prioritise: Prioritising candidate genes.

In PeteHaitch/RUVcorr: Removal of unwanted variation for gene-gene correlations and related analysis.

Description

prioritise returns a set of genes from a candidate set of genes that are correlated above a provided threshold with at least one of the provided reference genes.

Usage

prioritise(X, ref_index, cand_index, anno, Factor, Weights, threshold)

Arguments

X	A matrix of gene expression values.
ref_index	A vector of indices of reference genes.
cand_index	A vector of indices of candidate genes.
anno	A dataframe or a matrix containing the annotation of arrays in X.
Factor	A character string corresponding to a column name of anno; this should be the same used to generate Weights.
Weights	An object of class Weights or a list of weights. If NULL the unweighted correlation is used.
threshold	A value in the range [0,1].

Value

prioritise returns a matrix with three columns. The first column gives the names of the genes that were prioiritised, while the second column gives the number of correlations above the threshold for the gene in question. The columns gives the sum of the absolute value of all correlations with reference genes above the threshold.

Author(s)

Saskia Freytag

Examples

Y<-simulateGEdata(500, 500, 10, 2, 5, g=NULL, Sigma.eps=0.1, 250, 100, check.input=TRUE)
colnames(Y$Y)<-1:dim(Y$Y)[2]
anno<-as.matrix(sample(1:5, dim(Y$Y)[1], replace=TRUE))
colnames(anno)<-"Factor"
weights<-findWeights(Y$Y, anno, "Factor")
prioritise(Y$Y, 1:10, 51:150, anno, "Factor", weights, 0.6)

PeteHaitch/RUVcorr documentation built on May 8, 2019, 1:31 a.m.