Hi Carthika,
The mev files are directly loadable into MeV if you would prefer not to have to do
the log ratio ratios in Excel.  An annotation (.ann) file would be needed
to load related annotation.  The data directory an example of the annotation
file under 'TIGR_files' and there is a description in the MeV manual appendix on
file formats.
MeV simply uses the values in the TDMS file and does not perform a log transformation
on the input data.  I gather that since you are focusing on a fold change within a
particular array that you have replicate hybridizations of your experimental condition samples
against reference samples or some sort of control condition sample and that you are using
one sample TTEST to find significant genes.
If this is not the case please describe the design a bit more since your options will
vary based on whether you have one, two, or multiple experimental groups.
It is possible and likely that some genes will be statistically significant yet will have a
mean fold change that is not far from 1.   Roughly speaking, significance is a function
of mean difference over a measure of the variability of the measurements.
It can happen that a gene is not highly over expressed or under expressed yet the measurements
are so tight that the conclusion is made that there is a significant change given the
confidence in the mean value (low replicate variability).  This can happen even given
biological variation when thousands of genes are considered.
I think it is valid to use t-test or SAM to find statistically significant genes and report them
all or have them all ready in a supplemental table and then filter the list to focus on those
genes that have more biologically significant changes (mean fold change is larger).
*The fold change criteria or cutoff is arbitrary so I think reporting all statistically significant genes
sorted by mean fold change would be a good compromise.  That way you capture all
significant genes but the focus can be on those that were up or down regulated by
the greatest magnitude fold change.
**This is email going out to others in the MeV development group in case there are other
opinions or ideas to add.  Please let us know if there are additional question.
Thanks for using MeV and the TM4 suite.
Best Regards,
John Braisted
The Institute for Genomic Research

From: Carthika Luxmanan [mailto:carthika.luxmanan@anatomy.otago.ac.nz]
Sent: Wednesday, October 11, 2006 9:54 PM
To: mev
Subject: Log transformations...

Hi there.

I have used MIDAS to carry out some normalisations on my mucroarray data. The result is an MDS.mev file, which contains normalised data as specified in MIDAS. Then I open these MDS.mev files for each of my arrays in excel, and work out the Cy5/Cy3 ratio, log2 transform this ratio, and make a file containing my log transformed data for each of my arrays. Then I load this as a TDMS file in MeV, and carry out my analyses. My question is, do I need to manually log transform my data following MIDAS normalisation, or does MeV automatically take care of this? I am generally finding that my expression ratios are quite low when I look at differentially expressed genes from Mev, and I suspect that perhaps my data is being log transformed twice. One my top differentially expressed gene (with a very small p value) only has an dye ratio of 1.1 fold (after I anti-log it). I would appreciate your thoughts on this.

Thanks heaps


Carthika Luxmanan, BSc(Hons)

Dept. Anatomy & Structural Biology

University of Otago


New Zealand

Office: +64 3 479 5182

Lab: +64 3 479 5792