Title
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
Recommended Citation
Yuan, Qiuyue; Duren, Zhana (2022), "Integration of single-cell multi-omics data by regression analysis on unpaired observations", figshare Academic Research System, doi: 10.6084/m9.figshare.c.6103362.v1
https://doi.org/10.6084/m9.figshare.c.6103362.v1
Identifier
10.6084/m9.figshare.c.6103362.v1
Embargo Date
1-1-2022