f-scLVM: scalable and versatile factor analysis for single-cell RNA-seq.

f-scLVM: scalable and versatile factor analysis for single-cell RNA-seq.
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DOI:
10.1186/s13059-017-1334-8
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发表时间:
2017-11-07
期刊:
影响因子:
12.3
通讯作者:
Stegle O
Stegle O
中科院分区:
生物学1区
文献类型:
--
作者:
Buettner F;Pratanwanich N;McCarthy DJ;Marioni JC;Stegle O

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单细胞RNA测序(scRNA-seq)允许研究大细胞群体中基因表达的异质性。这种异质性可能是由于技术或生物因素造成的,使得分解变异来源变得困难。我们在这里描述f-scLVM(因子单细胞潜在变量模型),一种基于因子分析的方法,使用途径注释来指导对支撑异质性的可解释因素的推断。我们的模型联合估计单个因素的相关性,细化基因集注释,并推断没有注释的因素。在多个scRNA-seq数据集的应用中,我们发现f-scLVM将scRNA-seq数据集稳健地分解为可解释的组件,从而促进了新亚群的识别。本文的在线版本(doi:10.1186/s13059-017-1334-8)包含补充材料,可供授权用户使用。
Single-cell RNA-sequencing (scRNA-seq) allows studying heterogeneity in gene expression in large cell populations. Such heterogeneity can arise due to technical or biological factors, making decomposing sources of variation difficult. We here describe f-scLVM (factorial single-cell latent variable model), a method based on factor analysis that uses pathway annotations to guide the inference of interpretable factors underpinning the heterogeneity. Our model jointly estimates the relevance of individual factors, refines gene set annotations, and infers factors without annotation. In applications to multiple scRNA-seq datasets, we find that f-scLVM robustly decomposes scRNA-seq datasets into interpretable components, thereby facilitating the identification of novel subpopulations. The online version of this article (doi:10.1186/s13059-017-1334-8) contains supplementary material, which is available to authorized users.
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