plotCorrelation (version 3.5.4+galaxy0)

Matrix file from the multiBamSummary tool:

Correlation method:

Plotting type:

Minimum value for the heatmap intensities:

If not specified the value is set automatically.

Maximum value for the heatmap intensities:

If not specified the value is set automatically.

Color map to use for the heatmap:

Available color map names can be found here: http://matplotlib.org/examples/color/colormaps_reference.html

Title of the plot:

Title of the plot, to be printed on top of the generated image.

Plot the correlation value:

If set, then the correlation number is plotted on top of the heatmap.

Plot height:

Height in cm. The default for the plot height is 9.5 centimeters. The minimum value is 1 cm.

Plot width:

Width in cm. The default value is 11.0 centimeters. The minimum value is 1 cm.

Skip zeros:

If set, then zero counts that happen for *all* BAM/CRAM files given are ignored. This may result in fewer considered regions.

Image file format:

Note that the plotly output can be very large and not all options are supported.

Remove regions with very large counts:

If set, bins with very large counts are removed. Bins with abnormally high reads counts artificially increase pearson correlation; that's why, by default, plotCorrelation tries to remove outliers using the median absolute deviation (MAD) method applying a threshold of 200 to only consider extremely large deviations from the median. ENCODE blacklist page (https://sites. google.com/site/anshulkundaje/projects/blacklists) contains useful information about regions with unusually high counts.

Save the matrix of values underlying the heatmap:

What it does

This tools takes the default output of multiBamSummary or multiBigwigSummary, and computes the pairwise correlation among samples. Results can be visualized as scatterplots or as a heatmap of correlation coefficients (see below for examples).

Theoretical Background

The result of the correlation computation is a table of correlation coefficients that indicates how "strong" the relationship between two samples is and it will consist of numbers between -1 and 1. (-1 indicates perfect anti-correlation, 1 perfect correlation.)

We offer two different functions for the correlation computation: Pearson or Spearman.

The Pearson method measures the metric differences between samples and is therefore influenced by outliers. The Spearman method is based on rankings.

Output

The default output is a diagnostic plot -- either a scatterplot or a clustered heatmap displaying the values for each pair-wise correlation (see below for example plots).

Optionally, you can also obtain a table of the pairwise correlation coefficients.

/repository/static/images/a2134295b09d35aa/plotCorrelation_output.png

Example plots

The following is the output of plotCorrelation with our test ChIP-Seq datasets (to be found under "Shared Data" --> "Data Library").

Average coverages were computed over 10 kb bins for chromosome X, from bigWig files using multiBigwigSummary. This was then used with plotCorrelation to make a heatmap of Spearman correlation coefficients.

/repository/static/images/a2134295b09d35aa/plotCorrelation_galaxy_bw_heatmap_output.png

The scatterplot could look like this:

/repository/static/images/a2134295b09d35aa/plotCorrelation_scatterplot_PearsonCorr_bigwigScores.png

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.