ArticleThe annals of applied statistics2022
CONTRASTIVE LATENT VARIABLE MODELING WITH APPLICATION TO CASE-CONTROL SEQUENCING EXPERIMENTS.
Article in The annals of applied statistics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
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Who cites it
9 citing papers in PubMed.
- Contrastive Dimension Reduction: A Systematic Review.Wiley interdisciplinary reviews. Computational statistics · 2026Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- CONTRASTIVE LINEAR REGRESSION.The annals of applied statistics · 2025Article
- BuDDI: Bulk Deconvolution with Domain Invariance to predict cell-type-specific perturbations from bulk.PLoS computational biology · 2025Article
- BuDDI:bioRxiv : the preprint server for biology · 2024Article
- A Poisson reduced-rank regression model for association mapping in sequencing data.BMC bioinformatics · 2022Article
- CONTRASTIVE LATENT VARIABLE MODELING WITH APPLICATION TO CASE-CONTROL SEQUENCING EXPERIMENTS.The annals of applied statistics · 2022Article
- Leveraging Systems Immunology to Optimize Diagnosis and Treatment of Inborn Errors of Immunity.Frontiers in systems biology · 2022Article
- Hierarchical Gaussian Processes and Mixtures of Experts to Model COVID-19 Patient Trajectories.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
High-throughput RNA-sequencing (RNA-seq) technologies are powerful tools for understanding cellular state. Often, it is of interest to quantify and to summarize changes in cell state that occur between experimental or biological conditions. Differential expression is typically assessed using univariate tests to measure genewise shifts in expression. However, these methods largely ignore changes in transcriptional correlation. Furthermore, there is a need to identify the low-dimensional structure of the gene expression shift to identify collections of genes that change between conditions. Here, we propose contrastive latent variable models designed for count data to create a richer portrait of differential expression in sequencing data. These models disentangle the sources of transcriptional variation in different conditions in the context of an explicit model of variation at baseline. Moreover, we develop a model-based hypothesis testing framework that can test for global and gene subset-specific changes in expression. We evaluate our model through extensive simulations and analyses with count-based gene expression data from perturbation and observational sequencing experiments. We find that our methods effectively summarize and quantify complex transcriptional changes in case-control experimental sequencing data.
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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.