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plotPCA (version 3.5.4+galaxy0)
Note that the plotly output can be very large and not all options are supported.
Title of the plot, to be printed on top of the generated image.

What it does

This tool takes the default output file of multiBamSummary or multiBigwigSummary to perform a principal component analysis (PCA).


The result is a panel of two plots:

  1. Either the loadings (default) or the projections (--transpose) of the samples on the desired two principal components.
  2. The Scree plot for principal components where the bars represent the eigenvalues the red line traces the amount of variability is explained by the individual components in a cumulative manner.

Example plot


Theoretical Background

Principal component analysis (PCA) can be used, for example, to determine whether samples display greater variability between experimental conditions than between replicates of the same treatment. PCA is also useful to identify unexpected patterns, such as those caused by batch effects or outliers. Principal components represent the directions along which the variation in the data is maximal, so that the information (e.g., read coverage values) from thousands of regions can be represented by just a few dimensions.

PCA is not necessarily meant to identify unknown groupings or clustering; it is up to the researcher to determine the experimental or technical reason underlying the principal components.

For more information on the tools, please visit our help site.

For support or questions please post to Biostars. For bug reports and feature requests please open an issue on github.

This tool is developed by the Bioinformatics and Deep-Sequencing Unit at the Max Planck Institute for Immunobiology and Epigenetics.