Dear
Thank you for maintaining the kb_python package!
In the past, I have used kb_python exentisvely for processing single-cell RNA-seq data obtained from Smart-seq3 like datasets.
For a novel project, collaborators have now used a Smart-seq3-like set-up (with UMIs), but using a small cell subpopulation (e.g. hundreds of cells) as input rather than single-cell, to obtain a low-input bulk RNA-seq protocol. For analysis, would it be correct to use the same analysis set-up to process this dataset using kb_python?
Current command:
kb count
-o {analysis_dir}count_transcripts/
-w {bc_whitelist}
-t {threads}
--tcc
--h5ad
-i {analysis_dir}index.idx
-g {analysis_dir}ttg.txt
-x SMARTSEQ3
--verbose
{I1_fastq} {I2_fastq} {R1_fastq} {R2_fastq}
Thank you for any advice!
Dear
Thank you for maintaining the kb_python package!
In the past, I have used kb_python exentisvely for processing single-cell RNA-seq data obtained from Smart-seq3 like datasets.
For a novel project, collaborators have now used a Smart-seq3-like set-up (with UMIs), but using a small cell subpopulation (e.g. hundreds of cells) as input rather than single-cell, to obtain a low-input bulk RNA-seq protocol. For analysis, would it be correct to use the same analysis set-up to process this dataset using kb_python?
Current command:
kb count
-o {analysis_dir}count_transcripts/
-w {bc_whitelist}
-t {threads}
--tcc
--h5ad
-i {analysis_dir}index.idx
-g {analysis_dir}ttg.txt
-x SMARTSEQ3
--verbose
{I1_fastq} {I2_fastq} {R1_fastq} {R2_fastq}
Thank you for any advice!