DoLmFit: Apply multivariable linear regression for each row of input...

In YC3/mirNet: microRNA netowrk analyses

Description

DoLmFit applies multivariable linear regression to regress gene/microRNA epxpression data on phenotype of interest (poi) adjusting for potential confounding factors (cf).

Usage

DoLmFit(data, pheno.m)

Arguments

data	A matrix, the normalized gene/microRNA expression dataset, should be a numeric matrix, with rows referring to genes/microRNAs and columns to samples.
pheno	A data.frame with columns are different phenotype data. Sample phenotype in a scientific research could be treatment/control, normal/cancer or smoker/non-smoker. Different phenotypes should each be encoded as 0/1 when inputting to DoLmFit, for example, Normal-0; Cancer-1.

Value

A table with rows for all genes (ranked by significance) and columns of t-statistic, p-value, adjusted p-value (default to Benjamini–Hochberg procedure).

Examples

# prepare your normalized data matrix
data.m <- matrix(rnorm(120), nrow = 20, ncol = 6)

# prepare the phenotype info (0-control; 1-treatment)
poi.v <- c(0, 0, 0, 1, 1, 1)
cf.v <- c(0, 1, 1, 2, 2, 2)

# run function
DoLmFit(data = data.m, pheno = cbind(poi.v, cf.v))

YC3/mirNet documentation built on Sept. 3, 2020, 3:25 a.m.