A Snakemake workflow for performing differential expression analyses (DEA) of sc/snRNA-seq data powered by the R package Seurat's functions FindMarkers and FindAllMarkers.

• Test data
• Software
• Steps
• Configuration
• Authors
• Links

We provide a subset of cells from the following human T-Cell dataset as test case for the workflow:

Cano-Gamez, E., Soskic, B., Roumeliotis, T.I. et al. Single-cell transcriptomics identifies an effectorness gradient shaping the response of CD4+ T cells to cytokines. Nat Commun 11, 1801 (2020). https://doi.org/10.1038/s41467-020-15543-y

This work examines the transcriptional patterns of human naïve and memory CD4+ T cells to show that responses to cytokines differ substantially between these cell types. The analysis of the different data modalities is documented in the following repository: https://github.com/eddiecg/T-cell-effectorness

The folder test_data contains two sets of files starting with "Memory_Tcells" and "Naive_Tcells". The "counts.rds" files contain a raw count matrix and the "metadata.csv" the corresponding metadata annotation for the cells in the count matrix. Each count matrix and metadata table can be combined into a Seurat object as starting point for the workflow.

The workflow perfroms the following steps.

• Differential Expression Analysis (DEA)
  • using Seurat's FindMarkers or FindAllMarkers depending on the configuration (CSV)
  • feature list per comparison group and direction (up/down) for downstream analysis (eg enrichment analysis) (TXT)
  • (optional) feature score tables (with two columns: "feature" and "score") per comparison group using {score_formula} for downstream analyses (eg preranked enrichment analysis) (CSV).
• DEA result statistics: number of statistically significant results split by positive (up) and negative (down) change (CSV)
• DEA result filtering by
  • statistical significance (adjusted p-value)
  • effect-size (log 2 fold change)
  • expression (minimum percentage of expression) in one of the comparison groups
• Log Fold Change (LFC) matrix of filtered features by comparison groups (CSV)
• Visualizations
  • all and filtered DEA result statistics: number of features and direction (stacked Bar plots)
  • Volanco plot per comparison with configured cutoffs for statistical significance and effect-size
  • Clustered Heatmaps of the LFC matrix

Detailed specifications can be found here ./config/README.md

This project wouldn't be possible without the following software and their dependencies:

• adapted from the dea_seurat by Stephan Reichl

• GitHub Repository
• Snakemake Workflow Catalog Entry