Visualizing scRNA-Seq data at population scale with GloScope

IF 10.1 1区生物学 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Genome Biology Pub Date : 2024-10-08 DOI:10.1186/s13059-024-03398-1

Hao Wang, William Torous, Boying Gong, Elizabeth Purdom

引用次数: 0

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.

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利用 GloScope 在种群规模上可视化 scRNA-Seq 数据

越来越多的 scRNA-Seq 研究探索不同样本的细胞群以及样本异质性对生物表型的影响。然而，能充分解决样本间差异以进行此类群体级分析的生物信息学方法相对较少。我们提出了一个框架来表示样本的整个单细胞特征，我们称之为 GloScope 表示法。我们在从 12 个样本到超过 300 个样本的 scRNA-Seq 研究设计数据集上实施了 GloScope，并展示了 GloScope 如何让研究人员在样本级执行基本的生物信息任务，特别是可视化和质量控制评估。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Genome Biology Biochemistry, Genetics and Molecular Biology-Genetics

CiteScore

21.00

自引率

3.30%

发文量

241

审稿时长

2 months

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