[BioC] GAGE and PATHVIEW packages

Christian De Santis christian.desantis at stir.ac.uk
Mon Oct 7 13:49:55 CEST 2013

Hi Weijun,

Thanks for your prompt reply. It was very helpful to clarify my doubts, although it generated one more. 

"mol.sum" it is an excellent function, thanks for pointing it out. The default sum.method for this function is "sum". I am not sure what "sum" is exactly computing (and being a novice I have difficulties to look at the code directly), but I assume that it will return the sum of  the intensities associated with replicates ID. The reason why I am asking is that I am using arrays with an unbalanced number of replicates probes (i.e. 3 for gene A, 6 for gene B, etc.). I have the feeling that the "sum" option would, in my case, put a greater weight on those pathways with core genes more present on the array (i.e. gene B). I tried two different methods to test my hypothesis, and by using "sum" I indeed got one of our target pathways called significant in the top 3, while it does not show up by using "mean" for example (most other pathways are consistent). I would appreciate if you could help me clarify this doubt and make a decision. Am I correct, based on the design of my arrays, to avoid choosing the method "sum"? 

This should solve most of my doubts about your packages for now. Thanks again very much for your help. 

Best regards,

-----Original Message-----
From: Luo Weijun [mailto:luo_weijun at yahoo.com] 
Sent: 07 October 2013 01:11
To: Christian De Santis
Cc: bioconductor at r-project.org
Subject: Re: GAGE and PATHVIEW packages

Hi Christian,
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
 To: "luo_weijun at yahoo.com" <luo_weijun at yahoo.com>, "bioconductor at r-project.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  replicates?

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  into
 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:
ess.sets=c(gse16873.kegg.esg.up$essentialSets, gse16873.kegg.esg.dn$essentialSets)
gse16873.kegg.p.ess=lapply(gse16873.kegg.p, function(x) x[ess.sets,]) gse16873.kegg.sig.ess=sigGeneSet(gse16873.kegg.p.ess, outname="gse16873.kegg.ess")

  Any help on the above would be greatly  appreciated.
 Christian De Santis
 The University
 of Stirling has been ranked in the top 12 of UK universities  for graduate employment*.
 94% of
 our 2012 graduates were in work and/or further study within  six months of graduation.
 The University of
 Stirling is a charity registered in Scotland, number SC  011159.

The University of Stirling has been ranked in the top 12 of UK universities for graduate employment*.
94% of our 2012 graduates were in work and/or further study within six months of graduation.
*The Telegraph
The University of Stirling is a charity registered in Scotland, number SC 011159.

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