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Integration of single-cell multi-omics data by regression analysis on unpaired observations.

ABSTRACT: Despite recent developments, it is hard to profile all multi-omics single-cell data modalities on the same cell. Thus, huge amounts of single-cell genomics data of unpaired observations on different cells are generated. We propose a method named UnpairReg for the regression analysis on unpaired observations to integrate single-cell multi-omics data. On real and simulated data, UnpairReg provides an accurate estimation of cell gene expression where only chromatin accessibility data is available. The cis-regulatory network inferred from UnpairReg is highly consistent with eQTL mapping. UnpairReg improves cell type identification accuracy by joint analysis of single-cell gene expression and chromatin accessibility data.

SUBMITTER: Yuan Q

PROVIDER: S-EPMC9295346 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Integration of single-cell multi-omics data by regression analysis on unpaired observations.

Yuan Qiuyue Q Duren Zhana Z

Genome biology 20220719 1

Despite recent developments, it is hard to profile all multi-omics single-cell data modalities on the same cell. Thus, huge amounts of single-cell genomics data of unpaired observations on different cells are generated. We propose a method named UnpairReg for the regression analysis on unpaired observations to integrate single-cell multi-omics data. On real and simulated data, UnpairReg provides an accurate estimation of cell gene expression where only chromatin accessibility data is available. ...[more]

PMID: 35854350

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