Aggregate single-cell counts into pseudo-bulk samples and test differential expression with DESeq2 or edgeR.
npx skills add https://github.com/pwwang/immunopipe --skill pseudobulkdeg
Please help me install this Agent Skill. Skill: pseudobulkdeg Source: https://github.com/pwwang/immunopipe/tree/main/skills/pseudobulkdeg Command: npx skills add https://github.com/pwwang/immunopipe --skill pseudobulkdeg
Aggregates single-cell counts into sample-level pseudo-bulk data and identifies differentially expressed genes between conditions, accounting for biological replicates.
Provide a minimal configuration to compare two conditions after aggregating cells into pseudo-bulk samples.
High-intent search queries and answers about installing and using this skill.
You can perform pseudobulk DEG analysis on scRNA-seq data by aggregating single-cell counts into sample-level data and testing differential expression with DESeq2 or edgeR. This approach correctly accounts for biological replicates and per-cell-type variations across conditions.
Yes, pseudobulk DEG analysis supports complex experimental designs including batch effects, paired samples, and interactions across multiple conditions. Aggregating single-cell counts into sample-level pseudo-bulk data allows standard bulk RNA-seq models to account for these confounding factors robustly.
DESeq2 and edgeR are both supported for pseudobulk differential expression testing. The choice between them depends on your preferred statistical modeling framework, while the aggregation step consistently groups single-cell counts into sample-level matrices before either tool performs the testing and optional enrichment.
To run per-cell-type differential expression, the workflow groups metadata by cell type and aggregates single-cell counts into sample-level pseudo-bulk profiles. It then performs differential expression testing across multiple conditions and automatically generates publication-ready results and optional enrichment plots.
Yes, pseudobulk DEG analysis automatically performs enrichment analysis on significant differentially expressed markers. After aggregating scRNA-seq counts and testing with DESeq2 or edgeR, the workflow includes optional enrichment and plotting steps to produce publication-ready results.
pwwangLoad, preprocess, and integrate multi-sample scRNA-seq data into a Seurat object.
jaechang-hitsPerform GSEA and ORA on gene lists with Enrichr pathway lookups.
anthropicsFilter low-quality cells from single-cell RNA-seq data using MAD-based QC.
Victory-HugoPerform differential expression analysis on bulk RNA-seq data with PyDESeq2.
