gene_expr_ica: Custom ICA function for analyzing gene expression data.

In jinhyunju/icreport: Tools for creating a visual summary of matrix decomposition results.

Description

Performing ICA on a dataset and create a list object with results.

Usage

gene_expr_ica(phenotype.mx = NULL, info.df = NULL, check.covars = NULL,
  k.est = NULL, scale.pheno = FALSE, h.clust.cutoff = 0.3, n.runs = 5,
  max.iter = 10, n.cores = NULL, cor.threshold = 0.05,
  similarity.measure = "peaks", var.cutoff = 99)

Arguments

phenotype.mx	Phenotype matrix with diemnsions g x N
info.df	Dataframe that holds sample covariates (ex. population, gender, age, etc...)
check.covars	Column names of info.df which hold the covariates that should be used for association testing with IC coefficients.
k.est	Number of components to be estimated or method to estimate it.
scale.pheno	Logical value specifying the scaling of row of the phenotype.mx.
h.clust.cutoff	is the cutoff value used in hierarchical clustering. Default is set to 0.3.
n.runs	Number of runs for estimating k. Default value is set to 5.
max.iter	Maximum iterations for estimating k for each run. Default value is set to 10.
n.cores	Number of cores to be used for estimating k. Default is set to 1.
cor.threshold	Threshold for significant correlation calling. Default is set to 0.05.
similarity.measure	How to measure the similarity between ICs. If set to "peaks" only gene weights that are greater than 1 sd are used to calculate similarity.

Value

List with the following entries.

jinhyunju/icreport documentation built on May 19, 2019, 10:35 a.m.