ArticleProceedings of the National Academy of Sciences of the United States of America2018
Gene expression distribution deconvolution in single-cell RNA sequencing.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers.
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Who cites it
73 citing papers in PubMed.
- Highly Constrained Kinetic Models for Single-Cell Gene Expression Analysis.bioRxiv : the preprint server for biology · 2026Article
- Article
- Assessment of dispersion metrics for estimating single-cell transcriptional variability.PLoS computational biology · 2026Article
- A Manifold-Based Measure of Transcriptional Entropy for Quantifying Aging in Single Cells.bioRxiv : the preprint server for biology · 2026Article
- A negative binomial latent factor model for paired microbiome sequencing data.BMC bioinformatics · 2026Article
- distQTL: distribution quantitative trait loci identification by population-scale single-cell data.NAR genomics and bioinformatics · 2025Article
- Distilling Direct Effects via Conditional Differential Gene Expression Analysis.bioRxiv : the preprint server for biology · 2025Article
- distQTL: Distribution Quantitative Trait Loci Identification by Population-Scale Single-Cell Data.bioRxiv : the preprint server for biology · 2025Article
- scSorterDL: a deep neural network-enhanced ensemble LDAs for single cell classifications.Briefings in bioinformatics · 2025Article
- Method of moments framework for differential expression analysis of single-cell RNA sequencing data.Cell · 2024Article
- Statistically principled feature selection for single cell transcriptomics.bioRxiv : the preprint server for biology · 2024Article
- Leveraging gene correlations in single cell transcriptomic data.BMC bioinformatics · 2024Article
- Biophysical modeling with variational autoencoders for bimodal, single-cell RNA sequencing data.Nature methods · 2024Article
- AGImpute: imputation of scRNA-seq data based on a hybrid GAN with dropouts identification.Bioinformatics (Oxford, England) · 2024Article
- Adjustment of scRNA-seq data to improve cell-type decomposition of spatial transcriptomics.Briefings in bioinformatics · 2024Article
- Statistical methods for comparing two independent exponential-gamma means with application to single cell protein data.PloS one · 2024Article
- Inference after latent variable estimation for single-cell RNA sequencing data.Biostatistics (Oxford, England) · 2023Article
- Leveraging gene correlations in single cell transcriptomic data.bioRxiv : the preprint server for biology · 2023Article
- Cell-type-specific co-expression inference from single cell RNA-sequencing data.Nature communications · 2023Article
- A bivariate zero-inflated negative binomial model and its applications to biomedical settings.Statistical methods in medical research · 2023Article
13 more citing papers are in PubMed but not listed here.
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9 authors.
Funding
Abstract
Single-cell RNA sequencing (scRNA-seq) enables the quantification of each gene's expression distribution across cells, thus allowing the assessment of the dispersion, nonzero fraction, and other aspects of its distribution beyond the mean. These statistical characterizations of the gene expression distribution are critical for understanding expression variation and for selecting marker genes for population heterogeneity. However, scRNA-seq data are noisy, with each cell typically sequenced at low coverage, thus making it difficult to infer properties of the gene expression distribution from raw counts. Based on a reexamination of nine public datasets, we propose a simple technical noise model for scRNA-seq data with unique molecular identifiers (UMI). We develop deconvolution of single-cell expression distribution (DESCEND), a method that deconvolves the true cross-cell gene expression distribution from observed scRNA-seq counts, leading to improved estimates of properties of the distribution such as dispersion and nonzero fraction. DESCEND can adjust for cell-level covariates such as cell size, cell cycle, and batch effects. DESCEND's noise model and estimation accuracy are further evaluated through comparisons to RNA FISH data, through data splitting and simulations and through its effectiveness in removing known batch effects. We demonstrate how DESCEND can clarify and improve downstream analyses such as finding differentially expressed genes, identifying cell types, and selecting differentiation markers.
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