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Nat Commun. 2019 Jun 13;10(1):2611. doi: 10.1038/s41467-019-10500-w.

Simulating multiple faceted variability in single cell RNA sequencing.

Zhang X1,2, Xu C1, Yosef N3,4,5.

Author information

1
Department of Electrical Engineering and Computer Sciences, Center for Computational Biology, UC Berkeley, Berkeley, CA, 94720, USA.
2
Ragon Institute of Massachusetts General Hospital, MIT and Harvard, Cambridge, MA, 02139, USA.
3
Department of Electrical Engineering and Computer Sciences, Center for Computational Biology, UC Berkeley, Berkeley, CA, 94720, USA. niryosef@berkeley.edu.
4
Ragon Institute of Massachusetts General Hospital, MIT and Harvard, Cambridge, MA, 02139, USA. niryosef@berkeley.edu.
5
Chan-Zuckerberg Biohub, San Francisco, CA, 94158, USA. niryosef@berkeley.edu.

Abstract

The abundance of new computational methods for processing and interpreting transcriptomes at a single cell level raises the need for in silico platforms for evaluation and validation. Here, we present SymSim, a simulator that explicitly models the processes that give rise to data observed in single cell RNA-Seq experiments. The components of the SymSim pipeline pertain to the three primary sources of variation in single cell RNA-Seq data: noise intrinsic to the process of transcription, extrinsic variation indicative of different cell states (both discrete and continuous), and technical variation due to low sensitivity and measurement noise and bias. We demonstrate how SymSim can be used for benchmarking methods for clustering, differential expression and trajectory inference, and for examining the effects of various parameters on their performance. We also show how SymSim can be used to evaluate the number of cells required to detect a rare population under various scenarios.

PMID:
31197158
PMCID:
PMC6565723
DOI:
10.1038/s41467-019-10500-w
[Indexed for MEDLINE]
Free PMC Article

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