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

Yael Baran; Akhiad Bercovich; Arnau Sebe-Pedros; Yaniv Lubling; Amir Giladi; Elad Chomsky; Zohar Meir; Michael Hoichman; Aviezer Lifshitz; Amos Tanay

doi:10.1186/s13059-019-1812-2

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

Yael Baran, Akhiad Bercovich, Arnau Sebe-Pedros, Yaniv Lubling, Amir Giladi, Elad Chomsky, Zohar Meir, Michael Hoichman, Aviezer Lifshitz, Amos Tanay^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	English
Article number	206
Journal	Genome Biology
Volume	20
Issue number	1
DOIs	https://doi.org/10.1186/s13059-019-1812-2
State	Published - 11 Oct 2019
Externally published	Yes

Keywords

Clustering
Graph partition
Multinomial distribution
RNA-seq
Sampling variance
Smoothing
scRNA-seq

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10.1186/s13059-019-1812-2

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abstract = "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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AU - Sebe-Pedros, Arnau

AU - Lubling, Yaniv

AU - Giladi, Amir

AU - Chomsky, Elad

AU - Meir, Zohar

AU - Hoichman, Michael

AU - Lifshitz, Aviezer

AU - Tanay, Amos

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KW - Sampling variance

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

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