SVMDECON

a matrix represenging the mixture (genes X 1 sample)

a matrix representing the references (genes X cells), m should be subset to match B

v-SVR is applied with a linear kernel to solve for f,
and the best result from three values of v = 0.25, 0.5, 0.75
is saved, where &lt;U+2018&gt;best&lt;U+2019&gt; is defined as the lowest root mean squared error
between m and the deconvolution result, f x B.

Augments existing or de-novo cell-type signature matrices to deconvolve bulk gene expression data.
Useful for building signature matrices from single cell RNAseq data, determine cell type deconvolution
spillover, and hierarchical deconvolution to use spillover to increase deconvolution accuracy. Please cite:
Danziger SA et al. (2019) ADAPTS: Automated Deconvolution Augmentation of Profiles for
Tissue Specific cells <doi:10.1101/633958>. This package expands on the techniques outlined in Newman AM,
Liu CL, Green MR, Gentles AJ, Feng W, Xu Y,
Hoang CD, Diehn M, Alizadeh, AA (2015) <doi:10.1038/nmeth.3337>'s Nature Methods paper:
'Robust enumeration of cell subsets from tissue expression profiles' to allow a user to easily add
their own cell types (e.g. a tumor specific cell type) to Newman's LM22 or other signature matrix.

Samuel Danziger

SVMDECON: Use SVMDECONV to estimate the cell count percentage David L Gibbs, dgibbs@systemsbiology.org June 9, 2017

Description

Usage

Arguments

Value

Details