Tool for generating a principal component analysis (PCA) plot from multiBamCoverage or multiBigwigSummary output.
usage: plotPCA [-h] --corData FILE --plotFile FILE [--labels sample1 sample2 [sample1 sample2 ...]] [--plotTitle PLOTTITLE] [--plotFileFormat FILETYPE] [--version]
- Required arguments
--corData, -in Coverage file (generated by multiBamCoverage or multiBigwigSummary) --plotFile, -o File name to save the plot to. The extension determines the file format. For example: pca.pdf will save the PCA plot in PDF format. The available options are: .png, .eps, .pdf and .svg.
- Optional arguments
--labels, -l User defined labels instead of default labels from file names. Multiple labels have to be separated by spaces, e.g. –labels sample1 sample2 sample3 --plotTitle=, -T= Title of the plot, to be printed on top of the generated image. Leave blank for no title. --plotFileFormat
Image format type. If given, this option overrides the image format based on the plotFile ending. The available options are: png, eps, pdf and svg.
Possible choices: png, pdf, svg, eps
--version show program’s version number and exit
example usages: plotPCA -in coverages.npz -o pca.png
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 designed to discover unknown groupings or clustering; it is up to the researcher to identify the experimental or technical reason underlying the principal components.
$ deepTools2.0/bin/plotPCA -in readCounts.npz \ -o PCA_readCounts.png \ -T "PCA of read counts"
After perfoming the PCA on the values supplied as the input,
plotPCA will sort the principal components according to the amount of variability of the data that they explain. Based on this, you will obtain two plots:
- the values of the top two principal components per sample
- the Scree plot for the top five principal components where the bars represent the eigenvalues and the red line traces the variability that is explained by the individual components