Data-driven comparison of multiple high-dimensional single-cell expression profiles.

ABSTRACT: Comparing multiple single-cell expression datasets such as cytometry and scRNA-seq data between case and control donors provides information to elucidate the mechanisms of disease. We propose a completely data-driven computational biological method for this task. This overcomes the challenges of conventional cellular subset-based comparisons and facilitates further analyses such as machine learning and gene set analysis of single-cell expression datasets.

SUBMITTER: Okada D 

PROVIDER: S-EPMC8948086 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature