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author galaxyp
date Mon, 11 May 2015 12:31:49 -0400
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1 <tool id="gp_pyprophet" name="PyProphet" version="0.1.0">
2 <description></description>
3 <requirements>
4 <requirement type="package" version="0.3.2">pyprophet</requirement>
5 </requirements>
6 <command>
7 <![CDATA[
8 pyprophet
9 --apply_scorer $scorer
10 --apply_weights $weights
11 --num_processes "\${GALAXY_SLOTS:-24}"
12 $compute_prop
13 $use_all_groups
14 $ignore_nan
15 $random
16 --final_statistics.lambda $lambda
17 --semi_supervised_learner.initial_fdr $initial_fdr
18 --semi_supervised_learner.initial_lambda $iteration_lambda
19 --semi_supervised_learner.iteration_fdr $iteration_fdr
20 --semi_supervised_learner.iteration_lambda $iteration_lambda
21 --semi_supervised_learner.num_iter $num_iter
22 --xeval.fraction $xeval_fraction
23 --xeval.num_iter $xeval_num_iter
24
25 ${input}
26
27 ]]>
28 </command>
29 <inputs>
30 <param name="input" format="txt" type="data" label="Input files" help="" />
31 <param name="scorer" format="txt" type="data" optional="True" label="File of existing classifier"
32 help="(--apply_scorer)" />
33 <param name="weights" format="txt" type="data" optional="True" label="File of existing LDA weights"
34 help="(--apply_weights)" />
35
36 <param name="lambda" type="float" value="0.4" label="Final statistics lambda" help="(--final_statistics.lambda)" />
37 <param name="initial_fdr" type="float" value="0.15" label="Semi supervised learner initial fdr"
38 help="(--semi_supervised_learner.initial_fdr)" />
39 <param name="initial_lambda" type="float" value="0.4" label="Semi supervised learner initial lambda"
40 help="(--semi_supervised_learner.initial_lambda)" />
41 <param name="iteration_fdr" type="float" value="0.02" label="Semi supervised learner iteration fdr"
42 help="(--semi_supervised_learner.iteration_fdr)" />
43 <param name="iteration_lambda" type="float" value="0.4" label="Semi supervised learner iteration lambda"
44 help="(--semi_supervised_learner.iteration_lambda)" />
45 <param name="num_iter" type="integer" value="5" label="Semi supervised learner num iter"
46 help="(--semi_supervised_learner.num_iter)" />
47 <param name="xeval_fraction" type="float" value="0.5" label="Xeval fraction"
48 help="(--xeval.fraction)" />
49 <param name="xeval_num_iter" type="integer" value="5" label="Xeval num iter"
50 help="(--xeval.num_iter)" />
51 <param name="random" type="boolean" truevalue="--is_test" falsevalue="" checked="False"
52 label="Do not use random seed" help="(--is_test)" />
53 <param name="ignore_nan" type="boolean" truevalue="--ignore.invalid_score_columns" falsevalue="" checked="False"
54 label="Ignore score columns which only contain NaN or infinity values" help="(--ignore.invalid_score_columns)" />
55 <param name="use_all_groups" type="boolean" truevalue="--final_statistics.fdr_all_pg" falsevalue="" checked="False"
56 label="Use all peak groups for score and q-value calculation" help="(--final_statistics.fdr_all_pg)" />
57 <param name="compute_prop" type="boolean" truevalue="--compute.probabilities" falsevalue="" checked="False"
58 label="Compute approximate binned probability values" help="(--compute.probabilities)" />
59
60 </inputs>
61 <outputs>
62 <data format="tabular" name="output" />
63 </outputs>
64 <help>
65 <![CDATA[
66 **What it does**
67
68 The algorithm can take targeted proteomics data, learn a linear separation between true signal and the noise signal and then compute a q-value (false discovery rate) to achieve experiment-wide cutoffs.
69
70 This program is a reimplementation of the original algorithm by `Uwe Schmitt`_.
71
72 ..`Uwe Schmitt`: https://github.com/uweschmitt/pyprophet
73
74 ]]>
75 </help>
76 <citations>
77 <citation type="doi">10.1038/nmeth.1584</citation>
78 </citations>
79 </tool>