[BioC] LIMMA vs. dChip

Adaikalavan Ramasamy ramasamy at cancer.org.uk
Mon Mar 7 20:46:09 CET 2005

Your question is bit vague and you provide little information. I do not
think LIMMA has preprocessing capabilities for Affymetrix data.

1) How did you preprocess the data ?

2) How did you "analyse" your data in dChip ? What technique (e.g. fold
change, t-test, wilcoxon) did you use in dChip ?

3) How did you select the differentially expressed genes ? (e.g. via p-
value cutoff or biological significance).

One possibility is that you are using very different test statistics.
With 5 in each group, it is difficult to draw any conclusions as some
methods are more robust than others at small number of arrays.

Another is that you choose a threshold that includes a lot of noisy
gene. An extreme example is to select all genes with a p-value less than
1 in which case you get 100% agreement between the two methods.

And yet another, you may have made a coding/programming error somewhere.

Regards, Adai

On Mon, 2005-03-07 at 14:15 -0500, jun.yan.a at utoronto.ca wrote:
> Dear list member,
> I have a set of Affymetrix data of 10 arrays, HG_U133A, seperated into unpaired 
> two groups of 5 arrays each. I processed the data using LIMMA and dChip. For 
> dChip, I used all the default setting. The resulted differential expressed 
> genes of the two have only less than 50% in common.
> Why the number of the overlapped genes of the two results is so low? Is there 
> any problems? Can anyone help me?
> Thanks in advance,
> Jun
> _______________________________________________
> Bioconductor mailing list
> Bioconductor at stat.math.ethz.ch
> https://stat.ethz.ch/mailman/listinfo/bioconductor

More information about the Bioconductor mailing list