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Differential expression

pseudobulk approach

02_differential_expression/scRNA_pseudobulk.Rmd is a template that performs pseudobulk differential expression analysis using DESeq2. It takes as input:

  • information.R, containing basic facts about the project, analysis, and scientific question
  • Seurat object containing both raw counts and log normalized data
  • a metadata variable of interest and two levels of that variable to be compared
  • a column in the seurat object metadata containing cluster names/numbers
  • the name/number of the cluster of interest

The template aggregates single cell counts to the sample x cluster x factor_of_interest pseudobulk level, subsets to the cluster of interest, performs the desired statistical comparisons, and visualizes the results.

MAST - single cell approach

02_differential_expression/scRNA_MAST.Rmd is a template to visualize differentially expressed genes (DEG) results generated from MAST analysis. Main visualizations include:

  • Group-level mean expression shown in heatmap;
  • Volcano plots highlighting top DEGs;
  • An adapted seurat Dotplot to show % of cells expressing top DEGs;
  • An integrated Violin-Box-Scatter (VBS) plot displaying the normalized expression of top DEGs per single cell across contrast groups.

We separately prepare the Rscript 02_differential_expression/MAST_analysis.R to pre-compute the DEG results since MAST computation is relatively time-consuming. For our demo dataset, ~1000 cells and two group comparison, it took around 10-15 minutes. If working on an HPC, you may be interested in running this as a batch job. A sample sbatch script is provided in run_MAST_analysis.sh.

To run this Rscript, you should input below parameters:

  • --seurat_obj: the seurat object containing your scRNA-seq data, raw counts stored in the layer counts of assay RNA is required
  • --resolution_column: the column name of your choice of clustering method + resolution chosen, for example pca_res0.4, this is only required if you want to subset your data
  • --cluster_name: the cluster you want to subset for MAST analysis
  • --contrast: the column name in your seurat cell metadata indicating the group you want to do contrast for
  • --outputDir: the output directory you will find your intermediate results for running scRNA_MAST.Rmd Default: out.

If none of the parameters are supplied, just run Rscript 02_differential_expression/MAST_analysis.R will run it on our demo dataset.

To obtain more informative console logs from MAST analysis, please consider running:

Rscript --no-save --no-restore --verbose differential_expression/MAST_analysis.R > MAST.Rout 2>&1

You will expect to have three main outputs in your specified output folder:

  • processed_seurat.qs: the processed seurat object containing log-normalized data
  • MAST_RESULTS*: the MAST modeling object to allow for further follow-up analysis of your own choice
  • FULL_MAST_RESULTS_*: full MAST differential expression analysis results in csv format
  • SIG_MAST_RESULTS_padj<0.05*: significant DEGs using 0.05 as the threshold for FDR