From ac55c1f01b9138bb2b7649da35b8aa1c9e9903be Mon Sep 17 00:00:00 2001
From: Presisitence <Presisitence@users.noreply.github.com>
Date: Wed, 19 Aug 2026 22:06:38 +0800
Subject: [PATCH 1/2] Add plugin rgraph

RNA-seq downstream analysis and publication figures via local Rscript (DESeq2/edgeR/limma, volcano, heatmap, GSEA, WGCNA).
---
 plugins/Presisitence/rgraph/LICENSE           |  21 +
 plugins/Presisitence/rgraph/README.md         |  37 ++
 plugins/Presisitence/rgraph/mcp.json          |  11 +
 plugins/Presisitence/rgraph/plugin.json       |  22 +
 plugins/Presisitence/rgraph/pyproject.toml    |  23 +
 .../rgraph/rgraph_toolkit/__init__.py         |   6 +
 .../Presisitence/rgraph/rgraph_toolkit/cli.py |  34 ++
 .../rgraph/rgraph_toolkit/runner.py           | 291 ++++++++++++
 .../Presisitence/rgraph/rscripts/_common.R    | 167 +++++++
 .../Presisitence/rgraph/rscripts/build_gmt.R  |  57 +++
 .../rgraph/rscripts/correlation_heatmap.R     |  34 ++
 plugins/Presisitence/rgraph/rscripts/diff.R   | 102 +++++
 .../rgraph/rscripts/distribution.R            |  53 +++
 plugins/Presisitence/rgraph/rscripts/enrich.R |  82 ++++
 .../rgraph/rscripts/enrich_circos.R           |  94 ++++
 .../Presisitence/rgraph/rscripts/gene_bar.R   |  47 ++
 .../rgraph/rscripts/gene_correlation.R        |  44 ++
 .../Presisitence/rgraph/rscripts/go_plot.R    |  63 +++
 plugins/Presisitence/rgraph/rscripts/gsea.R   |  49 ++
 .../rgraph/rscripts/gsea_mountain.R           |  46 ++
 .../Presisitence/rgraph/rscripts/heatmap.R    |  73 +++
 .../rgraph/rscripts/interaction_diff.R        |  55 +++
 .../rgraph/rscripts/interactive_volcano.R     |  42 ++
 .../Presisitence/rgraph/rscripts/kegg_plot.R  |  44 ++
 plugins/Presisitence/rgraph/rscripts/kmeans.R |  86 ++++
 .../rgraph/rscripts/multilevel_scatter.R      |  43 ++
 .../Presisitence/rgraph/rscripts/network.R    |  87 ++++
 .../rgraph/rscripts/norm_matrix.R             |  36 ++
 .../Presisitence/rgraph/rscripts/normalize.R  |  37 ++
 .../rgraph/rscripts/pathway_gene_heatmap.R    |  70 +++
 .../rgraph/rscripts/pathway_trend.R           |  56 +++
 plugins/Presisitence/rgraph/rscripts/pca.R    |  56 +++
 plugins/Presisitence/rgraph/rscripts/ppi.R    |  50 +++
 .../Presisitence/rgraph/rscripts/quadrant.R   |  66 +++
 plugins/Presisitence/rgraph/rscripts/sankey.R |  43 ++
 .../rgraph/rscripts/segmented_volcano.R       |  46 ++
 plugins/Presisitence/rgraph/rscripts/ssgsea.R |  40 ++
 .../rgraph/rscripts/stacked_volcano.R         |  62 +++
 plugins/Presisitence/rgraph/rscripts/venn.R   |  49 ++
 .../rgraph/rscripts/virtual_knockout.R        |  69 +++
 .../Presisitence/rgraph/rscripts/volcano.R    |  61 +++
 plugins/Presisitence/rgraph/rscripts/wgcna.R  | 125 ++++++
 plugins/Presisitence/rgraph/server.py         | 421 ++++++++++++++++++
 .../rgraph/skills/rgraph/SKILL.md             |  19 +
 .../rgraph/tests/data/gene_count.csv          |  11 +
 .../rgraph/tests/data/sample_group.csv        |   7 +
 46 files changed, 3037 insertions(+)
 create mode 100644 plugins/Presisitence/rgraph/LICENSE
 create mode 100644 plugins/Presisitence/rgraph/README.md
 create mode 100644 plugins/Presisitence/rgraph/mcp.json
 create mode 100644 plugins/Presisitence/rgraph/plugin.json
 create mode 100644 plugins/Presisitence/rgraph/pyproject.toml
 create mode 100644 plugins/Presisitence/rgraph/rgraph_toolkit/__init__.py
 create mode 100644 plugins/Presisitence/rgraph/rgraph_toolkit/cli.py
 create mode 100644 plugins/Presisitence/rgraph/rgraph_toolkit/runner.py
 create mode 100644 plugins/Presisitence/rgraph/rscripts/_common.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/build_gmt.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/correlation_heatmap.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/diff.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/distribution.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/enrich.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/enrich_circos.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/gene_bar.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/gene_correlation.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/go_plot.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/gsea.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/gsea_mountain.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/heatmap.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/interaction_diff.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/interactive_volcano.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/kegg_plot.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/kmeans.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/multilevel_scatter.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/network.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/norm_matrix.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/normalize.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/pathway_gene_heatmap.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/pathway_trend.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/pca.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/ppi.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/quadrant.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/sankey.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/segmented_volcano.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/ssgsea.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/stacked_volcano.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/venn.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/virtual_knockout.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/volcano.R
 create mode 100644 plugins/Presisitence/rgraph/rscripts/wgcna.R
 create mode 100644 plugins/Presisitence/rgraph/server.py
 create mode 100644 plugins/Presisitence/rgraph/skills/rgraph/SKILL.md
 create mode 100644 plugins/Presisitence/rgraph/tests/data/gene_count.csv
 create mode 100644 plugins/Presisitence/rgraph/tests/data/sample_group.csv

diff --git a/plugins/Presisitence/rgraph/LICENSE b/plugins/Presisitence/rgraph/LICENSE
new file mode 100644
index 0000000..9637979
--- /dev/null
+++ b/plugins/Presisitence/rgraph/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 Presisitence
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/plugins/Presisitence/rgraph/README.md b/plugins/Presisitence/rgraph/README.md
new file mode 100644
index 0000000..d89561d
--- /dev/null
+++ b/plugins/Presisitence/rgraph/README.md
@@ -0,0 +1,37 @@
+# rgraph
+
+RNA-seq **downstream** analysis and figures for MiniMax Code. Parameterized R scripts
+(ggplot2, pheatmap, clusterProfiler, edgeR, limma, WGCNA, …) are exposed as MCP tools and
+rendered by the user's local **Rscript** to png + pdf.
+
+The Plugin consumes the user's own count/FPKM tables. It does not ship genomes or experimental matrices.
+`tests/data/` is a tiny synthetic `g1`–`g10` table for smoke tests.
+
+## Try it
+
+```text
+I have gene_count.csv and sample_group.csv. Run DESeq2 for treatment vs control, then draw a
+volcano plot (label top 8 genes, padj < 0.05, |log2FC| > 1) and a clustered heatmap of DEGs.
+```
+
+Expected result: the agent calls `rgraph_env`, then `rgraph_diff(method="deseq2")` and
+`rgraph_volcano` / `rgraph_heatmap`. Output png+pdf paths are returned. DEG calls use **padj**
+by default. If R or a package is missing, the tool returns install commands instead of crashing.
+
+## Requirements
+
+- Python 3.10+ and [uv](https://docs.astral.sh/uv/) on PATH.
+- R with `Rscript` on PATH, or set `RGRAPH_RSCRIPT` to the Rscript executable.
+- Common R packages: ggplot2, pheatmap, edgeR or DESeq2 or limma, clusterProfiler as needed.
+  Missing packages are reported with CRAN/Bioconductor install lines.
+- Windows, macOS, and Linux.
+
+## Data and network
+
+- Default analyses are local: user CSVs in, png/pdf out. No telemetry.
+- `rgraph_ppi` may contact STRING (`string-db.org`) when the user asks for a PPI edge table.
+- No credentials in the package.
+
+## License
+
+MIT. See [LICENSE](LICENSE).
diff --git a/plugins/Presisitence/rgraph/mcp.json b/plugins/Presisitence/rgraph/mcp.json
new file mode 100644
index 0000000..1cdbe22
--- /dev/null
+++ b/plugins/Presisitence/rgraph/mcp.json
@@ -0,0 +1,11 @@
+{
+  "$schema": "https://agent-plugins.org/schemas/1.0.0/mcp.schema.json",
+  "mcpServers": {
+    "rgraph": {
+      "type": "stdio",
+      "command": "uv",
+      "args": ["run", "server.py"],
+      "cwd": "${PLUGIN_ROOT}"
+    }
+  }
+}
diff --git a/plugins/Presisitence/rgraph/plugin.json b/plugins/Presisitence/rgraph/plugin.json
new file mode 100644
index 0000000..2db2c7d
--- /dev/null
+++ b/plugins/Presisitence/rgraph/plugin.json
@@ -0,0 +1,22 @@
+{
+  "$schema": "https://agent-plugins.org/schemas/1.0.0/plugin.schema.json",
+  "name": "rgraph",
+  "version": "0.1.0",
+  "description": "RNA-seq downstream analysis and publication figures via local Rscript (DESeq2/edgeR/limma, volcano, heatmap, GSEA, WGCNA).",
+  "author": {
+    "name": "Presisitence",
+    "url": "https://github.com/Presisitence"
+  },
+  "homepage": "https://github.com/Presisitence/rgraph-mcp",
+  "repository": "https://github.com/Presisitence/rgraph-mcp",
+  "license": "MIT",
+  "keywords": [
+    "minimax-code",
+    "plugin",
+    "mcp",
+    "rnaseq",
+    "deseq2",
+    "ggplot2",
+    "wgcna"
+  ]
+}
diff --git a/plugins/Presisitence/rgraph/pyproject.toml b/plugins/Presisitence/rgraph/pyproject.toml
new file mode 100644
index 0000000..3808ce4
--- /dev/null
+++ b/plugins/Presisitence/rgraph/pyproject.toml
@@ -0,0 +1,23 @@
+[project]
+name = "rgraph-mcp"
+version = "0.1.0"
+description = "RNAseq 下游可视化/分析 MCP 服务：把一套 R (ggplot2/pheatmap/clusterProfiler/WGCNA...) 出图脚本参数化封装为 MCP 工具，由 Rscript 引擎渲染"
+readme = "README.md"
+requires-python = ">=3.10"
+license = "MIT"
+dependencies = [
+    "mcp[cli]>=1.2.0",
+]
+
+[project.scripts]
+rgraph-cli = "rgraph_toolkit.cli:main"
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.hatch.build.targets.wheel]
+packages = ["rgraph_toolkit"]
+
+[tool.hatch.build.targets.wheel.force-include]
+"rscripts" = "rgraph_toolkit/rscripts"
diff --git a/plugins/Presisitence/rgraph/rgraph_toolkit/__init__.py b/plugins/Presisitence/rgraph/rgraph_toolkit/__init__.py
new file mode 100644
index 0000000..3677a2f
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rgraph_toolkit/__init__.py
@@ -0,0 +1,6 @@
+"""rgraph_toolkit: 把一套 RNAseq 下游 R 出图脚本参数化封装为 MCP 工具。"""
+from __future__ import annotations
+
+from . import runner
+
+__all__ = ["runner"]
diff --git a/plugins/Presisitence/rgraph/rgraph_toolkit/cli.py b/plugins/Presisitence/rgraph/rgraph_toolkit/cli.py
new file mode 100644
index 0000000..c87694e
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rgraph_toolkit/cli.py
@@ -0,0 +1,34 @@
+"""命令行自检入口：`rgraph-cli` 检查 R 引擎与关键包是否就绪。"""
+from __future__ import annotations
+
+import sys
+
+from . import runner
+
+_KEY_PKGS = [
+    "ggplot2", "dplyr", "tidyr", "stringr", "pheatmap", "ggrepel",
+    "DESeq2", "edgeR", "limma", "clusterProfiler", "enrichplot",
+    "pathview", "circlize", "ComplexHeatmap", "WGCNA", "VennDiagram",
+    "GSVA", "fgsea", "ggpubr", "patchwork", "reshape2",
+]
+
+
+def main() -> int:
+    rscript = runner.find_rscript()
+    print(f"Rscript: {rscript or '未找到 (设置 RGRAPH_RSCRIPT 或安装 R)'}")
+    print(f"rscripts 目录: {runner.rscripts_dir()}")
+    if not rscript:
+        return 1
+    status = runner.check_packages(_KEY_PKGS, rscript=rscript)
+    print("\nR 包状态:")
+    for pkg, ok in status.items():
+        print(f"  [{'x' if ok else ' '}] {pkg}")
+    missing = [p for p, ok in status.items() if not ok]
+    if missing:
+        print("\n缺失包安装建议:")
+        print("  " + runner._install_hint(missing))
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/plugins/Presisitence/rgraph/rgraph_toolkit/runner.py b/plugins/Presisitence/rgraph/rgraph_toolkit/runner.py
new file mode 100644
index 0000000..911353d
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rgraph_toolkit/runner.py
@@ -0,0 +1,291 @@
+"""Rscript 执行引擎：定位 R、序列化参数、运行 rscripts/ 下的 R 模板并解析结果。
+
+设计要点
+--------
+* **忠实 R 出图**：不在 Python 里重画，而是驱动一套清洗过的参数化 R 脚本（ggplot2/pheatmap/
+  clusterProfiler/WGCNA 等），产出与课程一致风格的 png/pdf。
+* **零额外 R 依赖传参**：参数以「R 源文件」形式落盘（`params <- list(...)`），R 端 `source()`
+  即可，不依赖 jsonlite 等包。
+* **优雅降级**：定位不到 Rscript → 返回可直接手动运行的脚本与命令；R 端缺包 → 返回缺失包名与
+  安装建议，而不是抛出晦涩的 R 报错。
+* **可复现**：以 `--no-init-file --no-site-file` 运行，屏蔽用户 .Rprofile/Rprofile.site
+  的自动加载横幅，避免污染输出。
+"""
+from __future__ import annotations
+
+import os
+import shutil
+import subprocess
+import tempfile
+from pathlib import Path
+from typing import Any
+
+# ---------------------------------------------------------------------------
+# Rscript 定位
+# ---------------------------------------------------------------------------
+
+_COMMON_DIRS = [
+    r"C:\Program Files\R",
+    r"C:\Program Files (x86)\R",
+]
+
+
+def find_rscript() -> str | None:
+    """按优先级定位 Rscript：环境变量 RGRAPH_RSCRIPT → PATH → 常见安装目录/注册表。"""
+    env = os.environ.get("RGRAPH_RSCRIPT")
+    if env and Path(env).exists():
+        return env
+
+    on_path = shutil.which("Rscript") or shutil.which("Rscript.exe")
+    if on_path:
+        return on_path
+
+    # 常见安装根目录下搜 R-*/bin/Rscript.exe
+    for root in _COMMON_DIRS:
+        p = Path(root)
+        if p.name.lower() == "bin" and (p / "Rscript.exe").exists():
+            return str(p / "Rscript.exe")
+        if p.exists():
+            for sub in sorted(p.glob("R-*"), reverse=True):
+                cand = sub / "bin" / "Rscript.exe"
+                if cand.exists():
+                    return str(cand)
+
+    # Windows 注册表
+    try:
+        import winreg  # type: ignore
+
+        for hive in (winreg.HKEY_LOCAL_MACHINE, winreg.HKEY_CURRENT_USER):
+            try:
+                with winreg.OpenKey(hive, r"SOFTWARE\R-core\R") as k:
+                    install_path, _ = winreg.QueryValueEx(k, "InstallPath")
+                cand = Path(install_path) / "bin" / "Rscript.exe"
+                if cand.exists():
+                    return str(cand)
+            except OSError:
+                continue
+    except Exception:  # noqa: BLE001
+        pass
+    return None
+
+
+# ---------------------------------------------------------------------------
+# 路径
+# ---------------------------------------------------------------------------
+
+def rscripts_dir() -> Path:
+    """定位 rscripts 目录（源码布局为包的同级；wheel 布局为包内）。"""
+    here = Path(__file__).resolve().parent
+    for cand in (here.parent / "rscripts", here / "rscripts"):
+        if cand.is_dir():
+            return cand
+    return here.parent / "rscripts"
+
+
+# ---------------------------------------------------------------------------
+# Python -> R 字面量序列化
+# ---------------------------------------------------------------------------
+
+def _r_str(s: str) -> str:
+    s = s.replace("\\", "\\\\").replace('"', '\\"').replace("\n", "\\n").replace("\t", "\\t")
+    return f'"{s}"'
+
+
+def to_r_literal(value: Any) -> str:
+    """把 Python 值转成 R 字面量表达式。"""
+    if value is None:
+        return "NULL"
+    if isinstance(value, bool):
+        return "TRUE" if value else "FALSE"
+    if isinstance(value, (int, float)):
+        if isinstance(value, float) and (value != value):  # NaN
+            return "NA"
+        return repr(value)
+    if isinstance(value, str):
+        return _r_str(value)
+    if isinstance(value, (list, tuple)):
+        if not value:
+            return "c()"
+        return "c(" + ", ".join(to_r_literal(v) for v in value) + ")"
+    if isinstance(value, dict):
+        parts = []
+        for k, v in value.items():
+            name = str(k)
+            key = name if name.isidentifier() else f"`{name}`"
+            parts.append(f"{key} = {to_r_literal(v)}")
+        return "list(" + ", ".join(parts) + ")"
+    # 兜底：字符串化
+    return _r_str(str(value))
+
+
+def write_params_file(params: dict[str, Any], path: Path) -> None:
+    """把参数写成 `params <- list(...)` 的 R 源文件。"""
+    body = to_r_literal(params)
+    path.write_text(f"params <- {body}\n", encoding="utf-8")
+
+
+# ---------------------------------------------------------------------------
+# 执行
+# ---------------------------------------------------------------------------
+
+_START_FLAGS = ["--no-init-file", "--no-site-file", "--no-restore"]
+
+
+def build_command(script_name: str, params_file: Path, rscript: str | None = None) -> list[str] | None:
+    rscript = rscript or find_rscript()
+    if not rscript:
+        return None
+    script = rscripts_dir() / script_name
+    common = rscripts_dir() / "_common.R"
+    # trailing args: [params_file, _common.R 路径]，R 端 source(args[[2]]) 再读 args[[1]]
+    return [rscript, *_START_FLAGS, str(script), str(params_file), str(common)]
+
+
+def run_script(
+    script_name: str,
+    params: dict[str, Any],
+    *,
+    outdir: str | None = None,
+    timeout: float = 600.0,
+) -> dict[str, Any]:
+    """运行一个 R 模板脚本，返回结构化结果。
+
+    返回字段：
+      status: ok | missing_packages | r_not_found | error
+      outputs: 产物文件绝对路径列表（R 端通过 `RGRAPH_OUTPUT:` 标记回传）
+      packages: 缺失的 R 包（status=missing_packages 时）
+      log: R 端 stdout+stderr（截断）
+      command / script / params_file: 便于手动复现
+    """
+    if outdir:
+        Path(outdir).mkdir(parents=True, exist_ok=True)
+        params = {**params, "outdir": outdir}
+
+    rscript = find_rscript()
+    # 参数文件落在 outdir（便于复现），否则临时目录
+    param_dir = Path(outdir) if outdir else Path(tempfile.mkdtemp(prefix="rgraph_"))
+    params_file = param_dir / f".rgraph_params_{script_name.replace('.R', '')}.R"
+    write_params_file(params, params_file)
+
+    script_path = rscripts_dir() / script_name
+    if not script_path.exists():
+        return {"status": "error", "error": f"R 模板不存在: {script_path}",
+                "script": str(script_path)}
+
+    cmd = build_command(script_name, params_file, rscript)
+    if cmd is None:
+        common = rscripts_dir() / "_common.R"
+        manual = f'"<Rscript路径>" {" ".join(_START_FLAGS)} "{script_path}" "{params_file}" "{common}"'
+        return {
+            "status": "r_not_found",
+            "message": ("未找到 Rscript。请安装 R 或设置环境变量 RGRAPH_RSCRIPT 指向 Rscript(.exe)。"
+                        "已生成可手动运行的脚本与参数文件。"),
+            "script": str(script_path),
+            "params_file": str(params_file),
+            "manual_command": manual,
+        }
+
+    try:
+        proc = subprocess.run(
+            cmd, capture_output=True, text=True, timeout=timeout,
+            encoding="utf-8", errors="replace", stdin=subprocess.DEVNULL,
+        )
+    except subprocess.TimeoutExpired:
+        return {"status": "error", "error": f"R 运行超时(>{timeout}s)", "command": cmd}
+
+    stdout = proc.stdout or ""
+    stderr = proc.stderr or ""
+    combined = stdout + ("\n" + stderr if stderr else "")
+
+    outputs: list[str] = []
+    missing: list[str] = []
+    for line in combined.splitlines():
+        line = line.strip()
+        if line.startswith("RGRAPH_OUTPUT:"):
+            outputs.append(line[len("RGRAPH_OUTPUT:"):].strip())
+        elif line.startswith("RGRAPH_MISSING_PACKAGES:"):
+            pk = line[len("RGRAPH_MISSING_PACKAGES:"):].strip()
+            missing = [x.strip() for x in pk.replace(";", ",").split(",") if x.strip()]
+
+    log_tail = combined if len(combined) < 6000 else combined[-6000:]
+
+    if missing:
+        return {
+            "status": "missing_packages",
+            "packages": missing,
+            "install_hint": _install_hint(missing),
+            "script": str(script_path),
+            "params_file": str(params_file),
+            "log": log_tail,
+        }
+
+    if proc.returncode != 0:
+        return {
+            "status": "error",
+            "returncode": proc.returncode,
+            "error": "R 脚本执行失败，见 log。",
+            "script": str(script_path),
+            "params_file": str(params_file),
+            "command": cmd,
+            "log": log_tail,
+        }
+
+    return {
+        "status": "ok",
+        "outputs": outputs,
+        "n_outputs": len(outputs),
+        "script": str(script_path),
+        "params_file": str(params_file),
+        "log": log_tail,
+    }
+
+
+# ---------------------------------------------------------------------------
+# 环境自检 / 安装建议
+# ---------------------------------------------------------------------------
+
+# CRAN vs Bioconductor 归属，用于给出正确的安装命令
+_BIOC = {
+    "DESeq2", "edgeR", "limma", "clusterProfiler", "enrichplot", "pathview",
+    "ComplexHeatmap", "GSVA", "GSEABase", "fgsea", "WGCNA", "AnnotationDbi",
+    "GO.db", "org.Hs.eg.db", "org.Mm.eg.db", "impute", "preprocessCore", "GENIE3",
+}
+
+
+def _install_hint(pkgs: list[str]) -> str:
+    cran = [p for p in pkgs if p not in _BIOC]
+    bioc = [p for p in pkgs if p in _BIOC]
+    lines = []
+    if cran:
+        quoted = ", ".join(f'"{p}"' for p in cran)
+        lines.append(f'install.packages(c({quoted}))')
+    if bioc:
+        quoted = ", ".join(f'"{p}"' for p in bioc)
+        lines.append('if (!require("BiocManager")) install.packages("BiocManager")')
+        lines.append(f'BiocManager::install(c({quoted}))')
+    return "  ;  ".join(lines)
+
+
+def check_packages(pkgs: list[str], rscript: str | None = None) -> dict[str, bool]:
+    """查询若干 R 包是否已安装。"""
+    rscript = rscript or find_rscript()
+    if not rscript:
+        return {p: False for p in pkgs}
+    vec = ", ".join(f'"{p}"' for p in pkgs)
+    code = f'i<-rownames(installed.packages()); for(x in c({vec})) cat(x,"=",x %in% i,"\\n")'
+    try:
+        proc = subprocess.run(
+            [rscript, *_START_FLAGS, "-e", code],
+            capture_output=True, text=True, timeout=120,
+            encoding="utf-8", errors="replace", stdin=subprocess.DEVNULL,
+        )
+    except Exception:  # noqa: BLE001
+        return {p: False for p in pkgs}
+    result: dict[str, bool] = {}
+    for line in (proc.stdout or "").splitlines():
+        if "=" in line:
+            name, _, val = line.partition("=")
+            result[name.strip()] = val.strip().upper().startswith("TRUE")
+    for p in pkgs:
+        result.setdefault(p, False)
+    return result
diff --git a/plugins/Presisitence/rgraph/rscripts/_common.R b/plugins/Presisitence/rgraph/rscripts/_common.R
new file mode 100644
index 0000000..afd6b49
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/_common.R
@@ -0,0 +1,167 @@
+# =============================================================================
+# _common.R  —  rgraph-mcp 共享 R 库
+# 所有出图/分析模板都 source() 本文件，统一：参数读取、包预检、样本表规整、
+# 出版级主题、配色、png+pdf 导出、产物回传标记。
+#
+# 相比原课程脚本的改进：
+#   * 不再 setwd() 到硬编码路径 / 不 rm(list=ls())；一切走参数与相对/绝对入参。
+#   * 统一 group / group_name 列名混用问题（二者缺一自动补齐）。
+#   * 缺包时输出 RGRAPH_MISSING_PACKAGES 标记并以状态码 3 退出，交由上层友好提示。
+#   * 产物统一通过 RGRAPH_OUTPUT 标记回传，Python 侧据此收集图片路径。
+# =============================================================================
+
+options(stringsAsFactors = FALSE, warn = 1)
+
+# ---- 参数读取 ---------------------------------------------------------------
+rgraph_load_params <- function() {
+  args <- commandArgs(trailingOnly = TRUE)
+  if (length(args) < 1) stop("缺少参数文件路径 (params file)")
+  e <- new.env()
+  sys.source(args[[1]], envir = e)
+  if (!exists("params", envir = e)) stop("参数文件未定义 params 列表")
+  get("params", envir = e)
+}
+
+rgraph_opt <- function(p, key, default = NULL) {
+  v <- p[[key]]
+  if (is.null(v)) default else v
+}
+
+`%||%` <- function(a, b) if (is.null(a)) b else a
+
+# ---- 包预检 -----------------------------------------------------------------
+rgraph_need <- function(pkgs) {
+  inst <- rownames(installed.packages())
+  miss <- setdiff(pkgs, inst)
+  if (length(miss) > 0) {
+    cat("RGRAPH_MISSING_PACKAGES:", paste(miss, collapse = ","), "\n")
+    quit(save = "no", status = 3)
+  }
+  invisible(TRUE)
+}
+
+rgraph_library <- function(pkgs) {
+  rgraph_need(pkgs)
+  for (p in pkgs) suppressMessages(suppressWarnings(library(p, character.only = TRUE)))
+  invisible(TRUE)
+}
+
+# ---- 产物回传 / 目录 --------------------------------------------------------
+rgraph_emit <- function(path) {
+  cat("RGRAPH_OUTPUT:", normalizePath(path, winslash = "/", mustWork = FALSE), "\n")
+}
+
+rgraph_ensure_dir <- function(path) {
+  if (!dir.exists(path)) dir.create(path, recursive = TRUE, showWarnings = FALSE)
+  invisible(path)
+}
+
+# ---- 读入：样本分组表（规整 group / group_name / TvsC） --------------------
+rgraph_read_sample_group <- function(path) {
+  sg <- read.csv(path, header = TRUE, check.names = FALSE, stringsAsFactors = FALSE)
+  if (!"sample_name" %in% names(sg)) stop("sample_group 缺少 sample_name 列")
+  sg <- sg[!is.na(sg$sample_name) & sg$sample_name != "", , drop = FALSE]
+  has_g <- "group" %in% names(sg)
+  has_gn <- "group_name" %in% names(sg)
+  if (has_g && !has_gn) sg$group_name <- sg$group
+  if (has_gn && !has_g) sg$group <- sg$group_name
+  if (!has_g && !has_gn) { sg$group <- sg$sample_name; sg$group_name <- sg$sample_name }
+  if (!"TvsC" %in% names(sg)) sg$TvsC <- NA_character_
+  sg
+}
+
+# ---- 读入：表达矩阵 (gene_id + 各样本列) -----------------------------------
+rgraph_read_matrix <- function(path) {
+  df <- read.csv(path, header = TRUE, check.names = FALSE, stringsAsFactors = FALSE)
+  if (!"gene_id" %in% names(df)) {
+    names(df)[1] <- "gene_id"  # 容错：首列视作 gene_id
+  }
+  df
+}
+
+# 校验 sample_group 中样本是否都在矩阵列里
+rgraph_check_samples <- function(mat, sample_list) {
+  miss <- setdiff(sample_list, colnames(mat))
+  if (length(miss) > 0) {
+    stop(paste0("以下样本不在表达矩阵中: ", paste(miss, collapse = ", ")))
+  }
+  invisible(TRUE)
+}
+
+# ---- 出版级主题 -------------------------------------------------------------
+theme_rgraph <- function(base_size = 12) {
+  ggplot2::theme_minimal(base_size = base_size) +
+    ggplot2::theme(
+      panel.grid = ggplot2::element_blank(),
+      panel.border = ggplot2::element_rect(colour = "black", fill = NA),
+      axis.text = ggplot2::element_text(color = "black"),
+      axis.title = ggplot2::element_text(color = "black"),
+      axis.ticks = ggplot2::element_line(color = "black", linewidth = 0.4),
+      axis.ticks.length = ggplot2::unit(0.1, "cm"),
+      legend.position = "right",
+      plot.title = ggplot2::element_text(hjust = 0.5, colour = "black")
+    )
+}
+
+# ---- 配色 -------------------------------------------------------------------
+.RGRAPH_PAL_COURSE <- c("#f03e3e", "#FF9933", "#CCCCFF", "#00CCCC", "#00CC66",
+                        "#009999", "#d4d445", "#0066CC", "#FFCCFF")
+# Okabe-Ito 色盲友好
+.RGRAPH_PAL_CB <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2",
+                    "#D55E00", "#CC79A7", "#999999", "#000000")
+
+rgraph_palette <- function(n, name = "course") {
+  base <- switch(name,
+                 colorblind = .RGRAPH_PAL_CB,
+                 course = .RGRAPH_PAL_COURSE,
+                 .RGRAPH_PAL_COURSE)
+  if (n <= length(base)) base[seq_len(n)] else grDevices::colorRampPalette(base)(n)
+}
+
+# 火山图/上下调三色（默认色盲友好红-蓝，可切换课程红-绿）
+rgraph_updown_colors <- function(scheme = "rb") {
+  if (identical(scheme, "rg")) {
+    c(Up = "red", Down = "green", No = "grey80")
+  } else {
+    c(Up = "#d73027", Down = "#4575b4", No = "grey80")
+  }
+}
+
+# ---- 保存：ggplot 对象 → png+pdf 并回传 ------------------------------------
+rgraph_save <- function(plot, outdir, base, width = 8, height = 6, dpi = 300,
+                        formats = c("png", "pdf")) {
+  rgraph_ensure_dir(outdir)
+  for (fmt in formats) {
+    f <- file.path(outdir, paste0(base, ".", fmt))
+    ggplot2::ggsave(f, plot = plot, width = width, height = height, dpi = dpi, bg = "white")
+    rgraph_emit(f)
+  }
+  invisible(TRUE)
+}
+
+# ---- 保存：base graphics (pheatmap/plotGOgraph/pathview) → png+pdf --------
+rgraph_save_base <- function(outdir, base, draw_fn, width = 8, height = 6, dpi = 300,
+                             formats = c("png", "pdf")) {
+  rgraph_ensure_dir(outdir)
+  for (fmt in formats) {
+    f <- file.path(outdir, paste0(base, ".", fmt))
+    if (fmt == "png") {
+      grDevices::png(f, width = width, height = height, units = "in", res = dpi)
+    } else {
+      grDevices::pdf(f, width = width, height = height)
+    }
+    draw_fn()
+    grDevices::dev.off()
+    rgraph_emit(f)
+  }
+  invisible(TRUE)
+}
+
+# ---- 写出 CSV 并回传 --------------------------------------------------------
+rgraph_write_csv <- function(df, outdir, base, row.names = FALSE) {
+  rgraph_ensure_dir(outdir)
+  f <- file.path(outdir, paste0(base, ".csv"))
+  write.csv(df, f, row.names = row.names)
+  rgraph_emit(f)
+  invisible(f)
+}
diff --git a/plugins/Presisitence/rgraph/rscripts/build_gmt.R b/plugins/Presisitence/rgraph/rscripts/build_gmt.R
new file mode 100644
index 0000000..cec383b
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/build_gmt.R
@@ -0,0 +1,57 @@
+# build_gmt.R —— 为 GSEA 构建 gmt 基因集
+# params: outdir, type("go"|"kegg"), orgdb(如 org.Hs.eg.db / 自建 org.Xxx.eg.db),
+#         source("orgdb"|"online"), keytype("GID"|"ENSEMBL"), kegg_species("hsa"),
+#         min_count(5), name(默认 GSEA_GO / GSEA_KEGG)
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])
+p <- rgraph_load_params()
+rgraph_library(c("dplyr", "tidyr", "stringr", "AnnotationDbi", p$orgdb))
+odb <- get(p$orgdb)
+
+type <- rgraph_opt(p, "type", "go")
+min_count <- rgraph_opt(p, "min_count", 5)
+keytype <- rgraph_opt(p, "keytype", "GID")
+
+to_gmt <- function(df, term_col, gene_col) {
+  df <- na.omit(df[, c(term_col, gene_col)])
+  names(df) <- c("term", "gene")
+  df$term <- gsub(":", "_", df$term)
+  df <- df %>% add_count(term, name = "n") %>% filter(n > min_count) %>% dplyr::select(term, gene)
+  wide <- df %>% group_by(term) %>% mutate(row = row_number()) %>%
+    tidyr::pivot_wider(names_from = row, values_from = gene) %>% ungroup()
+  wide <- wide %>% mutate(desc = "-", .after = 1)
+  wide
+}
+
+if (type == "go") {
+  g <- AnnotationDbi::select(odb, keys = AnnotationDbi::keys(odb, keytype = keytype),
+                             keytype = keytype, columns = c("GO"))
+  gcol <- intersect(c(keytype, "GID", "ENSEMBL"), names(g))[1]
+  wide <- to_gmt(g, "GO", gcol)
+  nm <- rgraph_opt(p, "name", "GSEA_GO")
+} else {
+  if (rgraph_opt(p, "source", "orgdb") == "online") {
+    sp <- rgraph_opt(p, "kegg_species", "hsa")
+    options(timeout = 600)
+    pe <- read.delim(sprintf("https://rest.kegg.jp/link/pathway/%s", sp), sep = "\t",
+                     col.names = c("ENTREZID", "KEGGID"))
+    pe$ENTREZID <- str_remove(pe$ENTREZID, paste0(sp, ":"))
+    pe$KEGGID <- str_remove(pe$KEGGID, "path:")
+    map <- AnnotationDbi::select(odb, keys = AnnotationDbi::keys(odb, keytype = "ENTREZID"),
+                                 keytype = "ENTREZID", columns = c("ENSEMBL"))
+    pe <- merge(pe, na.omit(map), by = "ENTREZID")
+    wide <- to_gmt(pe, "KEGGID", "ENSEMBL")
+  } else {
+    g <- AnnotationDbi::select(odb, keys = AnnotationDbi::keys(odb, keytype = keytype),
+                               keytype = keytype, columns = c("Pathway"))
+    gcol <- intersect(c(keytype, "GID"), names(g))[1]
+    wide <- to_gmt(g, "Pathway", gcol)
+  }
+  nm <- rgraph_opt(p, "name", "GSEA_KEGG")
+}
+
+rgraph_ensure_dir(p$outdir)
+f <- file.path(p$outdir, paste0(nm, ".gmt"))
+write.table(wide, f, col.names = FALSE, row.names = FALSE, sep = "\t", quote = FALSE, na = "")
+rgraph_emit(f)
+cat("RGRAPH_DONE: build_gmt", type, "genesets=", nrow(wide), "\n")
diff --git a/plugins/Presisitence/rgraph/rscripts/correlation_heatmap.R b/plugins/Presisitence/rgraph/rscripts/correlation_heatmap.R
new file mode 100644
index 0000000..4c11960
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/correlation_heatmap.R
@@ -0,0 +1,34 @@
+# correlation_heatmap.R —— 样本间 Pearson 相关性热图
+# params: fpkm(路径), sample_group(路径), outdir, dpi(默认300), width,height, name
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])
+p <- rgraph_load_params()
+rgraph_library(c("dplyr", "ggplot2", "scales", "reshape2"))
+
+fpkm <- rgraph_read_matrix(p$fpkm)
+sg <- rgraph_read_sample_group(p$sample_group)
+rgraph_check_samples(fpkm, sg$sample_name)
+
+mat <- fpkm[, sg$sample_name, drop = FALSE]
+mat[is.na(mat)] <- 0
+logm <- log2(mat + 1)
+
+cor_mat <- cor(logm, use = "pairwise.complete.obs")
+cor_df <- reshape2::melt(cor_mat)
+
+low  <- rgraph_opt(p, "low_color", "white")
+high <- rgraph_opt(p, "high_color", "blue")
+
+pl <- ggplot2::ggplot(cor_df, ggplot2::aes(x = Var2, y = Var1, fill = value)) +
+  ggplot2::geom_tile(color = "white", linewidth = 0.5) +
+  ggplot2::scale_fill_gradient(low = low, high = high, name = expression(R^2)) +
+  ggplot2::geom_text(ggplot2::aes(label = round(value, 3)), color = "black", size = 3) +
+  ggplot2::labs(x = NULL, y = NULL, title = "Pearson correlation between samples") +
+  theme_rgraph() +
+  ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, hjust = 1)) +
+  ggplot2::coord_fixed()
+
+rgraph_save(pl, p$outdir, rgraph_opt(p, "name", "correlation"),
+            width = rgraph_opt(p, "width", 6), height = rgraph_opt(p, "height", 6),
+            dpi = rgraph_opt(p, "dpi", 300))
+cat("RGRAPH_DONE: correlation_heatmap samples=", ncol(mat), "\n")
diff --git a/plugins/Presisitence/rgraph/rscripts/diff.R b/plugins/Presisitence/rgraph/rscripts/diff.R
new file mode 100644
index 0000000..4432f38
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/diff.R
@@ -0,0 +1,102 @@
+# diff.R —— 差异表达分析（DESeq2 / edgeR / edgeR_norep / limma）
+# params: count, sample_group, outdir, method("deseq2"|"edger"|"edger_norep"|"limma"),
+#         sig_metric("padj"|"pvalue",默认padj), pcut(0.05), log2fc(1),
+#         min_mean_count(1), dispersion(edger_norep用,默认0.1)
+# 输出: 01.Dse2_result.csv, 02.Deg_all.csv, 03.Deg_down.csv, 04.Deg_up.csv
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])
+p <- rgraph_load_params()
+
+method <- rgraph_opt(p, "method", "deseq2")
+sig_metric <- rgraph_opt(p, "sig_metric", "padj")
+pcut <- rgraph_opt(p, "pcut", 0.05)
+log2fc <- rgraph_opt(p, "log2fc", 1)
+min_mean <- rgraph_opt(p, "min_mean_count", 1)
+
+rgraph_library(c("dplyr"))
+count <- rgraph_read_matrix(p$count)
+sg <- rgraph_read_sample_group(p$sample_group)
+rgraph_check_samples(count, sg$sample_name)
+
+# 实验/对照组：优先 TvsC，其次取前两组
+if (any(!is.na(sg$TvsC)) && all(c("treatment", "control") %in% sg$TvsC)) {
+  treatment_g <- unique(sg$group[sg$TvsC == "treatment"])[1]
+  control_g   <- unique(sg$group[sg$TvsC == "control"])[1]
+} else {
+  gs <- unique(sg$group)
+  if (length(gs) < 2) stop("分组不足两组，无法差异分析")
+  treatment_g <- gs[1]; control_g <- gs[2]
+}
+
+mat <- count[, c("gene_id", sg$sample_name)]
+rownames(mat) <- mat$gene_id; mat$gene_id <- NULL
+mat[is.na(mat)] <- 0
+grp <- factor(sg$group[match(colnames(mat), sg$sample_name)])
+
+# ---- 各方法产出 stats(gene_id,log2FoldChange,pvalue,padj) 与 norm(矩阵) -----
+if (method == "deseq2") {
+  rgraph_library(c("DESeq2"))
+  coldata <- data.frame(row.names = colnames(mat), group = grp)
+  dds <- DESeq2::DESeqDataSetFromMatrix(round(as.matrix(mat)), coldata, design = ~group)
+  dds <- DESeq2::DESeq(dds)
+  norm <- as.data.frame(DESeq2::counts(dds, normalized = TRUE))
+  res <- as.data.frame(DESeq2::results(dds, contrast = c("group", treatment_g, control_g)))
+  stats <- data.frame(gene_id = rownames(res), log2FoldChange = res$log2FoldChange,
+                      pvalue = res$pvalue, padj = res$padj)
+} else if (method %in% c("edger", "edger_norep")) {
+  rgraph_library(c("edgeR"))
+  dge <- edgeR::DGEList(counts = as.matrix(mat), group = grp)
+  dge <- edgeR::calcNormFactors(dge, method = "TMM")
+  if (method == "edger_norep") {
+    disp <- rgraph_opt(p, "dispersion", 0.1)
+    dge <- edgeR::estimateDisp(dge, trend = "none", tagwise = FALSE)
+    dge$common.dispersion <- disp
+    dge$trended.dispersion <- rep(disp, nrow(dge))
+    dge$tagwise.dispersion <- rep(disp, nrow(dge))
+  } else {
+    dge <- edgeR::estimateDisp(dge)
+  }
+  et <- edgeR::exactTest(dge, pair = c(control_g, treatment_g))  # logFC = treatment/control
+  norm <- as.data.frame(edgeR::cpm(dge, normalized.lib.sizes = TRUE))
+  tt <- edgeR::topTags(et, n = Inf, adjust.method = "BH", sort.by = "none")$table
+  stats <- data.frame(gene_id = rownames(tt), log2FoldChange = tt$logFC,
+                      pvalue = tt$PValue, padj = tt$FDR)
+} else if (method == "limma") {
+  rgraph_library(c("limma"))
+  design <- model.matrix(~0 + grp)
+  colnames(design) <- make.names(levels(grp))
+  v <- limma::voom(as.matrix(mat), design)
+  norm <- as.data.frame(2^v$E)
+  fit <- limma::lmFit(v, design)
+  ct <- limma::makeContrasts(contrasts = paste0(make.names(treatment_g), "-", make.names(control_g)),
+                             levels = design)   # 修正为 treatment - control（原课程写反）
+  fit2 <- limma::eBayes(limma::contrasts.fit(fit, ct))
+  tt <- limma::topTable(fit2, number = Inf)
+  stats <- data.frame(gene_id = rownames(tt), log2FoldChange = tt$logFC,
+                      pvalue = tt$P.Value, padj = tt$adj.P.Val)
+} else {
+  stop(paste0("未知 method: ", method))
+}
+
+norm <- data.frame(gene_id = rownames(norm), norm, row.names = NULL, check.names = FALSE)
+result <- merge(norm, stats, by = "gene_id", all = TRUE)
+
+# ---- 过滤低表达 + 去 NA ----------------------------------------------------
+ns <- length(sg$sample_name); sc <- 2:(ns + 1)
+result <- result[rowSums(result[, sc] == 0) < ns * 0.5, ]
+result <- result[rowMeans(result[, sc]) >= min_mean, ]
+result <- result[!is.na(result$pvalue) & !is.na(result$padj), ]
+
+# ---- DEG 判定（默认 padj，可切 pvalue）-------------------------------------
+metric <- result[[sig_metric]]
+deg_all  <- result[metric < pcut & abs(result$log2FoldChange) > log2fc, ]
+deg_up   <- deg_all[deg_all$log2FoldChange >  log2fc, ]
+deg_down <- deg_all[deg_all$log2FoldChange < -log2fc, ]
+
+rgraph_write_csv(result,  p$outdir, "01.Dse2_result")
+rgraph_write_csv(deg_all, p$outdir, "02.Deg_all")
+rgraph_write_csv(deg_down, p$outdir, "03.Deg_down")
+rgraph_write_csv(deg_up,  p$outdir, "04.Deg_up")
+cat("RGRAPH_DONE: diff", method, "| treatment=", treatment_g, "control=", control_g,
+    "| all=", nrow(result), "DEG=", nrow(deg_all), "up=", nrow(deg_up),
+    "down=", nrow(deg_down), "\n")
diff --git a/plugins/Presisitence/rgraph/rscripts/distribution.R b/plugins/Presisitence/rgraph/rscripts/distribution.R
new file mode 100644
index 0000000..335a362
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/distribution.R
@@ -0,0 +1,53 @@
+# distribution.R —— 表达分布：箱线图 / 小提琴图 / 密度曲线
+# params: fpkm, sample_group, outdir, kind("box"|"violin"|"density"),
+#         palette, drop_zero_frac(默认0.5), dpi(300), width,height, name
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])
+p <- rgraph_load_params()
+rgraph_library(c("dplyr", "tidyr", "ggplot2"))
+
+kind <- rgraph_opt(p, "kind", "box")
+fpkm <- rgraph_read_matrix(p$fpkm)
+sg <- rgraph_read_sample_group(p$sample_group)
+rgraph_check_samples(fpkm, sg$sample_name)
+
+mat <- fpkm[, sg$sample_name, drop = FALSE]
+# 去除 0 占比过高的行
+zf <- rgraph_opt(p, "drop_zero_frac", 0.5)
+keep <- rowSums(mat == 0 | is.na(mat)) < ncol(mat) * zf
+mat <- mat[keep, , drop = FALSE]
+mat[is.na(mat)] <- 0
+
+long <- as.data.frame(mat) |>
+  tidyr::pivot_longer(cols = dplyr::everything(), names_to = "Sample", values_to = "FPKM") |>
+  dplyr::mutate(logFPKM = log2(FPKM + 1))
+long$Group <- sg$group_name[match(long$Sample, sg$sample_name)]
+long <- long |>
+  dplyr::mutate(Sample = factor(Sample, levels = unique(sg$sample_name)),
+                Group  = factor(Group,  levels = unique(sg$group_name)))
+
+cols <- rgraph_palette(length(unique(sg$group_name)), rgraph_opt(p, "palette", "course"))
+
+if (kind == "density") {
+  pl <- ggplot2::ggplot(long, ggplot2::aes(x = logFPKM, color = Group, group = Sample)) +
+    ggplot2::geom_density(linewidth = 0.6, alpha = 0.6) +
+    ggplot2::scale_color_manual(values = cols) +
+    ggplot2::labs(title = "Expression density", x = "log2(fpkm+1)", y = "Density") +
+    theme_rgraph()
+  w <- rgraph_opt(p, "width", 10); h <- rgraph_opt(p, "height", 6)
+} else {
+  base <- ggplot2::ggplot(long, ggplot2::aes(x = Sample, y = logFPKM, fill = Group, colour = Group)) +
+    ggplot2::scale_color_manual(values = cols) +
+    ggplot2::scale_fill_manual(values = cols) +
+    ggplot2::labs(title = "Gene expression distribution", x = "Sample", y = "log2(fpkm+1)") +
+    theme_rgraph() +
+    ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, hjust = 1))
+  geom <- if (kind == "violin") ggplot2::geom_violin(alpha = 0.5, linewidth = 0.6)
+          else ggplot2::geom_boxplot(alpha = 0.5, linewidth = 0.6, outlier.size = 0.4)
+  pl <- base + geom
+  w <- rgraph_opt(p, "width", 8); h <- rgraph_opt(p, "height", 8)
+}
+
+nm <- rgraph_opt(p, "name", switch(kind, violin = "violinplot", density = "density", "boxplot"))
+rgraph_save(pl, p$outdir, nm, width = w, height = h, dpi = rgraph_opt(p, "dpi", 300))
+cat("RGRAPH_DONE: distribution", kind, "genes=", nrow(mat), "\n")
diff --git a/plugins/Presisitence/rgraph/rscripts/enrich.R b/plugins/Presisitence/rgraph/rscripts/enrich.R
new file mode 100644
index 0000000..aed52a2
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/enrich.R
@@ -0,0 +1,82 @@
+# enrich.R —— GO / KEGG 富集分析（支持模式物种 OrgDb 与非模式物种自建基因集）
+# params:
+#   gene_list(路径,列 gene_id[,log2FoldChange]), outdir, type("go"|"kegg")
+#   orgdb(如 "org.Hs.eg.db"), id_type("ENSEMBL"|"SYMBOL"|"ENTREZID"|"GID")
+#   ont("all"|"BP"|"CC"|"MF")            # GO
+#   go_source("orgdb"|"custom")          # custom: 从 OrgDb 的 GO 列自建 TERM2GENE(非模式)
+#   kegg_source("online"|"gmt"), kegg_species(如 "hsa"), gmt(路径), kegg_info(路径,可选)
+#   pAdjustMethod("BH"), pvalueCutoff(1), qvalueCutoff(1)
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])
+p <- rgraph_load_params()
+rgraph_library(c("clusterProfiler", "dplyr", "stringr"))
+
+type <- rgraph_opt(p, "type", "go")
+padj_m <- rgraph_opt(p, "pAdjustMethod", "BH")
+pcut <- rgraph_opt(p, "pvalueCutoff", 1)
+qcut <- rgraph_opt(p, "qvalueCutoff", 1)
+id_type <- rgraph_opt(p, "id_type", "ENSEMBL")
+
+gl <- read.csv(p$gene_list, stringsAsFactors = FALSE, check.names = FALSE)
+genes_in <- gl$gene_id
+
+to_entrez <- function(ids) {
+  rgraph_library(c(p$orgdb))
+  suppressWarnings(clusterProfiler::bitr(ids, fromType = id_type, toType = "ENTREZID",
+                                         OrgDb = p$orgdb))$ENTREZID
+}
+
+if (type == "go") {
+  go_source <- rgraph_opt(p, "go_source", "orgdb")
+  ont <- rgraph_opt(p, "ont", "all")
+  if (go_source == "custom") {
+    rgraph_library(c(p$orgdb, "AnnotationDbi"))
+    odb <- get(p$orgdb)
+    g2go <- AnnotationDbi::select(odb, keys = AnnotationDbi::keys(odb), columns = c("GO"))
+    g2go <- na.omit(g2go[, c("GO", intersect(c("GID", "ENSEMBL"), names(g2go))[1])])
+    names(g2go) <- c("term", "gene")
+    er <- clusterProfiler::enricher(genes_in, TERM2GENE = g2go[, c("term", "gene")],
+                                    pAdjustMethod = padj_m, pvalueCutoff = pcut, qvalueCutoff = qcut)
+    res <- er@result
+  } else {
+    rgraph_library(c(p$orgdb))
+    genes <- if (id_type == "ENTREZID") genes_in else to_entrez(genes_in)
+    er <- clusterProfiler::enrichGO(gene = genes, OrgDb = p$orgdb, keyType = "ENTREZID",
+                                    ont = ont, pAdjustMethod = padj_m,
+                                    pvalueCutoff = pcut, qvalueCutoff = qcut)
+    res <- er@result
+    names(res)[names(res) == "ID"] <- "GOID"
+  }
+  names(res)[names(res) == "p.adjust"] <- "padj"
+  rgraph_write_csv(res, p$outdir, rgraph_opt(p, "name", "GO_enrich"))
+  cat("RGRAPH_DONE: enrich go terms=", nrow(res), "\n")
+} else {
+  kegg_source <- rgraph_opt(p, "kegg_source", "online")
+  if (kegg_source == "gmt") {
+    t2g <- clusterProfiler::read.gmt(p$gmt)
+    er <- clusterProfiler::enricher(genes_in, TERM2GENE = t2g, pAdjustMethod = padj_m,
+                                    pvalueCutoff = pcut, qvalueCutoff = qcut)
+    res <- er@result
+    names(res)[names(res) == "p.adjust"] <- "padj"
+  } else {
+    sp <- rgraph_opt(p, "kegg_species", "hsa")
+    options(timeout = 600)
+    pe <- read.delim(sprintf("https://rest.kegg.jp/link/pathway/%s", sp), sep = "\t",
+                     col.names = c("gene", "KEGGID"))
+    pe$gene <- str_remove(pe$gene, paste0(sp, ":"))
+    pe$KEGGID <- str_remove(pe$KEGGID, "path:")
+    t2g <- pe[, c("KEGGID", "gene")]
+    genes <- if (id_type == "ENTREZID") genes_in else to_entrez(genes_in)
+    er <- clusterProfiler::enricher(genes, TERM2GENE = t2g, pAdjustMethod = padj_m,
+                                    pvalueCutoff = pcut, qvalueCutoff = qcut)
+    res <- er@result
+    names(res)[names(res) == "ID"] <- "KEGGID"
+    names(res)[names(res) == "p.adjust"] <- "padj"
+    if (!is.null(p$kegg_info)) {
+      ki <- read.csv(p$kegg_info, stringsAsFactors = FALSE)
+      res <- merge(ki, res, by = "KEGGID", all.y = TRUE)
+    }
+  }
+  rgraph_write_csv(res, p$outdir, rgraph_opt(p, "name", "KEGG_enrich"))
+  cat("RGRAPH_DONE: enrich kegg pathways=", nrow(res), "\n")
+}
diff --git a/plugins/Presisitence/rgraph/rscripts/enrich_circos.R b/plugins/Presisitence/rgraph/rscripts/enrich_circos.R
new file mode 100644
index 0000000..607faee
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/enrich_circos.R
@@ -0,0 +1,94 @@
+# enrich_circos.R —— 富集分析圈图（circlize 多轨道：分类/背景基因/前景基因(上下调)/RichFactor）
+# 输入: enrich(GO_enrich.csv 或 KEGG_enrich.csv), 需列: pvalue,RichFactor,Bg_gene,Count[,Up,Down]
+# params: enrich, outdir, type("go"|"kegg"), topN(每类10), split_count(有Up/Down时按上下调拆分),
+#         show_axis(T), ifLog(F), circ1/2/3.color, dpi(300), width(8),height(8), name
+# 说明: 图例需 ComplexHeatmap（缺失则跳过图例，圈图主体仍由 circlize 正常绘制）。
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])
+p <- rgraph_load_params()
+rgraph_library(c("dplyr", "circlize"))
+has_ch <- requireNamespace("ComplexHeatmap", quietly = TRUE)
+
+type <- rgraph_opt(p, "type", "go")
+group_col <- if (type == "kegg") "Category" else "ONTOLOGY"
+id_col <- if (type == "kegg") "KEGGID" else "GOID"
+topN <- rgraph_opt(p, "topN", 10)
+show_axis <- rgraph_opt(p, "show_axis", TRUE)
+ifLog <- rgraph_opt(p, "ifLog", FALSE)
+c1 <- rgraph_opt(p, "circ1_color", c("#f7cb16", "#65c3fc", "#bfe046"))
+c2 <- rgraph_opt(p, "circ2_color", c("#fee5d9", "#fb6a4a"))
+c3 <- rgraph_opt(p, "circ3_color", c("#68a6e3", "#99CCFF"))
+
+df <- read.csv(p$enrich, stringsAsFactors = FALSE, check.names = FALSE)
+for (col in c(group_col, id_col, "pvalue", "RichFactor", "Bg_gene", "Count"))
+  if (!col %in% names(df)) stop(paste0("富集表缺少列: ", col))
+split_count <- rgraph_opt(p, "split_count", all(c("Up", "Down") %in% names(df)))
+df$.grp <- df[[group_col]]; df$.id <- df[[id_col]]
+df$neg_log10_pvalue <- -log10(df$pvalue)
+
+dt <- df %>% dplyr::group_by(.grp) %>% dplyr::slice_min(pvalue, n = topN, with_ties = FALSE) %>%
+  dplyr::arrange(dplyr::desc(RichFactor), .by_group = TRUE) %>% dplyr::ungroup()
+main.col <- c1[as.numeric(as.factor(dt$.grp))]
+p_max <- ceiling(max(dt$neg_log10_pvalue))
+color_assign <- circlize::colorRamp2(0:p_max, grDevices::colorRampPalette(c2)(p_max + 1))
+bgmax <- max(dt$Bg_gene, na.rm = TRUE)
+
+df1 <- data.frame(id = dt$.id, start = 0, end = bgmax)
+df2 <- data.frame(id = dt$.id, start = 0, end = if (show_axis) dt$Bg_gene else bgmax,
+                  Bg_gene = dt$Bg_gene, col = color_assign(dt$neg_log10_pvalue))
+if (split_count) {
+  tempLong <- if (ifLog) log10(bgmax + 1) else bgmax
+  frac <- dt$Up / pmax(dt$Up + dt$Down, 1)
+  df3 <- rbind(
+    data.frame(id = dt$.id, start = 0, end = frac * tempLong, count = dt$Up, col = c3[1]),
+    data.frame(id = dt$.id, start = frac * tempLong, end = tempLong, count = dt$Down, col = c3[2]))
+  df3$count <- ifelse(df3$count == 0, "", df3$count)
+} else {
+  df3 <- data.frame(id = dt$.id, start = 0, end = if (show_axis) dt$Count else bgmax,
+                    count = dt$Count, col = c3[1])
+}
+df4 <- data.frame(id = dt$.id, start = 0, end = bgmax, ratio = dt$RichFactor, col = main.col)
+if (ifLog) { df1$end <- log10(df1$end + 1); df2$end <- log10(df2$end + 1)
+  if (!split_count) df3$end <- log10(df3$end + 1); df4$end <- log10(df4$end + 1) }
+
+draw_fn <- function() {
+  par(omi = c(0.1, 0.1, 0.1, 1.5))
+  circlize::circos.par(track.margin = c(0.01, 0.01))
+  circlize::circos.genomicInitialize(df1, plotType = "none")
+  circlize::circos.trackPlotRegion(ylim = c(0, 1), track.height = 0.08, bg.border = NA, bg.col = main.col,
+    panel.fun = function(x, y) {
+      circlize::circos.text(mean(circlize::get.cell.meta.data("xlim")), 0.5,
+        circlize::get.cell.meta.data("sector.index"), cex = rgraph_opt(p, "circ1_size", 0.45),
+        facing = "bending.inside", niceFacing = TRUE) })
+  if (show_axis) for (si in circlize::get.all.sector.index())
+    circlize::circos.axis(h = "top", labels.cex = rgraph_opt(p, "axis_size", 0.3), sector.index = si,
+      track.index = 1, major.at = pretty(c(0, max(df1$end, na.rm = TRUE)), n = 5), labels.facing = "clockwise")
+  circlize::circos.genomicTrack(df2, ylim = c(0, 1), track.height = 0.1, bg.border = "white",
+    panel.fun = function(region, value, ...) {
+      circlize::circos.genomicRect(region, value, ytop = 0, ybottom = 1, col = value[, 2], border = NA, ...)
+      circlize::circos.genomicText(region, value, y = 0.5, labels = value[, 1], adj = c(0.5, 0.5),
+        cex = rgraph_opt(p, "circ2_size", 0.5), ...) })
+  circlize::circos.genomicTrack(df3, ylim = c(0, 1), track.height = 0.1, bg.border = "white",
+    panel.fun = function(region, value, ...) {
+      circlize::circos.genomicRect(region, value, ytop = 0, ybottom = 1, col = value[, 2], border = NA, ...)
+      circlize::circos.genomicText(region, value, y = 0.5, labels = value[, 1], adj = c(0.5, 0.5),
+        cex = rgraph_opt(p, "circ3_size", 0.3), ...) })
+  circlize::circos.genomicTrack(df4, ylim = c(0, max(dt$RichFactor, na.rm = TRUE)), track.height = 0.35,
+    bg.border = "white", bg.col = "grey97",
+    panel.fun = function(region, value, ...)
+      circlize::circos.genomicRect(region, value, ytop = 0, ybottom = value[, 1], col = value[, 2], border = NA, ...))
+  circlize::circos.clear()
+  if (has_ch) {
+    ml <- ComplexHeatmap::Legend(labels = unique(dt$.grp), type = "points", pch = 15,
+      legend_gp = grid::gpar(col = c1), title = group_col, size = grid::unit(3, "mm"))
+    lp <- ComplexHeatmap::Legend(col_fun = circlize::colorRamp2(round(seq(0, p_max, length.out = 6), 0),
+      grDevices::colorRampPalette(c2)(6)), title = "-log10(pvalue)")
+    ComplexHeatmap::draw(ComplexHeatmap::packLegend(ml, lp),
+      x = grid::unit(1, "snpc") * 0.85, y = grid::unit(1, "snpc") * 0.5, just = "left")
+  }
+}
+
+rgraph_save_base(p$outdir, rgraph_opt(p, "name", paste0(toupper(type), "_loop")), draw_fn,
+                 width = rgraph_opt(p, "width", 8), height = rgraph_opt(p, "height", 8),
+                 dpi = rgraph_opt(p, "dpi", 300))
+cat("RGRAPH_DONE: enrich_circos", type, "terms=", nrow(dt), "legend=", has_ch, "\n")
diff --git a/plugins/Presisitence/rgraph/rscripts/gene_bar.R b/plugins/Presisitence/rgraph/rscripts/gene_bar.R
new file mode 100644
index 0000000..776daf0
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/gene_bar.R
@@ -0,0 +1,47 @@
+# gene_bar.R —— 单基因表达柱形图（对照 vs 处理，均值±SEM，显著性星号）
+# params: deg(Deg_all.csv, 含 pvalue 与各样本归一化列), sample_group(需 TvsC 与 group/group_name),
+#         gene_list(CSV, 列 gene_id) 或 genes(基因向量), outdir, dpi(300), width(4),height(5)
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])
+p <- rgraph_load_params()
+rgraph_library(c("dplyr", "tidyr", "ggplot2"))
+
+deg <- read.csv(p$deg, check.names = FALSE)
+sg <- rgraph_read_sample_group(p$sample_group)
+if (!is.null(p$genes)) genes <- p$genes else genes <- read.csv(p$gene_list, check.names = FALSE)$gene_id
+
+deg$difference <- dplyr::case_when(deg$pvalue < 0.001 ~ "***", deg$pvalue < 0.01 ~ "**",
+                                   deg$pvalue < 0.05 ~ "*", TRUE ~ "")
+ctrl <- sg$sample_name[sg$TvsC == "control"]
+trt  <- sg$sample_name[sg$TvsC == "treatment"]
+if (length(ctrl) == 0 || length(trt) == 0) {  # 无 TvsC 时按前两组
+  gs <- unique(sg$group); trt <- sg$sample_name[sg$group == gs[1]]; ctrl <- sg$sample_name[sg$group == gs[2]]
+}
+ctrl_name <- sg$group_name[match(ctrl[1], sg$sample_name)]
+trt_name  <- sg$group_name[match(trt[1], sg$sample_name)]
+
+summ <- function(row, cols) {
+  v <- as.numeric(row[cols]); c(mean = mean(v), sem = ifelse(length(v) > 1, sd(v) / sqrt(length(v)), 0))
+}
+n_ok <- 0
+for (g in genes) {
+  cg <- deg[deg$gene_id == g, ]
+  if (nrow(cg) == 0) next
+  sc <- summ(cg[1, ], ctrl); st <- summ(cg[1, ], trt)
+  pd <- data.frame(group = factor(c(ctrl_name, trt_name), levels = c(ctrl_name, trt_name)),
+                   mean_exp = c(sc["mean"], st["mean"]), sem = c(sc["sem"], st["sem"]),
+                   difference = c("", cg$difference[1]))
+  pl <- ggplot2::ggplot(pd, ggplot2::aes(x = group, y = mean_exp, fill = group)) +
+    ggplot2::geom_col(width = 0.6) +
+    ggplot2::scale_fill_manual(values = setNames(c("#1f77b4", "#ff7f0e"), c(ctrl_name, trt_name)), name = "Group") +
+    ggplot2::geom_errorbar(ggplot2::aes(ymin = mean_exp - sem, ymax = mean_exp + sem), width = 0.2) +
+    ggplot2::geom_text(ggplot2::aes(y = mean_exp + sem, label = difference), vjust = 0, size = 5) +
+    ggplot2::labs(title = g, y = "Normalized Count", x = NULL) +
+    theme_rgraph() +
+    ggplot2::scale_y_continuous(expand = ggplot2::expansion(mult = c(0, 0.1)))
+  rgraph_save(pl, p$outdir, paste0(g, "_Expression"),
+              width = rgraph_opt(p, "width", 4), height = rgraph_opt(p, "height", 5),
+              dpi = rgraph_opt(p, "dpi", 300))
+  n_ok <- n_ok + 1
+}
+cat("RGRAPH_DONE: gene_bar genes=", n_ok, "\n")
diff --git a/plugins/Presisitence/rgraph/rscripts/gene_correlation.R b/plugins/Presisitence/rgraph/rscripts/gene_correlation.R
new file mode 100644
index 0000000..85f2f35
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/gene_correlation.R
@@ -0,0 +1,44 @@
+# gene_correlation.R —— 基因-基因相关性热图（气泡热图，圆点大小=|r|）
+# params: fpkm(gene_fpkm.csv), sample_group, outdir,
+#         genes1(向量) 或 gene_list1(CSV), genes2(向量) 或 gene_list2(CSV, 缺省=genes1),
+#         dpi(300), width,height, name
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])
+p <- rgraph_load_params()
+rgraph_library(c("dplyr", "ggplot2", "reshape2", "scales"))
+
+fpkm <- rgraph_read_matrix(p$fpkm)
+sg <- rgraph_read_sample_group(p$sample_group)
+g1 <- if (!is.null(p$genes1)) p$genes1 else read.csv(p$gene_list1, check.names = FALSE)$gene_id
+g2 <- if (!is.null(p$genes2)) p$genes2 else if (!is.null(p$gene_list2))
+  read.csv(p$gene_list2, check.names = FALSE)$gene_id else g1
+
+sub <- fpkm[fpkm$gene_id %in% unique(c(g1, g2)), c("gene_id", sg$sample_name)]
+rownames(sub) <- sub$gene_id; sub$gene_id <- NULL
+sub[is.na(sub)] <- 0
+logm <- log2(sub + 1)
+cm <- cor(t(logm), use = "pairwise.complete.obs")   # 基因 x 基因
+cm <- cm[rownames(cm) %in% g1, colnames(cm) %in% g2, drop = FALSE]
+
+rgraph_write_csv(as.data.frame(cbind(gene_id = rownames(cm), cm)), p$outdir, "correlation")
+
+df <- reshape2::melt(cm); df$size <- abs(df$value)
+pl <- ggplot2::ggplot(df, ggplot2::aes(x = Var2, y = Var1)) +
+  ggplot2::geom_tile(color = "black", fill = "white", linewidth = 0.4) +
+  ggplot2::geom_point(ggplot2::aes(fill = value, size = size, color = value), shape = 21,
+                      stroke = 0.4, alpha = 0.85) +
+  ggplot2::scale_color_gradient2(low = "#2167ad", mid = "#d8d4d4", high = "red", guide = "none") +
+  ggplot2::scale_size(range = c(4, 12), guide = "none") +
+  ggplot2::scale_fill_gradientn(colors = grDevices::colorRampPalette(c("#2167ad", "white", "red"))(100),
+                                limits = c(-1, 1), name = "r") +
+  ggplot2::labs(title = "Pearson correlation between genes", x = NULL, y = NULL) +
+  theme_rgraph() +
+  ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, hjust = 1)) +
+  ggplot2::coord_fixed()
+
+nr <- nrow(cm); nc <- ncol(cm)
+rgraph_save(pl, p$outdir, rgraph_opt(p, "name", "gene_correlation"),
+            width = rgraph_opt(p, "width", max(6, nc * 0.6)),
+            height = rgraph_opt(p, "height", max(6, nr * 0.6)),
+            dpi = rgraph_opt(p, "dpi", 300))
+cat("RGRAPH_DONE: gene_correlation rows=", nr, "cols=", nc, "\n")
diff --git a/plugins/Presisitence/rgraph/rscripts/go_plot.R b/plugins/Presisitence/rgraph/rscripts/go_plot.R
new file mode 100644
index 0000000..a43f3bb
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/go_plot.R
@@ -0,0 +1,63 @@
+# go_plot.R —— GO 富集条形图/气泡图（BP/CC/MF 三面板，纵向拼接）
+# 输入: GO_enrich.csv (列: ONTOLOGY, Description, GeneRatio, pvalue, Count)
+# params: enrich, outdir, kind("bar"|"dot"), top_n(每类10), desc_len(120),
+#         dpi(300), width(11), height(10), name
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])
+p <- rgraph_load_params()
+rgraph_library(c("dplyr", "ggplot2", "stringr", "patchwork"))
+
+parse_ratio <- function(x) vapply(x, function(s) {
+  s <- as.character(s)
+  if (grepl("/", s)) { a <- as.numeric(strsplit(s, "/")[[1]]); a[1] / a[2] } else as.numeric(s)
+}, numeric(1))
+
+kind <- rgraph_opt(p, "kind", "bar")
+top_n <- rgraph_opt(p, "top_n", 10)
+desc_len <- rgraph_opt(p, "desc_len", 120)
+
+d <- read.csv(p$enrich, stringsAsFactors = FALSE, check.names = FALSE)
+if (!"ONTOLOGY" %in% names(d)) stop("GO_enrich 缺少 ONTOLOGY 列")
+d$neg_log10_pvalue <- -log10(d$pvalue)
+d$GeneRatio <- parse_ratio(d$GeneRatio)
+d$Description <- substr(d$Description, 1, desc_len)
+
+d <- d %>% dplyr::group_by(ONTOLOGY) %>% dplyr::arrange(pvalue) %>%
+  dplyr::slice(seq_len(min(top_n, dplyr::n()))) %>% dplyr::ungroup()
+
+# 三类各自的低/高配色（沿用课程）
+pal <- list(BP = c("#f2c7b9", "#e74716"), CC = c("#90dee7", "#0a8a99"), MF = c("#b2eecf", "#41ae76"))
+onts <- intersect(c("BP", "CC", "MF"), unique(d$ONTOLOGY))
+
+panel <- function(ont, show_x) {
+  dd <- d %>% dplyr::filter(ONTOLOGY == ont) %>%
+    dplyr::arrange(if (kind == "dot") desc(GeneRatio) else desc(Count)) %>%
+    dplyr::mutate(Description = factor(Description, levels = rev(Description)))
+  cols <- pal[[ont]]
+  base <- ggplot2::ggplot(dd) +
+    ggplot2::facet_grid(ONTOLOGY ~ ., scales = "free_y", space = "free_y") +
+    theme_rgraph() +
+    ggplot2::theme(strip.text = ggplot2::element_text(face = "bold", size = 12, color = "white"),
+                   strip.background = ggplot2::element_rect(fill = cols[2], color = "black"),
+                   axis.text.y = ggplot2::element_text(size = 11, color = cols[2]),
+                   axis.text.x = if (show_x) ggplot2::element_text(size = 10) else ggplot2::element_blank()) +
+    ggplot2::scale_y_discrete(labels = function(y) str_wrap(y, width = 60)) +
+    ggplot2::labs(x = if (show_x) (if (kind == "dot") "GeneRatio" else "-log10(pvalue)") else NULL, y = NULL)
+  if (kind == "dot") {
+    base + ggplot2::geom_point(ggplot2::aes(x = GeneRatio, y = Description,
+                                            fill = neg_log10_pvalue, size = Count), shape = 21, colour = cols[2]) +
+      ggplot2::scale_fill_gradient(low = cols[1], high = cols[2], name = "-log10(pvalue)") +
+      ggplot2::scale_size_continuous(range = c(3, 6), name = "Count")
+  } else {
+    base + ggplot2::geom_col(ggplot2::aes(x = neg_log10_pvalue, y = Description, fill = Count), width = 0.7) +
+      ggplot2::scale_fill_gradient(low = cols[1], high = cols[2], name = "Count")
+  }
+}
+
+plots <- lapply(seq_along(onts), function(i) panel(onts[i], show_x = (i == length(onts))))
+pl <- Reduce(`/`, plots)
+
+rgraph_save(pl, p$outdir, rgraph_opt(p, "name", paste0("GO_", kind)),
+            width = rgraph_opt(p, "width", 11), height = rgraph_opt(p, "height", 10),
+            dpi = rgraph_opt(p, "dpi", 300))
+cat("RGRAPH_DONE: go_plot", kind, "ontologies=", paste(onts, collapse = "/"), "\n")
diff --git a/plugins/Presisitence/rgraph/rscripts/gsea.R b/plugins/Presisitence/rgraph/rscripts/gsea.R
new file mode 100644
index 0000000..495042b
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/gsea.R
@@ -0,0 +1,49 @@
+# gsea.R —— GSEA 分析（基于差异结果 log2FoldChange 排序 + gmt 基因集）
+# params: result(Dse2_result.csv), gmt(路径), outdir, minGSSize(5), maxGSSize(1000),
+#         pvalueCutoff(1), desc_map(可选 CSV: 列 ID,Description), top_n(每方向出图数,默认5),
+#         draw(bool,默认T), seed(123)
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])
+p <- rgraph_load_params()
+rgraph_library(c("clusterProfiler", "enrichplot", "ggplot2", "dplyr"))
+
+deg <- read.csv(p$result, check.names = FALSE)
+deg <- deg %>% dplyr::select(gene_id, log2FoldChange) %>% dplyr::filter(!is.na(log2FoldChange))
+gene_rank <- sort(setNames(deg$log2FoldChange, deg$gene_id), decreasing = TRUE)
+
+t2g <- clusterProfiler::read.gmt(p$gmt)
+set.seed(rgraph_opt(p, "seed", 123))
+gsea <- clusterProfiler::GSEA(geneList = gene_rank, TERM2GENE = t2g,
+                              pvalueCutoff = rgraph_opt(p, "pvalueCutoff", 1),
+                              pAdjustMethod = "BH",
+                              minGSSize = rgraph_opt(p, "minGSSize", 5),
+                              maxGSSize = rgraph_opt(p, "maxGSSize", 1000))
+res <- gsea@result
+
+if (!is.null(p$desc_map)) {
+  dm <- read.csv(p$desc_map, stringsAsFactors = FALSE)
+  if (all(c("ID", "Description") %in% names(dm))) {
+    res$Description <- dm$Description[match(res$ID, dm$ID)]
+  }
+}
+rgraph_write_csv(res, p$outdir, rgraph_opt(p, "name", "GSEA_result"))
+
+# ---- 各方向 top-N 通路 ES 图 -----------------------------------------------
+if (isTRUE(rgraph_opt(p, "draw", TRUE)) && nrow(res) > 0) {
+  top_n <- rgraph_opt(p, "top_n", 5)
+  draw_dir <- function(sub, sign_dir) {
+    if (nrow(sub) == 0) return(invisible())
+    sub <- sub[order(sub$pvalue), ]
+    sub <- utils::head(sub, top_n)
+    for (id in sub$ID) {
+      idx <- which(gsea@result$ID == id)
+      ttl <- paste0(res$Description[match(id, res$ID)], " [", id, "]")
+      pl <- enrichplot::gseaplot2(gsea, geneSetID = idx, ES_geom = "line", title = ttl)
+      rgraph_save(pl, file.path(p$outdir, sign_dir), gsub("[^A-Za-z0-9]", "_", id),
+                  width = 8, height = 8, dpi = rgraph_opt(p, "dpi", 300))
+    }
+  }
+  draw_dir(res[res$NES > 0, ], "UP")
+  draw_dir(res[res$NES < 0, ], "DOWN")
+}
+cat("RGRAPH_DONE: gsea genesets=", nrow(res), "\n")
diff --git a/plugins/Presisitence/rgraph/rscripts/gsea_mountain.R b/plugins/Presisitence/rgraph/rscripts/gsea_mountain.R
new file mode 100644
index 0000000..8192941
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/gsea_mountain.R
@@ -0,0 +1,46 @@
+# gsea_mountain.R —— GSEA 山峦图（多通路 core_enrichment 基因的 log2FC 分布 + NES 点）
+# params: gsea_result(gsea.R 产出的 CSV, 含 ID,Description,NES,pvalue,core_enrichment),
+#         result(Dse2_result.csv, 取 gene log2FC), outdir,
+#         geneset_ids(可选,要画的通路 ID 向量; 缺省取 pvalue 最小的前 n_top 个),
+#         n_top(15), dpi(300), width(15),height(13), name
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])
+p <- rgraph_load_params()
+rgraph_library(c("dplyr", "tidyr", "ggplot2", "ggridges", "ggnewscale"))
+
+deg <- read.csv(p$result, check.names = FALSE)
+gene_rank <- setNames(deg$log2FoldChange, deg$gene_id)
+
+gr <- read.csv(p$gsea_result, stringsAsFactors = FALSE, check.names = FALSE)
+if (!is.null(p$geneset_ids)) {
+  gr <- gr[gr$ID %in% p$geneset_ids, ]
+} else {
+  gr <- gr[order(gr$pvalue), ]
+  gr <- utils::head(gr, rgraph_opt(p, "n_top", 15))
+}
+if (nrow(gr) == 0) stop("没有可用于山峦图的通路")
+
+df <- gr %>%
+  dplyr::select(Description, NES, pvalue, core_enrichment) %>%
+  tidyr::separate_rows(core_enrichment, sep = "/") %>%
+  dplyr::mutate(log2FoldChange = gene_rank[core_enrichment],
+                neg_log10_pvalue = -log10(pvalue)) %>%
+  dplyr::filter(!is.na(log2FoldChange))
+
+min_fc <- min(df$log2FoldChange) - 1
+pl <- ggplot2::ggplot(df) +
+  ggridges::geom_density_ridges(ggplot2::aes(x = log2FoldChange, y = Description,
+                                             fill = neg_log10_pvalue), scale = 0.9) +
+  ggplot2::scale_fill_gradient(low = "#e6e2f9", high = "#998ec3", name = "-log10(pvalue)") +
+  ggnewscale::new_scale_fill() +
+  ggplot2::geom_point(ggplot2::aes(x = min_fc, y = Description, size = abs(NES), fill = NES),
+                      shape = 21) +
+  ggplot2::scale_fill_gradient2(low = "#4575b4", mid = "#f7f7f7", high = "#d73027", name = "NES") +
+  ggplot2::scale_size(range = c(5, 10), guide = "none") +
+  theme_rgraph() +
+  ggplot2::labs(x = "log2FoldChange", y = NULL)
+
+rgraph_save(pl, p$outdir, rgraph_opt(p, "name", "Mountain_plot"),
+            width = rgraph_opt(p, "width", 15), height = rgraph_opt(p, "height", 13),
+            dpi = rgraph_opt(p, "dpi", 300))
+cat("RGRAPH_DONE: gsea_mountain genesets=", nrow(gr), "\n")
diff --git a/plugins/Presisitence/rgraph/rscripts/heatmap.R b/plugins/Presisitence/rgraph/rscripts/heatmap.R
new file mode 100644
index 0000000..34ceeca
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/heatmap.R
@@ -0,0 +1,73 @@
+# heatmap.R —— 差异基因/指定基因表达聚类热图
+# 引擎: pheatmap(默认) 或 complexheatmap(按分组加顶部色块标签、列不聚类)
+# params: fpkm, sample_group, outdir, deg(可选 Deg_all.csv 路径), genes(可选基因向量),
+#         engine("pheatmap"|"complexheatmap"), scale("row"|"column"|"none"),
+#         cluster_rows(T), cluster_cols(T), show_rownames(默认自动),
+#         low/mid/high 颜色, dpi(300), width,height, name
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])
+p <- rgraph_load_params()
+rgraph_library(c("dplyr"))
+
+fpkm <- rgraph_read_matrix(p$fpkm)
+sg <- rgraph_read_sample_group(p$sample_group)
+rgraph_check_samples(fpkm, sg$sample_name)
+
+# 选基因
+if (!is.null(p$genes)) {
+  genes <- p$genes
+} else if (!is.null(p$deg)) {
+  deg <- read.csv(p$deg, check.names = FALSE)
+  genes <- deg$gene_id
+} else {
+  genes <- fpkm$gene_id
+}
+sub <- fpkm[fpkm$gene_id %in% genes, c("gene_id", sg$sample_name)]
+rownames(sub) <- sub$gene_id; sub$gene_id <- NULL
+sub <- as.matrix(sub); sub[is.na(sub)] <- 0
+sub <- log2(sub + 1)
+sub <- sub[rowSums(sub) != 0, , drop = FALSE]
+sub <- sub[, colSums(sub) != 0, drop = FALSE]
+if (nrow(sub) < 2) stop("可用于热图的基因不足 2 个")
+
+scale_by <- rgraph_opt(p, "scale", "row")
+show_rn <- rgraph_opt(p, "show_rownames", nrow(sub) <= 50)
+low  <- rgraph_opt(p, "low", "blue"); mid <- rgraph_opt(p, "mid", "white"); high <- rgraph_opt(p, "high", "red")
+nm <- rgraph_opt(p, "name", "heatmap")
+w <- rgraph_opt(p, "width", 8); h <- rgraph_opt(p, "height", 8); dpi <- rgraph_opt(p, "dpi", 300)
+engine <- rgraph_opt(p, "engine", "pheatmap")
+
+if (engine == "complexheatmap") {
+  rgraph_library(c("ComplexHeatmap", "circlize"))
+  sc <- t(scale(t(sub)))
+  col_fun <- circlize::colorRamp2(c(min(sc), 0, max(sc)), c(low, mid, high))
+  grp <- factor(sg$group_name[match(colnames(sub), sg$sample_name)],
+                levels = unique(sg$group_name))
+  gcol <- rgraph_palette(nlevels(grp), rgraph_opt(p, "palette", "course"))
+  names(gcol) <- levels(grp)
+  ha <- ComplexHeatmap::HeatmapAnnotation(
+    Group = ComplexHeatmap::anno_block(gp = grid::gpar(fill = gcol, col = NA),
+      labels = levels(grp), labels_gp = grid::gpar(col = "white", fontsize = 8, fontface = "bold")),
+    show_annotation_name = FALSE, height = grid::unit(0.3, "cm"))
+  ht <- ComplexHeatmap::Heatmap(sc, col = col_fun, cluster_rows = TRUE, cluster_columns = FALSE,
+    column_split = grp, show_row_names = show_rn, top_annotation = ha,
+    column_names_rot = 45, name = "Z-score")
+  rgraph_save_base(p$outdir, nm, function() ComplexHeatmap::draw(ht),
+                   width = w, height = h, dpi = dpi)
+} else {
+  rgraph_library(c("pheatmap"))
+  pal <- grDevices::colorRampPalette(c(low, mid, high))(100)
+  rgraph_ensure_dir(p$outdir)
+  for (fmt in c("png", "pdf")) {
+    f <- file.path(p$outdir, paste0(nm, ".", fmt))
+    pheatmap::pheatmap(sub, scale = scale_by,
+                       cluster_rows = rgraph_opt(p, "cluster_rows", TRUE),
+                       cluster_cols = rgraph_opt(p, "cluster_cols", TRUE),
+                       color = pal, border_color = NA,
+                       show_rownames = show_rn, show_colnames = TRUE,
+                       fontsize_col = 10, angle_col = 45,
+                       width = w, height = h, filename = f)
+    rgraph_emit(f)
+  }
+}
+cat("RGRAPH_DONE: heatmap engine=", engine, "genes=", nrow(sub), "samples=", ncol(sub), "\n")
diff --git a/plugins/Presisitence/rgraph/rscripts/interaction_diff.R b/plugins/Presisitence/rgraph/rscripts/interaction_diff.R
new file mode 100644
index 0000000..722c0a5
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/interaction_diff.R
@@ -0,0 +1,55 @@
+# interaction_diff.R —— 两因子交互作用差异分析（DESeq2 ~g1+g2+g1:g2，提取交互项）
+# 输入: count, sample_group(需 group_name1,group_name2,TvsC1,TvsC2)
+# params: count, sample_group, outdir, sig_metric("padj"|"pvalue"), pcut(0.05), log2fc(1),
+#         min_mean_count(2)
+# 输出: 01.DEseq2_result.csv + 02/03/04 Deg_all/down/up（可直接喂 rgraph_volcano）
+# 注: 需 DESeq2（本机默认缺，按提示安装）。
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])
+p <- rgraph_load_params()
+rgraph_library(c("dplyr", "DESeq2"))
+
+sig_metric <- rgraph_opt(p, "sig_metric", "padj")
+pcut <- rgraph_opt(p, "pcut", 0.05)
+log2fc <- rgraph_opt(p, "log2fc", 1)
+min_mean <- rgraph_opt(p, "min_mean_count", 2)
+
+count <- rgraph_read_matrix(p$count)
+sg <- read.csv(p$sample_group, header = TRUE, check.names = FALSE, stringsAsFactors = FALSE)
+sg <- sg[!is.na(sg$sample_name) & sg$sample_name != "", , drop = FALSE]
+for (col in c("group_name1", "group_name2", "TvsC1", "TvsC2"))
+  if (!col %in% names(sg)) stop(paste0("sample_group 缺少列: ", col, "（两因子交互需要）"))
+rgraph_check_samples(count, sg$sample_name)
+
+t1 <- unique(sg$group_name1[sg$TvsC1 == "treatment"]); c1 <- unique(sg$group_name1[sg$TvsC1 == "control"])
+t2 <- unique(sg$group_name2[sg$TvsC2 == "treatment"]); c2 <- unique(sg$group_name2[sg$TvsC2 == "control"])
+
+mat <- count[, c("gene_id", sg$sample_name)]
+rownames(mat) <- mat$gene_id; mat$gene_id <- NULL; mat[is.na(mat)] <- 0
+cd <- data.frame(row.names = sg$sample_name,
+                 g1 = factor(sg$group_name1, levels = c(c1, t1)),
+                 g2 = factor(sg$group_name2, levels = c(c2, t2)))
+dds <- DESeq2::DESeqDataSetFromMatrix(round(as.matrix(mat)), cd, design = ~ g1 + g2 + g1:g2)
+dds <- DESeq2::DESeq(dds)
+norm <- DESeq2::counts(dds, normalized = TRUE)
+norm <- data.frame(gene_id = rownames(norm), norm, row.names = NULL, check.names = FALSE)
+
+# 自动定位交互项系数（形如 g1XXX.g2YYY）
+inter <- grep("g1.*g2", DESeq2::resultsNames(dds), value = TRUE)
+if (length(inter) == 0) stop("未找到交互项系数，请检查分组设计")
+res <- as.data.frame(DESeq2::results(dds, name = inter[length(inter)]))
+res <- data.frame(gene_id = rownames(res), res[, c("log2FoldChange", "pvalue", "padj")], row.names = NULL)
+result <- merge(norm, res, by = "gene_id", all = TRUE)
+
+ns <- length(sg$sample_name); sc <- 2:(ns + 1)
+result <- result[rowSums(result[, sc] == 0) < ns * 0.5, ]
+result <- result[rowMeans(result[, sc]) >= min_mean, ]
+result <- result[!is.na(result$pvalue) & !is.na(result$padj), ]
+metric <- result[[sig_metric]]
+deg_all <- result[metric < pcut & abs(result$log2FoldChange) > log2fc, ]
+
+rgraph_write_csv(result, p$outdir, "01.DEseq2_result")
+rgraph_write_csv(deg_all, p$outdir, "02.Deg_all")
+rgraph_write_csv(deg_all[deg_all$log2FoldChange < -log2fc, ], p$outdir, "03.Deg_down")
+rgraph_write_csv(deg_all[deg_all$log2FoldChange > log2fc, ], p$outdir, "04.Deg_up")
+cat("RGRAPH_DONE: interaction_diff term=", inter[length(inter)], "DEG=", nrow(deg_all), "\n")
diff --git a/plugins/Presisitence/rgraph/rscripts/interactive_volcano.R b/plugins/Presisitence/rgraph/rscripts/interactive_volcano.R
new file mode 100644
index 0000000..0db00d3
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/interactive_volcano.R
@@ -0,0 +1,42 @@
+# interactive_volcano.R —— 交互式火山图（plotly HTML，悬停显示基因/log2FC/-log10p）
+# params: result(Degs.csv 或 Dse2_result.csv), outdir, sig_metric("pvalue"|"padj"),
+#         pcut(0.05), log2fc(1), color_scheme("rb"|"rg"), include_no(bool,默认T), name
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])
+p <- rgraph_load_params()
+rgraph_library(c("ggplot2", "dplyr", "plotly", "htmlwidgets"))
+
+sig <- rgraph_opt(p, "sig_metric", "pvalue")
+pcut <- rgraph_opt(p, "pcut", 0.05)
+log2fc <- rgraph_opt(p, "log2fc", 1)
+
+d <- read.csv(p$result, header = TRUE, check.names = FALSE)
+d <- d[!is.na(d[[sig]]) & !is.na(d$log2FoldChange), ]
+m <- d[[sig]]
+d$color <- ifelse(m < pcut & d$log2FoldChange > log2fc, "Up",
+           ifelse(m < pcut & d$log2FoldChange < -log2fc, "Down", "No"))
+if (!isTRUE(rgraph_opt(p, "include_no", TRUE))) d <- d[d$color != "No", ]
+cnt <- table(factor(d$color, levels = c("Up", "Down", "No")))
+labs3 <- c(Up = sprintf("Up (%d)", cnt["Up"]), Down = sprintf("Down (%d)", cnt["Down"]),
+           No = sprintf("No (%d)", cnt["No"]))
+cols <- rgraph_updown_colors(rgraph_opt(p, "color_scheme", "rb"))
+
+gcol <- if ("gene_id" %in% names(d)) d$gene_id else rownames(d)
+pl <- ggplot2::ggplot(d, ggplot2::aes(x = log2FoldChange, y = -log10(.data[[sig]]), color = color,
+        text = paste0("Gene: ", gcol, "<br>log2FC: ", round(log2FoldChange, 3),
+                      "<br>-log10(", sig, "): ", round(-log10(.data[[sig]]), 3)))) +
+  ggplot2::geom_point(alpha = 0.5, size = 1) +
+  ggplot2::scale_color_manual(values = cols, breaks = c("Up", "Down", "No"), labels = labs3) +
+  ggplot2::geom_hline(yintercept = -log10(pcut), linetype = "dashed", color = "grey") +
+  ggplot2::geom_vline(xintercept = c(-log2fc, log2fc), linetype = "dashed", color = "grey") +
+  ggplot2::labs(x = "log2 (Fold Change)", y = sprintf("-log10(%s)", sig), color = "Trend") +
+  theme_rgraph()
+
+ip <- plotly::ggplotly(pl, tooltip = "text", width = 850, height = 620)
+rgraph_ensure_dir(p$outdir)
+f <- file.path(p$outdir, paste0(rgraph_opt(p, "name", "volcano_interactive"), ".html"))
+# selfcontained=TRUE 需 pandoc；默认 FALSE(会伴生一个 _files 依赖目录)，无需 pandoc
+htmlwidgets::saveWidget(ip, f, selfcontained = isTRUE(rgraph_opt(p, "selfcontained", FALSE)),
+                        title = "Interactive Volcano Plot")
+rgraph_emit(f)
+cat("RGRAPH_DONE: interactive_volcano up=", cnt["Up"], "down=", cnt["Down"], "\n")
diff --git a/plugins/Presisitence/rgraph/rscripts/kegg_plot.R b/plugins/Presisitence/rgraph/rscripts/kegg_plot.R
new file mode 100644
index 0000000..0b6e8a5
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/kegg_plot.R
@@ -0,0 +1,44 @@
+# kegg_plot.R —— KEGG 富集条形图/气泡图
+# 输入: KEGG_enrich.csv (列: Description, GeneRatio, pvalue, Count)
+# params: enrich, outdir, kind("bar"|"dot"), top_n(30), dpi(300), width(9),height(7), name
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])
+p <- rgraph_load_params()
+rgraph_library(c("dplyr", "ggplot2", "stringr"))
+
+parse_ratio <- function(x) vapply(x, function(s) {
+  s <- as.character(s)
+  if (grepl("/", s)) { a <- as.numeric(strsplit(s, "/")[[1]]); a[1] / a[2] } else as.numeric(s)
+}, numeric(1))
+
+kind <- rgraph_opt(p, "kind", "dot")
+top_n <- rgraph_opt(p, "top_n", 30)
+
+d <- read.csv(p$enrich, stringsAsFactors = FALSE, check.names = FALSE)
+d$neg_log10_pvalue <- -log10(d$pvalue)
+d$GeneRatio <- parse_ratio(d$GeneRatio)
+d <- d %>% dplyr::arrange(pvalue) %>% dplyr::slice(seq_len(min(top_n, dplyr::n())))
+d$Description <- str_remove(d$Description, "\\(.*")   # 去掉括号后缀
+ord <- if (kind == "dot") order(d$neg_log10_pvalue, decreasing = TRUE) else order(d$GeneRatio, decreasing = TRUE)
+d$Description <- factor(d$Description, levels = rev(d$Description[ord]))
+
+if (kind == "dot") {
+  pl <- ggplot2::ggplot(d) +
+    ggplot2::geom_point(ggplot2::aes(x = GeneRatio, y = Description, fill = neg_log10_pvalue,
+                                     size = Count), shape = 21, colour = "grey20") +
+    ggplot2::scale_fill_gradient(low = "yellow", high = "red", name = "-log10(pvalue)") +
+    ggplot2::scale_size(range = c(2, 6), name = "Count") +
+    ggplot2::labs(x = "GeneRatio", y = NULL)
+} else {
+  pl <- ggplot2::ggplot(d) +
+    ggplot2::geom_col(ggplot2::aes(x = neg_log10_pvalue, y = Description, fill = GeneRatio)) +
+    ggplot2::scale_fill_gradient(low = "yellow", high = "red", name = "GeneRatio") +
+    ggplot2::labs(x = "-log10(pvalue)", y = NULL)
+}
+pl <- pl + theme_rgraph() +
+  ggplot2::scale_y_discrete(labels = function(y) str_wrap(y, width = 55))
+
+rgraph_save(pl, p$outdir, rgraph_opt(p, "name", paste0("KEGG_", kind)),
+            width = rgraph_opt(p, "width", 9), height = rgraph_opt(p, "height", 7),
+            dpi = rgraph_opt(p, "dpi", 300))
+cat("RGRAPH_DONE: kegg_plot", kind, "pathways=", nrow(d), "\n")
diff --git a/plugins/Presisitence/rgraph/rscripts/kmeans.R b/plugins/Presisitence/rgraph/rscripts/kmeans.R
new file mode 100644
index 0000000..364bb99
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/kmeans.R
@@ -0,0 +1,86 @@
+# kmeans.R —— 目标基因 K-means 聚类：肘部图 + 聚类热图 + 各簇表达趋势 + 成员表
+# params: fpkm, sample_group, outdir, gene_list(CSV, gene_id) 或 genes(向量),
+#         k(簇数,默认4), kmax(肘部图最大k,默认10), seed(123), dpi(300)
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])
+p <- rgraph_load_params()
+rgraph_library(c("dplyr", "tidyr", "ggplot2", "pheatmap"))
+
+k <- rgraph_opt(p, "k", 4)
+fpkm <- rgraph_read_matrix(p$fpkm)
+sg <- rgraph_read_sample_group(p$sample_group)
+genes <- if (!is.null(p$genes)) p$genes else read.csv(p$gene_list, check.names = FALSE)$gene_id
+
+sub <- fpkm[fpkm$gene_id %in% genes, c("gene_id", sg$sample_name), drop = FALSE]
+rownames(sub) <- sub$gene_id; sub$gene_id <- NULL
+m <- as.matrix(sub); m[is.na(m)] <- 0
+logm <- log2(m + 1)
+logm <- logm[rowSums(logm) != 0, , drop = FALSE]
+logm <- logm[, colSums(logm) != 0, drop = FALSE]
+z <- t(scale(t(logm)))
+z[is.na(z)] <- 0
+if (nrow(z) < k) stop("基因数少于簇数 k")
+
+# ---- 肘部图（base wss，不依赖 factoextra）---------------------------------
+set.seed(rgraph_opt(p, "seed", 123))
+kmax <- min(rgraph_opt(p, "kmax", 10), nrow(z) - 1)
+wss <- sapply(1:kmax, function(kk) kmeans(z, centers = kk, nstart = 10)$tot.withinss)
+elbow <- data.frame(k = 1:kmax, wss = wss)
+pl_e <- ggplot2::ggplot(elbow, ggplot2::aes(k, wss)) +
+  ggplot2::geom_line(color = "#4575b4") + ggplot2::geom_point(size = 2, color = "#4575b4") +
+  ggplot2::scale_x_continuous(breaks = 1:kmax) +
+  ggplot2::labs(x = "Number of clusters k", y = "Total within-cluster SS",
+                title = "Elbow method") + theme_rgraph()
+rgraph_save(pl_e, p$outdir, "elbow", width = 7, height = 5, dpi = rgraph_opt(p, "dpi", 300))
+
+# ---- K-means + 成员表 -------------------------------------------------------
+set.seed(rgraph_opt(p, "seed", 123))
+km <- kmeans(z, centers = k, nstart = 25)
+for (cn in 1:k)
+  rgraph_write_csv(data.frame(gene_id = names(km$cluster[km$cluster == cn])),
+                   p$outdir, paste0("cluster_", cn, "_genes"))
+
+# ---- 聚类热图（pheatmap: 行按簇排序 + 行/列注释）--------------------------
+ord <- order(km$cluster)
+zo <- z[ord, , drop = FALSE]
+cl <- km$cluster[ord]
+ann_row <- data.frame(Cluster = factor(cl)); rownames(ann_row) <- rownames(zo)
+ann_col <- data.frame(Group = factor(sg$group_name[match(colnames(zo), sg$sample_name)],
+                                     levels = unique(sg$group_name)))
+rownames(ann_col) <- colnames(zo)
+gcol <- rgraph_palette(nlevels(ann_col$Group), rgraph_opt(p, "palette", "course"))
+names(gcol) <- levels(ann_col$Group)
+ccol <- rgraph_palette(k, "colorblind")[1:k]; names(ccol) <- as.character(1:k)
+pal <- grDevices::colorRampPalette(c("blue", "white", "red"))(100)
+gaps <- cumsum(as.numeric(table(cl)))
+for (fmt in c("png", "pdf")) {
+  f <- file.path(p$outdir, paste0("kmeans_heatmap.", fmt))
+  rgraph_ensure_dir(p$outdir)
+  pheatmap::pheatmap(zo, scale = "none", cluster_rows = FALSE, cluster_cols = FALSE,
+                     color = pal, border_color = NA, show_rownames = nrow(zo) <= 60,
+                     annotation_row = ann_row, annotation_col = ann_col,
+                     annotation_colors = list(Cluster = ccol, Group = gcol),
+                     gaps_row = gaps, fontsize_col = 9, angle_col = 45,
+                     width = 8, height = max(5, nrow(zo) * 0.15 + 2), filename = f)
+  rgraph_emit(f)
+}
+
+# ---- 各簇表达趋势线 --------------------------------------------------------
+long <- as.data.frame(z) %>% dplyr::mutate(gene_id = rownames(z), Cluster = factor(km$cluster)) %>%
+  tidyr::pivot_longer(cols = -c(gene_id, Cluster), names_to = "Sample", values_to = "Expression")
+long$Sample <- factor(long$Sample, levels = colnames(z))
+for (cn in 1:k) {
+  cc <- long %>% dplyr::filter(Cluster == cn)
+  sm <- cc %>% dplyr::group_by(Sample) %>%
+    dplyr::summarise(mean = mean(Expression), lo = min(Expression), hi = max(Expression), .groups = "drop")
+  pl <- ggplot2::ggplot() +
+    ggplot2::geom_point(data = cc, ggplot2::aes(Sample, Expression), color = "grey70", size = 1.5, alpha = 0.5) +
+    ggplot2::geom_ribbon(data = sm, ggplot2::aes(Sample, ymin = lo, ymax = hi, group = 1), fill = "red", alpha = 0.2) +
+    ggplot2::geom_line(data = sm, ggplot2::aes(Sample, mean, group = 1), color = "red", linewidth = 1) +
+    ggplot2::geom_point(data = sm, ggplot2::aes(Sample, mean), fill = "red", shape = 21, size = 3, color = "white") +
+    ggplot2::labs(title = paste0("Cluster ", cn, " (n=", sum(km$cluster == cn), ")"),
+                  x = NULL, y = "Z-score") + theme_rgraph() +
+    ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, hjust = 1))
+  rgraph_save(pl, p$outdir, paste0("cluster_", cn, "_trend"), width = 8, height = 5, dpi = rgraph_opt(p, "dpi", 300))
+}
+cat("RGRAPH_DONE: kmeans k=", k, "genes=", nrow(z), "\n")
diff --git a/plugins/Presisitence/rgraph/rscripts/multilevel_scatter.R b/plugins/Presisitence/rgraph/rscripts/multilevel_scatter.R
new file mode 100644
index 0000000..6cdb7d2
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/multilevel_scatter.R
@@ -0,0 +1,43 @@
+# multilevel_scatter.R —— 多级分组基因表达散点图（逐基因；主分组 x，次分组着色）
+# 输入: expr(gene_expression.csv: gene_id+各样本), sample_group(多级分组表)
+# params: expr, sample_group, gene_list(CSV) 或 genes(向量), outdir,
+#         sample_col(默认自动 sample_name/sample_name1), x_col(默认自动 group_name/group_name1),
+#         color_col(默认 group_name2, 无则同 x_col), dpi(300), width(8),height(8)
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])
+p <- rgraph_load_params()
+rgraph_library(c("dplyr", "tidyr", "ggplot2"))
+
+sg <- read.csv(p$sample_group, header = TRUE, check.names = FALSE, stringsAsFactors = FALSE)
+pick <- function(cands, override) {
+  if (!is.null(override) && override %in% names(sg)) return(override)
+  hit <- intersect(cands, names(sg)); if (length(hit)) hit[1] else stop(paste0("缺少列: ", cands[1]))
+}
+sample_col <- pick(c("sample_name", "sample_name1"), rgraph_opt(p, "sample_col", NULL))
+x_col <- pick(c("group_name", "group_name1", "group"), rgraph_opt(p, "x_col", NULL))
+color_col <- rgraph_opt(p, "color_col", if ("group_name2" %in% names(sg)) "group_name2" else x_col)
+
+expr <- rgraph_read_matrix(p$expr)
+genes <- if (!is.null(p$genes)) p$genes else read.csv(p$gene_list, check.names = FALSE)$gene_id
+cols <- rgraph_palette(length(unique(sg[[color_col]])), rgraph_opt(p, "palette", "course"))
+
+n_ok <- 0
+for (g in genes) {
+  row <- expr[expr$gene_id == g, , drop = FALSE]
+  if (nrow(row) == 0) next
+  vals <- as.numeric(row[1, intersect(sg[[sample_col]], names(expr))])
+  df <- data.frame(sample = intersect(sg[[sample_col]], names(expr)), expression = vals)
+  df <- merge(df, sg, by.x = "sample", by.y = sample_col)
+  df$.x <- factor(df[[x_col]], levels = unique(sg[[x_col]]))
+  df$.col <- factor(df[[color_col]], levels = unique(sg[[color_col]]))
+  pl <- ggplot2::ggplot(df, ggplot2::aes(x = .x, y = expression, color = .col)) +
+    ggplot2::geom_point(position = ggplot2::position_dodge(0.6), size = 4, alpha = 0.7) +
+    ggplot2::scale_color_manual(values = cols, name = color_col) +
+    ggplot2::labs(title = g, x = NULL, y = "Expression") + theme_rgraph()
+  rgraph_save(pl, p$outdir, paste0(gsub("[^A-Za-z0-9_.-]", "_", g), "_expression"),
+              width = rgraph_opt(p, "width", 8), height = rgraph_opt(p, "height", 8),
+              dpi = rgraph_opt(p, "dpi", 300))
+  n_ok <- n_ok + 1
+}
+if (n_ok == 0) stop("gene_list 中没有基因命中表达矩阵")
+cat("RGRAPH_DONE: multilevel_scatter genes=", n_ok, "\n")
diff --git a/plugins/Presisitence/rgraph/rscripts/network.R b/plugins/Presisitence/rgraph/rscripts/network.R
new file mode 100644
index 0000000..25aa8f8
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/network.R
@@ -0,0 +1,87 @@
+# network.R —— ggNetView 网络图（相关/共表达/宏基因组共现网络，按模块着色）
+# 两种输入模式：
+#   mode="matrix": expr(gene_id/OTU + 样本列) → build_graph_from_mat 构相关网络
+#                  (method: WGCNA/cor/SPARCC/SpiecEasi/Hmisc)；适合共表达/宏基因组共现
+#   mode="edge":   edges(前两列 from,to [+ weight]) [+ nodes(首列=id,附注释)]
+#                  → build_graph_from_node_edge；可直接吃 rgraph_wgcna 的 Cytoscape 边表、rgraph_ppi 的 Target_PPi
+# params(通用): mode, outdir, name, layout("gephi"), layout_module("random"),
+#   group_by("Modularity"), fill_by("Modularity"), color_by, label(F 或前缀字串),
+#   pointlabel(如 "top5"), jitter(F), add_outer(F), dpi(300), width(9),height(8)
+# params(matrix): expr, sample_group(可选), genes(可选子集), method("WGCNA"), cor_method("pearson"),
+#   r_threshold(0.7), p_threshold(0.05), transform("none"), node_annotation(CSV,首列=id), max_features(500)
+# params(edge): edges, nodes(可选), from_col, to_col, weight_col, directed(F)
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])
+p <- rgraph_load_params()
+rgraph_library(c("ggNetView", "ggplot2", "dplyr"))
+
+mode <- rgraph_opt(p, "mode", "matrix")
+
+if (mode == "edge") {
+  edges <- read.csv(p$edges, header = TRUE, check.names = FALSE, stringsAsFactors = FALSE,
+                    sep = rgraph_opt(p, "sep", ","))
+  if (ncol(edges) < 2) {
+    # 容错：可能是制表符/空格分隔
+    edges <- read.table(p$edges, header = TRUE, check.names = FALSE, stringsAsFactors = FALSE)
+  }
+  fc <- rgraph_opt(p, "from_col", names(edges)[1])
+  tc <- rgraph_opt(p, "to_col", names(edges)[2])
+  wc <- rgraph_opt(p, "weight_col", { wi <- which(tolower(names(edges)) == "weight"); if (length(wi)) names(edges)[wi[1]] else NA })
+  edge_df <- data.frame(from = as.character(edges[[fc]]), to = as.character(edges[[tc]]),
+                        stringsAsFactors = FALSE)
+  if (!is.na(wc) && wc %in% names(edges)) edge_df$weight <- as.numeric(edges[[wc]])
+  if (!is.null(p$nodes)) {
+    node_df <- read.csv(p$nodes, header = TRUE, check.names = FALSE, stringsAsFactors = FALSE)
+  } else {
+    node_df <- data.frame(name = unique(c(edge_df$from, edge_df$to)), stringsAsFactors = FALSE)
+  }
+  obj <- build_graph_from_node_edge(node = node_df, edge = edge_df,
+                                    directed = isTRUE(rgraph_opt(p, "directed", FALSE)),
+                                    top_modules = rgraph_opt(p, "top_modules", 15))
+} else {
+  expr <- rgraph_read_matrix(p$expr)
+  if (!is.null(p$sample_group)) {
+    sg <- rgraph_read_sample_group(p$sample_group); samples <- sg$sample_name
+  } else {
+    samples <- setdiff(names(expr), c("gene_id", "Length"))
+  }
+  if (!is.null(p$genes)) expr <- expr[expr$gene_id %in% p$genes, , drop = FALSE]
+  rownames(expr) <- expr$gene_id
+  mat <- as.matrix(expr[, samples, drop = FALSE]); mode(mat) <- "numeric"
+  mat[!is.finite(mat)] <- 0
+  # 无基因子集时按方差取 top max_features，控制网络规模/耗时/可读性
+  if (is.null(p$genes)) {
+    mf <- rgraph_opt(p, "max_features", 500)
+    if (nrow(mat) > mf) mat <- mat[order(apply(mat, 1, var), decreasing = TRUE)[seq_len(mf)], , drop = FALSE]
+  }
+  ann <- NULL
+  if (!is.null(p$node_annotation)) ann <- read.csv(p$node_annotation, check.names = FALSE, stringsAsFactors = FALSE)
+  obj <- build_graph_from_mat(mat = mat,
+                              transfrom.method = rgraph_opt(p, "transform", "none"),
+                              method = rgraph_opt(p, "method", "WGCNA"),
+                              cor.method = rgraph_opt(p, "cor_method", "pearson"),
+                              proc = rgraph_opt(p, "proc", "BH"),
+                              r.threshold = rgraph_opt(p, "r_threshold", 0.7),
+                              p.threshold = rgraph_opt(p, "p_threshold", 0.05),
+                              node_annotation = ann,
+                              top_modules = rgraph_opt(p, "top_modules", 15))
+}
+
+pl <- ggNetView(graph_obj = obj,
+                layout = rgraph_opt(p, "layout", "gephi"),
+                layout.module = rgraph_opt(p, "layout_module", "random"),
+                group.by = rgraph_opt(p, "group_by", "Modularity"),
+                fill.by = rgraph_opt(p, "fill_by", "Modularity"),
+                color.by = rgraph_opt(p, "color_by", NULL),
+                pointsize = rgraph_opt(p, "pointsize", c(1, 6)),
+                jitter = isTRUE(rgraph_opt(p, "jitter", FALSE)),
+                add_outer = isTRUE(rgraph_opt(p, "add_outer", FALSE)),
+                label = rgraph_opt(p, "label", FALSE),
+                pointlabel = rgraph_opt(p, "pointlabel", NULL),
+                linealpha = rgraph_opt(p, "linealpha", 0.2),
+                linecolor = rgraph_opt(p, "linecolor", "#d9d9d9"))
+
+rgraph_save(pl, p$outdir, rgraph_opt(p, "name", "network"),
+            width = rgraph_opt(p, "width", 9), height = rgraph_opt(p, "height", 8),
+            dpi = rgraph_opt(p, "dpi", 300))
+cat("RGRAPH_DONE: network mode=", mode, "\n")
diff --git a/plugins/Presisitence/rgraph/rscripts/norm_matrix.R b/plugins/Presisitence/rgraph/rscripts/norm_matrix.R
new file mode 100644
index 0000000..286197d
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/norm_matrix.R
@@ -0,0 +1,36 @@
+# norm_matrix.R —— 表达矩阵标准化（DESeq2 中位数比值 / limma voom logCPM）
+# params: count(gene_count.csv), sample_group, outdir, method("deseq2"|"limma"),
+#         min_mean(limma 低表达过滤, 默认1), name(默认 normalized_count)
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])
+p <- rgraph_load_params()
+rgraph_library(c("dplyr"))
+
+method <- rgraph_opt(p, "method", "deseq2")
+count <- rgraph_read_matrix(p$count)
+sg <- rgraph_read_sample_group(p$sample_group)
+rgraph_check_samples(count, sg$sample_name)
+
+mat <- count[, c("gene_id", sg$sample_name)]
+rownames(mat) <- mat$gene_id; mat$gene_id <- NULL
+mat[is.na(mat)] <- 0
+grp <- factor(sg$group[match(colnames(mat), sg$sample_name)])
+
+if (method == "limma") {
+  rgraph_library(c("limma"))
+  ns <- length(sg$sample_name)
+  keep <- rowSums(mat == 0) < ns * 0.5 & rowMeans(mat) >= rgraph_opt(p, "min_mean", 1)
+  mat <- mat[keep, , drop = FALSE]
+  design <- model.matrix(~0 + grp)
+  v <- limma::voom(as.matrix(mat), design)
+  norm <- 2^v$E
+} else {
+  rgraph_library(c("DESeq2"))
+  coldata <- data.frame(row.names = colnames(mat), group = grp)
+  dds <- DESeq2::DESeqDataSetFromMatrix(round(as.matrix(mat)), coldata, design = ~group)
+  dds <- DESeq2::estimateSizeFactors(dds)
+  norm <- DESeq2::counts(dds, normalized = TRUE)
+}
+out <- data.frame(gene_id = rownames(norm), norm, row.names = NULL, check.names = FALSE)
+rgraph_write_csv(out, p$outdir, rgraph_opt(p, "name", "normalized_count"))
+cat("RGRAPH_DONE: norm_matrix", method, "genes=", nrow(out), "\n")
diff --git a/plugins/Presisitence/rgraph/rscripts/normalize.R b/plugins/Presisitence/rgraph/rscripts/normalize.R
new file mode 100644
index 0000000..c2573d7
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/normalize.R
@@ -0,0 +1,37 @@
+# normalize.R —— 由 count 计算 FPKM 或 TPM
+# params: count(gene_count.csv 路径), sample_group(路径,可选), outdir,
+#         method("fpkm"|"tpm"), out_name(默认 gene_fpkm / gene_tpm)
+# 输入列: gene_id, Length, <各样本 count 列>
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])          # _common.R
+p <- rgraph_load_params()   # 读取 .args[[1]] 指向的参数文件
+rgraph_library(c("dplyr"))
+
+method <- rgraph_opt(p, "method", "fpkm")
+gene_count <- rgraph_read_matrix(p$count)
+if (!"Length" %in% names(gene_count)) stop("gene_count 缺少 Length 列 (基因长度)，无法计算 FPKM/TPM")
+
+# 样本列：优先按 sample_group，否则用除 gene_id/Length 外的所有数值列
+if (!is.null(p$sample_group)) {
+  sg <- rgraph_read_sample_group(p$sample_group)
+  sample_list <- sg$sample_name
+  rgraph_check_samples(gene_count, sample_list)
+} else {
+  sample_list <- setdiff(names(gene_count), c("gene_id", "Length"))
+}
+
+out <- gene_count[, "gene_id", drop = FALSE]
+for (s in sample_list) {
+  x <- gene_count[[s]]
+  x[is.na(x)] <- 0
+  if (identical(method, "tpm")) {
+    rpk <- x / gene_count$Length
+    out[[s]] <- rpk / sum(rpk, na.rm = TRUE) * 1e6
+  } else {
+    out[[s]] <- (x * 1e9) / gene_count$Length / sum(x, na.rm = TRUE)
+  }
+}
+
+out_name <- rgraph_opt(p, "out_name", if (identical(method, "tpm")) "gene_tpm" else "gene_fpkm")
+rgraph_write_csv(out, p$outdir, out_name)
+cat("RGRAPH_DONE: normalize", method, "genes=", nrow(out), "samples=", length(sample_list), "\n")
diff --git a/plugins/Presisitence/rgraph/rscripts/pathway_gene_heatmap.R b/plugins/Presisitence/rgraph/rscripts/pathway_gene_heatmap.R
new file mode 100644
index 0000000..7db6b35
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/pathway_gene_heatmap.R
@@ -0,0 +1,70 @@
+# pathway_gene_heatmap.R —— 通路富集基因的相关性热图 / 聚类热图（逐通路出图）
+# 输入: enrich(GO/KEGG 富集表, 需 Description 及基因列 geneID/Gene_symbol), fpkm, sample_group
+# params: enrich, fpkm, sample_group, outdir, kind("correlation"|"cluster"),
+#         id_col(富集表里与 fpkm gene_id 同类型的基因列, 默认 geneID),
+#         min_count(通路基因数下限,默认3), max_pathways(最多出图通路数,默认6),
+#         pathway_col(通路命名列, 默认 GOID/KEGGID/Description), dpi(300)
+# 注: id_col 的基因需与 fpkm 的 gene_id 同一 ID 类型（本流程 rgraph_enrich→rgraph_diff 天然一致）。
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])
+p <- rgraph_load_params()
+rgraph_library(c("dplyr", "tidyr", "ggplot2", "reshape2"))
+
+kind <- rgraph_opt(p, "kind", "correlation")
+id_col <- rgraph_opt(p, "id_col", "geneID")
+et <- read.csv(p$enrich, stringsAsFactors = FALSE, check.names = FALSE)
+fpkm <- rgraph_read_matrix(p$fpkm)
+sg <- rgraph_read_sample_group(p$sample_group)
+if (!id_col %in% names(et)) stop(paste0("富集表缺少基因列: ", id_col))
+
+# 通路命名列
+pcol <- rgraph_opt(p, "pathway_col",
+                   intersect(c("GOID", "KEGGID", "Description"), names(et))[1])
+et <- et[et$Count >= rgraph_opt(p, "min_count", 3), , drop = FALSE]
+et <- et[order(-et$Count), , drop = FALSE]
+et <- utils::head(et, rgraph_opt(p, "max_pathways", 6))
+
+sel_fpkm <- fpkm[, c("gene_id", sg$sample_name), drop = FALSE]
+n_ok <- 0
+for (i in seq_len(nrow(et))) {
+  pid <- gsub("[^A-Za-z0-9_.-]", "_", as.character(et[[pcol]][i]))
+  genes <- unique(trimws(strsplit(as.character(et[[id_col]][i]), "/")[[1]]))
+  sub <- sel_fpkm[sel_fpkm$gene_id %in% genes, , drop = FALSE]
+  if (nrow(sub) < 2) next
+  rownames(sub) <- sub$gene_id; sub$gene_id <- NULL
+  m <- as.matrix(sub); m[is.na(m)] <- 0
+  logm <- log2(m + 1)
+  logm <- logm[rowSums(logm) != 0, , drop = FALSE]
+  if (nrow(logm) < 2) next
+
+  if (kind == "cluster") {
+    rgraph_library(c("pheatmap"))
+    pal <- grDevices::colorRampPalette(c("blue", "white", "red"))(100)
+    for (fmt in c("png", "pdf")) {
+      f <- file.path(p$outdir, paste0(pid, ".", fmt))
+      rgraph_ensure_dir(p$outdir)
+      pheatmap::pheatmap(logm, scale = "row", cluster_rows = TRUE, cluster_cols = TRUE,
+                         color = pal, border_color = "grey80", show_rownames = nrow(logm) <= 60,
+                         fontsize_col = 10, angle_col = 45,
+                         width = 8, height = max(4, nrow(logm) * 0.2 + 2), filename = f)
+      rgraph_emit(f)
+    }
+  } else {
+    cm <- cor(t(logm), use = "pairwise.complete.obs")
+    df <- reshape2::melt(cm); df$size <- abs(df$value)
+    pl <- ggplot2::ggplot(df, ggplot2::aes(x = Var2, y = Var1, fill = value)) +
+      ggplot2::geom_tile(color = "white", linewidth = 0.4) +
+      ggplot2::geom_text(ggplot2::aes(label = round(value, 2)), size = 2.6) +
+      ggplot2::scale_fill_gradientn(colors = grDevices::colorRampPalette(c("#f3ec98", "#f7440e"))(100),
+                                    name = expression(R^2)) +
+      ggplot2::labs(x = NULL, y = NULL, title = as.character(et[[pcol]][i])) +
+      theme_rgraph() +
+      ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, hjust = 1)) +
+      ggplot2::coord_fixed()
+    sz <- max(5, nrow(logm) * 0.5)
+    rgraph_save(pl, p$outdir, pid, width = sz, height = sz, dpi = rgraph_opt(p, "dpi", 300))
+  }
+  n_ok <- n_ok + 1
+}
+if (n_ok == 0) stop("无通路满足条件（可能 id_col 基因与 fpkm 的 gene_id 类型不一致）")
+cat("RGRAPH_DONE: pathway_gene_heatmap", kind, "pathways=", n_ok, "\n")
diff --git a/plugins/Presisitence/rgraph/rscripts/pathway_trend.R b/plugins/Presisitence/rgraph/rscripts/pathway_trend.R
new file mode 100644
index 0000000..b5cd7fe
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/pathway_trend.R
@@ -0,0 +1,56 @@
+# pathway_trend.R —— 通路上下调基因统计图（发散条形/堆叠条形）
+# 输入: enrich(富集表, 需 Description 与 geneID["a/b/c"]), deg(Dse2_result, 需 gene_id,log2FoldChange)
+# params: enrich, deg, outdir, kind("diverge"|"stack"), top_n(默认全部), dpi(300), width(10),height(8), name
+# 说明: 默认 enrich 的 geneID 与 deg 的 gene_id 为同一 ID 类型(直接匹配)。
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])
+p <- rgraph_load_params()
+rgraph_library(c("dplyr", "tidyr", "ggplot2"))
+
+kind <- rgraph_opt(p, "kind", "diverge")
+et <- read.csv(p$enrich, stringsAsFactors = FALSE, check.names = FALSE)
+deg <- read.csv(p$deg, stringsAsFactors = FALSE, check.names = FALSE)
+if (!"geneID" %in% names(et)) stop("富集表缺少 geneID 列")
+
+pg <- et %>% dplyr::rowwise() %>%
+  dplyr::mutate(gene_id = strsplit(as.character(geneID), "/")) %>%
+  tidyr::unnest(gene_id) %>% dplyr::ungroup() %>%
+  dplyr::select(Description, gene_id) %>% dplyr::distinct()
+pg <- merge(pg, deg[, c("gene_id", "log2FoldChange")], by = "gene_id")
+pg$Trend <- ifelse(pg$log2FoldChange > 0, "Up", "Down")
+
+stat <- pg %>% dplyr::group_by(Description, Trend) %>%
+  dplyr::summarise(Count = dplyr::n(), .groups = "drop") %>%
+  tidyr::pivot_wider(names_from = Trend, values_from = Count, values_fill = list(Count = 0))
+if (!"Up" %in% names(stat)) stat$Up <- 0
+if (!"Down" %in% names(stat)) stat$Down <- 0
+
+# top_n 通路（按总数）
+if (!is.null(p$top_n)) {
+  stat <- stat %>% dplyr::mutate(tot = Up + Down) %>% dplyr::arrange(dplyr::desc(tot)) %>%
+    dplyr::slice(seq_len(min(p$top_n, dplyr::n()))) %>% dplyr::select(-tot)
+}
+
+if (kind == "stack") {
+  long <- stat %>% tidyr::pivot_longer(cols = c(Down, Up), names_to = "Trend", values_to = "Count")
+  pl <- ggplot2::ggplot(long, ggplot2::aes(x = Description, y = Count, fill = Trend)) +
+    ggplot2::geom_col(position = "stack", width = 0.7) +
+    ggplot2::geom_text(ggplot2::aes(label = Count), position = ggplot2::position_stack(vjust = 0.5), size = 3) +
+    ggplot2::scale_fill_manual(values = c(Down = "#157d9f", Up = "#f4736c")) +
+    ggplot2::labs(x = NULL, y = "Number of DEGs") + theme_rgraph() + ggplot2::coord_flip()
+} else {
+  stat$Down <- -stat$Down
+  long <- stat %>% tidyr::pivot_longer(cols = c(Down, Up), names_to = "Trend", values_to = "Count")
+  xmax <- max(abs(long$Count))
+  pl <- ggplot2::ggplot(long, ggplot2::aes(x = Count, y = Description, fill = Trend)) +
+    ggplot2::geom_col() +
+    ggplot2::geom_vline(xintercept = 0, lty = 4, lwd = 0.6, alpha = 0.8) +
+    ggplot2::scale_fill_manual(values = c(Down = "#157d9f", Up = "#f4736c")) +
+    ggplot2::scale_x_continuous(labels = function(x) abs(x), limits = c(-xmax, xmax)) +
+    ggplot2::labs(x = "Number of DEGs", y = NULL) + theme_rgraph()
+}
+
+rgraph_save(pl, p$outdir, rgraph_opt(p, "name", "pathway_trend"),
+            width = rgraph_opt(p, "width", 10), height = rgraph_opt(p, "height", 8),
+            dpi = rgraph_opt(p, "dpi", 300))
+cat("RGRAPH_DONE: pathway_trend", kind, "pathways=", nrow(stat), "\n")
diff --git a/plugins/Presisitence/rgraph/rscripts/pca.R b/plugins/Presisitence/rgraph/rscripts/pca.R
new file mode 100644
index 0000000..076b974
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/pca.R
@@ -0,0 +1,56 @@
+# pca.R —— 样本 PCA (log2(fpkm+1) + prcomp)，支持置信椭圆与 ggrepel 标签
+# params: fpkm, sample_group, outdir, ellipse(bool,默认F), label(bool,默认T),
+#         palette("course"|"colorblind"), dpi(300), width,height, name
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])
+p <- rgraph_load_params()
+rgraph_library(c("dplyr", "ggplot2"))
+use_repel <- requireNamespace("ggrepel", quietly = TRUE)
+
+fpkm <- rgraph_read_matrix(p$fpkm)
+sg <- rgraph_read_sample_group(p$sample_group)
+rgraph_check_samples(fpkm, sg$sample_name)
+
+mat <- fpkm[, sg$sample_name, drop = FALSE]
+mat[is.na(mat)] <- 0
+mat <- mat[rowSums(mat != 0) > 0, , drop = FALSE]                 # 去全 0 行
+mat <- mat[apply(mat, 1, function(x) length(unique(x)) > 1), , drop = FALSE]  # 去常数行
+logm <- log2(mat + 1)
+
+pca <- prcomp(t(logm), scale. = TRUE)
+varexp <- (pca$sdev^2) / sum(pca$sdev^2)
+scores <- as.data.frame(pca$x)
+scores$Sample <- rownames(scores)
+scores$Group <- sg$group_name[match(scores$Sample, sg$sample_name)]
+scores$Group <- factor(scores$Group, levels = unique(sg$group_name))
+
+cols <- rgraph_palette(length(levels(scores$Group)), rgraph_opt(p, "palette", "course"))
+
+pl <- ggplot2::ggplot(scores, ggplot2::aes(x = PC1, y = PC2, color = Group)) +
+  ggplot2::geom_point(size = 4, alpha = 0.85) +
+  ggplot2::scale_color_manual(values = cols) +
+  ggplot2::geom_hline(yintercept = 0, linetype = "dashed", color = "#bebebe") +
+  ggplot2::geom_vline(xintercept = 0, linetype = "dashed", color = "#bebebe") +
+  ggplot2::labs(title = "Principal Component Analysis (PCA)",
+                x = sprintf("PC1 (%.2f%%)", varexp[1] * 100),
+                y = sprintf("PC2 (%.2f%%)", varexp[2] * 100)) +
+  theme_rgraph()
+
+if (isTRUE(rgraph_opt(p, "ellipse", FALSE)) && nlevels(scores$Group) > 1)
+  pl <- pl + ggplot2::stat_ellipse(level = 0.95, linewidth = 0.3)
+
+if (isTRUE(rgraph_opt(p, "label", TRUE))) {
+  if (use_repel) {
+    pl <- pl + ggrepel::geom_text_repel(ggplot2::aes(label = Sample), size = 3,
+                                        color = "black", max.overlaps = Inf)
+  } else {
+    pl <- pl + ggplot2::geom_text(ggplot2::aes(label = Sample), vjust = -0.6, size = 2.5,
+                                  color = "black")
+  }
+}
+
+rgraph_save(pl, p$outdir, rgraph_opt(p, "name", "pca"),
+            width = rgraph_opt(p, "width", 6), height = rgraph_opt(p, "height", 5.5),
+            dpi = rgraph_opt(p, "dpi", 300))
+cat("RGRAPH_DONE: pca genes_used=", nrow(logm), "PC1=", round(varexp[1] * 100, 2),
+    "PC2=", round(varexp[2] * 100, 2), "\n")
diff --git a/plugins/Presisitence/rgraph/rscripts/ppi.R b/plugins/Presisitence/rgraph/rscripts/ppi.R
new file mode 100644
index 0000000..d59e109
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/ppi.R
@@ -0,0 +1,50 @@
+# ppi.R —— 基于 STRING 的差异基因蛋白互作网络边表
+# params: deg(Deg_all.csv, 列 gene_id), info(9606.protein.info.txt),
+#         links(9606.protein.links.txt), outdir, score(400),
+#         id_is_symbol(bool,默认F), orgdb(id 非 symbol 时用于 ENSEMBL->SYMBOL)
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])
+p <- rgraph_load_params()
+rgraph_library(c("dplyr"))
+
+deg <- read.csv(p$deg, stringsAsFactors = FALSE)
+info <- read.csv(p$info, sep = "\t", stringsAsFactors = FALSE)
+links <- read.csv(p$links, sep = " ", stringsAsFactors = FALSE)
+
+if (isTRUE(rgraph_opt(p, "id_is_symbol", FALSE))) {
+  genes <- data.frame(gene_id = unique(deg$gene_id))
+} else {
+  rgraph_library(c("clusterProfiler", p$orgdb))
+  genes <- suppressWarnings(clusterProfiler::bitr(deg$gene_id, fromType = "ENSEMBL",
+                                                  toType = "SYMBOL", OrgDb = p$orgdb))
+  genes <- data.frame(gene_id = unique(genes$SYMBOL))
+}
+
+# symbol -> string_protein_id
+genes <- merge(genes, info[, c("string_protein_id", "preferred_name")],
+               by.x = "gene_id", by.y = "preferred_name")
+
+dl <- merge(links, genes, by.x = "protein1", by.y = "string_protein_id")
+names(dl)[names(dl) == "gene_id"] <- "from_symbol"
+dl <- merge(dl, genes, by.x = "protein2", by.y = "string_protein_id")
+names(dl)[names(dl) == "gene_id"] <- "to_symbol"
+names(dl)[names(dl) == "protein1"] <- "from_protein"
+names(dl)[names(dl) == "protein2"] <- "to_protein"
+
+dl <- dl %>%
+  dplyr::select(from_protein, to_protein, from_symbol, to_symbol, combined_score) %>%
+  dplyr::rowwise() %>%
+  dplyr::mutate(a = pmin(from_symbol, to_symbol), b = pmax(from_symbol, to_symbol)) %>%
+  dplyr::ungroup() %>%
+  dplyr::distinct(a, b, .keep_all = TRUE) %>%
+  dplyr::select(-a, -b) %>%
+  dplyr::filter(combined_score >= rgraph_opt(p, "score", 400))
+
+rgraph_ensure_dir(p$outdir)
+f1 <- file.path(p$outdir, "Target_PPi.tsv")
+write.table(dl, f1, row.names = FALSE, sep = "\t"); rgraph_emit(f1)
+nodes <- length(unique(c(dl$from_symbol, dl$to_symbol)))
+f2 <- file.path(p$outdir, "Edge_Node_count.tsv")
+write.table(data.frame(Node_count = nodes, Edge_count = nrow(dl)), f2, row.names = FALSE, sep = "\t")
+rgraph_emit(f2)
+cat("RGRAPH_DONE: ppi nodes=", nodes, "edges=", nrow(dl), "\n")
diff --git a/plugins/Presisitence/rgraph/rscripts/quadrant.R b/plugins/Presisitence/rgraph/rscripts/quadrant.R
new file mode 100644
index 0000000..910c23e
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/quadrant.R
@@ -0,0 +1,66 @@
+# quadrant.R —— 九象限图 / 二象限图（两组差异比较的 log2FC 散点）
+# params: x(比较1的DEG CSV,列 gene_id,log2FoldChange,pvalue), y(比较2的DEG CSV),
+#         x_name, y_name, mode("nine"|"four"), pcut(0.05), log2fc(1), outdir, dpi,width,height, name
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])
+p <- rgraph_load_params()
+rgraph_library(c("dplyr", "ggplot2"))
+
+mode <- rgraph_opt(p, "mode", "nine")
+pcut <- rgraph_opt(p, "pcut", 0.05)
+log2fc <- rgraph_opt(p, "log2fc", 1)
+xn <- rgraph_opt(p, "x_name", "compare1")
+yn <- rgraph_opt(p, "y_name", "compare2")
+
+read_cmp <- function(path, nm) {
+  d <- read.csv(path, check.names = FALSE)
+  d <- d[!is.na(d$pvalue) & d$pvalue < pcut, ]
+  if (mode == "four") d <- d[abs(d$log2FoldChange) > log2fc, ]
+  d <- d[, c("gene_id", "log2FoldChange")]
+  names(d)[2] <- nm
+  d
+}
+dx <- read_cmp(p$x, xn); dy <- read_cmp(p$y, yn)
+m <- merge(dx, dy, by = "gene_id")
+X <- m[[xn]]; Y <- m[[yn]]
+
+if (mode == "four") {
+  m <- m[(X > 0 & Y > 0) | (X < 0 & Y < 0), ]
+  X <- m[[xn]]; Y <- m[[yn]]
+  m$cls <- ifelse(abs(X) >= abs(Y), "High", "Low")
+  cnt <- table(m$cls)
+  cmap <- c(High = "#FF3333", Low = "#00CCCC")
+  labs2 <- c(High = sprintf("High (%d)", cnt["High"]), Low = sprintf("Low (%d)", cnt["Low"]))
+  brk <- c("High", "Low")
+} else {
+  m$cls <- ifelse(X >= log2fc & Y >= log2fc, "Q1",
+           ifelse(abs(X) < log2fc & Y >= log2fc, "Q2",
+           ifelse(X <= -log2fc & Y >= log2fc, "Q3",
+           ifelse(X >= log2fc & abs(Y) < log2fc, "Q4",
+           ifelse(abs(X) < log2fc & abs(Y) < log2fc, "Q5",
+           ifelse(X <= -log2fc & abs(Y) < log2fc, "Q6",
+           ifelse(X >= log2fc & Y <= -log2fc, "Q7",
+           ifelse(abs(X) < log2fc & Y <= -log2fc, "Q8",
+           ifelse(X <= -log2fc & Y <= -log2fc, "Q9", "No")))))))))
+  cnt <- table(factor(m$cls, levels = paste0("Q", 1:9)))
+  cmap <- setNames(c("#ede746", "#FF9933", "#FF3333", "#FFCCFF", "grey80",
+                     "#FF66FF", "#CCFFFF", "#00FF99", "#00CCCC"), paste0("Q", 1:9))
+  labs2 <- setNames(sprintf("quadrant.%d (%d)", 1:9, cnt), paste0("Q", 1:9))
+  brk <- paste0("Q", 1:9)
+}
+rgraph_write_csv(m, p$outdir, if (mode == "four") "Four_quadrant_table" else "Nine_quadrant_table")
+
+pl <- ggplot2::ggplot(m, ggplot2::aes(x = .data[[xn]], y = .data[[yn]], color = cls)) +
+  ggplot2::geom_point(alpha = 0.8, size = 0.7) +
+  ggplot2::scale_color_manual(values = cmap, breaks = brk, labels = labs2) +
+  ggplot2::geom_hline(yintercept = c(-log2fc, log2fc), linetype = "dashed", color = "grey") +
+  ggplot2::geom_vline(xintercept = c(-log2fc, log2fc), linetype = "dashed", color = "grey") +
+  ggplot2::labs(x = sprintf("log2FoldChange(%s)", xn), y = sprintf("log2FoldChange(%s)", yn),
+                color = sprintf("pvalue<%s", pcut)) +
+  theme_rgraph() +
+  ggplot2::guides(color = ggplot2::guide_legend(override.aes = list(size = 3)))
+
+rgraph_save(pl, p$outdir, rgraph_opt(p, "name", if (mode == "four") "Four_quadrant" else "Nine_quadrant"),
+            width = rgraph_opt(p, "width", 7.3), height = rgraph_opt(p, "height", 6),
+            dpi = rgraph_opt(p, "dpi", 300))
+cat("RGRAPH_DONE: quadrant", mode, "genes=", nrow(m), "\n")
diff --git a/plugins/Presisitence/rgraph/rscripts/sankey.R b/plugins/Presisitence/rgraph/rscripts/sankey.R
new file mode 100644
index 0000000..6cfefdd
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/sankey.R
@@ -0,0 +1,43 @@
+# sankey.R —— 富集通路-基因 桑基图（用 ggalluvial 重写；基因→通路流向）
+# 输入: enrich(富集表, 需 Description/通路名 与 geneID["a/b/c"])
+# params: enrich, outdir, id_col(基因列,默认geneID), pathway_col(通路名列,默认Description),
+#         top_n(通路数,默认8), dpi(300), width(12),height(8), name
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])
+p <- rgraph_load_params()
+rgraph_library(c("dplyr", "tidyr", "ggplot2", "ggalluvial"))
+
+id_col <- rgraph_opt(p, "id_col", "geneID")
+pcol <- rgraph_opt(p, "pathway_col", "Description")
+et <- read.csv(p$enrich, stringsAsFactors = FALSE, check.names = FALSE)
+if (!id_col %in% names(et)) stop(paste0("富集表缺少基因列: ", id_col))
+if ("Count" %in% names(et)) et <- et[order(-et$Count), , drop = FALSE]
+et <- utils::head(et, rgraph_opt(p, "top_n", 8))
+
+pairs <- et %>% dplyr::rowwise() %>%
+  dplyr::mutate(Gene = strsplit(as.character(.data[[id_col]]), "/")) %>%
+  tidyr::unnest(Gene) %>% dplyr::ungroup() %>%
+  dplyr::transmute(Gene = trimws(Gene), Pathway = .data[[pcol]]) %>%
+  dplyr::distinct()
+pairs$freq <- 1
+pairs$id <- seq_len(nrow(pairs))
+
+long <- ggalluvial::to_lodes_form(pairs, key = "axis", value = "stratum",
+                                  axes = c("Gene", "Pathway"), id = "id")
+ncol_need <- length(unique(long$stratum))
+fill <- rgraph_palette(ncol_need, rgraph_opt(p, "palette", "course"))
+
+pl <- ggplot2::ggplot(long, ggplot2::aes(x = axis, stratum = stratum, alluvium = id, label = stratum)) +
+  ggalluvial::geom_flow(ggplot2::aes(fill = stratum), width = 0.3, alpha = 0.5) +
+  ggalluvial::geom_stratum(ggplot2::aes(fill = stratum), width = 0.3, color = "grey40") +
+  ggplot2::geom_text(stat = ggalluvial::StatStratum, size = 2.6) +
+  ggplot2::scale_fill_manual(values = fill, guide = "none") +
+  ggplot2::scale_x_discrete(limits = c("Gene", "Pathway"), expand = c(0.1, 0.1)) +
+  ggplot2::labs(x = NULL, y = NULL) +
+  ggplot2::theme_void() +
+  ggplot2::theme(axis.text.x = ggplot2::element_text(face = "bold", size = 12))
+
+rgraph_save(pl, p$outdir, rgraph_opt(p, "name", "sankey"),
+            width = rgraph_opt(p, "width", 12), height = rgraph_opt(p, "height", 8),
+            dpi = rgraph_opt(p, "dpi", 300))
+cat("RGRAPH_DONE: sankey pathways=", nrow(et), "edges=", nrow(pairs), "\n")
diff --git a/plugins/Presisitence/rgraph/rscripts/segmented_volcano.R b/plugins/Presisitence/rgraph/rscripts/segmented_volcano.R
new file mode 100644
index 0000000..8d40445
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/segmented_volcano.R
@@ -0,0 +1,46 @@
+# segmented_volcano.R —— 分段式火山图（y 轴断裂，适合个别通路 p 值极端时）
+# params: result(Dse2_result.csv), outdir, sig_metric("pvalue"|"padj"), pcut(0.05), log2fc(1),
+#         color_scheme("rb"|"rg"), break_lower, break_upper(y 轴断裂区间), dpi,width,height, name
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])
+p <- rgraph_load_params()
+rgraph_library(c("ggplot2", "dplyr", "ggbreak"))
+
+sig <- rgraph_opt(p, "sig_metric", "pvalue")
+pcut <- rgraph_opt(p, "pcut", 0.05)
+log2fc <- rgraph_opt(p, "log2fc", 1)
+
+d <- read.csv(p$result, header = TRUE, check.names = FALSE)
+d <- d[!is.na(d[[sig]]) & !is.na(d$log2FoldChange), ]
+m <- d[[sig]]
+d$color <- ifelse(m < pcut & d$log2FoldChange > log2fc, "Up",
+           ifelse(m < pcut & d$log2FoldChange < -log2fc, "Down", "No"))
+cnt <- table(factor(d$color, levels = c("Up", "Down", "No")))
+labs3 <- c(Up = sprintf("Up (%d)", cnt["Up"]), Down = sprintf("Down (%d)", cnt["Down"]),
+           No = sprintf("No (%d)", cnt["No"]))
+cols <- rgraph_updown_colors(rgraph_opt(p, "color_scheme", "rb"))
+
+pl <- ggplot2::ggplot(d, ggplot2::aes(x = log2FoldChange, y = -log10(.data[[sig]]), color = color)) +
+  ggplot2::geom_point(alpha = 0.5, size = 1) +
+  ggplot2::scale_color_manual(values = cols, breaks = c("Up", "Down", "No"), labels = labs3) +
+  ggplot2::geom_hline(yintercept = -log10(pcut), linetype = "dashed", color = "grey") +
+  ggplot2::geom_vline(xintercept = c(-log2fc, log2fc), linetype = "dashed", color = "grey") +
+  ggplot2::labs(x = "log2 (Fold Change)", y = sprintf("-log10(%s)", sig),
+                color = sprintf("%s<%s\n|log2FC|>%s", sig, pcut, log2fc)) +
+  theme_rgraph() +
+  ggplot2::guides(color = ggplot2::guide_legend(override.aes = list(size = 3)))
+
+# y 轴断裂：默认自动在数据主体与极端值之间断开
+ymax <- max(-log10(d[[sig]]), na.rm = TRUE)
+bl <- rgraph_opt(p, "break_lower", NULL); bu <- rgraph_opt(p, "break_upper", NULL)
+if (is.null(bl) || is.null(bu)) {
+  q <- stats::quantile(-log10(d[[sig]]), 0.995, na.rm = TRUE)
+  if (ymax > q * 1.5) { bl <- ceiling(q); bu <- floor(ymax * 0.9) }
+}
+if (!is.null(bl) && !is.null(bu) && bu > bl)
+  pl <- pl + ggbreak::scale_y_break(c(bl, bu), space = 0.1, scales = c(0.5, 1))
+
+rgraph_save(pl, p$outdir, rgraph_opt(p, "name", "volcano_segmented"),
+            width = rgraph_opt(p, "width", 8), height = rgraph_opt(p, "height", 6.2),
+            dpi = rgraph_opt(p, "dpi", 300))
+cat("RGRAPH_DONE: segmented_volcano up=", cnt["Up"], "down=", cnt["Down"], "\n")
diff --git a/plugins/Presisitence/rgraph/rscripts/ssgsea.R b/plugins/Presisitence/rgraph/rscripts/ssgsea.R
new file mode 100644
index 0000000..a9f7922
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/ssgsea.R
@@ -0,0 +1,40 @@
+# ssgsea.R —— ssGSEA 单样本基因集富集打分 + 各基因集分组箱线图
+# params: expr(gene_expression.csv: gene_id+各样本), gmt(geneset.gmt), sample_group, outdir,
+#         normalize(bool,默认T), dpi(300)
+# 需 GSVA + GSEABase（本机默认缺，按提示 BiocManager::install(c("GSVA","GSEABase"))）
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])
+p <- rgraph_load_params()
+rgraph_library(c("GSVA", "GSEABase", "dplyr", "ggplot2"))
+
+sg <- rgraph_read_sample_group(p$sample_group)
+expr <- rgraph_read_matrix(p$expr)
+expr <- expr[, c("gene_id", sg$sample_name), drop = FALSE]
+ns <- length(sg$sample_name)
+keep <- rowSums(expr[, -1, drop = FALSE] > 0) / ns >= 0.5 &
+        rowMeans(expr[, -1, drop = FALSE]) >= 1
+expr <- expr[keep, , drop = FALSE]
+rownames(expr) <- expr$gene_id; expr$gene_id <- NULL
+mat <- as.matrix(expr)
+
+gs <- GSEABase::getGmt(p$gmt, geneIdType = GSEABase::SymbolIdentifier())
+param <- GSVA::ssgseaParam(exprData = mat, geneSets = gs,
+                           normalize = isTRUE(rgraph_opt(p, "normalize", TRUE)))
+score <- GSVA::gsva(param, verbose = FALSE)
+rgraph_write_csv(data.frame(geneset = rownames(score), score, check.names = FALSE),
+                 p$outdir, "ssGSEA_score", row.names = FALSE)
+
+cols <- rgraph_palette(length(unique(sg$group_name)), rgraph_opt(p, "palette", "course"))
+for (g in rownames(score)) {
+  df <- data.frame(Score = as.numeric(score[g, ]), sample_name = colnames(score))
+  df$group_name <- factor(sg$group_name[match(df$sample_name, sg$sample_name)],
+                          levels = unique(sg$group_name))
+  pl <- ggplot2::ggplot(df, ggplot2::aes(x = group_name, y = Score, fill = group_name, colour = group_name)) +
+    ggplot2::geom_boxplot(alpha = 0.5, linewidth = 0.6, width = 0.7, outlier.alpha = 0) +
+    ggplot2::scale_fill_manual(values = cols) + ggplot2::scale_color_manual(values = cols) +
+    ggplot2::labs(title = g, x = NULL, y = "ssGSEA Score") + theme_rgraph() +
+    ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, hjust = 1))
+  rgraph_save(pl, p$outdir, paste0(gsub("[^A-Za-z0-9_.-]", "_", g), "_box"),
+              width = 6, height = 6, dpi = rgraph_opt(p, "dpi", 300))
+}
+cat("RGRAPH_DONE: ssgsea genesets=", nrow(score), "samples=", ncol(score), "\n")
diff --git a/plugins/Presisitence/rgraph/rscripts/stacked_volcano.R b/plugins/Presisitence/rgraph/rscripts/stacked_volcano.R
new file mode 100644
index 0000000..0521010
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/stacked_volcano.R
@@ -0,0 +1,62 @@
+# stacked_volcano.R —— 堆叠火山图（多比较组 DEG 的 log2FC 抖动散点）
+# 输入: DEG_data.csv (列 gene_id, log2FoldChange, pvalue[, TvsC 分组])
+# params: deg_data, outdir, sig_metric("pvalue"|"padj"), pcut(0.05), log2fc(1),
+#         group_col("TvsC"), top_n(每组每方向标注数,5), dpi(300), width(10),height(9), name
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])
+p <- rgraph_load_params()
+rgraph_library(c("ggplot2", "dplyr"))
+use_repel <- requireNamespace("ggrepel", quietly = TRUE)
+
+sig <- rgraph_opt(p, "sig_metric", "pvalue")
+pcut <- rgraph_opt(p, "pcut", 0.05)
+log2fc <- rgraph_opt(p, "log2fc", 1)
+gcol <- rgraph_opt(p, "group_col", "TvsC")
+top_n <- rgraph_opt(p, "top_n", 5)
+
+d <- read.csv(p$deg_data, check.names = FALSE)
+if (!gcol %in% names(d)) d[[gcol]] <- "DEG"     # 无分组列时单组
+d$regulate <- ifelse(d[[sig]] < pcut & d$log2FoldChange > log2fc, "Up",
+              ifelse(d[[sig]] < pcut & d$log2FoldChange < -log2fc, "Down", "No"))
+d <- d[d$regulate != "No", ]
+d[[gcol]] <- factor(d[[gcol]], levels = unique(d[[gcol]]))
+set.seed(123)
+d$xj <- as.numeric(d[[gcol]]) + runif(nrow(d), -0.18, 0.18)
+
+# 每组背景柱（min/max*1.2）
+bar <- d %>% dplyr::group_by(.g = .data[[gcol]]) %>%
+  dplyr::summarise(minv = min(log2FoldChange) * 1.2, maxv = max(log2FoldChange) * 1.2,
+                   center = mean(as.numeric(.data[[gcol]])), .groups = "drop")
+bar$xc <- as.numeric(factor(bar$.g, levels = levels(d[[gcol]])))
+bcols <- rgraph_palette(nrow(bar), rgraph_opt(p, "palette", "course"))
+
+# 标注 top_n
+lab <- d %>% dplyr::group_by(.data[[gcol]]) %>%
+  dplyr::slice_max(log2FoldChange, n = top_n) %>% dplyr::ungroup()
+lab <- rbind(lab, d %>% dplyr::group_by(.data[[gcol]]) %>%
+               dplyr::slice_min(log2FoldChange, n = top_n) %>% dplyr::ungroup())
+lab <- lab[!duplicated(lab$gene_id), ]
+
+pl <- ggplot2::ggplot(d, ggplot2::aes(x = xj, y = log2FoldChange)) +
+  ggplot2::geom_col(data = bar, ggplot2::aes(x = xc, y = minv), fill = rep(bcols, 1), alpha = 0.15, width = 0.8) +
+  ggplot2::geom_col(data = bar, ggplot2::aes(x = xc, y = maxv), fill = rep(bcols, 1), alpha = 0.15, width = 0.8) +
+  ggplot2::geom_point(ggplot2::aes(size = abs(log2FoldChange), alpha = abs(log2FoldChange), color = regulate)) +
+  ggplot2::scale_color_manual(values = c(Down = "#3288bd", Up = "#d92523")) +
+  ggplot2::scale_size_continuous(range = c(0.5, 4)) +
+  ggplot2::scale_alpha_continuous(range = c(0.2, 0.85)) +
+  ggplot2::geom_tile(data = bar, ggplot2::aes(x = xc, y = 0), height = 0.8, width = 0.9,
+                     fill = bcols, alpha = 0.85) +
+  ggplot2::geom_text(data = bar, ggplot2::aes(x = xc, y = 0, label = .g), size = 5) +
+  ggplot2::scale_x_continuous(breaks = bar$xc, labels = bar$.g) +
+  ggplot2::labs(x = NULL, y = "log2FoldChange", color = "Trend", size = "|log2FoldChange|") +
+  theme_rgraph() +
+  ggplot2::guides(color = ggplot2::guide_legend(override.aes = list(size = 3)), alpha = "none")
+
+if (use_repel && nrow(lab) > 0)
+  pl <- pl + ggrepel::geom_text_repel(data = lab, ggplot2::aes(x = xj, y = log2FoldChange, label = gene_id),
+                                      size = 3.5, max.overlaps = Inf, segment.size = 0.3, segment.color = "gray40")
+
+rgraph_save(pl, p$outdir, rgraph_opt(p, "name", "multi_volcano"),
+            width = rgraph_opt(p, "width", 10), height = rgraph_opt(p, "height", 9),
+            dpi = rgraph_opt(p, "dpi", 300))
+cat("RGRAPH_DONE: stacked_volcano groups=", nrow(bar), "DEG=", nrow(d), "\n")
diff --git a/plugins/Presisitence/rgraph/rscripts/venn.R b/plugins/Presisitence/rgraph/rscripts/venn.R
new file mode 100644
index 0000000..33d70a3
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/venn.R
@@ -0,0 +1,49 @@
+# venn.R —— 韦恩图（2-5 组）+ 交集成员表
+# params: sets(CSV 路径向量) 或 folder(装 CSV 的目录), col(元素列名,默认自动: Element_name/gene_id/首列),
+#         names(可选,各集合名), outdir, name("venn"), dpi(300), width(7),height(7)
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])
+p <- rgraph_load_params()
+rgraph_library(c("VennDiagram"))
+suppressWarnings(try(futile.logger::flog.threshold(futile.logger::ERROR, name = "VennDiagramLogger"),
+                     silent = TRUE))
+
+pick_col <- function(df, col) {
+  if (!is.null(col) && col %in% names(df)) return(col)
+  for (c in c("Element_name", "gene_id", "Gene_symbol")) if (c %in% names(df)) return(c)
+  names(df)[1]
+}
+
+# 收集集合
+if (!is.null(p$folder)) {
+  files <- list.files(p$folder, pattern = "\\.csv$", full.names = TRUE)
+  set_names <- tools::file_path_sans_ext(basename(files))
+} else {
+  files <- p$sets
+  set_names <- if (!is.null(p$names)) p$names else tools::file_path_sans_ext(basename(files))
+}
+if (length(files) < 2 || length(files) > 5) stop("韦恩图支持 2-5 个集合")
+
+elem <- list()
+for (i in seq_along(files)) {
+  df <- read.csv(files[i], header = TRUE, check.names = FALSE, stringsAsFactors = FALSE)
+  v <- unique(df[[pick_col(df, p$col)]])
+  elem[[set_names[i]]] <- v[!is.na(v) & v != ""]
+}
+
+fill <- rgraph_palette(length(elem), rgraph_opt(p, "palette", "course"))
+grob <- VennDiagram::venn.diagram(elem, category.names = names(elem), filename = NULL,
+                                  fill = fill, col = NA, cat.col = "black", cat.cex = 0.9,
+                                  cex = 1.1, margin = 0.08)
+
+rgraph_save_base(p$outdir, rgraph_opt(p, "name", "venn"),
+                 function() { grid::grid.newpage(); grid::grid.draw(grob) },
+                 width = rgraph_opt(p, "width", 7), height = rgraph_opt(p, "height", 7),
+                 dpi = rgraph_opt(p, "dpi", 300))
+
+# 成员表
+all_g <- unique(unlist(elem))
+tab <- data.frame(Element = all_g)
+for (nm in names(elem)) tab[[nm]] <- all_g %in% elem[[nm]]
+rgraph_write_csv(tab, p$outdir, "veen_table")
+cat("RGRAPH_DONE: venn sets=", length(elem), "elements=", length(all_g), "\n")
diff --git a/plugins/Presisitence/rgraph/rscripts/virtual_knockout.R b/plugins/Presisitence/rgraph/rscripts/virtual_knockout.R
new file mode 100644
index 0000000..b153da2
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/virtual_knockout.R
@@ -0,0 +1,69 @@
+# virtual_knockout.R —— bulk RNA 虚拟敲除（GENIE3 调控网络 + bulkKnk 扰动 + 双轴火山图）
+# 输入: count(gene_count.csv), sample_group(需 group_name, pheno_data)
+# params: count, sample_group, outdir, ko_gene(敲除基因), ko_group(敲除样本所在分组),
+#         logfc(0.5), hv_num(高变基因数,5000), dpi(300)
+# 依赖: bulkKnk(课程作者本地源码包, install.packages("bulkKnk-main", repos=NULL, type="source"))
+#       + GENIE3(Bioconductor)。二者缺失将返回缺包提示。
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])
+p <- rgraph_load_params()
+rgraph_library(c("bulkKnk", "GENIE3", "ggplot2", "dplyr"))
+use_repel <- requireNamespace("ggrepel", quietly = TRUE)
+
+ko_gene <- p$ko_gene; ko_group <- p$ko_group
+logfc <- rgraph_opt(p, "logfc", 0.5)
+hv_num <- rgraph_opt(p, "hv_num", 5000)
+
+sg <- read.csv(p$sample_group, check.names = FALSE, stringsAsFactors = FALSE)
+expr <- read.csv(p$count, check.names = FALSE, stringsAsFactors = FALSE)
+if (length(unique(expr$gene_id)) < nrow(expr)) stop("gene_id 存在重复，请先去重")
+rownames(expr) <- expr$gene_id
+mat <- t(expr[, sg$sample_name])
+
+# 高变基因 + 确保 ko_gene 在内
+sub_expr <- bulkKnk::select_advanced_features(mat, n_features = hv_num)
+genes <- unique(colnames(sub_expr))
+if (!(ko_gene %in% genes)) sub_expr <- mat[, c(genes, ko_gene)]
+sub_expr <- as.matrix(sub_expr); mode(sub_expr) <- "numeric"
+
+# WGCNA 找相关模块 → 调控网络（GENIE3）→ DPI 剪枝
+wg <- bulkKnk::identify_hub_modules(sub_expr, trait_vec = sg$pheno_data)
+best <- unique(wg$best_genes); if (!(ko_gene %in% best)) best <- c(best, ko_gene)
+focus <- as.matrix(sub_expr[, best])
+netc <- bulkKnk::prune_network_dpi_fast(bulkKnk::infer_causal_network(focus)$clean_matrix)
+W <- sweep(t(netc), 2, colSums(t(netc)) + 1e-9, FUN = "/")
+
+pheno <- setNames(sg$group_name, sg$sample_name)
+common <- intersect(rownames(focus), names(pheno))
+ko <- bulkKnk::batch_virtual_knockout(expr_matrix = focus[common, ], adj_matrix = W,
+        target_genes = ko_gene, target_pheno = ko_group, pheno_vec = pheno[common])
+
+# 汇总
+gs <- data.frame(gene_id = colnames(ko$WT),
+                 WT_Mean = colMeans(ko$WT, na.rm = TRUE), KO_Mean = colMeans(ko$KO, na.rm = TRUE))
+gs$Diff <- gs$KO_Mean - gs$WT_Mean
+gs$log2FoldChange <- log2((gs$KO_Mean + 1e-9) / (gs$WT_Mean + 1e-9))
+rgraph_write_csv(gs, p$outdir, "1.virtual_knockout_summary")
+sig <- gs[abs(gs$log2FoldChange) > logfc & gs$gene_id != ko_gene, ]
+rgraph_write_csv(sig, p$outdir, "2.significant_perturbed_genes")
+
+# 双轴火山图（x=虚拟 log2FC, y=WT 基线表达）
+gs$color <- ifelse(gs$log2FoldChange > logfc, "Up regulated",
+             ifelse(gs$log2FoldChange < -logfc, "Down regulated", "Stable"))
+gs$color[gs$gene_id == ko_gene] <- "KO gene"
+cmap <- c("KO gene" = "#2ca02c", "Up regulated" = "#c0392b", "Down regulated" = "#2a80b9", "Stable" = "grey80")
+pl <- ggplot2::ggplot(gs, ggplot2::aes(x = log2FoldChange, y = WT_Mean, color = color)) +
+  ggplot2::geom_point(alpha = 0.7, size = 1) +
+  ggplot2::scale_color_manual(values = cmap, breaks = names(cmap)) +
+  ggplot2::geom_vline(xintercept = c(-logfc, logfc), linetype = "dashed", color = "grey") +
+  ggplot2::labs(title = "Virtual Knockout Effect", x = "Virtual Log2FC (KO vs WT)",
+                y = "Baseline Expression (WT)", color = NULL) +
+  theme_rgraph() +
+  ggplot2::guides(color = ggplot2::guide_legend(override.aes = list(size = 3)))
+lab <- gs[gs$gene_id == ko_gene, ]
+if (use_repel && nrow(lab) > 0)
+  pl <- pl + ggrepel::geom_label_repel(data = lab, ggplot2::aes(label = gene_id), color = "black",
+                                       size = 3, min.segment.length = 0, max.overlaps = Inf)
+rgraph_save(pl, p$outdir, rgraph_opt(p, "name", "dual_volcano"),
+            width = 7, height = 5, dpi = rgraph_opt(p, "dpi", 300))
+cat("RGRAPH_DONE: virtual_knockout ko=", ko_gene, "perturbed=", nrow(sig), "\n")
diff --git a/plugins/Presisitence/rgraph/rscripts/volcano.R b/plugins/Presisitence/rgraph/rscripts/volcano.R
new file mode 100644
index 0000000..352d878
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/volcano.R
@@ -0,0 +1,61 @@
+# volcano.R —— 火山图（可标注 top-N 或自定义基因；色盲友好红蓝配色）
+# params: result(Dse2_result.csv 路径), outdir, sig_metric("pvalue"|"padj",默认padj),
+#         pcut(0.05), log2fc(1), color_scheme("rb"|"rg"), label(bool),
+#         label_n(默认0), genes(可选,自定义标注基因向量), gene_col(默认gene_id),
+#         dpi(300), width,height, name
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])
+p <- rgraph_load_params()
+rgraph_library(c("ggplot2", "dplyr"))
+use_repel <- requireNamespace("ggrepel", quietly = TRUE)
+
+sig_metric <- rgraph_opt(p, "sig_metric", "padj")
+pcut <- rgraph_opt(p, "pcut", 0.05)
+log2fc <- rgraph_opt(p, "log2fc", 1)
+gene_col <- rgraph_opt(p, "gene_col", "gene_id")
+
+d <- read.csv(p$result, header = TRUE, check.names = FALSE)
+if (!sig_metric %in% names(d)) stop(paste0("结果表缺少列: ", sig_metric))
+d <- d[!is.na(d[[sig_metric]]) & !is.na(d$log2FoldChange), ]
+m <- d[[sig_metric]]
+d$color <- ifelse(m < pcut & d$log2FoldChange >  log2fc, "Up",
+            ifelse(m < pcut & d$log2FoldChange < -log2fc, "Down", "No"))
+
+cnt <- table(factor(d$color, levels = c("Up", "Down", "No")))
+labs3 <- c(Up = sprintf("Up (%d)", cnt["Up"]), Down = sprintf("Down (%d)", cnt["Down"]),
+           No = sprintf("No (%d)", cnt["No"]))
+cols <- rgraph_updown_colors(rgraph_opt(p, "color_scheme", "rb"))
+
+pl <- ggplot2::ggplot(d, ggplot2::aes(x = log2FoldChange, y = -log10(.data[[sig_metric]]),
+                                      color = color)) +
+  ggplot2::geom_point(alpha = 0.5, size = 1) +
+  ggplot2::scale_color_manual(values = cols, breaks = c("Up", "Down", "No"), labels = labs3) +
+  ggplot2::geom_hline(yintercept = -log10(pcut), linetype = "dashed", color = "grey") +
+  ggplot2::geom_vline(xintercept = c(-log2fc, log2fc), linetype = "dashed", color = "grey") +
+  ggplot2::labs(x = "log2 (Fold Change)", y = sprintf("-log10(%s)", sig_metric),
+                color = sprintf("%s<%s\n|log2FC|>%s", sig_metric, pcut, log2fc)) +
+  theme_rgraph() +
+  ggplot2::guides(color = ggplot2::guide_legend(override.aes = list(size = 3)))
+
+# ---- 标注基因：自定义清单优先，其次 top-N ----------------------------------
+sel <- NULL
+if (!is.null(p$genes)) {
+  sel <- d[d[[gene_col]] %in% p$genes, ]
+} else if (rgraph_opt(p, "label_n", 0) > 0) {
+  sel <- d[d$color != "No", ]
+  sel <- sel[order(sel[[sig_metric]]), ]
+  sel <- utils::head(sel, rgraph_opt(p, "label_n", 10))
+}
+if (!is.null(sel) && nrow(sel) > 0) {
+  aes_lab <- ggplot2::aes(x = log2FoldChange, y = -log10(.data[[sig_metric]]),
+                          label = .data[[gene_col]])
+  if (use_repel) pl <- pl + ggrepel::geom_text_repel(data = sel, mapping = aes_lab,
+                                                      size = 3, color = "black", max.overlaps = Inf)
+  else pl <- pl + ggplot2::geom_text(data = sel, mapping = aes_lab, size = 3, color = "black",
+                                     vjust = -0.6)
+}
+
+rgraph_save(pl, p$outdir, rgraph_opt(p, "name", "volcano"),
+            width = rgraph_opt(p, "width", 8), height = rgraph_opt(p, "height", 6.2),
+            dpi = rgraph_opt(p, "dpi", 300))
+cat("RGRAPH_DONE: volcano up=", cnt["Up"], "down=", cnt["Down"], "no=", cnt["No"], "\n")
diff --git a/plugins/Presisitence/rgraph/rscripts/wgcna.R b/plugins/Presisitence/rgraph/rscripts/wgcna.R
new file mode 100644
index 0000000..9312a3c
--- /dev/null
+++ b/plugins/Presisitence/rgraph/rscripts/wgcna.R
@@ -0,0 +1,125 @@
+# wgcna.R —— WGCNA 加权共表达网络分析（软阈值/模块/模块-分组相关/特征向量网络/Cytoscape 导出）
+# 输入: expr(gene_expression.csv: gene_id + 各样本), sample_group(需 group_name)
+# params: expr, sample_group, outdir, power(0=自动, 否则手动), min_module_size(30),
+#         merge_cut(0.25), network_type("unsigned"|"signed"), min_mean(1),
+#         max_genes(方差过滤上限,默认3000), export_module(可选:导出该颜色模块的 Cytoscape 边表),
+#         tomplot(bool,默认F,较慢), n_threads(0=全部), dpi(300)
+.args <- commandArgs(trailingOnly = TRUE)
+source(.args[[2]])
+p <- rgraph_load_params()
+rgraph_library(c("WGCNA", "dplyr"))
+options(stringsAsFactors = FALSE)
+
+expr <- rgraph_read_matrix(p$expr)
+sg <- rgraph_read_sample_group(p$sample_group)
+rgraph_check_samples(expr, sg$sample_name)
+rownames(expr) <- expr$gene_id
+ge <- expr[, sg$sample_name, drop = FALSE]
+ge[is.na(ge)] <- 0
+# 低表达过滤 + 方差 top 过滤（控制规模/耗时）
+ns <- ncol(ge)
+ge <- ge[rowSums(ge == 0) < ns * 0.5 & rowMeans(ge) >= rgraph_opt(p, "min_mean", 1), , drop = FALSE]
+maxg <- rgraph_opt(p, "max_genes", 3000)
+if (nrow(ge) > maxg) ge <- ge[order(apply(ge, 1, var), decreasing = TRUE)[seq_len(maxg)], , drop = FALSE]
+datExpr <- as.matrix(t(ge)); mode(datExpr) <- "numeric"
+nGenes <- ncol(datExpr); nSamples <- nrow(datExpr)
+
+nt <- rgraph_opt(p, "n_threads", 0)
+if (is.numeric(nt) && nt >= 2) WGCNA::enableWGCNAThreads(nThreads = nt) else try(WGCNA::allowWGCNAThreads(), silent = TRUE)
+net_type <- rgraph_opt(p, "network_type", "unsigned")
+
+# ---- 软阈值 ----------------------------------------------------------------
+powers <- 1:20
+sft <- WGCNA::pickSoftThreshold(datExpr, powerVector = powers, verbose = 0, networkType = net_type)
+rgraph_save_base(p$outdir, "01.power", function() {
+  par(mfrow = c(1, 2)); cex1 <- 0.9
+  plot(sft$fitIndices[, 1], -sign(sft$fitIndices[, 3]) * sft$fitIndices[, 2],
+       xlab = "Soft Threshold (power)", ylab = "Scale Free Topology Model Fit, signed R^2",
+       type = "n", main = "Scale independence")
+  text(sft$fitIndices[, 1], -sign(sft$fitIndices[, 3]) * sft$fitIndices[, 2], labels = powers, cex = cex1, col = "red")
+  abline(h = 0.85, col = "red")
+  plot(sft$fitIndices[, 1], sft$fitIndices[, 5], xlab = "Soft Threshold (power)",
+       ylab = "Mean Connectivity", type = "n", main = "Mean connectivity")
+  text(sft$fitIndices[, 1], sft$fitIndices[, 5], labels = powers, cex = cex1, col = "red")
+}, width = 12, height = 7, dpi = rgraph_opt(p, "dpi", 300))
+power <- rgraph_opt(p, "power", 0)
+if (is.null(power) || power <= 0) power <- if (!is.na(sft$powerEstimate)) sft$powerEstimate else 6
+cat("RGRAPH_INFO: power=", power, "\n")
+
+# ---- 构建网络 --------------------------------------------------------------
+net <- WGCNA::blockwiseModules(datExpr, corType = "pearson", power = power,
+        networkType = net_type, TOMType = net_type,
+        deepSplit = 2, minModuleSize = rgraph_opt(p, "min_module_size", 30),
+        mergeCutHeight = rgraph_opt(p, "merge_cut", 0.25), numericLabels = TRUE,
+        maxBlockSize = nGenes + 1, saveTOMs = FALSE, nThreads = 0)
+moduleColors <- WGCNA::labels2colors(net$colors)
+
+rgraph_save_base(p$outdir, "02.dendrogram", function() {
+  WGCNA::plotDendroAndColors(net$dendrograms[[1]], moduleColors[net$blockGenes[[1]]],
+    "Module colors", dendroLabels = FALSE, hang = 0.03, addGuide = TRUE, guideHang = 0.05)
+}, width = 9, height = 5, dpi = rgraph_opt(p, "dpi", 300))
+
+rgraph_write_csv(data.frame(gene_id = colnames(datExpr), Module = moduleColors),
+                 p$outdir, "gene_module")
+
+# ---- 模块-分组相关性 -------------------------------------------------------
+MEs <- WGCNA::orderMEs(WGCNA::moduleEigengenes(datExpr, moduleColors)$eigengenes)
+gfac <- as.factor(sg$group_name[match(rownames(datExpr), sg$sample_name)])
+gdesign <- model.matrix(~ gfac - 1); colnames(gdesign) <- levels(gfac)
+mgc <- cor(MEs, gdesign, use = "p")
+mgp <- WGCNA::corPvalueStudent(mgc, nSamples)
+txt <- paste(signif(mgc, 2), "\n(", signif(mgp, 1), ")", sep = ""); dim(txt) <- dim(mgc)
+rgraph_save_base(p$outdir, "05.module-group", function() {
+  par(mar = c(8, 10, 4, 4))
+  WGCNA::labeledHeatmap(Matrix = mgc, xLabels = colnames(gdesign), yLabels = names(MEs),
+    ySymbols = names(MEs), colorLabels = FALSE, colors = WGCNA::blueWhiteRed(50),
+    textMatrix = txt, setStdMargins = FALSE, cex.text = 0.7, zlim = c(-1, 1),
+    main = "Module-group relationships")
+}, width = 10, height = 8, dpi = rgraph_opt(p, "dpi", 300))
+
+# ---- MM(基因-模块隶属度) + geneInfo ---------------------------------------
+modNames <- substring(names(MEs), 3)
+MM <- as.data.frame(cor(datExpr, MEs, use = "p")); names(MM) <- paste0("MM", modNames)
+MMp <- as.data.frame(WGCNA::corPvalueStudent(as.matrix(MM), nSamples)); names(MMp) <- paste0("p.MM", modNames)
+geneInfo <- cbind(gene_id = colnames(datExpr), Module = moduleColors, MM, MMp)
+rgraph_write_csv(geneInfo, p$outdir, "geneInfo")
+
+# ---- 特征向量邻接热图 ------------------------------------------------------
+mecor <- cor(MEs, use = "p")
+rgraph_save_base(p$outdir, "08.eigengene_adjacency", function() {
+  par(mar = c(8, 10, 4, 3))
+  WGCNA::labeledHeatmap(Matrix = mecor, xLabels = names(MEs), yLabels = names(MEs),
+    ySymbols = names(MEs), colorLabels = FALSE, colors = WGCNA::blueWhiteRed(50),
+    setStdMargins = FALSE, cex.text = 0.7, zlim = c(-1, 1), main = "Eigengene adjacency")
+}, width = 8, height = 7, dpi = rgraph_opt(p, "dpi", 300))
+
+# ---- 可选: TOM 网络热图（较慢）--------------------------------------------
+if (isTRUE(rgraph_opt(p, "tomplot", FALSE))) {
+  TOM <- WGCNA::TOMsimilarityFromExpr(datExpr, power = power, networkType = net_type)
+  nSelect <- min(400, nGenes); set.seed(10); sel <- sample(nGenes, nSelect)
+  selTOM <- (1 - TOM)[sel, sel]; selTree <- hclust(as.dist(selTOM), method = "average")
+  plotDiss <- selTOM^7; diag(plotDiss) <- NA
+  rgraph_save_base(p$outdir, "06.network_heatmap",
+    function() WGCNA::TOMplot(plotDiss, selTree, moduleColors[sel], main = "Network heatmap (selected genes)"),
+    width = 8, height = 8, dpi = rgraph_opt(p, "dpi", 300))
+  # 可选 Cytoscape 导出（与 rgraph_network edge 模式串联）
+  em <- rgraph_opt(p, "export_module", NULL)
+  if (is.null(em)) {   # 未指定时自动取最大非-grey 模块
+    tb <- sort(table(moduleColors[moduleColors != "grey"]), decreasing = TRUE)
+    if (length(tb) > 0) em <- names(tb)[1]
+  }
+  if (!is.null(em)) {
+    inMod <- moduleColors == em; probes <- colnames(datExpr)[inMod]
+    modTOM <- TOM[inMod, inMod]; dimnames(modTOM) <- list(probes, probes)
+    ef <- file.path(p$outdir, paste0("Cytoscape_edges_", em, ".txt"))
+    nf <- file.path(p$outdir, paste0("Cytoscape_nodes_", em, ".txt"))
+    WGCNA::exportNetworkToCytoscape(modTOM, weighted = TRUE,
+             threshold = rgraph_opt(p, "export_threshold", 0.1),
+             nodeNames = probes, nodeAttr = moduleColors[inMod],
+             edgeFile = ef, nodeFile = nf)
+    rgraph_emit(ef); rgraph_emit(nf)
+    cat("RGRAPH_INFO: exported module=", em, "nodes=", length(probes), "\n")
+  }
+}
+cat("RGRAPH_DONE: wgcna genes=", nGenes, "samples=", nSamples, "modules=",
+    length(unique(moduleColors)), "\n")
diff --git a/plugins/Presisitence/rgraph/server.py b/plugins/Presisitence/rgraph/server.py
new file mode 100644
index 0000000..bea2bfa
--- /dev/null
+++ b/plugins/Presisitence/rgraph/server.py
@@ -0,0 +1,421 @@
+"""rgraph —— RNAseq 下游可视化/分析 MCP Server (FastMCP)。
+
+把一套清洗过、参数化的 R 出图脚本（rscripts/*.R）暴露为 MCP 工具，由本机 Rscript 引擎渲染，
+产出与课程一致风格的 png+pdf。定位不到 R 时返回可手动运行的脚本与命令；R 端缺包时返回缺失
+包名与安装建议。
+
+运行:
+    uv run --directory <this-dir> server.py
+"""
+from __future__ import annotations
+
+from typing import Any
+
+from mcp.server.fastmcp import FastMCP
+
+from rgraph_toolkit import runner
+
+mcp = FastMCP("rgraph")
+
+
+def _clean(**kw: Any) -> dict[str, Any]:
+    """丢弃值为 None 的参数，避免污染 R 端 params。"""
+    return {k: v for k, v in kw.items() if v is not None}
+
+
+def _run(script: str, params: dict[str, Any], outdir: str) -> dict[str, Any]:
+    return runner.run_script(script, params, outdir=outdir)
+
+
+# ============================ 环境自检 ============================
+@mcp.tool()
+def rgraph_env() -> dict[str, Any]:
+    """检查 R 引擎与关键 R 包是否就绪（首次使用/报错排查先跑这个）。返回 Rscript 路径、
+    各包安装状态与缺包安装建议。"""
+    rscript = runner.find_rscript()
+    pkgs = [
+        "ggplot2", "dplyr", "tidyr", "stringr", "pheatmap", "ggrepel", "reshape2",
+        "DESeq2", "edgeR", "limma", "clusterProfiler", "enrichplot", "pathview",
+        "circlize", "ComplexHeatmap", "WGCNA", "VennDiagram", "GSVA", "GSEABase", "fgsea",
+        "ggpubr", "patchwork", "ggridges", "ggbreak", "ggalluvial", "plotly", "factoextra",
+        "igraph", "ggraph", "tidygraph", "ggforce", "ggNetView", "GENIE3",
+    ]
+    status = runner.check_packages(pkgs, rscript=rscript) if rscript else {p: False for p in pkgs}
+    missing = [p for p, ok in status.items() if not ok]
+    return {
+        "rscript": rscript or "未找到 (请设置环境变量 RGRAPH_RSCRIPT 指向 Rscript.exe)",
+        "rscripts_dir": str(runner.rscripts_dir()),
+        "packages": status,
+        "missing": missing,
+        "install_hint": runner._install_hint(missing) if missing else "",
+    }
+
+
+# ============================ 01 表达量归一化 ============================
+@mcp.tool()
+def rgraph_normalize(count: str, outdir: str, method: str = "fpkm",
+                     sample_group: str | None = None) -> dict[str, Any]:
+    """由 count 计算 FPKM/TPM。count 为 gene_count.csv(列: gene_id,Length,<各样本>)。
+    method: fpkm/tpm。sample_group 可选(限定样本列)。产出 gene_fpkm.csv 或 gene_tpm.csv。"""
+    return _run("normalize.R", _clean(count=count, method=method, sample_group=sample_group), outdir)
+
+
+# ============================ 02 相关性 / PCA ============================
+@mcp.tool()
+def rgraph_correlation(fpkm: str, sample_group: str, outdir: str,
+                       dpi: int = 300, width: float = 6, height: float = 6) -> dict[str, Any]:
+    """样本间 Pearson 相关性热图(基于 log2(fpkm+1))。fpkm: gene_fpkm.csv；sample_group 含 sample_name。"""
+    return _run("correlation_heatmap.R",
+                _clean(fpkm=fpkm, sample_group=sample_group, dpi=dpi, width=width, height=height), outdir)
+
+
+@mcp.tool()
+def rgraph_pca(fpkm: str, sample_group: str, outdir: str, ellipse: bool = False,
+               label: bool = True, palette: str = "course", dpi: int = 300) -> dict[str, Any]:
+    """样本 PCA 散点图(log2(fpkm+1)+prcomp)。ellipse: 加 95% 置信椭圆；label: ggrepel 样本标签；
+    palette: course/colorblind。sample_group 需含 group_name。"""
+    return _run("pca.R", _clean(fpkm=fpkm, sample_group=sample_group, ellipse=ellipse,
+                                label=label, palette=palette, dpi=dpi), outdir)
+
+
+# ============================ 03 表达分布 ============================
+@mcp.tool()
+def rgraph_distribution(fpkm: str, sample_group: str, outdir: str, kind: str = "box",
+                        palette: str = "course", dpi: int = 300) -> dict[str, Any]:
+    """表达分布图。kind: box(箱线)/violin(小提琴)/density(密度)。按 group_name 着色。"""
+    return _run("distribution.R", _clean(fpkm=fpkm, sample_group=sample_group, kind=kind,
+                                         palette=palette, dpi=dpi), outdir)
+
+
+# ============================ 04 差异分析 ============================
+@mcp.tool()
+def rgraph_diff(count: str, sample_group: str, outdir: str, method: str = "deseq2",
+                sig_metric: str = "padj", pcut: float = 0.05, log2fc: float = 1.0,
+                min_mean_count: float = 1.0) -> dict[str, Any]:
+    """差异表达分析。method: deseq2/edger/edger_norep(无生物学重复)/limma。
+    sig_metric: padj(推荐,默认)/pvalue；pcut、log2fc 为阈值。分组由 sample_group 的 TvsC 列
+    (treatment/control)决定。产出 01.Dse2_result.csv 及 Deg_all/up/down。
+    注：DESeq2 需已安装(本机默认缺,可改 method=edger/limma 或按提示安装)。"""
+    return _run("diff.R", _clean(count=count, sample_group=sample_group, method=method,
+                                 sig_metric=sig_metric, pcut=pcut, log2fc=log2fc,
+                                 min_mean_count=min_mean_count), outdir)
+
+
+@mcp.tool()
+def rgraph_volcano(result: str, outdir: str, sig_metric: str = "padj", pcut: float = 0.05,
+                   log2fc: float = 1.0, color_scheme: str = "rb", label_n: int = 0,
+                   genes: list[str] | None = None) -> dict[str, Any]:
+    """火山图。result 为差异结果表(含 gene_id,log2FoldChange,pvalue,padj)。
+    color_scheme: rb(色盲友好红蓝,默认)/rg(课程红绿)；label_n: 标注显著性最高的前 N 个；
+    genes: 自定义标注基因清单(优先于 label_n)。"""
+    return _run("volcano.R", _clean(result=result, sig_metric=sig_metric, pcut=pcut, log2fc=log2fc,
+                                    color_scheme=color_scheme, label_n=label_n, genes=genes), outdir)
+
+
+@mcp.tool()
+def rgraph_heatmap(fpkm: str, sample_group: str, outdir: str, deg: str | None = None,
+                   genes: list[str] | None = None, engine: str = "pheatmap",
+                   scale: str = "row", cluster_cols: bool = True,
+                   show_rownames: bool | None = None) -> dict[str, Any]:
+    """差异/指定基因表达聚类热图(log2(fpkm+1))。基因来源: genes 优先，其次 deg(Deg_all.csv)，
+    否则全部。engine: pheatmap(默认) 或 complexheatmap(按 group_name 加顶部分组色块、列不聚类)。"""
+    return _run("heatmap.R", _clean(fpkm=fpkm, sample_group=sample_group, deg=deg, genes=genes,
+                                    engine=engine, scale=scale, cluster_cols=cluster_cols,
+                                    show_rownames=show_rownames), outdir)
+
+
+# ============================ 05 富集分析 (GO/KEGG) ============================
+@mcp.tool()
+def rgraph_enrich(gene_list: str, outdir: str, type: str = "go", orgdb: str = "org.Hs.eg.db",
+                  id_type: str = "ENSEMBL", ont: str = "all", go_source: str = "orgdb",
+                  kegg_source: str = "online", kegg_species: str = "hsa",
+                  gmt: str | None = None, kegg_info: str | None = None,
+                  pvalueCutoff: float = 1.0, qvalueCutoff: float = 1.0) -> dict[str, Any]:
+    """GO/KEGG 富集分析。gene_list 为含 gene_id 列的 CSV。type: go/kegg。
+    orgdb: 物种注释包(模式物种如 org.Hs.eg.db；非模式需先自建 org.Xxx.eg.db)。
+    go_source: orgdb(enrichGO) 或 custom(从 OrgDb 的 GO 列自建, 非模式物种)。
+    kegg_source: online(rest.kegg.jp, 需 kegg_species) 或 gmt(用自建 gmt)。
+    产出 GO_enrich.csv / KEGG_enrich.csv。注：OrgDb 需已安装，否则按提示安装。"""
+    return _run("enrich.R", _clean(gene_list=gene_list, type=type, orgdb=orgdb, id_type=id_type,
+                                   ont=ont, go_source=go_source, kegg_source=kegg_source,
+                                   kegg_species=kegg_species, gmt=gmt, kegg_info=kegg_info,
+                                   pvalueCutoff=pvalueCutoff, qvalueCutoff=qvalueCutoff), outdir)
+
+
+@mcp.tool()
+def rgraph_go_plot(enrich: str, outdir: str, kind: str = "bar", top_n: int = 10) -> dict[str, Any]:
+    """GO 富集图(BP/CC/MF 三面板纵向拼接)。enrich 为 GO_enrich.csv(需 ONTOLOGY/Description/
+    GeneRatio/pvalue/Count)。kind: bar(x=-log10p,填充Count) / dot(x=GeneRatio,填充-log10p,大小Count)。"""
+    return _run("go_plot.R", _clean(enrich=enrich, kind=kind, top_n=top_n), outdir)
+
+
+@mcp.tool()
+def rgraph_kegg_plot(enrich: str, outdir: str, kind: str = "dot", top_n: int = 30) -> dict[str, Any]:
+    """KEGG 富集图。enrich 为 KEGG_enrich.csv(需 Description/GeneRatio/pvalue/Count)。
+    kind: dot(气泡) / bar(柱形)。top_n: 取 pvalue 最小的前 N 条通路。"""
+    return _run("kegg_plot.R", _clean(enrich=enrich, kind=kind, top_n=top_n), outdir)
+
+
+# ============================ 06 PPI ============================
+@mcp.tool()
+def rgraph_ppi(deg: str, info: str, links: str, outdir: str, score: int = 400,
+               id_is_symbol: bool = False, orgdb: str = "org.Hs.eg.db") -> dict[str, Any]:
+    """由差异基因 + STRING 库构建蛋白互作边表。deg: Deg_all.csv(gene_id)；info/links 为 STRING 的
+    protein.info 与 protein.links 文件；score: combined_score 阈值。id_is_symbol=False 时用 orgdb 做
+    ENSEMBL→SYMBOL。产出 Target_PPi.tsv + Edge_Node_count.tsv(可导入 Cytoscape)。"""
+    return _run("ppi.R", _clean(deg=deg, info=info, links=links, score=score,
+                                id_is_symbol=id_is_symbol, orgdb=orgdb), outdir)
+
+
+# ============================ 07 GSEA ============================
+@mcp.tool()
+def rgraph_build_gmt(outdir: str, orgdb: str, type: str = "go", source: str = "orgdb",
+                     keytype: str = "GID", kegg_species: str = "hsa",
+                     min_count: int = 5) -> dict[str, Any]:
+    """为 GSEA 构建 gmt 基因集。type: go/kegg。source: orgdb(从注释包的 GO/Pathway 列) 或
+    online(KEGG 从 rest.kegg.jp + OrgDb 映射)。产出 GSEA_GO.gmt / GSEA_KEGG.gmt。需 orgdb 已安装。"""
+    return _run("build_gmt.R", _clean(type=type, orgdb=orgdb, source=source, keytype=keytype,
+                                     kegg_species=kegg_species, min_count=min_count), outdir)
+
+
+@mcp.tool()
+def rgraph_gsea(result: str, gmt: str, outdir: str, minGSSize: int = 5, maxGSSize: int = 1000,
+                pvalueCutoff: float = 1.0, top_n: int = 5, desc_map: str | None = None,
+                draw: bool = True) -> dict[str, Any]:
+    """GSEA 分析。result 为差异结果表(按 log2FoldChange 排序)，gmt 为基因集文件。
+    产出 GSEA_result.csv 及各方向 top-N 通路的 ES 图(UP/DOWN 子目录)。
+    desc_map(可选 CSV: ID,Description) 用于补充通路描述。"""
+    return _run("gsea.R", _clean(result=result, gmt=gmt, minGSSize=minGSSize, maxGSSize=maxGSSize,
+                                 pvalueCutoff=pvalueCutoff, top_n=top_n, desc_map=desc_map,
+                                 draw=draw), outdir)
+
+
+@mcp.tool()
+def rgraph_gsea_mountain(gsea_result: str, result: str, outdir: str,
+                         geneset_ids: list[str] | None = None, n_top: int = 15) -> dict[str, Any]:
+    """GSEA 山峦图(多通路 core_enrichment 基因的 log2FC 分布 + NES 点)。gsea_result 为 rgraph_gsea
+    产出的 CSV；result 为差异结果表(取基因 log2FC)。geneset_ids 指定通路，缺省取 pvalue 最小的前 n_top。"""
+    return _run("gsea_mountain.R", _clean(gsea_result=gsea_result, result=result,
+                                          geneset_ids=geneset_ids, n_top=n_top), outdir)
+
+
+# ============================ 非流程化绘图 (高级) ============================
+@mcp.tool()
+def rgraph_venn(outdir: str, sets: list[str] | None = None, folder: str | None = None,
+                col: str | None = None, names: list[str] | None = None) -> dict[str, Any]:
+    """韦恩图(2-5 组) + 交集成员表。sets: 各集合 CSV 路径列表；folder: 装 CSV 的目录(二选一)。
+    col: 元素列名(默认自动识别 Element_name/gene_id/首列)。产出 venn.png/pdf + veen_table.csv。"""
+    return _run("venn.R", _clean(sets=sets, folder=folder, col=col, names=names), outdir)
+
+
+@mcp.tool()
+def rgraph_quadrant(x: str, y: str, outdir: str, mode: str = "nine", x_name: str = "compare1",
+                    y_name: str = "compare2", pcut: float = 0.05, log2fc: float = 1.0) -> dict[str, Any]:
+    """九象限图/二象限图。x、y 为两个比较的差异结果 CSV(各含 gene_id,log2FoldChange,pvalue)。
+    mode: nine(九象限) / four(二象限,仅同向基因 High/Low)。产出散点图 + 象限表。"""
+    return _run("quadrant.R", _clean(x=x, y=y, mode=mode, x_name=x_name, y_name=y_name,
+                                     pcut=pcut, log2fc=log2fc), outdir)
+
+
+@mcp.tool()
+def rgraph_stacked_volcano(deg_data: str, outdir: str, sig_metric: str = "pvalue",
+                           pcut: float = 0.05, log2fc: float = 1.0, group_col: str = "TvsC",
+                           top_n: int = 5) -> dict[str, Any]:
+    """堆叠火山图(多比较组)。deg_data 含 gene_id,log2FoldChange,pvalue 及分组列(group_col,默认 TvsC)。
+    按组抽动散点，点大小/深浅=|log2FC|，红上蓝下，每组每方向标注 top_n 个基因。"""
+    return _run("stacked_volcano.R", _clean(deg_data=deg_data, sig_metric=sig_metric, pcut=pcut,
+                                            log2fc=log2fc, group_col=group_col, top_n=top_n), outdir)
+
+
+@mcp.tool()
+def rgraph_gene_bar(deg: str, sample_group: str, outdir: str, gene_list: str | None = None,
+                    genes: list[str] | None = None) -> dict[str, Any]:
+    """单基因表达柱形图(对照 vs 处理，均值±SEM，显著性星号)。deg 为 Deg_all.csv(含 pvalue 与各
+    样本归一化列)；sample_group 需 TvsC。基因来源: genes 优先，否则 gene_list(CSV)。每基因一张图。"""
+    return _run("gene_bar.R", _clean(deg=deg, sample_group=sample_group, gene_list=gene_list,
+                                     genes=genes), outdir)
+
+
+@mcp.tool()
+def rgraph_gene_correlation(fpkm: str, sample_group: str, outdir: str,
+                            gene_list1: str | None = None, gene_list2: str | None = None,
+                            genes1: list[str] | None = None,
+                            genes2: list[str] | None = None) -> dict[str, Any]:
+    """基因-基因相关性气泡热图(Pearson, 基于 log2(fpkm+1))。行基因=genes1/gene_list1，
+    列基因=genes2/gene_list2(缺省=genes1)。圆点大小=|r|，蓝-白-红。产出热图 + correlation.csv。"""
+    return _run("gene_correlation.R", _clean(fpkm=fpkm, sample_group=sample_group,
+                                             gene_list1=gene_list1, gene_list2=gene_list2,
+                                             genes1=genes1, genes2=genes2), outdir)
+
+
+@mcp.tool()
+def rgraph_pathway_trend(enrich: str, deg: str, outdir: str, kind: str = "diverge",
+                         top_n: int | None = None) -> dict[str, Any]:
+    """通路上下调基因统计图。enrich 为富集表(需 Description 与 geneID 列, 形如 a/b/c)；
+    deg 为差异结果表(需 gene_id,log2FoldChange)。kind: diverge(左右发散条形) / stack(堆叠条形)。
+    注: 默认 enrich 的 geneID 与 deg 的 gene_id 为同一 ID 类型(直接匹配)。"""
+    return _run("pathway_trend.R", _clean(enrich=enrich, deg=deg, kind=kind, top_n=top_n), outdir)
+
+
+@mcp.tool()
+def rgraph_segmented_volcano(result: str, outdir: str, sig_metric: str = "pvalue",
+                             pcut: float = 0.05, log2fc: float = 1.0, color_scheme: str = "rb",
+                             break_lower: float | None = None,
+                             break_upper: float | None = None) -> dict[str, Any]:
+    """分段式火山图(y 轴断裂，适合个别基因 p 值极端时)。break_lower/upper 手动指定断裂区间，
+    缺省自动推断。需 ggbreak 包(本机默认缺，按提示 install.packages('ggbreak'))。"""
+    return _run("segmented_volcano.R", _clean(result=result, sig_metric=sig_metric, pcut=pcut,
+                                              log2fc=log2fc, color_scheme=color_scheme,
+                                              break_lower=break_lower, break_upper=break_upper), outdir)
+
+
+@mcp.tool()
+def rgraph_pathway_gene_heatmap(enrich: str, fpkm: str, sample_group: str, outdir: str,
+                                kind: str = "correlation", id_col: str = "geneID",
+                                min_count: int = 3, max_pathways: int = 6) -> dict[str, Any]:
+    """通路富集基因的相关性热图/聚类热图（逐通路出图）。enrich 为 GO/KEGG 富集表
+    (需 Description 与基因列 id_col, 默认 geneID)；fpkm 为表达矩阵。kind: correlation(基因-基因
+    Pearson) / cluster(pheatmap 聚类热图)。注: id_col 基因需与 fpkm 的 gene_id 同 ID 类型。"""
+    return _run("pathway_gene_heatmap.R", _clean(enrich=enrich, fpkm=fpkm, sample_group=sample_group,
+                                                 kind=kind, id_col=id_col, min_count=min_count,
+                                                 max_pathways=max_pathways), outdir)
+
+
+@mcp.tool()
+def rgraph_kmeans(fpkm: str, sample_group: str, outdir: str, gene_list: str | None = None,
+                  genes: list[str] | None = None, k: int = 4, kmax: int = 10) -> dict[str, Any]:
+    """目标基因 K-means 聚类：肘部图 + 聚类热图(行按簇排序、行/列注释) + 各簇表达趋势线 +
+    各簇基因成员表。基因来源: genes 优先，否则 gene_list(CSV)。k: 簇数(可先看肘部图定 k)。"""
+    return _run("kmeans.R", _clean(fpkm=fpkm, sample_group=sample_group, gene_list=gene_list,
+                                   genes=genes, k=k, kmax=kmax), outdir)
+
+
+@mcp.tool()
+def rgraph_interactive_volcano(result: str, outdir: str, sig_metric: str = "pvalue",
+                               pcut: float = 0.05, log2fc: float = 1.0,
+                               color_scheme: str = "rb", include_no: bool = True) -> dict[str, Any]:
+    """交互式火山图(plotly HTML，悬停显示基因/log2FC/-log10p)。result 为差异结果表。
+    产出 .html（默认非 selfcontained，伴生 _files 目录，无需 pandoc）。"""
+    return _run("interactive_volcano.R", _clean(result=result, sig_metric=sig_metric, pcut=pcut,
+                                                log2fc=log2fc, color_scheme=color_scheme,
+                                                include_no=include_no), outdir)
+
+
+@mcp.tool()
+def rgraph_sankey(enrich: str, outdir: str, id_col: str = "geneID",
+                  pathway_col: str = "Description", top_n: int = 8) -> dict[str, Any]:
+    """富集通路-基因桑基图(ggalluvial; 基因→通路流向)。enrich 为富集表(需通路名列
+    pathway_col 与基因列 id_col, 形如 a/b/c)。top_n 限制展示通路数。"""
+    return _run("sankey.R", _clean(enrich=enrich, id_col=id_col, pathway_col=pathway_col,
+                                   top_n=top_n), outdir)
+
+
+@mcp.tool()
+def rgraph_ssgsea(expr: str, gmt: str, sample_group: str, outdir: str,
+                  normalize: bool = True) -> dict[str, Any]:
+    """ssGSEA 单样本基因集富集打分 + 各基因集分组箱线图。expr 为表达矩阵，gmt 为基因集。
+    需 GSVA + GSEABase（本机默认缺，按提示 BiocManager::install(c('GSVA','GSEABase'))）。"""
+    return _run("ssgsea.R", _clean(expr=expr, gmt=gmt, sample_group=sample_group,
+                                   normalize=normalize), outdir)
+
+
+@mcp.tool()
+def rgraph_multilevel_scatter(expr: str, sample_group: str, outdir: str,
+                              gene_list: str | None = None, genes: list[str] | None = None,
+                              x_col: str | None = None, color_col: str | None = None,
+                              sample_col: str | None = None) -> dict[str, Any]:
+    """多级分组基因表达散点图(逐基因)。主分组为 x、次分组着色。sample_group 可含
+    sample_name(1)/group_name(1)/group_name2 等多级列(缺省自动识别)。基因来源: genes 或 gene_list。"""
+    return _run("multilevel_scatter.R", _clean(expr=expr, sample_group=sample_group,
+                                               gene_list=gene_list, genes=genes, x_col=x_col,
+                                               color_col=color_col, sample_col=sample_col), outdir)
+
+
+@mcp.tool()
+def rgraph_norm_matrix(count: str, sample_group: str, outdir: str, method: str = "deseq2",
+                       min_mean: float = 1.0) -> dict[str, Any]:
+    """输出标准化表达矩阵。method: deseq2(中位数比值标准化 count) / limma(voom logCPM)。
+    产出 normalized_count.csv。注: deseq2 需安装 DESeq2；limma 本机已装。"""
+    return _run("norm_matrix.R", _clean(count=count, sample_group=sample_group, method=method,
+                                        min_mean=min_mean), outdir)
+
+
+@mcp.tool()
+def rgraph_interaction_diff(count: str, sample_group: str, outdir: str, sig_metric: str = "padj",
+                           pcut: float = 0.05, log2fc: float = 1.0,
+                           min_mean_count: float = 2.0) -> dict[str, Any]:
+    """两因子交互作用差异分析(DESeq2 ~g1+g2+g1:g2，提取交互项)。sample_group 需含
+    group_name1,group_name2,TvsC1,TvsC2。产出 01.DEseq2_result + Deg_all/down/up(可喂 rgraph_volcano)。
+    需 DESeq2（本机默认缺，按提示安装）。"""
+    return _run("interaction_diff.R", _clean(count=count, sample_group=sample_group,
+                                             sig_metric=sig_metric, pcut=pcut, log2fc=log2fc,
+                                             min_mean_count=min_mean_count), outdir)
+
+
+@mcp.tool()
+def rgraph_wgcna(expr: str, sample_group: str, outdir: str, power: int = 0,
+                 min_module_size: int = 30, merge_cut: float = 0.25,
+                 network_type: str = "unsigned", max_genes: int = 3000,
+                 tomplot: bool = False, export_module: str | None = None,
+                 export_threshold: float = 0.1) -> dict[str, Any]:
+    """WGCNA 加权共表达网络分析。产出：软阈值图、模块树状图、模块-分组相关热图、
+    特征向量邻接热图、gene_module/geneInfo 表。power=0 自动选最佳软阈值；max_genes 按方差限制
+    基因数(控制耗时)。tomplot=True 时额外绘网络热图并导出 Cytoscape 边/点表(用于与 rgraph_network
+    串联)；export_module 指定导出模块(缺省自动取最大非-grey 模块)，export_threshold 控制边数。"""
+    return _run("wgcna.R", _clean(expr=expr, sample_group=sample_group, power=power,
+                                  min_module_size=min_module_size, merge_cut=merge_cut,
+                                  network_type=network_type, max_genes=max_genes,
+                                  tomplot=tomplot, export_module=export_module,
+                                  export_threshold=export_threshold), outdir)
+
+
+@mcp.tool()
+def rgraph_enrich_circos(enrich: str, outdir: str, type: str = "go", topN: int = 10,
+                         split_count: bool | None = None, show_axis: bool = True) -> dict[str, Any]:
+    """富集分析圈图(circlize 四轨道: 分类/背景基因数/前景基因(上下调)/RichFactor)。
+    enrich 需含 pvalue,RichFactor,Bg_gene,Count[,Up,Down] 及 ONTOLOGY(GO)/Category(KEGG)。
+    type: go/kegg。图例需 ComplexHeatmap(缺失则略去图例，圈图主体照常输出)。"""
+    return _run("enrich_circos.R", _clean(enrich=enrich, type=type, topN=topN,
+                                          split_count=split_count, show_axis=show_axis), outdir)
+
+
+@mcp.tool()
+def rgraph_virtual_knockout(count: str, sample_group: str, outdir: str, ko_gene: str,
+                            ko_group: str, logfc: float = 0.5, hv_num: int = 5000) -> dict[str, Any]:
+    """bulk RNA 虚拟敲除（GENIE3 推断调控网络 + bulkKnk 扰动）。sample_group 需含 group_name
+    与 pheno_data。ko_gene/ko_group 为要敲除的基因与样本分组。产出扰动汇总/显著扰动基因
+    表 + 双轴火山图。依赖 bulkKnk(课程本地源码包，install.packages('bulkKnk-main',repos=NULL,type='source'))
+    与 GENIE3（Bioconductor）——本机默认缺，会返回缺包提示。"""
+    return _run("virtual_knockout.R", _clean(count=count, sample_group=sample_group,
+                                             ko_gene=ko_gene, ko_group=ko_group, logfc=logfc,
+                                             hv_num=hv_num), outdir)
+
+
+@mcp.tool()
+def rgraph_network(outdir: str, mode: str = "matrix", expr: str | None = None,
+                   sample_group: str | None = None, genes: list[str] | None = None,
+                   method: str = "WGCNA", cor_method: str = "pearson", r_threshold: float = 0.7,
+                   p_threshold: float = 0.05, transform: str = "none", max_features: int = 500,
+                   node_annotation: str | None = None, edges: str | None = None,
+                   nodes: str | None = None, from_col: str | None = None, to_col: str | None = None,
+                   weight_col: str | None = None, directed: bool = False, layout: str = "gephi",
+                   layout_module: str = "random", group_by: str = "Modularity",
+                   fill_by: str = "Modularity", label: bool = False, pointlabel: str | None = None,
+                   add_outer: bool = False) -> dict[str, Any]:
+    """网络图(ggNetView)：共表达/相关/宏基因组共现网络，按模块着色。
+    mode="matrix": expr(gene_id/OTU+样本) 构相关网络(method: WGCNA/cor/SPARCC/SpiecEasi/Hmisc；
+      genes 限定子集否则按方差取 max_features)。mode="edge": edges(前两列 from,to[+weight])
+      [+nodes] —— 可直接吃 rgraph_wgcna 的 Cytoscape 边表、rgraph_ppi 的 Target_PPi。
+    layout: gephi 等；label/pointlabel(如 'top5')/add_outer 控制标签与模块边界。需 ggNetView 包。"""
+    return _run("network.R", _clean(mode=mode, expr=expr, sample_group=sample_group, genes=genes,
+                                    method=method, cor_method=cor_method, r_threshold=r_threshold,
+                                    p_threshold=p_threshold, transform=transform,
+                                    max_features=max_features, node_annotation=node_annotation,
+                                    edges=edges, nodes=nodes, from_col=from_col, to_col=to_col,
+                                    weight_col=weight_col, directed=directed, layout=layout,
+                                    layout_module=layout_module, group_by=group_by, fill_by=fill_by,
+                                    label=label, pointlabel=pointlabel, add_outer=add_outer), outdir)
+
+
+if __name__ == "__main__":
+    mcp.run()
diff --git a/plugins/Presisitence/rgraph/skills/rgraph/SKILL.md b/plugins/Presisitence/rgraph/skills/rgraph/SKILL.md
new file mode 100644
index 0000000..d6005e7
--- /dev/null
+++ b/plugins/Presisitence/rgraph/skills/rgraph/SKILL.md
@@ -0,0 +1,19 @@
+---
+name: rgraph
+description: Use when the user already has an RNA-seq count or expression matrix and needs downstream analysis or figures (normalize, PCA, DESeq2/edgeR/limma, volcano, heatmap, GO/KEGG, GSEA, WGCNA). Drive the rgraph MCP tools with local Rscript; do not redraw in Python.
+---
+
+# rgraph
+
+Use the `rgraph` MCP tools. Prefer R-rendered png+pdf over matplotlib copies.
+
+## Typical order
+
+1. `rgraph_env` — confirm Rscript and packages
+2. `rgraph_normalize` / `rgraph_pca` / `rgraph_correlation` as needed
+3. `rgraph_diff` — default significance metric is **padj**, not raw p-value
+4. `rgraph_volcano`, `rgraph_heatmap`, `rgraph_enrich`, `rgraph_gsea`, `rgraph_wgcna` as requested
+
+Required table columns are in the Plugin README (`sample_name`/`group`, `gene_id`, counts).
+
+If Rscript is missing, return the generated `.R` script and the command to run it. If a package is missing, return the install hint from the tool. Do not pretend the figure was drawn.
diff --git a/plugins/Presisitence/rgraph/tests/data/gene_count.csv b/plugins/Presisitence/rgraph/tests/data/gene_count.csv
new file mode 100644
index 0000000..54b478e
--- /dev/null
+++ b/plugins/Presisitence/rgraph/tests/data/gene_count.csv
@@ -0,0 +1,11 @@
+gene_id,Length,A1,A2,A3,B1,B2,B3
+g1,1000,100,120,110,10,12,9
+g2,2000,50,40,45,200,220,210
+g3,1500,0,0,0,0,0,0
+g4,500,30,35,28,33,31,36
+g5,800,5,7,6,100,90,95
+g6,1200,80,88,84,20,18,22
+g7,900,300,320,290,15,17,14
+g8,1100,12,10,11,140,150,160
+g9,700,60,64,58,62,66,59
+g10,1600,2,3,1,80,85,90
diff --git a/plugins/Presisitence/rgraph/tests/data/sample_group.csv b/plugins/Presisitence/rgraph/tests/data/sample_group.csv
new file mode 100644
index 0000000..4c68281
--- /dev/null
+++ b/plugins/Presisitence/rgraph/tests/data/sample_group.csv
@@ -0,0 +1,7 @@
+sample_name,group_name,TvsC
+A1,A,treatment
+A2,A,treatment
+A3,A,treatment
+B1,B,control
+B2,B,control
+B3,B,control

From f4cce672f13ff5d2d91aa9d9ab191706408d5e8a Mon Sep 17 00:00:00 2001
From: Presisitence <Presisitence@users.noreply.github.com>
Date: Wed, 19 Aug 2026 22:12:05 +0800
Subject: [PATCH 2/2] Rename plugin rgraph to rnaseq-plot

Catalog name now states RNA-seq downstream plotting. MCP tool functions stay rgraph_*.
---
 plugins/Presisitence/rgraph/plugin.json       | 22 -----------------
 .../{rgraph => rnaseq-plot}/LICENSE           |  0
 .../{rgraph => rnaseq-plot}/README.md         | 12 ++++++----
 .../{rgraph => rnaseq-plot}/mcp.json          |  2 +-
 plugins/Presisitence/rnaseq-plot/plugin.json  | 24 +++++++++++++++++++
 .../{rgraph => rnaseq-plot}/pyproject.toml    |  6 ++---
 .../rgraph_toolkit/__init__.py                |  0
 .../rgraph_toolkit/cli.py                     |  0
 .../rgraph_toolkit/runner.py                  |  0
 .../rscripts/_common.R                        |  0
 .../rscripts/build_gmt.R                      |  0
 .../rscripts/correlation_heatmap.R            |  0
 .../{rgraph => rnaseq-plot}/rscripts/diff.R   |  0
 .../rscripts/distribution.R                   |  0
 .../{rgraph => rnaseq-plot}/rscripts/enrich.R |  0
 .../rscripts/enrich_circos.R                  |  0
 .../rscripts/gene_bar.R                       |  0
 .../rscripts/gene_correlation.R               |  0
 .../rscripts/go_plot.R                        |  0
 .../{rgraph => rnaseq-plot}/rscripts/gsea.R   |  0
 .../rscripts/gsea_mountain.R                  |  0
 .../rscripts/heatmap.R                        |  0
 .../rscripts/interaction_diff.R               |  0
 .../rscripts/interactive_volcano.R            |  0
 .../rscripts/kegg_plot.R                      |  0
 .../{rgraph => rnaseq-plot}/rscripts/kmeans.R |  0
 .../rscripts/multilevel_scatter.R             |  0
 .../rscripts/network.R                        |  0
 .../rscripts/norm_matrix.R                    |  0
 .../rscripts/normalize.R                      |  0
 .../rscripts/pathway_gene_heatmap.R           |  0
 .../rscripts/pathway_trend.R                  |  0
 .../{rgraph => rnaseq-plot}/rscripts/pca.R    |  0
 .../{rgraph => rnaseq-plot}/rscripts/ppi.R    |  0
 .../rscripts/quadrant.R                       |  0
 .../{rgraph => rnaseq-plot}/rscripts/sankey.R |  0
 .../rscripts/segmented_volcano.R              |  0
 .../{rgraph => rnaseq-plot}/rscripts/ssgsea.R |  0
 .../rscripts/stacked_volcano.R                |  0
 .../{rgraph => rnaseq-plot}/rscripts/venn.R   |  0
 .../rscripts/virtual_knockout.R               |  0
 .../rscripts/volcano.R                        |  0
 .../{rgraph => rnaseq-plot}/rscripts/wgcna.R  |  0
 .../{rgraph => rnaseq-plot}/server.py         |  9 +++----
 .../skills/rnaseq-plot}/SKILL.md              |  8 +++----
 .../tests/data/gene_count.csv                 |  0
 .../tests/data/sample_group.csv               |  0
 47 files changed, 44 insertions(+), 39 deletions(-)
 delete mode 100644 plugins/Presisitence/rgraph/plugin.json
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/LICENSE (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/README.md (73%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/mcp.json (91%)
 create mode 100644 plugins/Presisitence/rnaseq-plot/plugin.json
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/pyproject.toml (64%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rgraph_toolkit/__init__.py (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rgraph_toolkit/cli.py (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rgraph_toolkit/runner.py (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/_common.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/build_gmt.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/correlation_heatmap.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/diff.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/distribution.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/enrich.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/enrich_circos.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/gene_bar.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/gene_correlation.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/go_plot.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/gsea.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/gsea_mountain.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/heatmap.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/interaction_diff.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/interactive_volcano.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/kegg_plot.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/kmeans.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/multilevel_scatter.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/network.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/norm_matrix.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/normalize.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/pathway_gene_heatmap.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/pathway_trend.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/pca.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/ppi.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/quadrant.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/sankey.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/segmented_volcano.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/ssgsea.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/stacked_volcano.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/venn.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/virtual_knockout.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/volcano.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/rscripts/wgcna.R (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/server.py (98%)
 rename plugins/Presisitence/{rgraph/skills/rgraph => rnaseq-plot/skills/rnaseq-plot}/SKILL.md (64%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/tests/data/gene_count.csv (100%)
 rename plugins/Presisitence/{rgraph => rnaseq-plot}/tests/data/sample_group.csv (100%)

diff --git a/plugins/Presisitence/rgraph/plugin.json b/plugins/Presisitence/rgraph/plugin.json
deleted file mode 100644
index 2db2c7d..0000000
--- a/plugins/Presisitence/rgraph/plugin.json
+++ /dev/null
@@ -1,22 +0,0 @@
-{
-  "$schema": "https://agent-plugins.org/schemas/1.0.0/plugin.schema.json",
-  "name": "rgraph",
-  "version": "0.1.0",
-  "description": "RNA-seq downstream analysis and publication figures via local Rscript (DESeq2/edgeR/limma, volcano, heatmap, GSEA, WGCNA).",
-  "author": {
-    "name": "Presisitence",
-    "url": "https://github.com/Presisitence"
-  },
-  "homepage": "https://github.com/Presisitence/rgraph-mcp",
-  "repository": "https://github.com/Presisitence/rgraph-mcp",
-  "license": "MIT",
-  "keywords": [
-    "minimax-code",
-    "plugin",
-    "mcp",
-    "rnaseq",
-    "deseq2",
-    "ggplot2",
-    "wgcna"
-  ]
-}
diff --git a/plugins/Presisitence/rgraph/LICENSE b/plugins/Presisitence/rnaseq-plot/LICENSE
similarity index 100%
rename from plugins/Presisitence/rgraph/LICENSE
rename to plugins/Presisitence/rnaseq-plot/LICENSE
diff --git a/plugins/Presisitence/rgraph/README.md b/plugins/Presisitence/rnaseq-plot/README.md
similarity index 73%
rename from plugins/Presisitence/rgraph/README.md
rename to plugins/Presisitence/rnaseq-plot/README.md
index d89561d..c8a0ee6 100644
--- a/plugins/Presisitence/rgraph/README.md
+++ b/plugins/Presisitence/rnaseq-plot/README.md
@@ -1,12 +1,14 @@
-# rgraph
+# rnaseq-plot
 
-RNA-seq **downstream** analysis and figures for MiniMax Code. Parameterized R scripts
-(ggplot2, pheatmap, clusterProfiler, edgeR, limma, WGCNA, …) are exposed as MCP tools and
-rendered by the user's local **Rscript** to png + pdf.
+RNA-seq **downstream plotting** for MiniMax Code (formerly published as `rgraph`).
+Parameterized R scripts (ggplot2, pheatmap, clusterProfiler, edgeR, limma, WGCNA, …)
+are exposed as MCP tools and rendered by the user's local **Rscript** to png + pdf.
 
 The Plugin consumes the user's own count/FPKM tables. It does not ship genomes or experimental matrices.
 `tests/data/` is a tiny synthetic `g1`–`g10` table for smoke tests.
 
+MCP **tool names remain `rgraph_*`** (for example `rgraph_volcano`) so existing prompts keep working.
+
 ## Try it
 
 ```text
@@ -34,4 +36,4 @@ by default. If R or a package is missing, the tool returns install commands inst
 
 ## License
 
-MIT. See [LICENSE](LICENSE).
+MIT. See [LICENSE](LICENSE). Source: https://github.com/Presisitence/rnaseq-plot-mcp
diff --git a/plugins/Presisitence/rgraph/mcp.json b/plugins/Presisitence/rnaseq-plot/mcp.json
similarity index 91%
rename from plugins/Presisitence/rgraph/mcp.json
rename to plugins/Presisitence/rnaseq-plot/mcp.json
index 1cdbe22..0e13111 100644
--- a/plugins/Presisitence/rgraph/mcp.json
+++ b/plugins/Presisitence/rnaseq-plot/mcp.json
@@ -1,7 +1,7 @@
 {
   "$schema": "https://agent-plugins.org/schemas/1.0.0/mcp.schema.json",
   "mcpServers": {
-    "rgraph": {
+    "rnaseq-plot": {
       "type": "stdio",
       "command": "uv",
       "args": ["run", "server.py"],
diff --git a/plugins/Presisitence/rnaseq-plot/plugin.json b/plugins/Presisitence/rnaseq-plot/plugin.json
new file mode 100644
index 0000000..ab3aac5
--- /dev/null
+++ b/plugins/Presisitence/rnaseq-plot/plugin.json
@@ -0,0 +1,24 @@
+{
+  "$schema": "https://agent-plugins.org/schemas/1.0.0/plugin.schema.json",
+  "name": "rnaseq-plot",
+  "version": "0.1.1",
+  "description": "RNA-seq downstream plotting via local Rscript: DESeq2/edgeR/limma, volcano, heatmap, GSEA, WGCNA (png+pdf).",
+  "author": {
+    "name": "Presisitence",
+    "url": "https://github.com/Presisitence"
+  },
+  "homepage": "https://github.com/Presisitence/rnaseq-plot-mcp",
+  "repository": "https://github.com/Presisitence/rnaseq-plot-mcp",
+  "license": "MIT",
+  "keywords": [
+    "minimax-code",
+    "plugin",
+    "mcp",
+    "rnaseq",
+    "deseq2",
+    "volcano",
+    "heatmap",
+    "ggplot2",
+    "wgcna"
+  ]
+}
diff --git a/plugins/Presisitence/rgraph/pyproject.toml b/plugins/Presisitence/rnaseq-plot/pyproject.toml
similarity index 64%
rename from plugins/Presisitence/rgraph/pyproject.toml
rename to plugins/Presisitence/rnaseq-plot/pyproject.toml
index 3808ce4..17c9184 100644
--- a/plugins/Presisitence/rgraph/pyproject.toml
+++ b/plugins/Presisitence/rnaseq-plot/pyproject.toml
@@ -1,7 +1,7 @@
 [project]
-name = "rgraph-mcp"
-version = "0.1.0"
-description = "RNAseq 下游可视化/分析 MCP 服务：把一套 R (ggplot2/pheatmap/clusterProfiler/WGCNA...) 出图脚本参数化封装为 MCP 工具，由 Rscript 引擎渲染"
+name = "rnaseq-plot-mcp"
+version = "0.1.1"
+description = "RNA-seq 下游出图 MCP：把一套 R (ggplot2/pheatmap/clusterProfiler/WGCNA...) 脚本封装为工具，由本机 Rscript 渲染 png+pdf"
 readme = "README.md"
 requires-python = ">=3.10"
 license = "MIT"
diff --git a/plugins/Presisitence/rgraph/rgraph_toolkit/__init__.py b/plugins/Presisitence/rnaseq-plot/rgraph_toolkit/__init__.py
similarity index 100%
rename from plugins/Presisitence/rgraph/rgraph_toolkit/__init__.py
rename to plugins/Presisitence/rnaseq-plot/rgraph_toolkit/__init__.py
diff --git a/plugins/Presisitence/rgraph/rgraph_toolkit/cli.py b/plugins/Presisitence/rnaseq-plot/rgraph_toolkit/cli.py
similarity index 100%
rename from plugins/Presisitence/rgraph/rgraph_toolkit/cli.py
rename to plugins/Presisitence/rnaseq-plot/rgraph_toolkit/cli.py
diff --git a/plugins/Presisitence/rgraph/rgraph_toolkit/runner.py b/plugins/Presisitence/rnaseq-plot/rgraph_toolkit/runner.py
similarity index 100%
rename from plugins/Presisitence/rgraph/rgraph_toolkit/runner.py
rename to plugins/Presisitence/rnaseq-plot/rgraph_toolkit/runner.py
diff --git a/plugins/Presisitence/rgraph/rscripts/_common.R b/plugins/Presisitence/rnaseq-plot/rscripts/_common.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/_common.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/_common.R
diff --git a/plugins/Presisitence/rgraph/rscripts/build_gmt.R b/plugins/Presisitence/rnaseq-plot/rscripts/build_gmt.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/build_gmt.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/build_gmt.R
diff --git a/plugins/Presisitence/rgraph/rscripts/correlation_heatmap.R b/plugins/Presisitence/rnaseq-plot/rscripts/correlation_heatmap.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/correlation_heatmap.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/correlation_heatmap.R
diff --git a/plugins/Presisitence/rgraph/rscripts/diff.R b/plugins/Presisitence/rnaseq-plot/rscripts/diff.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/diff.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/diff.R
diff --git a/plugins/Presisitence/rgraph/rscripts/distribution.R b/plugins/Presisitence/rnaseq-plot/rscripts/distribution.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/distribution.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/distribution.R
diff --git a/plugins/Presisitence/rgraph/rscripts/enrich.R b/plugins/Presisitence/rnaseq-plot/rscripts/enrich.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/enrich.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/enrich.R
diff --git a/plugins/Presisitence/rgraph/rscripts/enrich_circos.R b/plugins/Presisitence/rnaseq-plot/rscripts/enrich_circos.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/enrich_circos.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/enrich_circos.R
diff --git a/plugins/Presisitence/rgraph/rscripts/gene_bar.R b/plugins/Presisitence/rnaseq-plot/rscripts/gene_bar.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/gene_bar.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/gene_bar.R
diff --git a/plugins/Presisitence/rgraph/rscripts/gene_correlation.R b/plugins/Presisitence/rnaseq-plot/rscripts/gene_correlation.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/gene_correlation.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/gene_correlation.R
diff --git a/plugins/Presisitence/rgraph/rscripts/go_plot.R b/plugins/Presisitence/rnaseq-plot/rscripts/go_plot.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/go_plot.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/go_plot.R
diff --git a/plugins/Presisitence/rgraph/rscripts/gsea.R b/plugins/Presisitence/rnaseq-plot/rscripts/gsea.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/gsea.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/gsea.R
diff --git a/plugins/Presisitence/rgraph/rscripts/gsea_mountain.R b/plugins/Presisitence/rnaseq-plot/rscripts/gsea_mountain.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/gsea_mountain.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/gsea_mountain.R
diff --git a/plugins/Presisitence/rgraph/rscripts/heatmap.R b/plugins/Presisitence/rnaseq-plot/rscripts/heatmap.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/heatmap.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/heatmap.R
diff --git a/plugins/Presisitence/rgraph/rscripts/interaction_diff.R b/plugins/Presisitence/rnaseq-plot/rscripts/interaction_diff.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/interaction_diff.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/interaction_diff.R
diff --git a/plugins/Presisitence/rgraph/rscripts/interactive_volcano.R b/plugins/Presisitence/rnaseq-plot/rscripts/interactive_volcano.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/interactive_volcano.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/interactive_volcano.R
diff --git a/plugins/Presisitence/rgraph/rscripts/kegg_plot.R b/plugins/Presisitence/rnaseq-plot/rscripts/kegg_plot.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/kegg_plot.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/kegg_plot.R
diff --git a/plugins/Presisitence/rgraph/rscripts/kmeans.R b/plugins/Presisitence/rnaseq-plot/rscripts/kmeans.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/kmeans.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/kmeans.R
diff --git a/plugins/Presisitence/rgraph/rscripts/multilevel_scatter.R b/plugins/Presisitence/rnaseq-plot/rscripts/multilevel_scatter.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/multilevel_scatter.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/multilevel_scatter.R
diff --git a/plugins/Presisitence/rgraph/rscripts/network.R b/plugins/Presisitence/rnaseq-plot/rscripts/network.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/network.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/network.R
diff --git a/plugins/Presisitence/rgraph/rscripts/norm_matrix.R b/plugins/Presisitence/rnaseq-plot/rscripts/norm_matrix.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/norm_matrix.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/norm_matrix.R
diff --git a/plugins/Presisitence/rgraph/rscripts/normalize.R b/plugins/Presisitence/rnaseq-plot/rscripts/normalize.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/normalize.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/normalize.R
diff --git a/plugins/Presisitence/rgraph/rscripts/pathway_gene_heatmap.R b/plugins/Presisitence/rnaseq-plot/rscripts/pathway_gene_heatmap.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/pathway_gene_heatmap.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/pathway_gene_heatmap.R
diff --git a/plugins/Presisitence/rgraph/rscripts/pathway_trend.R b/plugins/Presisitence/rnaseq-plot/rscripts/pathway_trend.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/pathway_trend.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/pathway_trend.R
diff --git a/plugins/Presisitence/rgraph/rscripts/pca.R b/plugins/Presisitence/rnaseq-plot/rscripts/pca.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/pca.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/pca.R
diff --git a/plugins/Presisitence/rgraph/rscripts/ppi.R b/plugins/Presisitence/rnaseq-plot/rscripts/ppi.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/ppi.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/ppi.R
diff --git a/plugins/Presisitence/rgraph/rscripts/quadrant.R b/plugins/Presisitence/rnaseq-plot/rscripts/quadrant.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/quadrant.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/quadrant.R
diff --git a/plugins/Presisitence/rgraph/rscripts/sankey.R b/plugins/Presisitence/rnaseq-plot/rscripts/sankey.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/sankey.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/sankey.R
diff --git a/plugins/Presisitence/rgraph/rscripts/segmented_volcano.R b/plugins/Presisitence/rnaseq-plot/rscripts/segmented_volcano.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/segmented_volcano.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/segmented_volcano.R
diff --git a/plugins/Presisitence/rgraph/rscripts/ssgsea.R b/plugins/Presisitence/rnaseq-plot/rscripts/ssgsea.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/ssgsea.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/ssgsea.R
diff --git a/plugins/Presisitence/rgraph/rscripts/stacked_volcano.R b/plugins/Presisitence/rnaseq-plot/rscripts/stacked_volcano.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/stacked_volcano.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/stacked_volcano.R
diff --git a/plugins/Presisitence/rgraph/rscripts/venn.R b/plugins/Presisitence/rnaseq-plot/rscripts/venn.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/venn.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/venn.R
diff --git a/plugins/Presisitence/rgraph/rscripts/virtual_knockout.R b/plugins/Presisitence/rnaseq-plot/rscripts/virtual_knockout.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/virtual_knockout.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/virtual_knockout.R
diff --git a/plugins/Presisitence/rgraph/rscripts/volcano.R b/plugins/Presisitence/rnaseq-plot/rscripts/volcano.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/volcano.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/volcano.R
diff --git a/plugins/Presisitence/rgraph/rscripts/wgcna.R b/plugins/Presisitence/rnaseq-plot/rscripts/wgcna.R
similarity index 100%
rename from plugins/Presisitence/rgraph/rscripts/wgcna.R
rename to plugins/Presisitence/rnaseq-plot/rscripts/wgcna.R
diff --git a/plugins/Presisitence/rgraph/server.py b/plugins/Presisitence/rnaseq-plot/server.py
similarity index 98%
rename from plugins/Presisitence/rgraph/server.py
rename to plugins/Presisitence/rnaseq-plot/server.py
index bea2bfa..b4cbced 100644
--- a/plugins/Presisitence/rgraph/server.py
+++ b/plugins/Presisitence/rnaseq-plot/server.py
@@ -1,8 +1,9 @@
-"""rgraph —— RNAseq 下游可视化/分析 MCP Server (FastMCP)。
+"""rnaseq-plot —— RNA-seq 下游出图 MCP Server (FastMCP)。
 
 把一套清洗过、参数化的 R 出图脚本（rscripts/*.R）暴露为 MCP 工具，由本机 Rscript 引擎渲染，
-产出与课程一致风格的 png+pdf。定位不到 R 时返回可手动运行的脚本与命令；R 端缺包时返回缺失
-包名与安装建议。
+产出 png+pdf。工具函数名保持 `rgraph_*`，与既有客户端配置兼容。
+
+定位不到 R 时返回可手动运行的脚本与命令；R 端缺包时返回缺失包名与安装建议。
 
 运行:
     uv run --directory <this-dir> server.py
@@ -15,7 +16,7 @@
 
 from rgraph_toolkit import runner
 
-mcp = FastMCP("rgraph")
+mcp = FastMCP("rnaseq-plot")
 
 
 def _clean(**kw: Any) -> dict[str, Any]:
diff --git a/plugins/Presisitence/rgraph/skills/rgraph/SKILL.md b/plugins/Presisitence/rnaseq-plot/skills/rnaseq-plot/SKILL.md
similarity index 64%
rename from plugins/Presisitence/rgraph/skills/rgraph/SKILL.md
rename to plugins/Presisitence/rnaseq-plot/skills/rnaseq-plot/SKILL.md
index d6005e7..ce057cf 100644
--- a/plugins/Presisitence/rgraph/skills/rgraph/SKILL.md
+++ b/plugins/Presisitence/rnaseq-plot/skills/rnaseq-plot/SKILL.md
@@ -1,11 +1,11 @@
 ---
-name: rgraph
-description: Use when the user already has an RNA-seq count or expression matrix and needs downstream analysis or figures (normalize, PCA, DESeq2/edgeR/limma, volcano, heatmap, GO/KEGG, GSEA, WGCNA). Drive the rgraph MCP tools with local Rscript; do not redraw in Python.
+name: rnaseq-plot
+description: Use when the user already has an RNA-seq count or expression matrix and needs downstream plots or analysis (normalize, PCA, DESeq2/edgeR/limma, volcano, heatmap, GO/KEGG, GSEA, WGCNA). Call the rnaseq-plot MCP tools named rgraph_*; do not redraw in Python.
 ---
 
-# rgraph
+# rnaseq-plot
 
-Use the `rgraph` MCP tools. Prefer R-rendered png+pdf over matplotlib copies.
+Use the `rnaseq-plot` MCP server. Tool functions are still named `rgraph_*`. Prefer R-rendered png+pdf over matplotlib copies.
 
 ## Typical order
 
diff --git a/plugins/Presisitence/rgraph/tests/data/gene_count.csv b/plugins/Presisitence/rnaseq-plot/tests/data/gene_count.csv
similarity index 100%
rename from plugins/Presisitence/rgraph/tests/data/gene_count.csv
rename to plugins/Presisitence/rnaseq-plot/tests/data/gene_count.csv
diff --git a/plugins/Presisitence/rgraph/tests/data/sample_group.csv b/plugins/Presisitence/rnaseq-plot/tests/data/sample_group.csv
similarity index 100%
rename from plugins/Presisitence/rgraph/tests/data/sample_group.csv
rename to plugins/Presisitence/rnaseq-plot/tests/data/sample_group.csv