BSDE: barycenter single-cell differential expression for case–control studies

Abstract

Motivation Single-cell sequencing brings about a revolutionarily high resolution for finding differentially expressed genes by disentangling highly heterogeneous cell tissues. Yet, such analysis is so far mostly focused on comparing between different cell types from the same individual. As single-cell sequencing becomes cheaper and easier to use, an increasing number of datasets from case-control studies are becoming available, which call for new methods for identifying differential expressions between case and control individuals. Results To bridge this gap, we propose Barycenter Single-cell Differential Expression (BSDE), a nonparametric method for finding differentially expressed genes for case-control studies. Through the use of optimal transportation for aggregating distributions and computing their distances, our method overcomes the restrictive parametric assumptions imposed by standard mixed-effect-modeling approaches. Through simulations, we show that BSDE can accurately detect a variety of differential expressions while maintaining the type-I error at a prescribed level. Further, 1345 and 1568 cell type specific differentially expressed genes are identified by BSDE from datasets on pulmonary fibrosis and multiple sclerosis, among which the top findings are supported by previous results from the literature. Availability R package BSDE is freely available from doi.org/10.5281/zenodo.6332254. For real data analysis with the R package, see doi.org/10.5281/zenodo.6332566. These can also be accessed thorough GitHub at github.com/mqzhanglab/BSDE and github.com/mqzhanglab/BSDE_pipeline. The two single cell sequencing datasets can be download with UCSC cell browser from cells.ucsc.edu/?ds=ms and cells.ucsc.edu/?ds=lung-pf-control. Supplementary information Supplementary data are available at Bioinformatics online.

Benchmarking of analytical combinations for COVID-19 outcome prediction using single-cell RNA sequencing data

“…We are now in the era where the technology enables us to collect large pools of cells from multiple patients across multiple conditions. The current single-cell literature has mostly focused on analysing gene and cell level changes [2,3], for example dissecting the transcriptional heterogeneity in the population of single cells and identifying genes that mark the cell types [4]. Recently, there is an increasing number of studies designed at an individual level, such as between normal and patients (i.e.…”