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One Cell At a Time (OCAT): a unified framework to integrate and analyze single-cell RNA-seq data.

ABSTRACT: Integrative analysis of large-scale single-cell RNA sequencing (scRNA-seq) datasets can aggregate complementary biological information from different datasets. However, most existing methods fail to efficiently integrate multiple large-scale scRNA-seq datasets. We propose OCAT, One Cell At a Time, a machine learning method that sparsely encodes single-cell gene expression to integrate data from multiple sources without highly variable gene selection or explicit batch effect correction. We demonstrate that OCAT efficiently integrates multiple scRNA-seq datasets and achieves the state-of-the-art performance in cell type clustering, especially in challenging scenarios of non-overlapping cell types. In addition, OCAT can efficaciously facilitate a variety of downstream analyses.

SUBMITTER: Wang CX

PROVIDER: S-EPMC9019955 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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One Cell At a Time (OCAT): a unified framework to integrate and analyze single-cell RNA-seq data.

Wang Chloe X CX Zhang Lin L Wang Bo B

Genome biology 20220420 1

Integrative analysis of large-scale single-cell RNA sequencing (scRNA-seq) datasets can aggregate complementary biological information from different datasets. However, most existing methods fail to efficiently integrate multiple large-scale scRNA-seq datasets. We propose OCAT, One Cell At a Time, a machine learning method that sparsely encodes single-cell gene expression to integrate data from multiple sources without highly variable gene selection or explicit batch effect correction. We demons ...[more]

PMID: 35443717

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