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

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.

SUBMITTER: Baran Y 

PROVIDER: S-EPMC6790056 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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

Baran Yael Y   Bercovich Akhiad A   Sebe-Pedros Arnau A   Lubling Yaniv Y   Giladi Amir A   Chomsky Elad E   Meir Zohar Z   Hoichman Michael M   Lifshitz Aviezer A   Tanay Amos A  

Genome biology 20191011 1

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  ...[more]

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