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annotate multiple_testing_adjustment.xml @ 1:2e7d47c0b027 draft

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"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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2e7d47c0b027 "planemo upload for repository https://malex@toolshed.g2.bx.psu.edu/repos/malex/secimtools"
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1 <tool id="secimtools_multiple_testing_adjustment" name="Multiple Testing Adjustment (MTA)" version="@WRAPPER_VERSION@">
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2 <description>of p-values.</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><![CDATA[
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8 multiple_testing_adjustment.py
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9 --input $input
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10 --uniqID $uniqID
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11 --pval "$pval"
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12 --alpha $alpha
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13 --outadjusted $outadjusted
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14 --flags $flags
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15 ]]></command>
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16 <inputs>
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17 <param name="input" type="data" format="tabular" label="Wide Dataset" help="Input your tab-separated wide format dataset. If not tab separated see TIP below."/>
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18 <param name="uniqID" type="text" size="30" value="" label="Unique Feature ID" help="Name of the column in your Wide Dataset that has unique identifiers.."/>
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19 <param name="pval" type="text" size="30" value="" label="p-value column" help="Name of the column in your wide dataset that contains the p-values."/>
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20 <param name="alpha" type="float" size="6" value="0.05" label="α" help="Value of α to be used for multiple correction. Default α = 0.05."/>
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21 </inputs>
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22 <outputs>
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23 <data format="tabular" name="outadjusted" label="${tool.name} on ${on_string}: Adjusted pval."/>
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24 <data format="tabular" name="flags" label="${tool.name} on ${on_string}: Flags."/>
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25 </outputs>
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26 <tests>
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27 <test>
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28 <param name="input" value="ST000006_anova_fixed_with_group_summary.tsv"/>
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29 <param name="uniqID" value="Retention_Index" />
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30 <param name="pval" value="prob_greater_than_t_for_diff_Chardonnay, Carneros, CA 2003 (CH01)-Chardonnay, Carneros, CA 2003 (CH02)" />
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31 <param name="alpha" value="0.05" />
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32 <output name="outadjusted" file="ST000006_multiple_testing_adjustment_outadjusted.tsv" />
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33 <output name="flags" file="ST000006_multiple_testing_adjustment_flags.tsv" />
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34 </test>
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35 </tests>
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36 <help><![CDATA[
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37
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38 @TIP_AND_WARNING@
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39
2e7d47c0b027 "planemo upload for repository https://malex@toolshed.g2.bx.psu.edu/repos/malex/secimtools"
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40 **Tool Description**
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41
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42 The tool is designed to adjust p-values for multiple comparisons using three different methods:
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43
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44 (1) The Bonferroni method and two false discovery rate (FDR) methods, (2) the Benjamini-Hochberg method (BH) and (3) the Benjamini-Yekutieli method (BY).
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45 The p-value correction can be carried out on p-values generated from the following tools: Analysis of Variance (ANOVA) Fixed Effects Model, Kruskal-Wallis Non-Parametric Test, T-test (Single Group) and T-test (Paired and/or Unpaired) in addition to p-values generated outside of these tools.
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46 The user can specify the total type I error α value.
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47
2e7d47c0b027 "planemo upload for repository https://malex@toolshed.g2.bx.psu.edu/repos/malex/secimtools"
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48 More details about the PH and BY methods are available in the papers:
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49
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50 Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the royal statistical society. Series B (Methodological), 289-300.
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51
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52 Benjamini, Y., and Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of statistics, 1165-1188.
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53
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54 -------------------------------------------------------------------------------------------
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55
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56 **Input**
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57
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58 - Two input datasets are required.
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59
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60 @WIDE@
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61
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62 @UNIQID@
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63
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64 **Name for p-value column**
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65
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66 - Name of the column in your Wide Dataset that contains the p-values.
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67
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68 **α**
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69
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70 - Value of α to be used for multiple correction. Default α = 0.05.
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71
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72 -------------------------------------------------------------------------------------------
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73
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74 **Output**
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75
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76 The tool produces two TSV files:
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77
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78 (1) One TSV that contains the following five columns:
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79 a column with unique feature IDs,
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80 a column of the original p-values and
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81 the last three columns contain the p-values adjusted using the 3 methods described above which are reflected in the column name.
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82 (2) The second TSV file contains flags where all significant values are flagged as 1 and non-significant values are flagged as 0.
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83
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84 ]]></help>
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85 <expand macro="citations"/>
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86 </tool>