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

Abstract

Background 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. Results 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. Conclusion 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.

Improved Quality Metrics for Association and Reproducibility in Chromatin Accessibility Data Using Mutual Information

“…While similar simulation studies have been conducted on Hi-C sequencing data [30], to our knowledge, no prior study has examined the behavior of statistical metrics on ATAC-seq data. That said, there are several statistics and methodologies that may be used to analyze this data type, such as Poisson regression [49]. Improving on this simulation design could help generate a framework that allows researchers to develop new statistical tools for hypothesis testing.…”