Dear all, We found similar things when we looked at the standard and small sample (amplification) protocols from Affymetrix (Wilson et al 2004, Biotechniques. 2004 Mar;36(3):498-506). Whilst raw expression levels from unamplified and amplified arrays were not directly comparable, fold-changes were (i.e. if you divided the chips into unamplified and amplified sets and compared fold changes calculated within each set, there was good correspondence). We also used the RNA degradation plots to assess the similarity/differences between the arrays. With regards to the initial starting amount of RNA used in the amplification experiments we found little difference in starting from 10 or 100ng; 1ng was harder. Best Claire > -----Original Message----- > From: bioconductor-bounces@stat.math.ethz.ch > [mailto:bioconductor-bounces@stat.math.ethz.ch] On Behalf Of > Robert Gentleman > Sent: 09 February 2005 15:17 > To: Swati Ranade > Cc: bioconductor@stat.math.ethz.ch > Subject: Re: [BioC] Microarray data analysis for experiments > using amplifiedRNA > > One of the problems with amplified mRNA is that not all mRNA species > are going to get amplified at the same rate (and probably for > some the > rate is zero), also, as I understand it the resulting mRNAs > will tend > not to be full length. So this affects the binding, and hence the > estimated expression levels (and I do not believe it matters what > platform you are using; there will most likely be some > peculiarities). > So, that basically means that you want all samples to have been > amplified using the same method, and in some sense the same amount. > Otherwise, you are not really comparing like with like. The RNA > degredation plots can be quite helpful in this regard - as they can > help to pinpoint arrays that might be substantially different. > > Robert > > > > > On Feb 9, 2005, at 5:36 AM, Swati Ranade wrote: > > > Hi, > > I have done some experiments using amplified RNA (Rayan > Baugh's method > > which > > I modified slightly) probes with affy chips. The study design is a > > simple > > comparison of knockout mutant vs wild type. My question > was: Is it ok > > to use > > the same statistical algorithms one would apply for > standard microarray > > experiments or do I need to follow a different strategy? > Can anybody > > give > > pointers? > > > > Thanks, > > > > Swati > > > > Swati Ranade > > Bauer Center for Genomics Research > > 7 Divinity Av > > Cambridge > > MA 02138 > > > > _______________________________________________ > > Bioconductor mailing list > > Bioconductor@stat.math.ethz.ch > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > > > > +------------------------------------------------------------- > ---------- > ----------------+ > | Robert Gentleman phone: (206) 667-7700 > > | > | Head, Program in Computational Biology fax: (206) 667-1319 | > | Division of Public Health Sciences office: M2-B865 > > | > | Fred Hutchinson Cancer Research Center > > | > | email: rgentlem@fhcrc.org > > | > +------------------------------------------------------------- > ---------- > ----------------+ > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > -------------------------------------------------------- This email is confidential and intended solely for the use o...{{dropped}}