Hello, I have been asked to look at data from the GEO to make a comparison with current microarray data. The GEO data is from an Affymetrix Murine Genome U74 Version 2 Array experiment, from 2003 and the description says that the data is the "average difference" with "global scaling" applied. This means there are negative values for some probesets and the values range from -831.8 to 12785, but the majority sit around 15-300. My question is what is the best way to proceed? Do I take this as normalized data? A boxplot of the data shows some variation between the samples. Should I just remove the negative values and then proceed with limma with log2 of the data. Or will I need to make further transformations before I apply the linear model? if so, which? Im wary of over manipulating the data as its only a 2x2 comparison to begin with. Thanks for your advice. Alice P Think before you print This e-mail transmission and any attachments that accompany it may contain information that is privileged, confidential or otherwise exempt from disclosure under applicable law and is intended solely for the use of the individual(s) to whom it was intended to be addressed. If you have received this e-mail by mistake, or you are not the intended recipient, any disclosure, dissemination, distribution, copying or other use or retention of this communication or its substance is prohibited. If you have received this communication in error, please immediately reply to the author via e-mail that you received this message by mistake and also permanently delete the original and all copies of this e-mail and any attachments from your computer. Thank you.

Hi Alice you could try vsn. It applies a logarithm-like transformation that avoids the steepness of the usual logarithm for small values and the singularity at 0. Its bottom-end behaviour is fit based on the data's background noise characteristics, and it has worked well in conjunction with subsequent linear modeling in many previous cases. Conceptually, this modification of the usual logarithm at the bottom end is equivalent to a "background correction" followed by usual logarithm. Best wishes Wolfgang On 18/08/10 01:30, Alice Johnstone wrote: > Hello, > > I have been asked to look at data from the GEO to make a comparison with current microarray data. The GEO data is from an Affymetrix Murine Genome U74 Version 2 Array experiment, from 2003 and the description says that the data is the "average difference" with "global scaling" applied. > > This means there are negative values for some probesets and the values range from -831.8 to 12785, but the majority sit around 15-300. > > My question is what is the best way to proceed? Do I take this as normalized data? A boxplot of the data shows some variation between the samples. Should I just remove the negative values and then proceed with limma with log2 of the data. > Or will I need to make further transformations before I apply the linear model? if so, which? > Im wary of over manipulating the data as its only a 2x2 comparison to begin with. > > Thanks for your advice. > Alice > P Think before you print > This e-mail transmission and any attachments that accompany it may contain information that is privileged, confidential or otherwise exempt from disclosure under applicable law and is intended solely for the use of the individual(s) to whom it was intended to be addressed. > If you have received this e-mail by mistake, or you are not the intended recipient, any disclosure, dissemination, distribution, copying or other use or retention of this communication or its substance is prohibited. If you have received this communication in error, please immediately reply to the author via e-mail that you received this message by mistake and also permanently delete the original and all copies of this e-mail and any attachments from your computer. Thank you. > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor -- Wolfgang Huber EMBL http://www.embl.de/research/units/genome_biology/huber