SCADIE

SCADIE performs simultaneous estimation of cell type-specific gene expression profiles and cell type proportions to enable cell type-specific differential expression analysis of bulk RNA-Seq data.

Key Features:

• Iterative simultaneous estimation: Uses an iterative algorithm that concurrently estimates cell type-specific gene expression and sample-wise cell type proportions.
• Group-level cell type-specific DE: Performs differential expression analysis at the group level for individual cell types using bulk gene expression data.
• Penalty and objective function: Incorporates a unique penalty and objective function to improve identification of cell type-specific differentially expressed genes.
• Compatibility with deconvolution methods: Maintains performance across various deconvolution methods and is resilient to differences in input data sources and quality.
• Detection beyond single-cell RNA-Seq: Can detect cell type-specific differentially expressed genes that may be overlooked by single-cell RNA-Seq analyses.

Scientific Applications:

• Deconvolving heterogeneous tissues: Dissects cell type-specific expression changes within heterogeneous tissue samples using bulk RNA-Seq.
• Elucidating cellular dynamics: Identifies cell type-specific differential expression relevant to cellular dynamics in health and disease.
• Complementing single-cell studies: Supplements single-cell RNA-Seq by revealing additional differentially expressed genes at the cell type level.
• Biomedical and translational research: Enhances interpretation of bulk RNA-Seq for studies in biomedical research and personalized medicine.

Methodology:

An iterative algorithm concurrently estimates cell type-specific gene expression profiles and cell type proportions using a unique penalty and objective function to perform group-level cell type-specific differential expression analysis and operates across various deconvolution methods.