summarizeByRegion: Compute Summary Statistics of Genome Regions
In reb: Regional Expression Biases

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

Splits the data into subsets based on genome mapping information, computes summary statistics for each region, and returns the results in a convenient form. (cgma stands for Comparative Genomic Microarray Analysis)

This function supplies a t.test function at the empirically derived significance threshold (p.value = 0.005)

1	cgma(eset, genome, chrom="ALL",ref=NULL,center=TRUE,aggrfun=NULL, p.value=0.005, FUN=t.test, verbose=TRUE, explode=FALSE ,...)

`eset`	an exprSet object
`genome`	an chromLocation object, such as on produced by buildChromLocation or buildChromMap
`chrom`	a character vector specifying the chromosomes to analyze
`ref`	a vector containing the index of reference samples from which to make comparisons. Defaults to NULL (internally referenced samples)
`center`	boolean - re-center gene expression matrix columns. Helpful if `ref` is used
`aggrfun`	a function to summarizes/aggregates gene expression values that map to the same locations. If NULL, all values are included. Also see `absMax`
`p.value`	p.value cutoff, NA for all results, or TRUE for all t.stats and p.values
`FUN`	function by which to summarize the data
`verbose`	boolean - print verbose output during execution?
`explode`	boolean - explode summary matrix into a full expression set?
`...`	further arguments pass to or used by the function

Gene expression values are separated into subsets that based on the 'chromLocation' object argument. For example, buildChromMap can be used to produce a 'chromLocation' object composed of the genes that populate human chromosome 1p and chromosome 1q. The gene expression values from each of these regions are extracted from the 'exprSet' and a summary statistic is computed for each region.

cgma is most straightforwardly used to identify regional gene expression biases when comparing a test sample to a reference sample. For example, a number of simple tests can be used to determine if a genomic region contains a disproportionate number of positive or negative log transformed gene expression ratios. The presence of such a regional expression bias can indicates an underlying genomic abnormality.

If multiple clones map to the same genomic locus the aggregate.by.loc argument can be used to include a summary value for the overlapping expression values rather then include all of the individual gene expression values. For example, if 50 copies of the actin gene are on a particular array and actin changes expression under a given condition, it may appear as though a regional expression bias exists as 50 values in a small region change expression.

regmap is usually the best way to plot results of this function. idiogram can also be used if you set the "explode" argument to TRUE.

buildChromLocation.2 can be used to create a chromLocation object in which the genes can be divided a number of different ways. Separating the data by chromosome arm was the original intent. If you use buildChromLocation.2 with the "arms" argument to build your chromLocation object, set the "chrom" argument to "arms" in this function.

`m`	A matrix of summary statistics

Kyle A. Furge

Crawley and Furge, Genome Biol. 2002;3(12):RESEARCH0075. Epub 2002 Nov 25.

buildChromMap,tBinomTest,regmap,buildChromLocation.2

## Not run: 
## 
## NOTE: This requires an annotation package to work.
##       In this example packages "hu6800" and "golubEsets" are used.
##       They can be downloaded from http://www.bioconductor.org
##       "hu6800" is under MetaData, "golubEsets" is under Experimental Data.

if(require(hu6800) && require(golubEsets)) {
   data(Golub_Train)
   cloc <- buildChromMap("hu6800",c("1p","1q","2p","2q","3p","3q"))

   ## For one-color expression data
   ## compare the ALL samples to the AML samples
   ## not particularly informative in this example

   aml.ix <- which(Golub_Train$"ALL.AML" == "AML")
   bias <- cgma(eset=Golub_Train,ref=aml.ix,genome=cloc)
   regmap(bias,col=.rwb) 
} else print("This example requires the hu6800 and golubEsets data
   packages.")

## A more interesting example

## The mcr.eset is a two-color gene expression exprSet
## where cytogenetically complex (MCR), 
## cytogenetically simple (CN) leukemia samples
## and normal control (MNC) samples were profiled against
## a pooled-cell line reference
## The MCR eset data was obtained with permission. See PMID: 15377468

## Notice the dimished expression on chromosome 5 in the MCR samples
## and the enhanced expression on chromosome 11
## This reflects chromosome gains and losses as validated by CGH

   data("mcr.eset")
   data(idiogramExample)
   norms <- grep("MNC",colnames(mcr.eset@exprs))
   bias <- cgma(mcr.eset@exprs,vai.chr,ref=norms)
   regmap(bias,col=topo.colors(50)) 

## End(Not run)