Principal components (PC) analysis (PCA) is an exploratory technique that is used to describe the structure of high dimensional data by reducing its dimensionality. It is a linear transformation that converts n original variables (typically: genes or transcripts) into n new variables, which are called PCs, they have three important properties:

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If read quantification (i.e. mapping to a transcript model) was performed by Partek E/M algorithm, PCA can be invoked on a quantification output data node (Gene counts or Transcript counts) or, after normalisation, on a Normalized counts data node. Select a node on the canvas and then PCA in the Visualization section of the toolbox. The PCA task creates a new task node, and to open it and see the result, do one of the following: select the PCA task node, proceed to the toolbox and go to the Task result; or double-click on the PCA task node.

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The plot can be customised by using the controls on the left (Figure 3). Color by shows the sample attributes as listed in the Data tab or you can set it to Fixed to have all the dots of the same color. Size by option works in the same way, but affects the dot sizes.

Connect by option is particularly useful for dependent study designs, where you can highlight the samples based on the same biological source by the connecting lines. Example on Figure 4 depicts results of a study where each RNA sample was processed by both RNA-seq and  gene expression array; the lines connect the same samples.

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Next, Show legend turns the legend on or off. Select all selects all the dots, while Show axis turns the coordinate axis on or off. To change the size of the dots, use the Dot size slider. Grid cells increases or decreases the size of the cells in the plot grid and the Major grid interval specifies the frequency of major grid lines (fat lines). E.g. setting the Major grid interval to 4 highlights every 4th grid line.

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Once you are pleased with the appearance of the dot plot, push Save image button to save it to the local machine. The resulting dialog (Figure 7) controls the resolution of the image file. The image will be saved in .png format, and the default filename is PCA plot.png. Or, if you are not happy with your edits, you can always revert to the initial view by pushing the Reset button.

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