essGene {gage} | R Documentation |
This function extracts data for essential member genes in a gene set. Essential genes are genes that have changes over noise level.
essGene(gs, exprs, ref = NULL, samp = NULL, gsets = NULL, compare = "paired", use.fold = TRUE, rank.abs = FALSE, use.chi = FALSE, chi.p = 0.05, ...)
gs |
character, either the name of an interesting gene set in a gene set
collection passed by |
exprs |
an expression matrix or matrix-like data structure, with genes as rows and samples as columns. |
ref |
a numeric vector of column numbers for the reference condition or phenotype (i.e. the control group) in the exprs data matrix. Default ref = NULL, all columns are considered as target experiments. |
samp |
a numeric vector of column numbers for the target condition or phenotype (i.e. the experiment group) in the exprs data matrix. Default samp = NULL, all columns other than ref are considered as target experiments. |
gsets |
a named list, each element contains a gene set that is a character
vector of gene IDs or symbols. For example, type |
compare |
character, which comparison scheme to be used: 'paired', 'unpaired', '1ongroup', 'as.group'. 'paired' is the default, ref and samp are of equal length and one-on-one paired by the original experimental design; 'as.group', group-on-group comparison between ref and samp; 'unpaired' (used to be '1on1'), one-on-one comparison between all possible ref and samp combinations, although the original experimental design may not be one-on-one paired; '1ongroup', comparison between one samp column at a time vs the average of all ref columns. |
use.fold |
Boolean, whether the input |
rank.abs |
boolean, whether to sort the essential gene data based on absoluate changes. Default to be FALSE. |
use.chi |
boolean, whether to use chi-square test to select the essential genes. Default to be FALSE, use the mean plus standard deviation of all gene changes instead. Check details for more information. |
chi.p |
numeric value between 0 and 1, cutoff p-value for the chi-square test to select the essential genes. Default to 0.05. |
... |
other arguments to be passed into the inside |
There are two different criteria for essential gene selection. One uses a chi-square test to determin whether the change of a gene is more than noise. A second considers any changes beyond 1 standard deviation from mean of all genes as real.
Note that essential genes are different from core genes considered in
esset.grp
function. Essential genes may change in a different
direction than the overall change of a gene set. But core genes need to
change in the in the interesting direction(s) of the gene set test.
A expression data matrix extracted for the essential genes, with
similar structure as exprs
.
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;
geneData
output and visualization of expression data
for selected genes;
esset.grp
non-redundant signcant gene set list;
data(gse16873) cn=colnames(gse16873) hn=grep('HN',cn, ignore.case =TRUE) dcis=grep('DCIS',cn, ignore.case =TRUE) #kegg test for 1-directional changes data(kegg.gs) gse16873.kegg.p <- gage(gse16873, gsets = kegg.gs, ref = hn, samp = dcis) rownames(gse16873.kegg.p$greater)[1:3] gs=unique(unlist(kegg.gs[rownames(gse16873.kegg.p$greater)[1:3]])) essData=essGene(gs, gse16873, ref =hn, samp =dcis) head(essData) ref1=1:6 samp1=7:12 #generated text file for data table, pdf files for heatmap and scatterplot for (gs in rownames(gse16873.kegg.p$greater)[1:3]) { outname = gsub(" |:|/", "_", substr(gs, 10, 100)) geneData(genes = kegg.gs[[gs]], exprs = essData, ref = ref1, samp = samp1, outname = outname, txt = TRUE, heatmap = TRUE, Colv = FALSE, Rowv = FALSE, dendrogram = "none", limit = 3, scatterplot = TRUE) }