|
0
|
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>
|
