![]() ![]() Download the starting data ("CEL" files) at the top of the page and unzip this file to get 8 files ending in ".CEL".You also have the option of performing other algorithms such as RMA, GCRMA, and dChip.See the manuals from Affymetrix for more information about these processes,Īnd the Statistical Algorithms Description Document.This can be done using a Bioconductor/R version of the methods in the Microarray Suite 5.0 (MAS5, the standard Affymetrix algorithm) Data from a probeset (a series of oligos designed to a specific gene target) needs to be summarized by calculating an expression values for that probeset.Note that these analysis protocols are generally specific to Affymetric chips.If you wish, you can skip these steps and download summarized (probeset-level) data.Preprocessing and normalization of Affymetrix expression data Scanned images were quantified (including measurement of background) using standard software.Ĭlass 1 exercises Part 0.Human tissue samples were hybridized on Affymetrix (one-color) arrays and chips were scanned.įor each tissue, at least two independent samples were hybridized to separate chips. Preliminary information: Image analysis and calculation of expression value Type '?m圜ommand' to get a help page about the command 'm圜ommand'. These commands can be pasted into the program. R is a free, very powerful statistics environment but it requires commands to perform every step of an analysis pipeline. (a set of packages that run in R) to do most of the mathematical analyses. try to find what functions specific groups of genes (with similar expression profiles) have in common.cluster a differentially expressed subset of all genes to identify those with similar expression profiles.flag low intensity data (most probably background noise).use a common statistical test to identify differentially expressed genes.calculate expression ratios of genes between two different tissues.calculate Absent/Present calls which attempts to label genes that are "expressed".preprocess the raw data (summarize probe measurements into one measurement for each probe).What we'll be doing to analyze these data: Many other tissues were also profiled but won't be used for these exercises. You'll be using a sample of expression data from a study using Affymetrix (one color) U95A arrays that were hybridized to tissues from fetal and human liver and brain tissue.Įach hybridization was performed in duplicate. Processed Data (starting with MAS5) Introduction Microarray analysis exercises 1 - with R Microarray analysis exercises 1 - with R WIBR Microarray Analysis Course - 2007 Starting Data (probe data) ![]()
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