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

Description

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.

Publication Date

1-1-2022

Publisher

figshare Academic Research System

DOI

10.6084/m9.figshare.c.6103362.v1

Document Type

Data Set

Identifier

10.6084/m9.figshare.c.6103362.v1

Embargo Date

1-1-2022

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