ArticleBiostatistics (Oxford, England)2023
Inference after latent variable estimation for single-cell RNA sequencing data.
Article in Biostatistics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- The Lomb-Scargle periodogram-based differentially expressed gene detection along pseudotime.Nucleic acids research · 2026Article
- Compound models and Pearson residuals for single-cell RNA-seq data without UMIs.Genome biology · 2026Article
- Multimodal analysis reveals cellular diversity and divergent circuits of the zona incerta.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- Quantifying stability via count splitting to guide model selection in RNA velocity analyses.bioRxiv : the preprint server for biology · 2026Article
- Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data.Cell reports methods · 2026Article
- Classification with Missing Data - AbioRxiv : the preprint server for biology · 2026Article
- Topological stratification of continuous genetic variation in large biobanks.PLoS genetics · 2026Article
- CanSig Benchmarks Methods for Reproducible Cancer Cell State Discovery from Single-Cell Transcriptomic Data.Cancer research · 2026Article
- Molecular characterization of directly reprogrammed neurons from human fibroblasts using single cell RNA sequencing.Scientific reports · 2026Article
- Quantifying stability via count splitting to guide model selection in RNA velocity analyses.Bioinformatics advances · 2026Article
- Model-based dimensionality reduction for single-cell RNA-seq using generalized bilinear models.Biostatistics (Oxford, England) · 2025Article
- Testing for a difference in means of a single feature after clustering.Biostatistics (Oxford, England) · 2025Article
- SpaTM: topic models for inferring spatially informed transcriptional programs.Briefings in bioinformatics · 2025Article
- PseudotimeDE-fast: fast testing of differential gene expression along cell pseudotime.Bioinformatics (Oxford, England) · 2025Article
- Tree-based differential testing using inferential uncertainty for RNA-seq.Genome research · 2025Article
- Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data.bioRxiv : the preprint server for biology · 2025Article
- BISON: bi-clustering of spatial omics data with feature selection.Bioinformatics (Oxford, England) · 2025Article
- CHOIR improves significance-based detection of cell types and states from single-cell data.Nature genetics · 2025Article
- Modeling the dynamics of EMT reveals genes associated with pan-cancer intermediate states and plasticity.NPJ systems biology and applications · 2025Article
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5 authors.
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Abstract
In the analysis of single-cell RNA sequencing data, researchers often characterize the variation between cells by estimating a latent variable, such as cell type or pseudotime, representing some aspect of the cell's state. They then test each gene for association with the estimated latent variable. If the same data are used for both of these steps, then standard methods for computing p-values in the second step will fail to achieve statistical guarantees such as Type 1 error control. Furthermore, approaches such as sample splitting that can be applied to solve similar problems in other settings are not applicable in this context. In this article, we introduce count splitting, a flexible framework that allows us to carry out valid inference in this setting, for virtually any latent variable estimation technique and inference approach, under a Poisson assumption. We demonstrate the Type 1 error control and power of count splitting in a simulation study and apply count splitting to a data set of pluripotent stem cells differentiating to cardiomyocytes.
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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.