Journal
BRIEFINGS IN BIOINFORMATICS
Volume 24, Issue 1, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbac616
Keywords
cell type deconvolution; scRNA-seq; reference signature matrix; harmonize information
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Computational cell type deconvolution can reveal cell type proportion heterogeneity in samples. This study introduces tranSig, a novel Bayesian framework, to improve signature matrix inference from single-cell RNA sequencing data. The simulations and applications show that tranSig is accurate and robust in defining gene expression signatures of different cell types, facilitating improved in silico cell type deconvolutions.
Computational cell type deconvolution on bulk transcriptomics data can reveal cell type proportion heterogeneity across samples. One critical factor for accurate deconvolution is the reference signature matrix for different cell types. Compared with inferring reference signature matrices from cell lines, rapidly accumulating single-cell RNA-sequencing (scRNA-seq) data provide a richer and less biased resource. However, deriving cell type signature from scRNA-seq data is challenging due to high biological and technical noises. In this article, we introduce a novel Bayesian framework, tranSig, to improve signature matrix inference from scRNA-seq by leveraging shared cell type-specific expression patterns across different tissues and studies. Our simulations show that tranSig is robust to the number of signature genes and tissues specified in the model. Applications of tranSig to bulk RNA sequencing data from peripheral blood, bronchoalveolar lavage and aorta demonstrate its accuracy and power to characterize biological heterogeneity across groups. In summary, tranSig offers an accurate and robust approach to defining gene expression signatures of different cell types, facilitating improved in silico cell type deconvolutions.
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