[BioC] GAGE and PATHVIEW packages
luo_weijun at yahoo.com
Mon Oct 7 02:11:25 CEST 2013
Please see my point-to-point answers below.
On Fri, 10/4/13, Christian De Santis <christian.desantis at stir.ac.uk> wrote:
Subject: GAGE and PATHVIEW packages
.org" <bioconductor at r-project.org>
Date: Friday, October 4, 2013, 11:27 AM
Dear Luo and list,
> I am successfully using GAGE and pathview for my
analyses and I like the package a lot. So, thanks for
developing it. I have some points on which I would
appreciate some help and/or clarification.
Thanks for the comments.
> AVERAGE VALUE - The first time I run the analysis with
GAGE, I used an identical setup parameters as the example
prepared by you in the manual. I have 8 replicates per
treatment and I initially used unique column names for each
sample (i.e. “DIET02_1,
DIET02_2, DIET02_3, etc.) as per your example with HN and
DCIS. However, I have discovered (following a casual
mistake) that if instead of having a unique name samples are
named with the treatments they belong (i.e.
“DIET02” for all 8 replicates), the subsequent
gage analysis it generates one single value for that
treatment. By comparing the p values of both the above cases
I have found that they are identical. Am I correct to assume
that in the latter case every value assigned to the
treatment are an average of the
It is the average, i.e. p-value is the genometric mean, while statistics is the mean of the columns with the same name. The average mechanism is there to accomdate special needs or mistakes, but it is not recommended to use the same name for replicate samples.
> DUPLICATE PROBES – My array has got several
duplicate or triplicate probes which are correctly annotated
with the same KO number. How are these probes handled by the
gage analysis? For example, if I have three probes for my
gene X which are annotated with
the same KO number, are these going to be counted 3 times
into the “set size”? Or are the values for that
KO number going to be merged into one?
Duplicate probes will be count for multiple times, which is not good. Because gene set analysis like GAGE really assume one independent variable per gene. You may summarize over duplicate probes before feed into GAGE. You can check ?mol.sum in pathview package for that.
> “COMPARE” argument of “gage”
function – My experiment consists of 5 treatments (x 8
replicates). None of the treatments is a proper
“control”. Is it correct if I use as an argument
“1ongroup” choosing one of the treatment as a
ref? I have also tried the
“as.group” option but when I look at the results
I do not get a comparison of the chosen reference with the
remaining groups, but instead one single value named
“exp1”. I have also tried “paired”
which gives completely different results.
If you set ref or samp other than NULL, GAGE assume it is a two state comparison. Compare argument may assume one value of 1ongrp, paired, unpaired, as.group based on needs. They are all for two state comparison, but to do it based on whether you samples are paired or not etc. If you want to do multiple state comparison/test, you should do before GAGE on each gene, then feed the single-column results into gage with “ref = NULL, samp = NULL”. If you want to do a two-state comparison, you should specify a control state, either all 4 groups other than your inntersting group, or the median of all groups for each gene.
> HEATMAP OUTPUT of “esset.grp” function
– Is there any quick way to generate an output heatmap
(as for sigGeneSet) removing the redundant pathways
identified with function “esset.grp”? At the
moment I am doing this manually and plotting the results
heatmap.2 from gplot. Is this the only way?
You can do this quickly using esset.grp+ sigGeneSet, assuming you follow the examples till you get gse16873.kegg.esg.up and gse16873.kegg.esg.dn:
gse16873.kegg.p.ess=lapply(gse16873.kegg.p, function(x) x[ess.sets,])
Any help on the above would be greatly
Christian De Santis
of Stirling has been ranked in the top 12 of UK universities
for graduate employment*.
our 2012 graduates were in work and/or further study within
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