heter.gage: GAGE analysis for heterogeneous data
In gage: Generally Applicable Gene-set Enrichment for Pathway Analysis

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

heter.gage is a wrapper function of gage for heterogeneous data. pairData prepares the heterogeneous data and related arguments for GAGE analysis.

heter.gage(exprs, gsets, ref.list, samp.list, comp.list = "paired",
use.fold = TRUE, ...)

pairData(exprs, ref.list, samp.list, comp.list = "paired", use.fold =
TRUE, ...)

`exprs`	an expression matrix or matrix-like data structure, with genes as rows and samples as columns.
`gsets`	a named list, each element contains a gene set that is a character vector of gene IDs or symbols. For example, type head(kegg.gs). A gene set can also be a "smc" object defined in PGSEA package. Make sure that the same gene ID system is used for both `gsets` and `exprs`.
`ref.list`	a list of `ref` inputs for `gage` function. In other words, each element of the list is a column number vector for the reference condition or phenotype (i.e. the control group) in the exprs data matrix.
`samp.list`	a list of `samp` inputs for `gage` function. In other words, each element of the list is a column number vector for the target condition or phenotype (i.e. the experiment group) in the exprs data matrix.
`comp.list`	a list or a vector of `compare` input(s) for `gage` function. The length of the list or vector should equal to the length of `ref.list` and `samp.list` or 1. In the latter case, all analyses will use the same comparison scheme. The same as `compare`, the element value(s) in `comp.list` can be 'paired', 'unpaired', '1ongroup' or 'as.group'. Default to be 'paired'.
`use.fold`	Boolean, whether to use fold changes or t-test statistics as per gene statistics. Default use.fold= TRUE.
`...`	other arguments to be passed into `gage`.

comp.list can be a list or vector of mixture values of 'paired' and 'unpaired' matching the experiment layouts of the heterogeneous data. In such cases, each ref-samp pairs and corresponding columns in the result data matrix after calling pairData are assigned different weights when calling gage in the next step. The inclusion of '1ongroup' and 'as.group' in comp.list would make weight assignment very complicated especially when the sample sizes are different for the individual experiments of the heterogeneous data.

The output of pairData is a list of 2 elements:

`exprs`	a data matrix derived from the input expression data matrix `exprs`, but ready for column-wise gene est tests. In the matrix, genes are rows, and columns are the per gene test statistics from the ref-samp pairwise comparison.
`weights`	weights assigned to columns of the output data matrix `exprs` when calling `gage` next. The value may be NULL if `comp.list` are all 'paired'.

The result returned by heter.gage function is the same as result of gage, i.e. either a single data matrix (same.dir = FALSE, test for two-directional changes) or a named list of two data matrix (same.dir = TRUE, test for single-direction changes) for the results of up- ($greater) and down- ($less) regulated gene sets. Check help information for gage for details.

Weijun Luo <luo_weijun@yahoo.com>

Luo, W., Friedman, M., Shedden K., Hankenson, K. and Woolf, P GAGE: Generally Applicable Gene Set Enrichment for Pathways Analysis. BMC Bioinformatics 2009, 10:161

gage the main function for GAGE analysis; gagePipe pipeline for multiple GAGE analysis in a batch

data(gse16873)
cn=colnames(gse16873)
hn=grep('HN',cn, ignore.case =TRUE)
dcis=grep('DCIS',cn, ignore.case =TRUE)
data(kegg.gs)

library(gageData)
data(gse16873.2)
cn2=colnames(gse16873.2)
hn2=grep('HN',cn2, ignore.case =TRUE)
dcis2=grep('DCIS',cn2, ignore.case =TRUE)

#combined the two half dataset
gse16873=cbind(gse16873, gse16873.2)
refList=list(hn, hn2+12)
sampList=list(dcis, dcis2+12)

#quick look at the heterogeneity of the combined data
summary(gse16873[,hn[c(1:2,7:8)]])
#if graphic devices open:
#boxplot(data.frame(gse16873))
gse16873.kegg.heter.p <- heter.gage(gse16873, gsets = kegg.gs,
    ref.list = refList, samp.list = sampList)
gse16873.kegg.heter.2d.p <- heter.gage(gse16873, gsets = kegg.gs,
    ref.list = refList, samp.list = sampList, same.dir = FALSE)
str(gse16873.kegg.heter.p)
head(gse16873.kegg.heter.p$greater[, 1:5])