MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions

被引：227

作者：

Baran, Yael ^{[1
]}

Bercovich, Akhiad ^{[1
]}

Sebe-Pedros, Arnau ^{[1
]}

Lubling, Yaniv ^{[1
]}

Giladi, Amir ^{[2
]}

Chomsky, Elad ^{[1
]}

Meir, Zohar ^{[1
]}

Hoichman, Michael ^{[1
]}

Lifshitz, Aviezer ^{[1
]}

Tanay, Amos ^{[1
]}

机构：

[1] Weizmann Inst Sci, Dept Comp Sci & Appl Math, Rehovot, Israel

[2] Weizmann Inst Sci, Dept Immunol, Rehovot, Israel

来源：

GENOME BIOLOGY | 2019年 / 20卷 / 01期

基金：

欧洲研究理事会;

关键词：

RNA-seq; scRNA-seq; Graph partition; Multinomial distribution; Sampling variance; Clustering; Smoothing; TRANSCRIPTIONAL HETEROGENEITY; SEQUENCING DATA; RECONSTRUCTION; DYNAMICS;

D O I：

10.1186/s13059-019-1812-2

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 [微生物学]; 090105 [作物生产系统与生态工程];

摘要：

scRNA-seq profiles each represent a highly partial sample of mRNA molecules from a unique cell that can never be resampled, and robust analysis must separate the sampling effect from biological variance. We describe a methodology for partitioning scRNA-seq datasets into metacells: disjoint and homogenous groups of profiles that could have been resampled from the same cell. Unlike clustering analysis, our algorithm specializes at obtaining granular as opposed to maximal groups. We show how to use metacells as building blocks for complex quantitative transcriptional maps while avoiding data smoothing. Our algorithms are implemented in the MetaCell R/C++ software package.

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