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Dozer: Debiased personalized gene co-expression networks for population-scale scRNA-seq data
Shan Lu1, Sündüz Keleş1,2
1Department of Statistics, University of Wisconsin, Madison, WI, USA.
Biorxiv : the Preprint Server for Biology
|May 10, 2023
Summary
Dozer debiases gene-gene correlation estimates from single-cell RNA sequencing (scRNA-seq) data, enabling accurate quantification of gene co-expression networks and variation across individuals. This improves biological insights from large-scale scRNA-seq studies.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Population-scale single-cell RNA sequencing (scRNA-seq) offers opportunities to study gene co-expression networks across individuals.
- Technical limitations and noise in scRNA-seq lead to biased correlation estimates, particularly for lowly expressed genes.
Conclusions:
- Dozer enables accurate estimation of personalized co-expression networks from scRNA-seq data.
- The method facilitates novel analyses, such as identifying gene groups linked to induced pluripotent stem cell differentiation efficiency.
- Dozer reveals distinct co-expression modules in Alzheimer's disease and control brain tissue, highlighting its utility in disease research.
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