Ok thanks, Kasper Daniel Hansen wrote: > This is well known and is (one of) the reason(s) for the difficulty with > analyzing microarrays. Note that if you compute fold change, it looks a > lot better which is why we use fold change. Also note that > normalization (hopefully) will help you a bit. > > But in the end there will be genes where the fold change is not great > either (like 'C" in your example), and you will need to learn how to > live with it. > > There are no great way to correct for it, since the correction typically > depends on the probe used to measure the signal. If you had the money > and ability to generate a sample in which _all_ genes are expressed and > you then do a dilution experiment you might be able to estimate this. > There are attempts at correcting for this computationally, but they are > typically not very impressive. > > For more details, see the 100s of papers on microarray analysis and > probe effects. > > Kasper > > On Jul 23, 2009, at 8:25 , David martin wrote: > >> Hello, >> I have a naive question. In my experiment i have computed different >> standard curves for different genes. >> I have noticed that my data is not following a linear pattern (sorry >> i'm not statistician so don't know if this is the word). >> For e.g >> GENES [1?g] [2?g] [5?g] [10?g] >> gene A 500 800 1800 3500 >> gene B 450 650 1700 3400 >> gene C 200 300 600 1300 >> >> As you can see the intensities are not as I would expect (there is no >> linear intensity based on the concentration); Since my data shows that >> there is a bias i would like to correct the data and adjust according. >> For instance, i think there might be a coeffecient, which based on the >> intensitiy would help to correct the intenstities. At the end if for a >> gene X i find an intensity of 500 i could assume that it should higher >> than that to have a 2 fold change and that it should probably be >> somewhere around 1000. >> >> Thanks for your help ?? >> >> thanks for your help. >> >> _______________________________________________ >> 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 > > _______________________________________________ > 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 >