Deconvolution analysis of cell-type expression from bulk tissues by integrating with single-cell expression reference.

ABSTRACT: To understand phenotypic variations and key factors which affect disease susceptibility of complex traits, it is important to decipher cell-type tissue compositions. To study cellular compositions of bulk tissue samples, one can evaluate cellular abundances and cell-type-specific gene expression patterns from the tissue transcriptome profiles. We develop both fixed and mixed models to reconstruct cellular expression fractions for bulk-profiled samples by using reference single-cell (sc) RNA-sequencing (RNA-seq) reference data. In benchmark evaluations of estimating cellular expression fractions, the mixed-effect models provide similar results as an elegant machine learning algorithm named cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORTx), which is a we

SUBMITTER: Luo Y 

PROVIDER: S-EPMC9669104 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature