Hi Chuming,   As per your experimental information, you have replicates. Because, you have samples from same tissue with 2 different region across all patients.  So, you should be able to fit linear model. What I guess, there is something wrong in your analysis steps. So, better to send sessional information to list.   Regards, Prashantha Prashantha Hebbar Kiradi, Dept. of Biotechnology, Manipal Life Sciences Center, Manipal University, Manipal, India Email:prashantha.hebbar@manipal.edu --- On Mon, 1/25/10, Wolfgang Huber wrote: From: Wolfgang Huber Subject: Re: [BioC] Agilent G4112A Arrays To: "Naomi Altman" Cc: "Chuming Chen" , "Prashantha Hebbar" , bioconductor@stat.math.ethz.ch Date: Monday, January 25, 2010, 8:06 PM Hi Chuming 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.     Best wishes     Wolfgang 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. > > Regards, > 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 >> >> Chuming >> >> >> 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. >>> >>> Regards, >>> >>> Prashantha Hebbar Kiradi, >>> Dept. of Biotechnology, >>> Manipal Life Sciences Center, >>> Manipal University, >>> Manipal, India >>> >>> >>> --- On *Mon, 1/25/10, Chuming Chen //* wrote: >>> >>> >>>     From: Chuming Chen >>>     Subject: [BioC] Agilent G4112A Arrays >>>     To: bioconductor@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 >>> >>>     _______________________________________________ >>>     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 > > 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 > > _______________________________________________ > 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 [[alternative HTML version deleted]]