geneRecommender (version 1.44.0)

gr.main: A gene recommender algorithm to identify genes coexpressed with a query set of genes

Description

The function gr.main implements the Gene Recommender algorithm described in Owen et al (2003). Note that in order for gr.main to provide meaningful results, the normalization procedure gr.normalize must first be applied to the gene expression matrix.

Usage

gr.main(normalized.dataset, query, fun = median, ngenes = NULL, extra = FALSE)

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 below for details.

ngenes

The number of genes to return in the result. It's default value is the number of genes found at 50 percent recall.

extra

A logical value. When false, the output list will contain only one item, result. When true, several other quantities (listed below) will be calculated and added to the output list.

Value

A list containing entries:

result

An array of dimensions (ngenes, 2). Column 1 contains the resulting genes, with the highest scoring genes listed first. Column 2 contains character strings, indicating whether the corresponding gene is from the query list or not.

fifty.percent.recall

Number of genes found at 50 percent recall.

experiments.included

Experiments included in the analysis.

experiments.excluded

Experiments excluded from the analysis.

s.g.i

An array used as a measure of biological significance for each gene. The output is ranked by this quantity.

z.g.i

An array used as a measure of statistical significance for each gene.

contribution

An array indicating the contribution of each experiment to each gene result. For a given gene and a given experiment, the contribution indicates how strongly the experiment suggests that the gene should be high ranking. Using notation from the article, contribution is defined as $\bar{Y}_{Q,j} \times Y_{ij}$.

Details

Given data from a large number of microarray experiments and a query set of genes, which genes have expression profiles that are similar to the query? The Gene Recommender algorithm (Owen et al, 2003) answers this question by first identifying the set of experiments over which the query genes behave similarly. Next, the algorithm ranks all the genes based on the strength of the correlation with the query across the chosen set of experiments.

The algorithm must choose how generous to be in including experiments. How many experiments should be included? The algorithm tries every number of experiments and chooses the number which minimizes a score. In the paper, the score was defined as the median of the ranks of the query genes. In gr.main, the score can be computed with the user-defined function, fun.

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.normalize, gr.cv

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.main(normalized.data, my.query, ngenes = 10)