Binomial models uncover biological variation during feature selection of droplet-based single-cell RNA sequencing.

IF 3.8 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS PLoS Computational Biology Pub Date : 2024-09-06 eCollection Date: 2024-09-01 DOI:10.1371/journal.pcbi.1012386

Breanne Sparta, Timothy Hamilton, Gunalan Natesan, Samuel D Aragones, Eric J Deeds

引用次数: 0

Abstract

Effective analysis of single-cell RNA sequencing (scRNA-seq) data requires a rigorous distinction between technical noise and biological variation. In this work, we propose a simple feature selection model, termed "Differentially Distributed Genes" or DDGs, where a binomial sampling process for each mRNA species produces a null model of technical variation. Using scRNA-seq data where cell identities have been established a priori, we find that the DDG model of biological variation outperforms existing methods. We demonstrate that DDGs distinguish a validated set of real biologically varying genes, minimize neighborhood distortion, and enable accurate partitioning of cells into their established cell-type groups.

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二项式模型揭示基于液滴的单细胞 RNA 测序特征选择过程中的生物变异。

有效分析单细胞 RNA 测序（scRNA-seq）数据需要严格区分技术噪声和生物变异。在这项工作中，我们提出了一个简单的特征选择模型，称为 "差异分布基因"（Differentially Distributed Genes）或 DDGs，其中每个 mRNA 物种的二项式采样过程会产生一个技术变异的空模型。利用事先确定了细胞身份的 scRNA-seq 数据，我们发现 DDG 生物变异模型优于现有方法。我们证明了 DDGs 能区分一组经过验证的真实生物变异基因，最大程度地减少邻域失真，并能准确地将细胞划分到既定的细胞类型组中。

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

PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY

CiteScore

7.10

自引率

4.70%

发文量

820

审稿时长

2.5 months

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