annotate run_order_regression.xml @ 1:2e7d47c0b027 draft

"planemo upload for repository https://malex@toolshed.g2.bx.psu.edu/repos/malex/secimtools"
author malex
date Mon, 08 Mar 2021 22:04:06 +0000
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1 <tool id="secimtools_run_order_regression" name="Run Order Regression (ROR)" version="@WRAPPER_VERSION@">
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2 <description>using the order samples were run.</description>
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3 <macros>
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4 <import>macros.xml</import>
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5 </macros>
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6 <expand macro="requirements" />
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7 <command detect_errors="exit_code"><![CDATA[
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8 run_order_regression.py
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9 --input $input
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10 --design $design
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11 --ID $uniqID
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12 --group $group
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13 --order $order
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14 --fig $order_plots
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15 --table $order_summary
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16 --flags $flags
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17 ]]></command>
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18 <inputs>
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19 <param name="input" type="data" format="tabular" label="Wide Dataset" help="Input your tab-separated wide format dataset. If file is not tab separated see TIP below."/>
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20 <param name="design" type="data" format="tabular" label="Design File" help="Input your design file(tab-separated). Note you need a 'sampleID' column. If not tab separated see TIP below."/>
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21 <param name="uniqID" type="text" size="30" value="" label="Unique Feature ID" help="Name of the column in your wide dataset that has unique identifiers."/>
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22 <param name="group" type="text" size="30" value="" label="Group/Treatment" help="Name of the column in your design file that contains group classifications."/>
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23 <param name="order" type="text" size="30" value="" label="Run Order ID" help="The name of the column in your design file that contains the order the samples were run."/>
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24 </inputs>
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25 <outputs>
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26 <data name="order_plots" format="pdf" label="${tool.name} on ${on_string}: Plots" />
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27 <data name="order_summary" format="tabular" label="${tool.name} on ${on_string}: Summary"/>
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28 <data name="flags" format="tabular" label="${tool.name} on ${on_string}: Flags"/>
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29 </outputs>
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30 <tests>
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31 <test>
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32 <param name="input" value="ST000006_data.tsv"/>
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33 <param name="design" value="ST000006_design.tsv"/>
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34 <param name="uniqID" value="Retention_Index" />
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35 <param name="group" value="White_wine_type_and_source" />
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36 <param name="order" value="run_Order_fake_variable" />
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37 <output name="order_plots" file="ST000006_run_order_regression_figure.pdf" compare="sim_size" delta="10000" />
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38 <output name="order_summary" file="ST000006_run_order_regression_table.tsv" />
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39 <output name="flags" file="ST000006_run_order_regression_flags.tsv" />
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40 </test>
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41 </tests>
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42 <help><![CDATA[
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43
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44 @TIP_AND_WARNING@
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45
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46 **Tool Description**
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47
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48 **NOTE:** The tool is intended to evaluate the impact of sample run order on feature (row) values. Not applicable in the absence of known run order.
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49
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50 It uses linear regression to identify features where the regression slope is not zero for nominal levels of significance.
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51
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52 The tool fits a simple linear regression by feature (row) using values for each feature as a response and sample run order as a linear predictor.
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53 The goal is to identify a linear trend that changes over time and determine whether the trends are statistically significant.
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54 The tool generates flags if the slope is statistically significant for two different levels of statistical significance ( alpha = 0.05 and alpha = 0.01).
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55
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56 NOTE: Groups with one element are excluded from the analysis.
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57
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58 --------------------------------------------------------------------------------
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59
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60 **Input**
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61
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62 - Two input datasets are required.
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63
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64 @WIDE@
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65
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66 **NOTE:** The sample IDs must match the sample IDs in the Design File (below). Extra columns will automatically be ignored.
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67
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68 @METADATA@
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69
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70 @UNIQID@
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71
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72 @GROUP@
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73
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74 - **NOTE:** Groups with one element will be excluded.
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75
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76 @RUNORDER@
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79 -----------------------------------------------------------------------------------
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80
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81 **Output**
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82
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83 This tool outputs three different files:
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84
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85 (1) a TSV file of regression summaries including the values of the regression slope, corresponding p-value and r-squared value.
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86
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87 (2) a TSV file with the corresponding flags for two levels of statistical significance (alpha = 0.05 and alpha = 0.01).
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88
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89 (3) and a PDF file with fitted regression plots for each feature. The values of the feature are displayed on the plot together with the regression line, bands, slopes, and corresponding p and r-squared values. The values are colored according to group classification.
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90
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91 ]]></help>
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92 <expand macro="citations"/>
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93 </tool>