debCAM

debCAM performs unsupervised and semi-supervised deconvolution of complex tissue expression data to identify constituent molecular subtypes and estimate their proportions using Convex Analysis of Mixtures (CAM).

Key Features:

• Convex Analysis of Mixtures (CAM): Applies CAM to automatically identify tissue- or cell-specific molecular markers and infer the number of constituent subtypes from bulk expression profiles.
• Subtype Proportion Estimation: Estimates the proportions of identified subtypes across individual samples.
• Subtype Expression Profile Reconstruction: Infers tissue- or cell-specific expression profiles for each detected subtype.
• Semi-Supervised and Supervised Deconvolution: Supports deconvolution using prior knowledge including molecular markers, S matrices, or A matrices and enables integration of CAM-derived and known markers.
• Multi-Omics Data Compatibility: Processes diverse biological datasets including gene expression, DNA methylation, proteomics, and imaging data.

Scientific Applications:

• Tissue Heterogeneity Analysis: Deconvolutes bulk tissue profiles to identify molecularly distinct cellular or tissue subtypes.
• Disease and Tissue Remodeling Studies: Enables investigation of subtype composition changes associated with disease progression or biological remodeling.
• Multi-Omics Deconvolution: Supports subtype identification and proportion estimation across gene expression, methylation, proteomics, and imaging datasets.

Methodology:

debCAM applies Convex Analysis of Mixtures (CAM) to bulk molecular profiles to identify subtype-specific markers, estimate the number of subtypes, compute subtype proportions across samples, and reconstruct subtype-specific expression profiles, with optional incorporation of prior marker, S matrix, or A matrix information for supervised or semi-supervised deconvolution.