geneRecommender (version 1.44.0)

gr.cv: A function for cross validation

Description

gr.cv performs leave-one-out cross validation with gr.main for each element of the query.

Usage

gr.cv(normalized.dataset, query, fun = median)

Arguments

normalized.dataset

A matrix or ExpressionSet containing the normalized gene expression data. The rows correspond to genes, the columns correspond to experiments, and the entries correspond to the gene expression levels. The rows must be labeled. The values contained in normalized.dataset must be either finite or NA.

query

A vector containing the query set of genes. These should correspond to the row names of normalized.dataset. The query must contain at least 2 elements

fun

A function used in choosing the number of experiments to include in the calculation. See the help file for gr.main for details.

Value

A vector containing the rank of each element in the query produced by applying gr.main to the query with that element removed.

Details

In addition to measuring performance, the results of the cross validation can be used to determine if some element(s) in the query might not belong. If one of the elements in the output vector was very large, one would suspect that the associated gene was regulated differently than the other genes in the query.

References

Art B. Owen, Josh Stuart, Kathy Mach, Anne M. Villeneuve, and Stuart Kim. A Gene Recommender Algorithm to Identify Coexpressed Genes in C. elegans. Genome Research 13:1828-1837, 2003.

See Also

gr.main, gr.normalize

Examples

#This example uses the geneData dataset from the Biobase package
data(geneData)
my.query <- c("31730_at", "31331_at", "31712_at", "31441_at")
normalized.data <- gr.normalize(geneData)
gr.cv(normalized.data, my.query)