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 journalArticlepeer-review


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 languageEnglish
Article number206
JournalGenome Biology
Issue number1
StatePublished - 11 Oct 2019
Externally publishedYes


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


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