Mercurial > repos > bgruening > deeptools_plot_correlation

annotate plotCorrelation.xml @ 15:f264afa9fa6f draft

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author bgruening
date Mon, 15 May 2017 03:57:38 -0400
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children 90d5df0a49bd
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1 <tool id="deeptools_plot_correlation" name="plotCorrelation" version="@WRAPPER_VERSION@.0">
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2 <description>Create a heatmap or scatterplot of correlation scores between different samples </description>
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3 <macros>
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4 <token name="@BINARY@">plotCorrelation</token>
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5 <import>deepTools_macros.xml</import>
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6 </macros>
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7 <expand macro="requirements"/>
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8 <command>
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9 <![CDATA[
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10 @BINARY@
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11 --corData "$corData"
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12 --plotFile "$outFileName"
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13 --corMethod "$corMethod"
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14 --whatToPlot "$plotting_type.whatToPlot"
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15 #if str($plotting_type.whatToPlot) == 'heatmap':
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16 @HEATMAP_OPTIONS@
14
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17 --plotWidth $plotting_type.plotWidth
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18 --plotHeight $plotting_type.plotHeight
12
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19 #else:
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20 --plotTitle '$plotting_type.plotTitle'
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21 #end if
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22 $skipZeros
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23 --plotFileFormat "$outFileFormat"
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24 $removeOutliers
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25 #if $outFileCorMatrix:
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26 --outFileCorMatrix "$matrix"
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27 #end if
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28
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29 ]]>
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30 </command>
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31 <inputs>
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32 <param name="corData" format="deeptools_coverage_matrix" type="data" label="Matrix file from the multiBamSummary tool"/>
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33 <expand macro="corMethod" />
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34
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35 <conditional name="plotting_type" >
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36 <param argument="--whatToPlot" type="select" label="Plotting type">
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37 <option value="heatmap" selected="True">Heatmap</option>
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38 <option value="scatterplot">Scatterplot</option>
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39 </param>
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40 <when value="heatmap">
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41 <expand macro="heatmap_options" />
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42 <expand macro="plotWidthHeight" PLOTWIDTH="11.0" PLOTHEIGHT="9.5" />
12
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43 </when>
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44 <when value="scatterplot">
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45 <expand macro="plotTitle" />
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46 </when>
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47 </conditional>
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48
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49 <expand macro="skipZeros" />
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50
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51 <expand macro="input_image_file_format" />
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52
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53 <param argument="--removeOutliers" type="boolean"
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54 truevalue="--removeOutliers" falsevalue="" label="Remove regions with very large counts"
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55 help="If set, bins with very large counts are removed. Bins
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56 with abnormally high reads counts artificially
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57 increase pearson correlation; that's why, by default,
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58 plotCorrelation tries to remove outliers using the median
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59 absolute deviation (MAD) method applying a threshold
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60 of 200 to only consider extremely large deviations
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61 from the median. ENCODE blacklist page (https://sites.
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62 google.com/site/anshulkundaje/projects/blacklists)
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63 contains useful information about regions with
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64 unusually high counts."/>
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65
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66 <param name="outFileCorMatrix" type="boolean" label="Save the matrix of values underlying the heatmap"/>
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67
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68 </inputs>
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69 <outputs>
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70 <expand macro="output_image_file_format_not_nested" />
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71 <data format="tabular" name="matrix" label="${tool.name} on ${on_string}: Correlation matrix">
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72 <filter>outFileCorMatrix is True</filter>
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73 </data>
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74 </outputs>
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75 <tests>
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76 <test>
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77 <param name="corData" value="multiBamSummary_result1.npz" ftype="deeptools_coverage_matrix" />
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78 <param name="outFileFormat" value="png" />
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79 <param name="outFileCorMatrix" value="True" />
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80 <output name="matrix" file="plotCorrelation_result1.tabular" ftype="tabular" />
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81 <output name="outFileName" file="plotCorrelation_result1.png" ftype="png" compare="sim_size" delta="300" />
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82 </test>
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83 <test>
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84 <param name="corData" value="multiBamSummary_result1.npz" ftype="deeptools_coverage_matrix" />
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85 <param name="outFileFormat" value="png" />
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86 <param name="whatToPlot" value="scatterplot" />
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87 <param name="removeOutliers" value="True" />
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88 <param name="plotTitle" value="Test Plot" />
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89 <output name="outFileName" file="plotCorrelation_result2.png" ftype="png" compare="sim_size" delta="300" />
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90 </test>
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91 </tests>
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92 <help>
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93 <![CDATA[
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94 What it does
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95 ------------
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96
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97 This tools takes the default output of ``multiBamSummary`` or ``multiBigwigSummary``, and computes the pairwise correlation among samples.
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98 Results can be visualized as **scatterplots** or as a **heatmap** of correlation coefficients (see below for examples).
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99
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100 Theoretical Background
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101 ----------------------
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102
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103 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.)
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104
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105 We offer two different functions for the correlation computation: *Pearson* or *Spearman*.
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106
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107 The *Pearson method* measures the **metric differences** between samples and is therefore influenced by outliers.
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108 The *Spearman method* is based on **rankings**.
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109
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110 Output
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111 ------
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112
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113 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).
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114
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115 Optionally, you can also obtain a table of the pairwise correlation coefficients.
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116
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117 .. image:: $PATH_TO_IMAGES/plotCorrelation_output.png
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118 :width: 600
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119 :height: 271
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120
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121 Example plots
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122 -------------
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123
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124 The following is the output of ``plotCorrelation`` with our test ChIP-Seq datasets (to be found under "Shared Data" --> "Data Library").
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125
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126 Average coverages were computed over 10 kb bins for chromosome X,
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127 from bigWig files using ``multiBigwigSummary``. This was then used with ``plotCorrelation`` to make a heatmap of Spearman correlation coefficients.
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128
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129 .. image:: $PATH_TO_IMAGES/plotCorrelation_galaxy_bw_heatmap_output.png
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130 :width: 600
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131 :height: 518
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132
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133 The scatterplot could look like this:
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134
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135 .. image:: $PATH_TO_IMAGES/plotCorrelation_scatterplot_PearsonCorr_bigwigScores.png
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136 :width: 600
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137 :height: 600
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138
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139 -----
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140
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141 @REFERENCES@
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142 ]]>
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143 </help>
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144 <expand macro="citations" />
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145 </tool>