Visualizing scRNA-Seq data at population scale with GloScope

Hao Wang; William Torous; Boying Gong; Elizabeth Purdom

doi:10.1186/s13059-024-03398-1

Visualizing scRNA-Seq data at population scale with GloScope

Genome Biol. 2024 Oct 8;25(1):259. doi: 10.1186/s13059-024-03398-1.

Authors

Hao Wang¹, William Torous², Boying Gong¹, Elizabeth Purdom^{3

4}

Affiliations

¹ Division of Biostatistics, University of California, Berkeley, CA, USA.
² Department of Statistics, University of California, Berkeley, CA, USA.
³ Department of Statistics, University of California, Berkeley, CA, USA. epurdom@berkeley.edu.
⁴ Center for Computational Biology, University of California, Berkeley, CA, USA. epurdom@berkeley.edu.

Abstract

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.

Keywords: Batch effect detection and visualization; Density estimation; Single-cell sequencing data; scRNA-Seq.

MeSH terms

Animals
Computational Biology / methods
Humans
RNA-Seq* / methods
Single-Cell Analysis* / methods
Single-Cell Gene Expression Analysis
Software*

Abstract

MeSH terms

Grants and funding