estimate_joint_effects: Wrapper to joint CNA effect estimation using GLMNet

In SingerLab/gac: Genetic Analysis of Cells

Wrapper to joint CNA effect estimation using GLMNet

Description

Function is still in development

Usage

estimate_joint_effects(cnr, trait, pheno0, pheno1, exclude.cluster = "HC")

Arguments

cnr	a cnr bundle
trait	character, name of the trait of interest to analyze. Must be a column in the phenotype matrix (Y). e.g. "binary1"
pheno0	character, phenotype(s) to use as baseline, e.g. "0"
pheno1	character, phenotype(s) to use as alternate, e.g. "1"
exclude.cluster	character, list of clusters to exclude, e.g. hypersegmented, Stroma, etc. Default "HC"

Value

A CNR object containing joint CNA effect estimates using GLMNet. Analysis is carried out in both 'X' and 'genes' copy number matrices.

Effects are estimated at two Lambda; min, 1se. Lambda values are appended as attributes to the chromInfo, and gene.index, for the bin, respectively.

Examples

data(cnr)

cnr <- estimate_joint_effects(cnr, trait = "binary1", pheno0 = 0, pheno1 = 1)

SingerLab/gac documentation built on March 23, 2024, 5:15 a.m.