Published February 24, 2025
| Version 2025.02.24
Dataset
Open
Single-cell RNA-Seq-based deconvolution of hairy cell leukemia reveals novel disease drivers and identifies DUSP1 as potential therapeutic target
Creators
- Bohn, Jan-Paul (Contact person)1
-
Sturm, Gregor
(Contact person)2
- Mair, Anna1
- Scheiber, Alexandra1
- Kugler, Valentina3
- Feichtner, Andreas3
- Fritz, Alexandra4
- Torres-Quesada, Omar3
- Jaeger, Ulrich5
- Brunner, Andrea6
- Pircher, Andreas1
- Sopper, Sieghart1
- Eduard, Stefan3
- Trajanoski, Zlatko7
- Salcher, Stefan1
- Wolf, Dominik1
- 1. Department of Internal Medicine V, Hematology & Oncology, Comprehensive Cancer Center Innsbruck (CCCI) and Tyrolean Cancer Research Institute (TKFI), Medical University of Innsbruck, Innsbruck, Austria
- 2. Biocenter, Institute of Bioinformatics, Medical University of Innsbruck, Innsbruck, Austria.
- 3. Institute of Biochemistry and Center for Molecular Biosciences, University of Innsbruck, Innsbruck, Austria
- 4. Tyrolean Cancer Research Institute (TKFI)
- 5. Department of Medicine I, Division of Hematology and Hemostaseology, Medical University of Vienna
- 6. Institute of Pathology, Neuropathology and Molecular Pathology, Medical University of Innsbruck, Innsbruck, Austria
- 7. Biocenter, Institute of Bioinformatics, Medical University of Innsbruck, Innsbruck, Austria
Description
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.
Files
01_raw_counts.zip
Files
(3.4 GB)
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