BeELISA

Author: Aun Al Atiat

BeELISA is a cross-platform desktop application for the standardized and reproducible analysis of enzyme-linked immunosorbent assay (ELISA) data in biomedical research.

Goal of the project

This software was developed as part of the Bachelor thesis:

Statistical Evaluation of Glycoprotein Biomarkers in Cancer Using an Analytical Application for ELISA Data Processing and Biomarker Pattern Recognition (not public)

Aun Al Atiat Brandenburg University of Technology Cottbus-Senftenberg Faculty 2 (Environment and Natural Sciences), Institute of Biotechnology. 2026

Overview

BeELISA provides a structured workflow for quantifying protein biomarkers from 96-well plate ELISA experiments. It covers the complete analytical pipeline: raw optical density (OD) import, standard curve fitting, concentration back-calculation, quality control assessment, and correlation with clinical metadata.

The application was developed with reproducibility as a primary objective. All analytical parameters — including curve model selection, clinical metadata parsers (e.g., TNM, UICC), and detection threshold definitions — are dataset-specific yet designed to remain reusable across different datasets. Clinical metadata parsers are applied only when the corresponding columns (e.g., “TNM” or “UICC”) are present in the dataset.

All you need

BeELISA requires at least three input tables/files: one plate OD file, the corresponding plate ID file, and one metadata file. The plate OD and plate ID files should follow the same 96-well plate layout and can be provided as .csv or .xlsx files. The plate ID file defines which sample is located in each well, while the plate OD file contains the corresponding optical density measurement for each well. The metadata file is a normal table with a header row and columns describing the samples; it must contain a sample ID column that matches the IDs used in the plate ID file. The plate design itself is configured inside the software.

Plate ID file 1 2 3 4 5 6 7 8 9 10 11 12 A STD1 STD1 S001 S002 S003 S004 S005 S006 S007 S008 S009 S010 B STD2 STD2 S011 S012 S013 S014 S015 S016 S017 S018 S019 S020 C STD3 STD3 S021 S022 S023 S024 S025 S026 S027 S028 S029 S030 D STD4 STD4 S031 S032 S033 S034 S035 S036 S037 S038 S039 S040 E STD5 STD5 S041 S042 S043 S044 S045 S046 S047 S048 S049 S050 F NEG NEG S051 S052 S053 S054 S055 S056 S057 S058 S059 S060 G S061 S062 S063 S064 S065 S066 S067 S068 S069 S070 S071 S072 H S073 S074 S075 S076 S077 S078 S079 S080 S081 S082 S083 S084 The ID file contains the sample or calibrator identifier assigned to each well. resources/synthetic_data_1/plates/PLATE_001_ID.xlsx

Plate OD file 1 2 3 4 5 6 7 8 9 10 11 12 A 2.102 2.087 0.812 0.743 1.031 0.655 0.912 1.223 0.534 0.689 0.771 0.845 B 1.742 1.735 0.621 0.583 0.711 0.902 0.488 0.557 0.632 0.784 0.846 0.923 C 1.203 1.190 0.445 0.512 0.601 0.734 0.821 0.944 0.377 0.425 0.503 0.588 D 0.742 0.755 0.389 0.467 0.529 0.612 0.703 0.812 0.356 0.441 0.532 0.608 E 0.332 0.341 0.288 0.355 0.402 0.481 0.552 0.624 0.298 0.344 0.421 0.509 F 0.091 0.087 0.251 0.316 0.389 0.456 0.518 0.587 0.246 0.309 0.376 0.462 G 0.271 0.335 0.412 0.506 0.577 0.643 0.725 0.809 0.286 0.359 0.421 0.498 H 0.312 0.384 0.456 0.529 0.604 0.681 0.759 0.833 0.337 0.408 0.492 0.561 The OD file contains the optical density reading for each well in the same layout. resources/synthetic_data_1/plates/PLATE_001_OD.xlsx

Metadata file sample_id group age sex stage S001 PDAC 61 F II S002 PDAC 57 M III S003 Control 45 F NA S004 PDAC 69 M IV S005 Control 52 F NA The metadata file contains additional information for each sample. The sample ID column must match the IDs in the plate ID file. resources/synthetic_data_1/metadata.xlsx

Scientific Methodology

Standard Curve Fitting

Calibrant OD measurements are fitted to the following models:

• Linear regression
• Log-linear regression
• Exponential regression
• Four-parameter logistic (4PL)
• Five-parameter logistic (5PL)

Model selection is performed automatically using the Bayesian Information Criterion (BIC), which penalizes model complexity. The model with the lowest BIC is applied to back-calculate unknown concentrations via the inverse curve function.

Limits of Detection and Quantification

The limit of detection (LOD) and limit of quantification (LOQ) are calculated from blank or negative control replicates:

• LOD = mean(blank) + 3 * SD(blank)
• LOQ = mean(blank) + 10 * SD(blank)

When per-plate blank replicates are insufficient, a global estimate pooled across all plates is used as a fallback.

Inter-Plate Factor Correction

When multiple plates are analyzed together, systematic differences in signal intensity between plates (e.g., due to different reagent lots, incubation conditions, or plate reader variation) can bias concentration estimates. BeELISA implements a multiplicative inter-plate factor correction based on calibrant wells, analogous to the ΔΔCt normalisation used in qPCR. (Ruijter et al., 2015 — Between-run correction for multi-plate qPCR experiments. Biomolecular Detection and Quantification.) (https://doi.org/10.1016/j.bdq.2015.07.001)

For each calibrant dilution level k, the across-plate median OD of all plates is used as a reference r_k. A plate-specific correction factor is then computed as:

F_plate = exp( median_k( log( m_{p,k} / r_k ) ) )

where m_{p,k} is the median OD of calibrant replicates for plate p at level k, and the outer median is taken over all valid calibrant levels (those with r_k > 0). Corrected ODs are obtained by dividing all wells on the plate by F_plate. LOD and LOQ thresholds are scaled by the same factor.

If plates are assigned to groups, correction factors are computed independently within each group so that between-group biological differences are preserved.

Quality Control

Replicate agreement is assessed by the coefficient of variation (CV):

• Calibrant replicates: warning threshold > 15%
• Sample and control replicates: warning threshold > 20%

Clinical Correlation

Quantified biomarker concentrations are correlated with ordinal clinical staging variables (TNM classification, UICC stage) using Spearman rank correlation. Multiple testing correction is applied using the Benjamini-Hochberg false discovery rate (FDR) procedure. Pattern visualization uses locally weighted scatterplot smoothing (LOWESS). Batch effects across plates are assessed by principal component analysis (PCA) of plate-level QC metrics.

ROC / Diagnostic Performance Analysis

When a binary clinical outcome is available (e.g. disease vs control), BeELISA evaluates the diagnostic performance of the biomarker using Receiver Operating Characteristic (ROC) analysis.

The ROC curve is generated by varying the decision threshold of the selected score variable (e.g. concentration or OD value) and calculating the corresponding sensitivity (true positive rate) and false positive rate (1 − specificity).

Overall classification performance is summarized by the area under the ROC curve (AUC):

• AUC = 0.5 indicates random classification
• AUC = 1.0 indicates perfect discrimination

The optimal cutoff value is determined using the Youden index (sensitivity + specificity − 1), which identifies the threshold that maximizes the combined sensitivity and specificity.

The ROC plot reports:

• AUC with 95% confidence interval
• Optimal cutoff value
• Sensitivity and specificity at the cutoff
• Sample sizes of the positive and negative groups

Uncertainty of the ROC curve is estimated using bootstrap resampling to generate a 95% confidence band.

Features

• Import of raw plate reader data (CSV, Excel) for standard 96-well plates
• Configurable well classification: calibrant, sample, blank, negative control, positive control
• Automatic standard curve model selection via BIC
• Per-plate and global LOD/LOQ calculation
• CV-based replicate QC with configurable thresholds
• Dilution factor correction
• per group Multiplicative inter-plate factor correction of standard curves
• Result classification: below detection, borderline, quantifiable
• TNM/UICC clinical staging integration
• Spearman correlation with Benjamini-Hochberg FDR correction
• Plate-level PCA for batch effect visualization
• ROC-based diagnostic performance analysis with AUC estimation, optimal cutoff determination (Youden index), and sensitivity/specificity reporting
• Session save and restore (.beelisa format)
• Cross-platform: Windows, macOS, Linux

Download

Windows Windows 10 / 11 · MSI Installer Run the installer

macOS macOS 12+ · DMG · Open the DMG and drag BeELISA to Applications.

Linux (AppImage) All distributions · No install needed: chmod +x BeELISA.AppImage && ./BeELISA.AppImage

Linux (Flatpak) All distributions · Install via: flatpak install BeELISA.flatpak

_{Links above always point to the latest release. All releases and release notes are on the Releases page.}

Framework and Build System

BeELISA is implemented in Python and built as a native desktop application using the BeeWare ecosystem.

The graphical user interface (GUI) is developed with:

• BeeWare Toga — a native, cross-platform GUI toolkit for Python.

Application packaging and distribution are handled using:

• BeeWare Briefcase — a tool for building standalone installers for Windows, macOS, and Linux.

The BeeWare project enables Python applications to run as fully native desktop software without requiring users to install Python separately.

For more information, see:

• BeeWare: https://beeware.org
• Toga: https://beeware.org/project/projects/libraries/toga/
• Briefcase: https://beeware.org/project/projects/tools/briefcase/

Development Dependencies

Python 3.11+

Core libraries:

pandas>=2.2,<2.4 numpy>=1.26,<2.0 scipy==1.13.1 scikit-learn>=1.4,<1.6 matplotlib>=3.8,<3.11 seaborn==0.13.2 openpyxl>=3.1,<4.0 toga

License

Copyright (C) 2026 Aun Al Atiat.

This software is distributed under the terms of the GNU General Public License v3.0 (GPLv3) or any later version. See the LICENSE file for the full license text.

Citation

If you use BeELISA in research, please cite as:

Al Atiat, A. (2026). BeELISA: A Software Framework for ELISA Data Analysis (Version 1.0.7) [Computer software]. GitHub. https://github.com/AlAtiat/beelisa