pseudobulkdeg

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.