Analysis of microarray data - VSN International

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50Analysis of Microarray Data in GenStatTo merge the mean intensities with the Estimate results, the columnf_Name must be converted to a text to match the type of the columnProbes in the Estimates spreadsheet. To do this, right click on thecolumn and select the popup menu Convert to Text. Now go to theEstimates spreadsheet window and use the Spread | Manipulate |Merge menu which opens the dialog below. The options are setcorrectly by default, so just click OK to merge the m_Intensitycolumn.Now, complete the Volcano Plot menu asshown on the previous page. The Optionsbutton allows you to set the titles, symbolsand colours used on the plot. The optionsshown below left were used to produce thefollowing graph (right).
Analysis of Microarray Data in GenStat 51Cluster microarray probes or targetsThis menu allows you to group similar probes or targets togetherthrough the technique of cluster analysis. This can be used tocheck that the similarity between slides reflects that expectedfrom the experimental design, to see which groups ofgenes/probes behave in a similar manner between targets. A twowayclustering using both slides and targets can be used to produce a ‘heat map’ of the probe x targetmatrix.Cluster Probes/GenesThis menu can be used to group together probes or genes that behave in a similar manner over the varioustreatments in the experiment. As we often have a very large number of probes on a slide, there is an optionto restrict the clustering to probes with the largest levels of differential expression. The data can be putinto the menu in two formats, either as a pointer (see Help | GenStat Guides | Introduction, section 10.7 onpage 328) to the results by slide or target, or using single variate with all a factor indexing the variousslides or targets.For example, using the probes effects from the ANOVAof the nine Affymetrix slides saved in “Hyb-ANOVA.gwb”, we can look to see which probes havesimilar responses over the three treatments. Opening theStats | Microarray | Cluster | Probes/Genes menu, we getthe dialog to the right. As the effects are in three columnspointed to by the Effects pointer, we select the PointerData Format as shown. We then select Effects as the Log-Ratios and enter IDProbes as the Probes/Genes. With apointer, the ‘Targets or Slides’ field can be left blank.The cluster method we use is K means and we specify thenumber of groups to cluster into as 20, and that we onlywant to cluster the top 10% of probes. Note: the K-Means algorithm is much more space efficient withlarge number of probes, as a hierarchical cluster analysis must calculate a full n x n similarity matrix,where n is the number of probes being clustered.The Options dialog, shown to the right, controls the graphs andoutput from the clustering. If you use a Trellis plot with largenumbers of probes, then the graph size in memory is often too largefor most computers. Selecting the option “Display Mean Responseper group” only shows the average over the probes in the groupsdefined by the clustering, considerably reducing the memoryrequired to display the graph. The two graphs with and without thisoption are shown on the next page. The option “Display ClusterGroups in a Spreadsheet”produces the followingspreadsheet that gives theprobe groupings and the effectsin columns S[1]…S[3] (onecolumn for each treatment).
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Analysis of microarray data - VSN International

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