DiSC: a statistical tool for fast differential expression analysis of individual-level single-cell RNA-seq data
The R software package "SingleCellStat" is freely available on CRAN (https://cran.r-project.org/web/packages/SingleCellStat/index.html) and GitHub (https://github.com/Lujun995/DiSC). The replication code for reproducing the analyses in this study is publicly accessible at https://github.com/Lujun995/DiSC_Replication_Code. The scRNA-seq expression matrix and metadata utilized in our simulations and analyses can be retrieved from https://cells.ucsc.edu/autism/rawMatrix.zip, https://cellxgene.cziscience.com/collections/1ca90a2d-2943-483d-b678-b809bf464c30, and https://covid19.cog.sanger.ac.uk/submissions/release1/haniffa21.processed.h5ad.
Authors
- Lu Yang (ORCID: https://orcid.org/0000-0002-5234-3190)
- Jun Chen (ORCID: https://orcid.org/0000-0002-4493-9082)
- Shuwen Zhang (ORCID: https://orcid.org/0000-0003-3555-6677)
- Weihua Guan
- Yingxue Ren (ORCID: https://orcid.org/0000-0003-4529-5722)
- Lujun Zhang (ORCID: https://orcid.org/0009-0007-2062-5082)
Institutions
- University of Minnesota (US)
- Jacksonville College (US)
- Minnesota Department of Health (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- Bioinformatics
- Published
- 2025-05-30
- DOI
- https://doi.org/10.1093/bioinformatics/btaf327
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institutes of Health
- Center for Individualized Medicine, Mayo Clinic