Principal Components Analysis (PCA)


Principal Component Analysis (PCA) is an unsupervised or class-free approach to finding the most informative or explanatory features in a dataset. In particular, PCA substantially reduces the complexity of data in which a large number of variables (e.g. thousands) are interrelated, such as in large-scale gene expression data obtained across a variety of different samples or conditions. CodeLinker provides two options for PCA analysis:  Orientation by Genes or Orientation by Samples, the former of which allows you to distinguish sample sets while the latter allows you to distinguish gene sets.

To visualize your analyses, there are specialized plots tuned for the algorithms you used to perform the original analysis. Each plot type can be customised and you can export your plot in PNG, SVG, or PDF formats.