README.md

In debCAM: Deconvolution by Convex Analysis of Mixtures

debCAM

The goal of debCAM is to perform computational deconvolution to dissect complex tissues into molecularly distinctive tissue or cell subtypes based on bulk expression profiles. for tissue heterogeneity characterization. It provides basic functions for unsupervised deconvolution on mixture expression profiles by CAM and some auxiliary functions to help understand the subpopulation-specific results. debCAM also implements functions to perform supervised deconlution based on prior knowledge of molecular markers, S matrix or A matrix. Semi-suprvised deconvolution can also be achieved by combining molecular markers from CAM and from prior knowledge to analyze mixture expressions.

Installation

You can install the latest version of debCAM following the instructions in debCAM's bioconductor page: http://bioconductor.org/packages/debCAM.

Or, you can install it from github by

devtools::install_github("Lululuella/debCAM")

Example

This is a basic example which shows you how to decompose a data matrix by CAM:

## specify the range of underlying subpopulation number
rCAM <- CAM(data, K = 2:5)

debCAM documentation built on Nov. 8, 2020, 5:33 p.m.