Simulating multiple faceted variability in single cell RNA sequencing

Xiuwei Zhang; Chenling Xu; Nir Yosef

doi:10.1038/s41467-019-10500-w

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Simulating multiple faceted variability in single cell RNA sequencing

Journal article

an open version is available

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Simulating multiple faceted variability in single cell RNA sequencing

Xiuwei Zhang, Chenling Xu and Nir Yosef

Nature communications, Vol.10, 2611

13/Jun/2019

DOI: https://doi.org/10.1038/s41467-019-10500-w

PMCID: PMC6565723

PMID: 31197158

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https://doi.org/10.1038/s41467-019-10500-wView

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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.

Cluster Analysis

Computer Simulation

Datasets as Topic

Gene Expression Profiling - methods

High-Throughput Nucleotide Sequencing - methods

Humans

Kinetics

Models, Genetic

Sequence Analysis, RNA - methods

Single-Cell Analysis - methods

Transcriptome - genetics

Details

Title: Simulating multiple faceted variability in single cell RNA sequencing
Creators: Xiuwei Zhang - Ragon Institute of Massachusetts General Hospital, MIT and Harvard, Cambridge, MA, 02139, USA
Chenling Xu - University of California, Berkeley
Nir Yosef (Corresponding Author) - University of California, Berkeley
Resource Type: Journal article
Publication Details: Nature communications, Vol.10, 2611; 13/Jun/2019
Number of pages: 16
Language: English
DOI: https://doi.org/10.1038/s41467-019-10500-w
PMID: 31197158
PMCID: PMC6565723
Grant note: X.Z. was supported by grant #220558 from the Ragon Institute of MGH, MIT and Harvard. C.X. and N.Y. were supported by NIH/NHLBI grant U19 AI-090023–09. Author contributions - X.Z., C.X., and N.Y. conceived the study and wrote the paper. X.Z. and C.X. wrote the software and conducted the analysis.
Record Identifier: 993423583603596

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