Thanks everyone for your replies. I was indeed inadvertently calculating deltaCt, but its great to know that what I was doing was correct! I get differences in values for deltaCt using different control genes, but the trend is always the same and these changes are usually quite small, except for a few occasions where there seems to have been problems determining the value for Ct in some of the technical replicates. (so if GAPDH had a Ct value of ~24 cycles for 2 technical replicates, but was undetermined for 2 others). However I guess this is more of a problem with the technology and possibly the person running the card than with the choice of genes. Regards, Jim Mark Cowley wrote: > Hi James, > by normalising to housekeeper genes, you have probably inadvertently > calculated deltaCt. > There are some other great references by Pfaffl and Bustin on the subject > > cheers, > Mark > > ----------------------------------------------------- > Mark Cowley, BSc (Bioinformatics)(Hons) > > Peter Wills Bioinformatics Centre > Garvan Institute of Medical Research, Sydney, Australia > ----------------------------------------------------- > > On 04/09/2008, at 8:05 PM, James Perkins wrote: > >> Hi Bas, >> >> Thanks for your reply. I have built an eset with detector as the rows >> and sample as the columns. However I have not been able to populate >> it with delta Ct since I do not have this data. >> >> How did you calculate deltaCt? Using the proprietary software? I >> don't have access to this I have just been given the Ct and the Ct >> Avg for each detector. >> >> I have been normalising each gene to the houskeeping genes, averaging >> across samples and dividing case by control to get the fold change. >> I've then been comparing the resultant fold changes depending on >> choice of normaliser against each other to see if there is a >> difference, which there is with *some* control genes. >> >> Kind regards, >> >> James >> >> Bas Jansen wrote: >>> Hi James: >>> >>> On Mon, Sep 1, 2008 at 1:25 PM, James Perkins >>> <jperkins at="" biochem.ucl.ac.uk=""> wrote: >>> >>>> Hi, >>>> >>>> >>>> Apologies for the long list of questions, I have searched the >>>> mailing list >>>> but can't find much info about these arrays. >>>> >>>> >>>> I am looking at low density PCR cards. They measure the expression >>>> levels of >>>> 96 different transcripts from a very small sample of human or >>>> animal tissue. >>>> There are actually 384 reactions going on but in our case each is >>>> done in >>>> quadruplicate (can be through biological or technical repetition). >>>> >>>> I wondered if there was a favoured way to normalise this data. The >>>> most >>>> cited paper I have found is the Vandesompele 2002 paper using the >>>> geometric >>>> mean of a number of control genes, implemented in R in the SLqPCR. >>>> >>>> Has anything else been developed that could be used with these >>>> cards? I >>>> guess quantile normalisation is out of the question since this >>>> makes some >>>> assumption that the majority of genes don't change in expression. >>>> >>> >>> As far as I know nothing has been developed in Bioconductor for >>> these cards. >>> When I analyzed them, I first created an ExpressionSet following the >>> (excellent!) directions given in the the Biobase vignette 'An >>> introduction to Bioconductor's ExpressionSet class' by Falcon et al. >>> Then I processed the normalized data (deltaCt) using the LMGene >>> package in order to perform gene-by-gene ANOVA and to identify >>> differentially expressed genes. I have repeated the whole procedure >>> using different control genes (read: different deltaCt values for the >>> same gene), but in my case I got the same results with the different >>> controls. Hope this helps. >>> >>> Kind regards, >>> Bas >>> >> >> _______________________________________________ >> 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 >