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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

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

Okada Daigo D Cheng Jian Hao JH Zheng Cheng C Yamada Ryo R

Journal of human genetics 20211101 4

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. ...[more]

PMID: 34719682

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