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When measuring only two variables, such as height and weight in a dozen patients, it is easy to plot this data and to visually assess the correlation between these two factors. However, in a typical microarray experiment, the expression of thousands of genes is measured across many conditions such as treatments or time points. Therefore, it becomes impossible to make a visual inspection of the relationship between genes or conditions in such a multi-dimensional matrix. One way to make sense of this data is to reduce its dimensionality. Several data decomposition techniques are available for this purpose: Principal Components Analysis (PCA) is among these techniques that reduces the data into two dimensions.
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