A Poisson reduced-rank regression model for association mapping in sequencing data

• Published in: BMC bioinformatics Vol. 23; no. 1; pp. 529 - 22
• Main Authors: Fitzgerald, Tiana, Jones, Andrew, Engelhardt, Barbara E
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 08.12.2022; BioMed Central; BMC
• Subjects: Association mapping; Count-based models; Datasets; Disease; Estimates; Gene expression; Gene mapping; Gene sequencing; Generalized linear models; Genetic research; Genomes; Genotype & phenotype; Genotypes; Hypotheses; Hypothesis testing; Mapping; Mathematical models; Methods; Poisson distribution; Probabilistic models; Reduced-rank regression; Regression analysis; Regression models; Ribonucleic acid; RNA; RNA sequencing; Single-cell RNA-sequencing; Sparsity; Statistical analysis; Statistical models; Variables; United States; Reduced-rank regression; Count-based models; Association mapping; Single-cell RNA-sequencing
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-022-05054-6

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. 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. 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.