Alignment of single-cell RNA-seq samples without overcorrection using kernel density matching
Autor: | Qi Zhan, Mengjie Chen, Jinlin Miao, Yang I. Li, Zepeng Mu, Zhaohui Zheng, Ping Zhu, Lili Wang |
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Rok vydání: | 2021 |
Předmět: |
Matching (statistics)
genetic processes Kernel density estimation Population Method RNA-Seq Computational biology Biology 03 medical and health sciences 0302 clinical medicine Gene expression Genetics Cluster Analysis Humans natural sciences Primary cell Cluster analysis education Genetics (clinical) 030304 developmental biology 0303 health sciences education.field_of_study Gene Expression Profiling RNA Single-Cell Analysis 030217 neurology & neurosurgery |
Zdroj: | Genome Res |
ISSN: | 1549-5469 1088-9051 |
Popis: | Single-cell RNA sequencing (scRNA-seq) technology is poised to replace bulk cell RNA sequencing for many biological and medical applications as it allows users to measure gene expression levels in a cell type–specific manner. However, data produced by scRNA-seq often exhibit batch effects that can be specific to a cell type, to a sample, or to an experiment, which prevent integration or comparisons across multiple experiments. Here, we present Dmatch, a method that leverages an external expression atlas of human primary cells and kernel density matching to align multiple scRNA-seq experiments for downstream biological analysis. Dmatch facilitates alignment of scRNA-seq data sets with cell types that may overlap only partially and thus allows integration of multiple distinct scRNA-seq experiments to extract biological insights. In simulation, Dmatch compares favorably to other alignment methods, both in terms of reducing sample-specific clustering and in terms of avoiding overcorrection. When applied to scRNA-seq data collected from clinical samples in a healthy individual and five autoimmune disease patients, Dmatch enabled cell type–specific differential gene expression comparisons across biopsy sites and disease conditions and uncovered a shared population of pro-inflammatory monocytes across biopsy sites in RA patients. We further show that Dmatch increases the number of eQTLs mapped from population scRNA-seq data. Dmatch is fast, scalable, and improves the utility of scRNA-seq for several important applications. Dmatch is freely available online. |
Databáze: | OpenAIRE |
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