It is important to remember that statistical significance refers to the investigator's ability to reproduce the result. A result can be statistically significant without having biological significance. The reported R-sq might be statistically significant but biologically insignificant. --Naomi At 06:57 AM 10/6/2006, Sean Davis wrote: >On Thursday 05 October 2006 21:14, Lana Schaffer wrote: > > Hi, > > This experiment involves the expression analysis between 2 batches of > > AGE variable samples which were normalized separately because the > > batches did not cluster together. The age groups of the second set > > were in between the ages from the first set and all the data are desired > > to be analyzed together. Now what happened is that the separately > > normalized expression values became graphed together by the lab researchers > > and plotted expression values vs age. Now with the diseased samples there > > were genes which showed a "trend" with age where the R-squared were between > > .16 and .4. From my training I get that this trend only is explains 16-40% > > of the data and would not be significant. However, using Prism these > > R-squares are called significantly different from zero. These researchers > > explain to me that this is the way data is presented in their field and > > that an R-squared of .16-.4 is considered excellent results. Indeed, with > > non-diseased individuals the R-squared are zero for these genes. I > > understand that in their field "any" trend is better than no trend, > > especially since the samples are hetergeneous. However, this is not what > > is taught in statistics. These graphs will be submitted to Journals under > > my authorship and I am a bit shaken-up. > > Would you please comment to me about your thoughs about the combination > > of the 2 sets of expression values and the significance of the R-squared > > values. Thanks, > > Lana > >Lana, > >There are two issues it seems. The first is normalization of two separate >batches, which is appropriate. What isn't clear is whether doing so >introduces bias in downstream analyses--this you will need to judge for >yourself. > >The second is of the significance of the results of the R-squared value. To >convince yourself and your collaborators of the significance or lack thereof >of the computed values, one can test whether the R-squared is significant or >not. I would suggest using a permutation-based analysis, but the method is >up to you. Judging an R-squared value by looking at the raw number is >probably not a valid method for determining significance. > >Sean > >_______________________________________________ >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 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