# HG changeset patch
# User galaxyp
# Date 1431361909 14400
# Node ID f795005c14b794856c71e47daadfdee8d2ca4129

Uploaded

diff -r 000000000000 -r f795005c14b7 .shed.yml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/.shed.yml	Mon May 11 12:31:49 2015 -0400
@@ -0,0 +1,3 @@
+# repository published to https://toolshed.g2.bx.psu.edu/repos/galaxyp/pyprophet
+owner: galaxyp
+name: pyprophet
diff -r 000000000000 -r f795005c14b7 pyprophet.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/pyprophet.xml	Mon May 11 12:31:49 2015 -0400
@@ -0,0 +1,79 @@
+<tool id="gp_pyprophet" name="PyProphet" version="0.1.0">
+    <description></description>
+    <requirements>
+        <requirement type="package" version="0.3.2">pyprophet</requirement>
+    </requirements>
+    <command>
+<![CDATA[
+        pyprophet
+            --apply_scorer $scorer
+            --apply_weights $weights
+            --num_processes "\${GALAXY_SLOTS:-24}"
+            $compute_prop
+            $use_all_groups
+            $ignore_nan
+            $random
+            --final_statistics.lambda $lambda
+            --semi_supervised_learner.initial_fdr $initial_fdr
+            --semi_supervised_learner.initial_lambda $iteration_lambda
+            --semi_supervised_learner.iteration_fdr $iteration_fdr
+            --semi_supervised_learner.iteration_lambda $iteration_lambda
+            --semi_supervised_learner.num_iter $num_iter
+            --xeval.fraction $xeval_fraction
+            --xeval.num_iter $xeval_num_iter
+
+            ${input}
+
+]]>
+    </command>
+    <inputs>
+        <param name="input" format="txt" type="data" label="Input files" help="" />
+        <param name="scorer" format="txt" type="data" optional="True" label="File of existing classifier"
+            help="(--apply_scorer)" />
+        <param name="weights" format="txt" type="data" optional="True" label="File of existing LDA weights"
+            help="(--apply_weights)" />
+
+        <param name="lambda" type="float" value="0.4" label="Final statistics lambda" help="(--final_statistics.lambda)" />
+        <param name="initial_fdr" type="float" value="0.15" label="Semi supervised learner initial fdr"
+            help="(--semi_supervised_learner.initial_fdr)" />
+        <param name="initial_lambda" type="float" value="0.4" label="Semi supervised learner initial lambda"
+            help="(--semi_supervised_learner.initial_lambda)" />
+        <param name="iteration_fdr" type="float" value="0.02" label="Semi supervised learner iteration fdr"
+            help="(--semi_supervised_learner.iteration_fdr)" />
+        <param name="iteration_lambda" type="float" value="0.4" label="Semi supervised learner iteration lambda"
+            help="(--semi_supervised_learner.iteration_lambda)" />
+        <param name="num_iter" type="integer" value="5" label="Semi supervised learner num iter"
+            help="(--semi_supervised_learner.num_iter)" />
+        <param name="xeval_fraction" type="float" value="0.5" label="Xeval fraction"
+            help="(--xeval.fraction)" />
+        <param name="xeval_num_iter" type="integer" value="5" label="Xeval num iter"
+            help="(--xeval.num_iter)" />
+        <param name="random" type="boolean" truevalue="--is_test" falsevalue="" checked="False"
+            label="Do not use random seed" help="(--is_test)" />
+        <param name="ignore_nan" type="boolean" truevalue="--ignore.invalid_score_columns" falsevalue="" checked="False"
+            label="Ignore score columns which only contain NaN or infinity values" help="(--ignore.invalid_score_columns)" />
+        <param name="use_all_groups" type="boolean" truevalue="--final_statistics.fdr_all_pg" falsevalue="" checked="False"
+            label="Use all peak groups for score and q-value calculation" help="(--final_statistics.fdr_all_pg)" />
+        <param name="compute_prop" type="boolean" truevalue="--compute.probabilities" falsevalue="" checked="False"
+            label="Compute approximate binned probability values" help="(--compute.probabilities)" />
+
+    </inputs>
+    <outputs>
+        <data format="tabular" name="output" />
+    </outputs>
+  <help>
+<![CDATA[
+**What it does**
+
+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. 
+
+This program is a reimplementation of the original algorithm by `Uwe Schmitt`_.
+
+..`Uwe Schmitt`: https://github.com/uweschmitt/pyprophet
+
+]]>
+    </help>
+    <citations>
+        <citation type="doi">10.1038/nmeth.1584</citation>
+    </citations>
+</tool>
diff -r 000000000000 -r f795005c14b7 tool_dependencies.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/tool_dependencies.xml	Mon May 11 12:31:49 2015 -0400
@@ -0,0 +1,6 @@
+<?xml version="1.0"?>
+<tool_dependency>
+  <package name="pyprophet" version="0.13.2">
+    <repository changeset_revision="30c076cbe970" name="package_pyprophet_0_13_2" owner="iuc" toolshed="https://testtoolshed.g2.bx.psu.edu" />
+  </package>
+</tool_dependency>