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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Vydané v:	Frontiers in genetics Ročník 12; s. 616686
Hlavní autori:	He, Zhiqiang, Pan, Yueyun, Shao, Fang, Wang, Hui
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland Frontiers Media S.A 05.02.2021
Predmet:	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
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Shrnutí:	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.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 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