{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Fitzgerald T"],"funding":["Leona M. and Harry B. Helmsley Charitable Trust","NCI NIH HHS","National Institutes of Health","NIH HHS","National Science Foundation"],"pagination":["529"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9733401"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["23(1)"],"pubmed_abstract":["<h4>Background</h4>Single-cell RNA-sequencing (scRNA-seq) technologies allow for the study of gene expression in individual cells. Often, it is of interest to understand how transcriptional activity is associated with cell-specific covariates, such as cell type, genotype, or measures of cell health. Traditional approaches for this type of association mapping assume independence between the outcome variables (or genes), and perform a separate regression for each. However, these methods are computationally costly and ignore the substantial correlation structure of gene expression. Furthermore, count-based scRNA-seq data pose challenges for traditional models based on Gaussian assumptions.<h4>Results</h4>We aim to resolve these issues by developing a reduced-rank regression model that identifies low-dimensional linear associations between a large number of cell-specific covariates and high-dimensional gene expression readouts. Our probabilistic model uses a Poisson likelihood in order to account for the unique structure of scRNA-seq counts. We demonstrate the performance of our model using simulations, and we apply our model to a scRNA-seq dataset, a spatial gene expression dataset, and a bulk RNA-seq dataset to show its behavior in three distinct analyses.<h4>Conclusion</h4>We show that our statistical modeling approach, which is based on reduced-rank regression, captures associations between gene expression and cell- and sample-specific covariates by leveraging low-dimensional representations of transcriptional states."],"journal":["BMC bioinformatics"],"pubmed_title":["A Poisson reduced-rank regression model for association mapping in sequencing data."],"pmcid":["PMC9733401"],"funding_grant_id":["AWD1005627","U2C CA233195","AWD1006624","5U2CCA233195"],"pubmed_authors":["Fitzgerald T","Engelhardt BE","Jones A"],"additional_accession":[]},"is_claimable":false,"name":"A Poisson reduced-rank regression model for association mapping in sequencing data.","description":"<h4>Background</h4>Single-cell RNA-sequencing (scRNA-seq) technologies allow for the study of gene expression in individual cells. Often, it is of interest to understand how transcriptional activity is associated with cell-specific covariates, such as cell type, genotype, or measures of cell health. Traditional approaches for this type of association mapping assume independence between the outcome variables (or genes), and perform a separate regression for each. However, these methods are computationally costly and ignore the substantial correlation structure of gene expression. Furthermore, count-based scRNA-seq data pose challenges for traditional models based on Gaussian assumptions.<h4>Results</h4>We aim to resolve these issues by developing a reduced-rank regression model that identifies low-dimensional linear associations between a large number of cell-specific covariates and high-dimensional gene expression readouts. Our probabilistic model uses a Poisson likelihood in order to account for the unique structure of scRNA-seq counts. We demonstrate the performance of our model using simulations, and we apply our model to a scRNA-seq dataset, a spatial gene expression dataset, and a bulk RNA-seq dataset to show its behavior in three distinct analyses.<h4>Conclusion</h4>We show that our statistical modeling approach, which is based on reduced-rank regression, captures associations between gene expression and cell- and sample-specific covariates by leveraging low-dimensional representations of transcriptional states.","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Dec","modification":"2026-06-20T03:23:29.586Z","creation":"2025-04-05T21:25:58.942Z"},"accession":"S-EPMC9733401","cross_references":{"pubmed":["36482321"],"doi":["10.1186/s12859-022-05054-6"]}}