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


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Article Info

DATE

2022-04-20

AUTHORS

Chloe X. Wang, Lin Zhang, Bo Wang

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. More... »

PAGES

102

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s13059-022-02659-1

    DOI

    http://dx.doi.org/10.1186/s13059-022-02659-1

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1147246645

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/35443717


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