Hi, I would like to know if there is a way to estimate standard deviations for all genes, using noise information of genes with similar intensity levels? This would be helpful when trying to obtain significant fold change from experiments without replicates. Thanks for your ideas. Lana [[alternative HTML version deleted]]

Dear Lana, You could consider to try and evaluate a recently released BioC package, called 'plgem' (Power Law Global Error Model), that is available from the developmental repository under <http: www.bioconductor.org="" repository="" devel="" package="" html="" plgem.html=""> . Briefly, it represent a method that estimates SD of single genes, based on a global behavior of all genes in a dataset of replicated samples. It takes advantage of the fact that the SD of a given gene depends on the average expression level of the gene itself, following a power law. After installing the package (which depends on MASS) there is a quite straightforward wrapper that fits the model to the data, computes model-based differential expression statistics and outputs a list of significantly changing genes, based on a set of random resamplings of the data used for fitting the model. In case you do not have enough replicates in the dataset to perform the resampling step, the first n (default is 100) genes are selected. You first need to create from your data an object of class ‘exprSet’ with a ‘phenodata’ slot that contains a covariate called ‘conditionName’, in which you provide some coding of your classes (e.g. ‘treated’, ‘ctrl’, etc.). The only important thing here is that you give the same value to samples you wish to be treated as replicates. Other covariates in addition to ‘conditionName’ are allowed, but will be ignored. Then simply type: >run.plgem(esdata)->list.of.significant.genes where ‘esdata’ is an object of class ‘exprSet’ as described above. This will assume by default that your baseline samples are the first encountered in your ‘phenodata’ and that you want to perform the selection at an overall significance level of 0.001. To change some of these or other defaults, please refer to the help pages and to the vignette provided in the package. Of course you will need at least one condition with 3 replicates in order to fit the model, but in the remaining experimental conditions the SD can be estimated even from single samples. Reference article: <http: www.biomedcentral.com="" 1471-2105="" 5="" 203=""> I will be happy to help you if encounter any difficulties. Good luck! Norman Norman Pavelka Department of Biotechnology and Bioscience University of Milano-Bicocca Piazza della Scienza, 2 20126 Milan, Italy Phone: +39 02 6448 3556 Fax: +39 02 6448 3552 > Date: Wed, 20 Apr 2005 09:34:50 -0700 > From: "Lana Schaffer" <schaffer@scripps.edu> > Subject: [BioC] computing SD using a list of gene expressions > To: <bioconductor@stat.math.ethz.ch> > Message-ID: <002a01c545c6$e00089e0$54508389@menton> > Content-Type: text/plain > > Hi, > I would like to know if there is a way to estimate standard deviations > for all genes, using > noise information of genes with similar intensity levels? > This would be helpful when trying to obtain significant fold change > from experiments without > replicates. > Thanks for your ideas. > Lana > [[alternative text/enriched version deleted]]