Journal
GENOME BIOLOGY
Volume 20, Issue 1, Pages -Publisher
BMC
DOI: 10.1186/s13059-019-1812-2
Keywords
RNA-seq; scRNA-seq; Graph partition; Multinomial distribution; Sampling variance; Clustering; Smoothing
Funding
- ERC [724824]
- Chan Zuckerberg Association
- Wolfson foundation
- FAMRI
- European Research Council (ERC) [724824] Funding Source: European Research Council (ERC)
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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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