There are usually several probesets map to one gene in Affymetrix. It's worth mentioning that the expression after RMA is probeset expression. RemoveProbes(listOutProbes=NULL, pombe_filter_out, "yeast2cdf", "yeast2probe") #Get the raw subset data for Cerevisiae, use RemoveProbes() function We use the RemoveProbes.r to remove the mappings from the x- and y-coordinates to the pombe probesets in the cdf environment so that when R is told to look at the environment, R cannot detect that those probesets are there. For these probesets, we can obtain 4,640 genenames from yeast2GENENAME. After filtering we are left with 5,900 cerevisiae and spike-in probesets. There are 5,028 pombe probesets in this mask file. #Get the Gene Name for Yeast, need ProbeID if the gene name is 'NA'ĬerevisiaeGeneName<-YeastGeneName #Get the whole 10928 Probeset ID from yeast2GENENAMEĬerevisiaeProbeID <- YeastProbeID I have one thread on Filter out pombe probeset from cerevisiae probesets for yeast2 Affymetrix chip in Bioconductor mailing list. Yeast2 affymetrix chip contains probesets for both cerevisiae and pombe, we only need cerevisiae probesets, so we should extract only the relevant data before normalisation. To load the data, use the following command:įns2 = list.celfiles(path="data2", full.names=TRUE) These files are stored in the directory data2. The raw data files are named as follows,yeast01.cel, yeast02.cel. At each hour three microarray experiments were carried out for each strain. Two strains, wild type and mutants, of the yeast Saccharomyces cerevisiae were monitored for four hours. Alternatively, you can download pdf file for a presentation " Tutorial: analysing Microarray data using BioConductor" which I gave recently. The whole R code will also be available upon request. The full tutorial with lots of figures will be available soon. The following is part of this tutorial on using R and Bioconductor to analyse microarray data, including how to load microarray data into R, pre-process microarray data, identify differential expression and infer network. ![]() Please refer to Bioconductor on how to install Bioconductor and these packages. ![]() These packages have been integrated into Bioconductor. We use several R packages: affy, affyPLM, smida, limma, time course and GeneNet. Bioconductor is an open source and open development software project for the analysis and comprehension of genomic data. I am currently working on identifying differential expression and network inference for microarray data using R packages in Bioconductor.
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