difExprs: Differential expression analysis using two different methods.

In juancholkovich/coexnet: coexnet: An R package to build CO-EXpression NETworks from Microarray Data

Description

Using the expression matrix calculate the differential expressed genes to two class analysis and fixing an expected FDR value. The methods are SAM and ACDE.

Usage

difExprs(expData, treatment, fdr, DifferentialMethod, plotting = FALSE)

Arguments

expData	A matrix with the expression matrix, it may be stored in a SummarizedExperiment object.
treatment	A vector with the ientifiers of the classes, 0 to control and 1 to case.
fdr	The expected FDR value.
DifferentialMethod	The method to calculate the differential expressed genes, can be "sam" or "acde"
plotting	The option to show the result in a plot. By default FALSE.

Value

A data.frame with the expression matrix to the expressed diferential genes only.

Author(s)

Juan David Henao <[e-mail]>

References

Tusher, V. G., Tibshirani, R., & Chu, G. (2001). Significance analysis of microarrays applied to the ionizing radiation response. Proceedings of the National Academy of Sciences, 98(9), 5116-5121.

Acosta J and Lopez-Kleine L (2015). acde: Artificial Components Detection of Differentially Expressed Genes. R package version 1.4.0.

See Also

exprMat to obtain the expression matrix.

Examples

## Creating the expression matrix

# The matrix have 200 genes and 20 samples

n <- 200
m <- 20

# The vector with treatment samples and control samples

treat <- c(rep(0,10),rep(1,10))

# Calculating the expression values normalized

mat <- as.matrix(rexp(n, rate = 1))
norm <- t(apply(mat, 1, function(nm) rnorm(m, mean=nm, sd=1)))

## Running the function using the two approaches

sam <- difExprs(expData = norm,treatment = treat,fdr = 0.2,DifferentialMethod = "sam")
acde <- difExprs(expData = norm,treatment = treat,fdr = 0.2,DifferentialMethod = "acde")

juancholkovich/coexnet documentation built on May 20, 2019, 3:18 a.m.