Statistically principled feature selection for single cell transcriptomics
Feature selection is a critical step in scRNAseq for downstream analyses. We explore the pitfalls that can arise from incautious feature selection and present a statistical method to facilitate improved outcomes.
Authors
- Arthur D. Lander (ORCID: https://orcid.org/0000-0002-4380-5525)
- Emmanuel Dollinger (ORCID: https://orcid.org/0000-0002-2562-7980)
- Kai Silkwood
- Qing Nie (ORCID: https://orcid.org/0000-0002-8804-3368)
- Scott X. Atwood (ORCID: https://orcid.org/0000-0001-7407-9792)
Institutions
- University of California, Irvine (US)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2025-10-02
- DOI
- https://doi.org/10.1186/s12859-025-06240-y
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institutes of Health