In simple terms, Principal Components Analysis (PCA) is a technique that uses all expression values to find underlying variables (known as principal components) that best differentiate your samples. Principal components are dimensions along which your data points are most spread out. Each component will describe a proportion of the total underlying variation between genes and samples (i.e. the global variation in the dataset). Components are ordered by the % of the underlying variation that they describe, with PC1 explaining the most variation.
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A PCA contribution of components plot shows what % of the global variation in the dataset is explained by each component. This is useful for determining how many biological groups there are in the data and how big an impact on the data each biological group has. For example if there were two biological groups with very strong differences, and no other biological groups we might expect a large proportion of variance in PC1 and a low proportion in all other components.
