plmDEmodel.default: Creates a class to hold Expression Data and Other...

In plmDE: Additive partially linear models for differential gene expression analysis

Description

Given gene expression data as well as phenotypic measurements, plmDEmodel encodes them within a single object on which fitGAPLM and limmaPLM can be run.

Usage

## Default S3 method:
plmDEmodel(genes, expressionValues, sampleInfo, ...)

Arguments

genes	A vector of the genes (or probes/targets) from which each one is to be tested for differential expression.
expressionValues	A data frame or matrix containing the expression data of these genes. Each column should represent one sample and each row one gene.
sampleInfo	Information about the samples in expressionValues. This data frame must be specially formatted: first column must list samples (and must match the column names of expressionValues), second column must list the subgroups (i.e. 'disease', 'control') each sample belongs to, additional columns hold the measurements of some aspect of interest for each sample.
...	Parameters from plmDEmodel that are unused in this method.

Value

Returns an object of type plmDE on which fitGAPLM may be run to test for differential expression under a specified model.

Author(s)

Jonas Mueller

Examples

## create an object of type \code{plmDE} containing disease 
## with "control" and "disease" and measurements of weight and severity:
ExpressionData = as.data.frame(matrix(abs(rnorm(10000, 1, 1.5)), ncol = 100))
names(ExpressionData) = sapply(1:100, function(x) paste("Sample", x))
Genes = sapply(1:100, function(x) paste("Gene", x))
DataInfo = data.frame(sample = names(ExpressionData), group = c(rep("Control", 50),
 rep("Diseased", 50)), weight = abs(rnorm(100, 50, 20)), severity = c(rep(0, 50),
 abs(rnorm(50, 100, 20))))
plmDEobject = plmDEmodel(Genes, ExpressionData, DataInfo)

plmDE documentation built on May 29, 2017, 6:37 p.m.