ArticleGenes2022
A Two-Part Mixed Model for Differential Expression Analysis in Single-Cell High-Throughput Gene Expression Data.
Article in Genes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Differential expression analysis in single-cell and spatial RNA-seq without model assumptions.Cell reports methods · 2026Article
- Differential expression analysis in single cell and spatial RNASeq without model assumptions.bioRxiv : the preprint server for biology · 2025Article
- Unravelling infiltrating T-cell heterogeneity in kidney renal clear cell carcinoma: Integrative single-cell and spatial transcriptomic profiling.Journal of cellular and molecular medicine · 2024Article
- Article
- Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges.Entropy (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 methods, our approach shows improved power for detecting differential expressed genes in single-cell high-throughput gene expression data.
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