MuSiC

MuSiC deconvolves bulk RNA-seq data to infer cell type composition of complex tissues using cell-type-specific gene expression profiles derived from single-cell RNA-seq data.

Key Features:

• Single-cell reference profiles: Uses cell-type-specific gene expression signatures extracted from single-cell RNA-seq datasets as references for bulk deconvolution.
• Cross-subject and cross-cell weighting: Applies a weighting mechanism that prioritizes genes with consistent expression patterns across subjects and cells to improve robustness.
• Cross-dataset and cross-species transferability: Enables application of single-cell-derived expression signatures across different datasets and species.
• Validation on complex tissues: Demonstrated performance on pancreatic islet and whole kidney expression data from humans, mice, and rats.

Scientific Applications:

• Cellular heterogeneity characterization: Quantifies relative cell type proportions within complex tissues from bulk RNA-seq data.
• Disease mechanism investigation: Identifies cell type contributions and potential cellular targets involved in disease processes.
• Comparative and integrative studies: Facilitates comparison and integration of cellular composition across datasets, conditions, or species.

Methodology:

Derives cell-type-specific gene expression signatures from single-cell RNA-seq, weights genes by cross-subject and cross-cell expression consistency, and applies those weighted signatures to deconvolute bulk RNA-seq samples, with validation on pancreatic islet and whole kidney data from humans, mice, and rats.