Single-cell RNA-Seq-based deconvolution of hairy cell leukemia reveals novel disease drivers and identifies DUSP1 as potential therapeutic target

Microwell-based (BD Rhapsody) scRNA-seq of Hairy Cell Leukemia Patients published in Single-cell RNA-Seq-based deconvolution of hairy cell leukemia reveals novel disease drivers and identifies DUSP1 as potential therapeutic target, Jan-Paul Bohn et al. Submitted. The files will be made available upon publication. Description of the files 01_raw_counts: count matrices as CSV as generated by the BD Rhapsody WTA analysis pipeline 10_prepare_adata: Load BD Rhapsody WTA analysis pipeline outputs into AnnData objects and add metadata. 20_scrnaseq_qc: Use a nextflow pipeline (stored in lib/single-cell-analysis-nf) to perform threshold-based filtering of single-cell data and apply SOLO for doublet detection. 30_merge_adata: Merge samples into a single AnnData object, train a scVI model for batch effect removal, and annotate cell-types based on unsupervised clustering 40_cluster_analysis: Identify and investigate subclusters representing cell-states that go beyond the major cell-types 50_de_analysis: Generate pseudobulk and perform differential gene expression analysis using DESeq2 (based on a wrapper script stored in lib/deseq2_workflow) 70_downstream_analysis: Perform pathway analyses and generate figures for publication based on the data generated in the previous steps containers: Conda environments used for the analysis packed up as singularity containers.