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

Description Usage Arguments Details Value Note Author(s) References See Also Examples

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.

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

`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.

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.

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}.

The results of gr.main will differ from the results generated from the C code released by Owen et al (2003). This is due to differences in the implementation. See the vignette for details.

Gregory J. Hather ghather@stat.berkeley.edu
with contributions from from Art B. Owen art@stat.stanford.edu
and Terence P. Speed terry@stat.berkeley.edu.

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.

gr.normalize, gr.cv

#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)

Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colMeans, colSums, colnames, do.call,
    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    lapply, lengths, mapply, match, mget, order, paste, pmax, pmax.int,
    pmin, pmin.int, rank, rbind, rowMeans, rowSums, rownames, sapply,
    setdiff, sort, table, tapply, union, unique, unsplit, which,
    which.max, which.min

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

$main.result
      [,1]             [,2]              
 [1,] "31730_at"       "Within query"    
 [2,] "31331_at"       "Within query"    
 [3,] "31455_r_at"     "Not within query"
 [4,] "AFFX-BioB-5_at" "Not within query"
 [5,] "AFFX-DapX-3_at" "Not within query"
 [6,] "31712_at"       "Within query"    
 [7,] "31441_at"       "Within query"    
 [8,] "31558_at"       "Not within query"
 [9,] "31668_f_at"     "Not within query"
[10,] "31429_at"       "Not within query"