A computational method for direct imputation of cell type-specific expression profiles and cellular compositions from...

Area of Science:

• Genomics
• Computational Biology
• Biostatistics

Background:

• Cell type-specific gene expression is crucial for understanding complex diseases.
• Bulk tissue RNA sequencing (RNA-Seq) studies often lack cell type resolution.
• Existing computational methods for cell type deconvolution rarely impute cell type-specific expression profiles.

Purpose of the Study:

• To develop a computational method for estimating both cellular composition and cell type-specific gene expression from bulk RNA-Seq data.
• To leverage external prior information, such as single-cell RNA-Seq and population-wide expression profiles.
• To introduce CellR, a novel tool addressing cross-individual gene expression variations.

Main Methods:

• CellR adjusts weights of cell-specific gene markers using external data.
• The deconvolution problem is framed as a linear programming model.
• A multi-variate stochastic search algorithm estimates cell type-specific expression profiles, considering inter/intra-cellular correlations.

Main Results:

• CellR effectively estimates cellular composition and cell type-specific expression profiles.
• Analyses on schizophrenia, Alzheimer's disease, Huntington's disease, and type 2 diabetes validated CellR's efficiency.
• The method revealed specific cell type contributions to various complex diseases.

Conclusions:

• CellR enables accurate cell type-specific re-analysis of bulk tissue gene expression data.
• The tool outperforms competing approaches in deconvolution and expression imputation.
• CellR provides valuable insights into the cellular basis of complex diseases.