Bioconductor Europe Conference 2023

Abstract

Differential detection workflows for multi-patient single-cell RNA-seq data. Traditional differential gene expression (DGE) analyses only allow for assessing differences in the average expression between cells or samples. However, in scRNA-seq data, other differences between count distributions can be observed, such as differences in the number of modes and differential variability. One particularly interesting distributional characteristic of gene expression that is not explicitly captured by most existing frameworks is the fraction of cells in a group in which the gene is detected. It has been reported repeatedly that gene expression profiles may exhibit characteristic bimodal expression patterns, in which the expression of otherwise abundant genes is either strongly positive or undetected within individual cells. In this work, we show the potential of differential detection (DD) strategies for scRNA-seq data analysis. First, we benchmark several DD strategies; we start with a simple logistic regression model on the binarised scRNA-seq expression matrix, and gradually increase the model complexity to account for overdispersion and allow for model-based normalisation. In the context of multi-patient datasets, we additionally assess the potential of pseudobulking on the model performance and type 1 error control. Second, we combine results from our differential detection tests and a traditional DGE analysis on the same data using a two-stage testing paradigm. Finally, we show the added value of jointly performing a DGE and a DD analysis on the same data on a large multi-patient case study.