TY - JOUR
T1 - A Two-Part Mixed Model for Differential Expression Analysis in Single-Cell High-Throughput Gene Expression Data
AU - Shi, Yang
AU - Lee, Ji Hyun
AU - Kang, Huining
AU - Jiang, Hui
N1 - Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/2
Y1 - 2022/2
N2 - The high-throughput gene expression data generated from recent single-cell RNA sequencing (scRNA-seq) and parallel single-cell reverse transcription quantitative real-time PCR (scRT-qPCR) technologies enable biologists to study the function of transcriptome at the level of individual cells. Compared with bulk RNA-seq and RT-qPCR gene expression data, single-cell data show notable distinct features, including excessive zero expression values, high variability, and clustered design. We propose to model single-cell high-throughput gene expression data using a two-part mixed model, which not only adequately accounts for the aforementioned features of single-cell expression data but also provides the flexibility of adjusting for covariates. An efficient computational algorithm, automatic differentiation, is used for estimating the model parameters. Compared with existing meth-ods, our approach shows improved power for detecting differential expressed genes in single-cell high-throughput gene expression data.
AB - The high-throughput gene expression data generated from recent single-cell RNA sequencing (scRNA-seq) and parallel single-cell reverse transcription quantitative real-time PCR (scRT-qPCR) technologies enable biologists to study the function of transcriptome at the level of individual cells. Compared with bulk RNA-seq and RT-qPCR gene expression data, single-cell data show notable distinct features, including excessive zero expression values, high variability, and clustered design. We propose to model single-cell high-throughput gene expression data using a two-part mixed model, which not only adequately accounts for the aforementioned features of single-cell expression data but also provides the flexibility of adjusting for covariates. An efficient computational algorithm, automatic differentiation, is used for estimating the model parameters. Compared with existing meth-ods, our approach shows improved power for detecting differential expressed genes in single-cell high-throughput gene expression data.
KW - Automatic differentiation
KW - Differential expression
KW - Single-cell RNA-seq
KW - Single-cell RT-qPCR
KW - Two-part mixed-model
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U2 - 10.3390/genes13020377
DO - 10.3390/genes13020377
M3 - Article
C2 - 35205420
AN - SCOPUS:85125310850
SN - 2073-4425
VL - 13
JO - Genes
JF - Genes
IS - 2
M1 - 377
ER -