ArticleNature methods2023
Isolating salient variations of interest in single-cell data with contrastiveVI.
Article in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
35 citing papers in PubMed.
- Donor-specific, but not sex-specific, signatures dominate cell-type classification in lung scRNA-seq data.Biochemistry and biophysics reports · 2026Article
- Contrastive Dimension Reduction: A Systematic Review.Wiley interdisciplinary reviews. Computational statistics · 2026Article
- Benchmarking the prediction of responding cells to perturbations affecting both gene expression and cellular abundance using scRNA sequencing.Scientific reports · 2026Article
- Leveraging Lineage Barcodes as Natural Augmentations for Contrastive Learning of Cell Fate in scRNA-seq Data.bioRxiv : the preprint server for biology · 2026Article
- Proteostasis sustains T cell differentiation potential and tumor-infiltrating lymphocyte function.Cell · 2026Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- Patches: A Representation Learning Framework for Decoding Shared and Condition-Specific Transcriptional Programs in Wound Healing.bioRxiv : the preprint server for biology · 2026Article
- Systematic background selection with BasCoD enhances contrastive dimension reduction in single cell genomics.Nature communications · 2026Article
- Benchmarking LLM-based agents for single-cell omics analysis.Genome biology · 2026Article
- Proteostasis sustains T cell differentiation potential and tumor-infiltrating lymphocyte function.bioRxiv : the preprint server for biology · 2026Article
- Quantifying pathological progression from single-cell transcriptomic data with scPSS.Genome research · 2026Article
- Integrative Learning of Disentangled Representations from Single-Cell RNA-Sequencing Datasets.Computational and structural biotechnology journal · 2026Article
- Haruka Resolves Perturbation Response Heterogeneity in Spatial Cell Niches.bioRxiv : the preprint server for biology · 2025Article
- Interpretable phenotype decoding from multicondition sequencing data with ALPINE.Genome research · 2025Article
- Benchmarking deep learning methods for biologically conserved single-cell integration.Genome biology · 2025Article
- Decoding cell fate: integrated experimental and computational analysis at the single-cell level.Bioinformatics (Oxford, England) · 2025Review
- Modeling heterogeneity in single-cell perturbation states enhances detection of response eQTLs.Nature genetics · 2025Article
- Deep generative modeling of sample-level heterogeneity in single-cell genomics.Nature methods · 2025Article
- Scvi-hub: an actionable repository for model-driven single-cell analysis.Nature methods · 2025Article
- CONTRASTIVE LINEAR REGRESSION.The annals of applied statistics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Single-cell datasets are routinely collected to investigate changes in cellular state between control cells and the corresponding cells in a treatment condition, such as exposure to a drug or infection by a pathogen. To better understand heterogeneity in treatment response, it is desirable to deconvolve variations enriched in treated cells from those shared with controls. However, standard computational models of single-cell data are not designed to explicitly separate these variations. Here, we introduce contrastive variational inference (contrastiveVI; https://github.com/suinleelab/contrastiveVI ), a framework for deconvolving variations in treatment-control single-cell RNA sequencing (scRNA-seq) datasets into shared and treatment-specific latent variables. Using three treatment-control scRNA-seq datasets, we apply contrastiveVI to perform a variety of analysis tasks, including visualization, clustering and differential expression testing. We find that contrastiveVI consistently achieves results that agree with known ground truths and often highlights subtle phenomena that may be difficult to ascertain with standard workflows. We conclude by generalizing contrastiveVI to accommodate joint transcriptome and surface protein measurements.
Indexed as
Identifiers
37550579What OpenQuestion holds
Registered trials
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.