ArticleBioinformatics (Oxford, England)2019
DECENT: differential expression with capture efficiency adjustmeNT for single-cell RNA-seq data.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 61 citations in OpenAlex.
- What are the applications of single-cell RNA sequencing in cancer research: a systematic review.Journal of experimental & clinical cancer research : CR · 2021Pooled it
- DEAPLOG: Differential Expression Analysis and Pseudo-Temporal Locating and Ordering of Genes in Single-Cell Transcriptomic Data.Interdisciplinary sciences, computational life sciences · 2026Article
- Evaluation of statistical differential analysis methods for identification of senescent cells using single-cell transcriptomics.Cell reports methods · 2026Article
- Leveraging neighborhood representations of single-cell data to achieve sensitive DE testing with miloDE.Genome biology · 2024Article
- scBoolSeq: Linking scRNA-seq statistics and Boolean dynamics.PLoS computational biology · 2024Article
- Single-cell RNA sequencing data imputation using bi-level feature propagation.Briefings in bioinformatics · 2024Article
- Single-Cell RNA Sequencing and Its Applications in Pituitary Research.Neuroendocrinology · 2024Review
- Article
- iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects.BMC bioinformatics · 2023Article
- Intracellular pH dynamics regulates intestinal stem cell lineage specification.Nature communications · 2023Article
- Sensitive cluster-free differential expression testing.bioRxiv : the preprint server for biology · 2023Article
- Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges.Entropy (Basel, Switzerland) · 2022Review
- Article
- Anti-bias training for (sc)RNA-seq: experimental and computational approaches to improve precision.Briefings in bioinformatics · 2021Article
- SwarnSeq: An improved statistical approach for differential expression analysis of single-cell RNA-seq data.Genomics · 2021Article
- Atlas of breast cancer infiltrated B-lymphocytes revealed by paired single-cell RNA-sequencing and antigen receptor profiling.Nature communications · 2021Article
- Article
- A systematic evaluation of single-cell RNA-sequencing imputation methods.Genome biology · 2020Article
- Single-Cell Analysis of the Gene Expression Effects of Developmental Lead (Pb) Exposure on the Mouse Hippocampus.Toxicological sciences : an official journal of the Society of Toxicology · 2020Article
- DISC: a highly scalable and accurate inference of gene expression and structure for single-cell transcriptomes using semi-supervised deep learning.Genome biology · 2020Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationDropout is a common phenomenon in single-cell RNA-seq (scRNA-seq) data, and when left unaddressed it affects the validity of the statistical analyses. Despite this, few current methods for differential expression (DE) analysis of scRNA-seq data explicitly model the process that gives rise to the dropout events. We develop DECENT, a method for DE analysis of scRNA-seq data that explicitly and accurately models the molecule capture process in scRNA-seq experiments.
resultsWe show that DECENT demonstrates improved DE performance over existing DE methods that do not explicitly model dropout. This improvement is consistently observed across several public scRNA-seq datasets generated using different technological platforms. The gain in improvement is especially large when the capture process is overdispersed. DECENT maintains type I error well while achieving better sensitivity. Its performance without spike-ins is almost as good as when spike-ins are used to calibrate the capture model. AVAILABILITY AND IMPLEMENTATION: The method is implemented as a publicly available R package available from https://github.com/cz-ye/DECENT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.