1. MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions
- Author
-
Amir Giladi, Akhiad Bercovich, Michael Hoichman, Aviezer Lifshitz, Elad Chomsky, Yaniv Lubling, Zohar Meir, Amos Tanay, Arnau Sebé-Pedrós, and Yael Baran
- Subjects
Sampling variance ,lcsh:QH426-470 ,Method ,RNA-Seq ,Disjoint sets ,Biology ,CD8-Positive T-Lymphocytes ,Clustering ,03 medical and health sciences ,0302 clinical medicine ,Multinomial distribution ,scRNA-seq ,Cluster analysis ,lcsh:QH301-705.5 ,030304 developmental biology ,0303 health sciences ,Sequence Analysis, RNA ,Graph partition ,Genomics ,lcsh:Genetics ,lcsh:Biology (General) ,Graph (abstract data type) ,RNA-seq ,Single-Cell Analysis ,Robust analysis ,Algorithm ,030217 neurology & neurosurgery ,Smoothing ,Algorithms ,Software - 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.
- Published
- 2019