Framework for integrating scRNA-seq and scATAC-seq to reveal signatures and trajectories of immune cells

Abstract

Studying the dynamics and epigenetic signatures of immune cells is important to understand the differences in immune response of individuals. Vast amount of scRNA-seq and scATAC-seq data are available and computational tools exist to integrate these two data modalities. However, most computational tools are performing poorly when both measurements were done on parallel samples and lack keeping all the dynamics in the data after integration. Thus, to use the already existing data, we propose a framework designed for trajectory and comparison analysis. For integration we build on an already published neural network-based tool scDART and propose improvements in construction of the gene-activity matrix similar to MAESTRO to provide higher accuracy. In addition, we suggest implementation of further downstream analyses such as differential gene and accessibility analysis and gene set enrichment analysis specifically picked for immunological comparison studies.