Identifying Differentially Expressed Genes of Zero Inflated Single Cell RNA Sequencing Data Using Mixed Model Score Tests

Single cell RNA sequencing (scRNA-seq) allows quantitative measurement and comparison of gene expression at the resolution of single cells. Ignoring the batch effects and zero inflation of scRNA-seq data, many proposed differentially expressed (DE) methods might generate bias. We propose a method, s...

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Bibliographic Details
Published in:Frontiers in genetics Vol. 12; p. 616686
Main Authors: He, Zhiqiang, Pan, Yueyun, Shao, Fang, Wang, Hui
Format: Journal Article
Language:English
Published: Switzerland Frontiers Media S.A 05.02.2021
Subjects:
differential expression analyses
generalized linear mixed model
Genetics
observational weights
score test
single cell RNA sequencing
zero inflation
score test
observational weights
differential expression analyses
single cell RNA sequencing
generalized linear mixed model
zero inflation
ISSN:1664-8021, 1664-8021
Online Access:Get full text
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Summary:Single cell RNA sequencing (scRNA-seq) allows quantitative measurement and comparison of gene expression at the resolution of single cells. Ignoring the batch effects and zero inflation of scRNA-seq data, many proposed differentially expressed (DE) methods might generate bias. We propose a method, single cell mixed model score tests (scMMSTs), to efficiently identify DE genes of scRNA-seq data with batch effects using the generalized linear mixed model (GLMM). scMMSTs treat the batch effect as a random effect. For zero inflation, scMMSTs use a weighting strategy to calculate observational weights for counts independently under zero-inflated and zero-truncated distributions. Counts data with calculated weights were subsequently analyzed using weighted GLMMs. The theoretical null distributions of the score statistics were constructed by mixed Chi-square distributions. Intensive simulations and two real datasets were used to compare edgeR-zinbwave, DESeq2-zinbwave, and scMMSTs. Our study demonstrates that scMMSTs, as supplement to standard methods, are advantageous to define DE genes of zero-inflated scRNA-seq data with batch effects.
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This article was submitted to Computational Genomics, a section of the journal Frontiers in Genetics
Edited by: Alfredo Pulvirenti, University of Catania, Italy
Reviewed by: Tiejun Tong, Hong Kong Baptist University, Hong Kong; Shiquan Sun, Xi’an Jiaotong University, China
ISSN:1664-8021
1664-8021
DOI:10.3389/fgene.2021.616686