Visualizing scRNA-Seq data at population scale with GloScope
Increasingly, scRNA-Seq studies explore cell populations across different samples and the effect of sample heterogeneity on organism's phenotype. However, relatively few bioinformatic methods have been developed which adequately address the variation between samples for such population-level analyses. We propose a framework for representing the entire single-cell profile of a sample, which we call a GloScope representation. We implement GloScope on scRNA-Seq datasets from study designs ranging from 12 to over 300 samples and demonstrate how GloScope allows researchers to perform essential bioinformatic tasks at the sample-level, in particular visualization and quality control assessment.
Authors
- William Torous
- Boying Gong (ORCID: https://orcid.org/0000-0001-7326-4603)
- Elizabeth Purdom (ORCID: https://orcid.org/0000-0001-9455-7990)
- Hao Wang (ORCID: https://orcid.org/0000-0002-0749-474X)
Institutions
- University of California, Berkeley (US)
Publication Details
- Journal
- Genome biology
- Published
- 2024-10-08
- DOI
- https://doi.org/10.1186/s13059-024-03398-1
- Citations
- 4
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.70
Funders
- Chan Zuckerberg Initiative