[BioC] Agilent G4112A Arrays

Naomi Altman naomi at stat.psu.edu
Mon Jan 25 16:38:10 CET 2010

The more data one has, the fewer assumptions one needs.  In the 
absence of replication, you cannot get p-values without very strong 
assumptions.  e.g. you could assume that the vast majority of the 
genes do not differentially express, that their M-values have equal 
variance and that the M-values are normally distributed.  Then you 
could use e.g. the IQR of the M-values to estimate the sd and use 
this to pick a fold cut-off for DE.  You have no reasonable way to 
estimate FDR with this approach, but it might be slightly better than 
using 2-fold - or then again, it might not.  Without replication, 
there is no way to know.

Naomi Altman

At 08:53 AM 1/25/2010, Chuming Chen wrote:
>Hi Prashantha,
>Thank you for your suggestion. My target file is as below. Although 
>I couldn't fit a linear model, I still wonder whether I can do some 
>statistic on M (log ratio) values and use the p-value to get the 
>differentially expressed genes.
>SlideNumber    FileName    Cy3    Cy5
>1    B1vsT1.txt    B1    T1
>2    B2vsT2.txt    B2    T2
>3    B3vsT3.txt    B3    T3
>4    B4vsT4.txt    B4    T4
>5    B5vsT5.txt    B5    T5
>Prashantha Hebbar wrote:
>>Dear Chen,
>>You need not to look for any other packages. Since, you do not have 
>>any replicates, do not fit linear model, instead just do 
>>normalization with in arrays and look at the M (log ratio) values.
>>Prashantha Hebbar Kiradi,
>>Dept. of Biotechnology,
>>Manipal Life Sciences Center,
>>Manipal University,
>>Manipal, India
>>--- On *Mon, 1/25/10, Chuming Chen /<chumingchen at gmail.com>/* wrote:
>>     From: Chuming Chen <chumingchen at gmail.com>
>>     Subject: [BioC] Agilent G4112A Arrays
>>     To: bioconductor at stat.math.ethz.ch
>>     Date: Monday, January 25, 2010, 6:32 AM
>>     Dear All,
>>     I am trying to find out the differentially expressed genes from
>>     some Agilent Human Whole Genome (G4112A) Arrays data.
>>     I have tried LIMMA package, but LIMMA gave the error message "no
>>     residual degrees of freedom in linear model fits" and stopped. My
>>     guess is that my data has no replicates in the experiment.
>>     Is there any other packages I can use to find differentially
>>     expressed genes which does not require replicates in the experiment?
>>     Thanks for your help.
>>     Chuming
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Naomi S. Altman                                814-865-3791 (voice)
Associate Professor
Dept. of Statistics                              814-863-7114 (fax)
Penn State University                         814-865-1348 (Statistics)
University Park, PA 16802-2111

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