![]() If you imagine a race with 3 participants where the winner and runner-up are very close together while the third person broke her leg and comes in way, way after the first two, then Pearson would be strongly influenced by the fact that the third person had a great distance to the first ones while Spearman would only care about the fact that person 1 came in first, person 2 came in second and person 3 got the third rank, the distances between them are ignored. The Spearman method is based on rankings. More precisely, it is defined as the covariance of two variables divided by the product of their standard deviation. The Pearson method measures the metric differences between samples and is therefore influenced by outliers. We offer two different functions for the correlation computation: Pearson or Spearman. (-1 indicates perfect anti-correlation, 1 perfect correlation.) 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. PlotCorrelation computes the overall similarity between two or more files based on read coverage (or other scores) within genomic regions, which must be calculated using either multiBamSummary or multiBigwigSummary. PlotCorrelation -in results_file –whatToPlot heatmap –corMethod pearson -o heatmap.png Background ¶ Note that this is ONLY for plotting, the correlation is unaffected. Plot the natural log of the scatter plot after adding 1. The default scales these such that the full range of dots is displayed. This option is only valid when plotting a heatmap. If set, then the correlation number is plotted on top of the heatmap. Available values can be seen here: -plotNumbers Maximum value for the heatmap intensities.If not specified, the value is set automatically -colorMapĬolor map to use for the heatmap. If not specified, the value is set automatically -zMax, -max Minimum value for the heatmap intensities. Save matrix with pairwise correlation values to a tab-separated file. Show program’s version number and exit Output optional options ¶ -outFileCorMatrix The ENCODE blacklist page ( ) contains useful information about regions with unusually high countsthat may be worth removing. Bins with abnormally high reads counts artificially increase pearson correlation that’s why, multiBamSummary 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. If set, bins with very large counts are removed. The available options are: png, eps, pdf and svg. If given, this option overrides the image format based on the plotFile ending. Possible choices: png, pdf, svg, eps, plotly Title of the plot, to be printed on top of the generated image. –labels sample1 sample2 sample3 -plotTitle, -T Multiple labels have to be separated by spaces, e.g. User defined labels instead of default labels from file names. skipZerosīy setting this option, genomic regions that have zero or missing (nan) values in all samples are excluded. The file extension determines the format, so heatmap.pdf will save the heatmap in PDF format. Usage : plotCorrelation - corData FILE - corMethod ] Required arguments ¶ -corData, -inĬompressed matrix of values generated by multiBigwigSummary or multiBamSummary -corMethod, -cĬhoose between a heatmap or pairwise scatter plots Optional arguments ¶ -plotFile, -oįile to save the heatmap to.
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