Dear Wolfgang,
 
It is really nice and surprise to have your attention! Thank you!
 
I am sorry that the question was too vague. The detailed figure is that we would like to study the gene expression profiling in human peripheral blood and identify DEGs (differential expressed genes) between male and female. As I mentioned in my previous email, the raw data were preprocessed in two approaches: one is simply by RMA and the other, after RMA, the expression data were further adjusted by ComBat  (http://statistics.byu.edu/johnson/ComBat/) to removal potential batch effects. The dataset was relatively small including 12 Male and 12 Female. At the end, we got two DEG lists by SAM at FDR=0.05. The basic idea is to show by removing potential batch effects, we are capable to extract more information from gene expression data representing the difference between male and female in peripheral blood. On the other hand, we also would like to check whether the additional batch effect adjustment will introduce artificial DEGs.
 
Based on the preliminary result, we observe that the difference between male and female in peripheral blood are very impressive, especially for (x, y) chromosome specific genes. Hence, when plotted ROC curves for both methods, both DEG lists easily reached the maximum AUC=1. And the same situation for hierarchical clustering heatmap, both DEG lists achieved perfect discrimination. 
 
Thanks again!
 
Qinghua



________________________________
发件人： Wolfgang Huber <whuber@embl.de>

抄 送： bioconductor <bioconductor@stat.math.ethz.ch>; qinghua.xu@as.biomerieux.com
发送日期： 2009/12/28 (周一) 4:56:18 下午
主 题： Re: [BioC] 转发： Statistical approach to compare differentiall expressed gene lists

Dear Qinghua

I am afraid your question may be too vague. You will need to define more
precisely what you mean by "better". Then, it should be straightforward
to compute a quantitative criterion. It wouldn't be wise to wait for
someone else to define what is "better" for you.

Also, for any analysis method I know of, gene lists depend in a trivial
manner on a cut-off (e.g. for p-value, score...), and if you want to do
something more meaningful than exegesis of someone's cut-off choice,
than I'd suggest to plot ROC curves for both methods, using a reference
set of genes that is enriched for "truely differentially expressed".

Best wishes
    Wolfgang


> Dear all,
> 
> I have identified two lists of differential expressed gene from the
> same expression data but treated with different normalisation
> methods. List A contains 995 genes and list B contains 2400 genes.
> More than nine hundreds genes are overlapped between two lists,
> namely most of genes in list A are also included in list B. The idea
> is to check whether list B is better than list A.
> 
> In addition to visualisation approach (like hierarchical clustering
> heatmap) or biological interpretations,  I am wondering is there any
> other statistical approach available to compare two differential
> expressed gene lists?
> 
> I would appreciate any advice, or pointers to any references for
> this!
> 
> Bests, Qinghua
> 
> 
> 
> ___________________________________________________________ 好玩贺卡等你发，邮
> 箱贺卡全新上线！ 
> 
> 
> ------------------------------------------------------------------------
> 
> 
> _______________________________________________ Bioconductor mailing
> list Bioconductor@stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/bioconductor Search the
> archives:
> http://news.gmane.org/gmane.science.biology.informatics.conductor
> 
> 
> ------------------------------------------------------------------------
> 
> 
> _______________________________________________ Bioconductor mailing
> list Bioconductor@stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/bioconductor Search the
> archives:
> http://news.gmane.org/gmane.science.biology.informatics.conductor

-- 
Best wishes
    Wolfgang


--
Wolfgang Huber
EMBL
http://www.embl.de/research/units/genome_biology/huber/contact


      ___________________________________________________________ 
  好玩贺卡等你发，邮箱贺卡全新上线！ 

	[[alternative HTML version deleted]]

