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.

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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

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article

Visualizing scRNA-Seq data at population scale with GloScope

William Torous, Boying Gong, Elizabeth Purdom, Hao Wang
4 citations
Genome biology
Single-cell and spatial transcriptomics
0.70
article

Visualizing scRNA-Seq data at population scale with GloScope

William Torous, Boying Gong, Elizabeth Purdom, Hao Wang
article en
4 citations

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.

Genome biologyVol. 25(1)
University of California, Berkeley (US)
Chan Zuckerberg Initiative
Openalex Percentile: Top 29%
Single-cell and spatial transcriptomics
0.70
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