Journal
GENOME BIOLOGY
Volume 23, Issue 1, Pages -Publisher
BMC
DOI: 10.1186/s13059-022-02726-7
Keywords
Single-cell multi-omics; Regression model on unpaired observations; Cis-regulatory network
Funding
- NIH [P20 GM139769]
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This study proposes a method called UnpairReg for regression analysis and integration of single-cell multi-omics data. The results show that UnpairReg accurately estimates cell gene expression when only chromatin accessibility data is available and improves the accuracy of cell type identification.
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.
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