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1 <tool id="rgQQ1" name="QQ Plots:">
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2 <code file="rgQQ_code.py"/>
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3
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4 <description>for p values from an analysis </description>
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5
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6 <command interpreter="python">
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7 rgQQ.py "$input1" "$title" "$sample" "$cols" "$allqq" "$height" "$width" "$logtrans" "$allqq.id" "$__new_file_path__"
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8 </command>
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9
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10 <inputs>
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11 <page>
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12 <param name="input1" type="data" label="Choose the History dataset containing p values to QQ plot"
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13 size="80" format="tabular" help="Dataset missing? See Tip below" />
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14 <param name="title" type="text" size="80" label = "Descriptive title for QQ plot" value="QQ" />
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15
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16 <param name="logtrans" type="boolean" label = "Use a log scale - recommended for p values in range 0-1.0"
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17 truevalue="true" falsevalue="false"/>
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18 <param name="sample" type="float" label="Random sample fraction - set to 1.0 for all data points" value="0.01"
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19 help="If you have a million values, the QQ plots will be huge - a random sample of 1% will be fine" />
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20 <param name="height" type="integer" label="PDF image height (inches)" value="6" />
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21 <param name="width" type="integer" label="PDF image width (inches)" value="6" />
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22 </page>
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23 <page>
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24 <param name="cols" type="select" display="checkboxes" multiple="True"
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25 help="Choose from these numeric columns in the data file to make a quantile-quantile plot against a uniform distribution"
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26 label="Columns (p values 0-1 eg) to make QQ plots" dynamic_options="get_columns( input1 )" />
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27 </page>
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28 </inputs>
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29
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30 <outputs>
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31 <data format="pdf" name="allqq" label="${title}.html"/>
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32 </outputs>
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33
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34 <tests>
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35 <test>
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36 <param name='input1' value='tinywga.pphe' />
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37 <param name='title' value="rgQQtest1" />
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38 <param name='logtrans' value="false" />
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39 <param name='sample' value='1.0' />
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40 <param name='height' value='8' />
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41 <param name='width' value='10' />
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42 <param name='cols' value='3' />
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43 <output name='allqq' file='rgQQtest1.pdf' ftype='binary' compare="diff" lines_diff="29"/>
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44 </test>
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45 </tests>
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46
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47 <help>
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48
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49 .. class:: infomark
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50
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51 **Explanation**
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52
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53 A quantile-quantile (QQ) plot is a good way to see systematic departures from the null expectation of uniform p-values
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54 from a genomic analysis. If the QQ plot shows departure from the null (ie a uniform 0-1 distribution), you hope that this will be
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55 in the very smallest p-values suggesting that there might be some interesting results to look at. A log scale will help emphasise departures
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56 from the null at low p values more clear
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57
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58 -----
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59
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60 .. class:: infomark
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61
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62 **Syntax**
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63
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64 This tool has 2 pages. On the first one you choose the data set and output options, then on the second page, the
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65 column names are shown so you can choose the one containing the p values you wish to plot.
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66
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67 - **History data** is one of your history tabular data sets
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68 - **Descriptive Title** is the text to appear in the output file names to remind you what the plots are!
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69 - **Use a Log scale** is recommended for p values in the range 0-1 as it highlights departures from the null at small p values
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70 - **Random Sample Fraction** is the fraction of points to randomly sample - highly recommended for >5k or so values
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71 - **Height and Width** will determine the scale of the pdf images
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72
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73
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74 -----
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75
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76 .. class:: infomark
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77
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78 **Summary**
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79
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80 Generate a uniform QQ plot for any large number of p values from an analysis.
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81 Essentially a plot of n ranked p values against their rank as a centile - ie rank/n
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82
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83 Works well where you have a column containing p values from
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84 a statistical test of some sort. These will be plotted against the values expected under the null. Departure
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85 from the diagonal suggests one distribution is more extreme than the other. You hope your p values are
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86 smaller than expected under the null.
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87
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88 The sampling fraction will help cut down the size of the pdfs. If there are fewer than 5k points on any plot, all will be shown.
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89 Otherwise the sampling fraction will be used or 5k, whichever is larger.
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90
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91 Note that the use of a log scale is ill-advised if you are plotting log transformed p values because the
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92 uniform distribution chosen for the qq plot is always 0-1 and log transformation is applied if required.
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93 The most useful plots for p values are log QQ plots of untransformed p values in the range 0-1
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94
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95 Originally designed and written for family based data from the CAMP Illumina run of 2007 by
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96 ross lazarus (ross.lazarus@gmail.com)
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97
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98 </help>
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99 </tool>
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