HIDE-Deconv

HIDE-Deconv characterizes cellular remodeling from bulk RNA-seq data using hierarchical cell-type deconvolution across multiple levels of cellular resolution.

Key Features:

• Hierarchical deconvolution: Jointly optimizes cellular compositions across multiple levels of a cell-type hierarchy while maintaining consistency between resolutions.
• Integrated workflow: Provides single-cell reference preprocessing, estimation of cellular compositions, and downstream analysis of deconvolution results.
• Downstream analysis capabilities: Includes visualization, clustering, differential composition, and survival analysis.

Scientific Applications:

• Lung adenocarcinoma: Revealed biologically relevant cellular remodeling concealed at broader levels of cellular resolution.
• Sepsis: Revealed biologically relevant cellular remodeling concealed at broader levels of cellular resolution.
• COVID-19: Revealed biologically relevant cellular remodeling concealed at broader levels of cellular resolution.
• Systemic lupus erythematosus: Revealed biologically relevant cellular remodeling concealed at broader levels of cellular resolution.

Methodology:

HIDE-Deconv uses a hierarchical deconvolution framework that jointly optimizes cellular compositions across multiple levels of a cell-type hierarchy.