[BioC] Agilent G4112A Arrays
whuber at embl.de
Mon Jan 25 21:06:06 CET 2010
if you want to work with the approximation that M-values have equal
variances, then preprocessing the data with a method that provides
variance stabilisation (e.g. vsn) will likely be useful.
Furthermore, it might be useful to discard a fraction of genes with low
A-values, since they are more likely to be either not expressed, or so
weakly expressed that you would find it more difficult to validate them.
Naomi Altman wrote:
> 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.
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