An Efficient and Flexible Method for Deconvoluting Bulk RNA-Seq Data with Single-Cell RNA-Seq Data.

Area of Science:

• Computational biology
• Genomics
• Translational medicine

Background:

• Understanding cellular heterogeneity in complex diseases is crucial for etiology research, diagnosis, and prevention.
• Bulk RNA sequencing (RNA-seq) data provides a global view of gene expression but lacks cellular resolution.
• Single-cell RNA sequencing (scRNA-seq) offers cell-type-specific gene expression profiles.

Purpose of the Study:

• To develop a novel computational statistical method, Multi-Omics Matrix Factorization (MOMF), for estimating cell type compositions from bulk RNA-seq data.
• To leverage scRNA-seq data for accurate cell type proportion estimation in bulk samples.
• To apply MOMF to real-world disease datasets for validation and biological insight.

Main Methods:

• Developed Multi-Omics Matrix Factorization (MOMF), a statistical method for deconvolution of bulk RNA-seq data.
• MOMF directly models the count nature of gene expression data.
• MOMF incorporates uncertainty in cell type-specific mean gene expression levels derived from scRNA-seq data.

Main Results:

• MOMF accurately estimates cell type compositions from bulk RNA-seq data.
• Demonstrated MOMF's efficacy in Glioblastoma (GBM), colorectal cancer (CRC), and type II diabetes (T2D) studies.
• Identified disease-related cell types, such as oligodendrocyte progenitor cells in GBM and macrophage cells in CRC, associated with patient survival.

Conclusions:

• MOMF provides a robust computational approach for dissecting cellular heterogeneity in complex diseases.
• Accurate cell type proportion estimation using MOMF can reveal cell types critical to disease progression and patient outcomes.
• This method facilitates deeper understanding of disease etiology and supports potential diagnostic and preventative strategies.