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comparison secimtools/multiple_testing_adjustment.xml @ 0:b54326490b4d draft

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date Mon, 08 Mar 2021 20:55:03 +0000
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1 <tool id="secimtools_multiple_testing_adjustment" name="Multiple Testing Adjustment (MTA)" version="@WRAPPER_VERSION@">
2 <description>of p-values.</description>
3 <macros>
4 <import>macros.xml</import>
5 </macros>
6 <expand macro="requirements" />
7 <command><![CDATA[
8 multiple_testing_adjustment.py
9 --input $input
10 --uniqID $uniqID
11 --pval "$pval"
12 --alpha $alpha
13 --outadjusted $outadjusted
14 --flags $flags
15 ]]></command>
16 <inputs>
17 <param name="input" type="data" format="tabular" label="Wide Dataset" help="Input your tab-separated wide format dataset. If not tab separated see TIP below."/>
18 <param name="uniqID" type="text" size="30" value="" label="Unique Feature ID" help="Name of the column in your Wide Dataset that has unique identifiers.."/>
19 <param name="pval" type="text" size="30" value="" label="p-value column" help="Name of the column in your wide dataset that contains the p-values."/>
20 <param name="alpha" type="float" size="6" value="0.05" label="α" help="Value of α to be used for multiple correction. Default α = 0.05."/>
21 </inputs>
22 <outputs>
23 <data format="tabular" name="outadjusted" label="${tool.name} on ${on_string}: Adjusted pval."/>
24 <data format="tabular" name="flags" label="${tool.name} on ${on_string}: Flags."/>
25 </outputs>
26 <tests>
27 <test>
28 <param name="input" value="ST000006_anova_fixed_with_group_summary.tsv"/>
29 <param name="uniqID" value="Retention_Index" />
30 <param name="pval" value="prob_greater_than_t_for_diff_Chardonnay, Carneros, CA 2003 (CH01)-Chardonnay, Carneros, CA 2003 (CH02)" />
31 <param name="alpha" value="0.05" />
32 <output name="outadjusted" file="ST000006_multiple_testing_adjustment_outadjusted.tsv" />
33 <output name="flags" file="ST000006_multiple_testing_adjustment_flags.tsv" />
34 </test>
35 </tests>
36 <help><![CDATA[
37
38 @TIP_AND_WARNING@
39
40 **Tool Description**
41
42 The tool is designed to adjust p-values for multiple comparisons using three different methods:
43
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).
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.
46 The user can specify the total type I error α value.
47
48 More details about the PH and BY methods are available in the papers:
49
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.
51
52 Benjamini, Y., and Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of statistics, 1165-1188.
53
54 -------------------------------------------------------------------------------------------
55
56 **Input**
57
58 - Two input datasets are required.
59
60 @WIDE@
61
62 @UNIQID@
63
64 **Name for p-value column**
65
66 - Name of the column in your Wide Dataset that contains the p-values.
67
68 **α**
69
70 - Value of α to be used for multiple correction. Default α = 0.05.
71
72 -------------------------------------------------------------------------------------------
73
74 **Output**
75
76 The tool produces two TSV files:
77
78 (1) One TSV that contains the following five columns:
79 a column with unique feature IDs,
80 a column of the original p-values and
81 the last three columns contain the p-values adjusted using the 3 methods described above which are reflected in the column name.
82 (2) The second TSV file contains flags where all significant values are flagged as 1 and non-significant values are flagged as 0.
83
84 ]]></help>
85 <expand macro="citations"/>
86 </tool>