# HG changeset patch
# User iuc
# Date 1533225464 14400
# Node ID e41ec5af7472be072410c13cf014696499b38b5d

planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/heinz commit b0b2c64a46bdd9beebdfb7fc5312f75346483763

diff -r 000000000000 -r e41ec5af7472 bum.R
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/bum.R	Thu Aug 02 11:57:44 2018 -0400
@@ -0,0 +1,34 @@
+# Author: Cico Zhang
+# Usage: Rscript bum.R --input p-values.txt --output result.txt --verbose TRUE
+
+# Set up R error handling to go to stderr
+options(show.error.messages=F, error=function(){cat(geterrmessage(),file=stderr());q("no",1,F)})
+
+# Avoid crashing Galaxy with an UTF8 error on German LC settings
+#loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8")
+
+# Import required libraries
+suppressPackageStartupMessages({
+  library('getopt')
+  library('BioNet')
+})
+
+# Take in trailing command line arguments
+args <- commandArgs(trailingOnly = TRUE)
+
+# Get options using the spec as defined by the enclosed list
+# Read the options from the default: commandArgs(TRUE)
+option_specification <- matrix(c(
+  'input', 'i', 2, 'character',
+  'output', 'o', 2, 'character'
+), byrow=TRUE, ncol=4);
+
+# Parse options
+options <- getopt(option_specification);
+
+pvals <- read.table(options$input)
+bum <- fitBumModel(pvals,plot=FALSE)
+mat <- c(bum$lambda, bum$a)
+#bumtablename <- paste(options$output,sep="\t")
+write.table(x=mat, file=options$output,quote=FALSE, row.names=FALSE, col.names=FALSE)
+message ("Parameters have been estimated successfully!")
diff -r 000000000000 -r e41ec5af7472 bum.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/bum.xml	Thu Aug 02 11:57:44 2018 -0400
@@ -0,0 +1,41 @@
+<tool id="heinz_bum" name="Fit a BUM model" version="1.0">
+    <description>with p-values</description>
+    <requirements>
+        <requirement type="package" version="1.20.0">r-getopt</requirement>
+        <requirement type="package" version="1.34.0">bioconductor-bionet</requirement>
+    </requirements>
+    <command detect_errors="exit_code"><![CDATA[
+Rscript '$__tool_directory__/bum.R' --input '$p_values' --output '$dist_params'
+    ]]></command>
+    <inputs>
+        <param type="data" name="p_values" label="Input file" format="txt" help="
+        The input file should only contain p-values, one per line." />
+    </inputs>
+    <outputs>
+        <data name="dist_params" format="txt" label="${tool.name} on ${on_string}: BUM params"/>
+    </outputs>
+    <tests>
+      <!--Ensure the stochastic result is similar enough-->
+        <test>
+            <param name="p_values" value="BUM_input.txt" />
+            <output name="dist_params">
+                <assert_contents>
+                    <has_text text="0.684" />
+                    <has_text text="0.376" />
+                </assert_contents>
+            </output>
+        </test>
+    </tests>
+    <help><![CDATA[
+BUM is the abbreviation for "Beta-Uniform Mixture (BUM) distribution."
+
+This tool (part of the package "Bionet") is used for fitting Beta-Uniform
+mixture model to a P-value distribution, the output of which is two model
+parameters: lambda and alpha, kept in a text file, the first line is
+lambda; the second, alpha.
+    ]]></help>
+    <citations>
+        <citation type="doi">10.1093/bioinformatics/btq089</citation>
+        <citation type="doi">10.1093/bioinformatics/btn161</citation>
+    </citations>
+</tool>
diff -r 000000000000 -r e41ec5af7472 heinz.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/heinz.xml	Thu Aug 02 11:57:44 2018 -0400
@@ -0,0 +1,76 @@
+<tool id="heinz" name="Identify optimal scoring subnetwork" version="1.0">
+    <description>using Heinz</description>
+    <requirements>
+        <requirement type="package" version="2.0">heinz</requirement>
+    </requirements>
+    <command detect_errors="aggressive"><![CDATA[
+heinz -m \${GALAXY_SLOTS:-2} -n '$score' -e '$edge' 1> '$subnetwork'
+    ]]></command>
+    <inputs>
+        <param type="data" name="score" format="txt" label="File containing Heinz scores"
+        help="The file should contain two columns: the node identifier (e.g. gene) and the corresponding Heinz score" />
+        <param type="data" name="edge" format="txt" label="Edge file"
+        help="In this file, two nodes define one edge, one edge per line" />
+    </inputs>
+    <outputs>
+        <data name="subnetwork" format="txt" label="${tool.name} on ${on_string}: Optimal subgraph" />
+    </outputs>
+    <tests>
+        <test>
+            <param name="score" value="NodesPCST.txt" />
+            <param name="edge" value="EdgesPCST.txt" />
+            <output name="subnetwork">
+                <assert_contents>
+                    <has_text text='label="a\n5\n0\n0"' />
+                    <has_text text='label="b\n-4\n0\n1",shape=box' />
+                    <has_text text="Total weight: 36" />
+                    <has_text text="0 -- 1" />
+                    <has_text text="1 -- 3" />
+                    <has_text text="5 -- 7" />
+                </assert_contents>
+            </output>
+        </test>
+    </tests>
+    <help><![CDATA[
+**Note**: You are currently using a version of Heinz based on IBM CPLEX Community version,
+which is limiting the capacity of Heinz in handling big networks. For an unlimited version of Heinz,
+you need to get a license of IBM CPLEX and compile Heinz from scratch from here https://github.com/ls-cwi/heinz.
+We are currently looking for an alternative to IBM CPLEX in Heinz to make an unlimited version of Heinz available
+to the public as soon as possible.
+
+Score file --- the output file of the tool "Calculate Heinz scores": two columns delimited
+by a tab without headers, the first column is node identifier (e.g., genes, KEGG ORTHOLOGY (KO));
+the second, Heinz score.
+
+========= ===================
+BRCA2    -6.991782933819368
+--------- -------------------
+BRCA1    -5.206139799106934
+--------- -------------------
+AACS     -0.9299868303078357
+--------- -------------------
+ABCC11    -5.845009850430119
+========= ===================
+
+
+Edge file: the background network Heinz uses in the form of a list of edges; each line is made up of
+two node identifiers (e.g., genes, KEGG ORTHOLOGY (KO)) delimited by a tab. In practice, we could
+prepare this file using different pathway databases, such as Reactome, STRING and KEGG. Which database
+to choose depends on the question to solve.
+
+========= =========
+ACTR1B    ACVR2B
+--------- ---------
+ZSWIM9    FOXP3
+--------- ---------
+LGALS4    PRKX
+--------- ---------
+NPTX1     CIAO1
+========= =========
+
+
+    ]]></help>
+    <citations>
+        <citation type="doi">10.1093/bioinformatics/btn161</citation>
+    </citations>
+</tool>
diff -r 000000000000 -r e41ec5af7472 heinz_scoring.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/heinz_scoring.py	Thu Aug 02 11:57:44 2018 -0400
@@ -0,0 +1,94 @@
+#!/usr/bin/env python
+"""Calculate scores for Heinz.
+
+This script transform a p-value into a score:
+    1. Use alpha and lambda to calculate a threshold P-value.
+    2. Calculate a score based on each P-value by alpha and the threshold.
+
+For more details, please refer to the paper doi:10.1093/bioinformatics/btn161
+
+Input:
+    P-values from DESeq2 result: first column: names, second column P-values
+Output:
+    Scores, which will be used as the input of Heinz.
+    First column: names, second column: scores.
+
+Python 3 is required.
+"""
+# Implemented by: Chao (Cico) Zhang
+# Homepage: https://Hi-IT.org
+# Date: 14 Mar 2017
+# Last modified: 23 May 2018
+
+import argparse
+import sys
+
+import numpy as np
+import pandas as pd
+
+
+parser = argparse.ArgumentParser(description='Transform a P-value into a '
+                                 'score which can be used as the input of '
+                                 'Heinz')
+parser.add_argument('-n', '--node', required=True, dest='nodes',
+                    metavar='nodes_pvalue.txt', type=str,
+                    help='Input file of nodes with P-values')
+parser.add_argument('-f', '--fdr', required=True, dest='fdr',
+                    metavar='0.007', type=float, help='Choose a value of FDR')
+parser.add_argument('-m', '--model', required=False, dest='param_file',
+                    metavar='param.txt', type=str,
+                    help='A txt file contains model params as input')
+parser.add_argument('-a', '--alpha', required=False, dest='alpha',
+                    metavar='0.234', type=float, default=0.5,
+                    help='Single parameter alpha as input if txt input is '
+                    'not provided')
+parser.add_argument('-l', '--lambda', required=False, dest='lam',
+                    metavar='0.345', type=float, default=0.5,
+                    help='Single parameter lambda as input if txt input is '
+                    'not provided')
+parser.add_argument('-o', '--output', required=True, dest='output',
+                    metavar='scores.txt', type=str,
+                    help='The output file to store the calculated scores')
+args = parser.parse_args()
+
+# Check if the parameters are complete
+if args.output is None:
+    sys.exit('Output file is not designated.')
+
+if args.nodes is None:
+    sys.exit('Nodes with p-values must be provided.')
+
+if args.fdr is None:
+    sys.exit('FDR must be provided')
+
+if args.fdr >= 1 or args.fdr <= 0:
+    sys.exit('FDR must greater than 0 and smaller than 1')
+
+# run heinz-print according to the input type
+if args.param_file is not None:  # if BUM output is provided
+    with open(args.param_file) as p:
+        params = p.readlines()
+        lam = float(params[0])  # Maybe this is a bug
+        alpha = float(params[1])  # Maybe this is a bug
+# if BUM output is not provided
+elif args.alpha is not None and args.lam is not None:
+    lam = args.lam
+    alpha = args.alpha
+else:  # The input is not complete
+    sys.exit('The parameters of the model are incomplete.')
+
+# Calculate the threshold P-value
+pie = lam + (1 - lam) * alpha
+p_threshold = np.power((pie - lam * args.fdr) / (args.fdr - lam * args.fdr),
+                       1 / (alpha - 1))
+print(p_threshold)
+# Calculate the scores
+input_pvalues = pd.read_csv(args.nodes, sep='\t', names=['node', 'pvalue'])
+input_pvalues.loc[:, 'score'] = input_pvalues.pvalue.apply(lambda x:
+                                                           (alpha - 1) *
+                                                           (np.log(x) -
+                                                            np.log(
+                                                                p_threshold)))
+# print(input_pvalues.loc[:, ['node', 'score']])
+input_pvalues.loc[:, ['node', 'score']].to_csv(args.output, sep='\t',
+                                               index=False, header=False)
diff -r 000000000000 -r e41ec5af7472 heinz_scoring.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/heinz_scoring.xml	Thu Aug 02 11:57:44 2018 -0400
@@ -0,0 +1,90 @@
+<tool id="heinz_scoring" name="Calculate a Heinz score" version="1.0">
+    <description>for each node</description>
+    <requirements>
+        <requirement type="package" version="0.19.2">pandas</requirement>
+        <requirement type="package" version="1.12.0">numpy</requirement>
+    </requirements>
+    <command detect_errors="aggressive"><![CDATA[
+python '$__tool_directory__/heinz_scoring.py' -n '$node' -f '$FDR' -o '$score'
+#if str( $input_type.input_type_selector ) == "bum_type":
+    -l '$input_type.lambda' -a '$input_type.alpha'
+#else:
+    -m '$input_type.input_bum'
+#end if
+    ]]></command>
+    <inputs>
+        <param type="data" name="node" format="txt" label="A node file with p-values"
+        help="The file should contain two columns: the node identifier (e.g. gene) and the corresponding p-value" />
+        <param type="float" name="FDR" value="0.5" min="0" max="1" label="FDR value"
+        help="False Discovery Rate (FDR), e.g. 0.0001"/>
+        <conditional name="input_type">
+            <param name="input_type_selector" type="select" label="Choose your input type for BUM parameters">
+                <option value="bum_output" selected="True">The output file of BUM model</option>
+                <option value="bum_type">Manually type the parameters of BUM model.</option>
+            </param>
+            <when value="bum_type">
+                <param type="float" name="lambda" value="0.5" min="0" max="1" label="lambda" />
+                <param type="float" name="alpha" value="0.5" min="0" max="1" label="alpha" />
+            </when>
+            <when value="bum_output">
+                <param type="data" name="input_bum" format="txt" label="Output file of BUM model as input:
+                lambda on the first line and alpha, the second" />
+            </when>
+        </conditional>
+    </inputs>
+    <outputs>
+        <data name="score" format="txt" label="${tool.name} on ${on_string}: Heinz scores" />
+    </outputs>
+    <tests>
+        <!--Ensure the manual input of BUM parameters work-->
+        <test>
+            <param name="node" value="genes_with_a_p_value.csv" />
+            <param name="FDR" value="0.001" />
+            <param name="input_type_selector" value="bum_type" />
+            <param name="lambda" value="0.546" />
+            <param name="alpha" value="0.453" />
+            <output name="score" file="Heinz_score_using_manual_BUM_params.txt" />
+        </test>
+        <!--Ensure the use of BUM output file as input work-->
+        <test>
+            <param name="node" value="genes_with_a_p_value.csv" />
+            <param name="FDR" value="0.001" />
+            <param name="input_type_selector" value="bum_output" />
+            <param name="input_bum" value="BUM_output.txt" />
+            <output name="score" file="Heinz_score_using_BUM_output.txt" />
+        </test>
+    </tests>
+    <help><![CDATA[
+This tool is used to calculate a Heinz score for each node (e.g., a gene). This
+score further serves as the input of the tool "Identify optimal scoring subnetwork".
+
+The input "Node file with p-values" should be like this:
+
+========= ===================
+K10970    0.00278208628672861
+--------- -------------------
+K10780    0.0029566591795884
+--------- -------------------
+K01484    0.0157152504694443
+--------- -------------------
+K09055    0.0188894478579773
+========= ===================
+
+
+The first column is "node identifier" (e.g., a gene name); the second column, "p-value";
+The columns are delimited by a tab; no headers are needed.
+
+The section "Choose your input type for BUM parameters" requires two alternative types of input:
+
+1."The output of the BUM model" (The tool "Fit a BUM model"): the first line of the file
+is lambda; the second, alpha.
+
+2."Manually type the parameters of BUM model": lambda and alpha.
+
+
+    ]]></help>
+    <citations>
+        <citation type="doi">10.1093/bioinformatics/btn161</citation>
+        <citation type="doi">10.1093/bioinformatics/btg148</citation>
+    </citations>
+</tool>
diff -r 000000000000 -r e41ec5af7472 test-data/BUM_input.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/BUM_input.txt	Thu Aug 02 11:57:44 2018 -0400
@@ -0,0 +1,1000 @@
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+0.865267703405176
+0.00337493056633076
+0.30400350300163
+0.514564453330559
+0.150859876840911
+0.528228602944177
+0.040236592014929
+0.140563657143996
+0.140426371242889
+0.758223225206527
+0.184407866286835
+0.237803827253682
+0.901539046853114
+0.333050038387453
+0.781529363473918
+0.194190412648925
+0.476659487150017
+0.953836884980725
+0.784592981944965
+0.583406195272662
+0.662890565822532
+0.83318628321259
+0.709236399906408
+0.729917970326033
+0.854039297563908
+0.224122140131679
+0.0892458988605694
+0.965188985280667
+0.608957726415198
+0.1545456981837
+0.00112303024929184
+0.493677809317058
+0.320267370941394
+0.957479463898128
+0.663938331651806
+0.421226891355979
diff -r 000000000000 -r e41ec5af7472 test-data/BUM_output.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/BUM_output.txt	Thu Aug 02 11:57:44 2018 -0400
@@ -0,0 +1,2 @@
+0.546281463286217
+0.453175526745403
diff -r 000000000000 -r e41ec5af7472 test-data/EdgesPCST.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/EdgesPCST.txt	Thu Aug 02 11:57:44 2018 -0400
@@ -0,0 +1,10 @@
+a	b
+b	c
+a	c
+c	d
+b	d
+d	f
+c	e
+f	h
+f	g
+e	f
diff -r 000000000000 -r e41ec5af7472 test-data/Heinz_output.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/Heinz_output.txt	Thu Aug 02 11:57:44 2018 -0400
@@ -0,0 +1,22 @@
+// Successfully parsed '/export/galaxy-central/database/files/000/dataset_183.dat': contains 8 nodes, 10 edges and 1 component(s)
+
+// Considering component 1/1: contains 8 nodes
+// Identified 4 dependent node pairs
+// Added 4 analyze constraints
+// Solution with weight 36 and 6 nodes found
+graph G {
+	overlap=scale
+	layout=neato
+	0 [label="a\n5\n0\n0"]
+	1 [label="b\n-4\n0\n1",shape=box]
+	3 [label="d\n30\n0\n3"]
+	5 [label="f\n-15\n0\n5",shape=box]
+	6 [label="g\n10\n0\n6"]
+	7 [label="h\n10\n0\n7"]
+	label="Total weight: 36"
+	0 -- 1
+	1 -- 3
+	3 -- 5
+	5 -- 6
+	5 -- 7
+}
diff -r 000000000000 -r e41ec5af7472 test-data/Heinz_score_using_BUM_output.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/Heinz_score_using_BUM_output.txt	Thu Aug 02 11:57:44 2018 -0400
@@ -0,0 +1,3 @@
+GRHPR	-6.060224431226826
+B4GALT6	-7.230950844434514
+FGF17	-7.312812620557818
diff -r 000000000000 -r e41ec5af7472 test-data/Heinz_score_using_manual_BUM_params.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/Heinz_score_using_manual_BUM_params.txt	Thu Aug 02 11:57:44 2018 -0400
@@ -0,0 +1,3 @@
+GRHPR	-6.058859736554457
+B4GALT6	-7.229961944568557
+FGF17	-7.3118499977365765
diff -r 000000000000 -r e41ec5af7472 test-data/NodesPCST.txt
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/NodesPCST.txt	Thu Aug 02 11:57:44 2018 -0400
@@ -0,0 +1,8 @@
+a	5
+b	-4
+c	-5
+d	30
+e	-5
+f	-15
+g	10
+h	10
diff -r 000000000000 -r e41ec5af7472 test-data/genes_with_a_p_value.csv
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/genes_with_a_p_value.csv	Thu Aug 02 11:57:44 2018 -0400
@@ -0,0 +1,3 @@
+GRHPR	0.0843889413906522
+B4GALT6	0.717943620714012
+FGF17	0.833884563412679
diff -r 000000000000 -r e41ec5af7472 test-data/heinz_graph.pdf
Binary file test-data/heinz_graph.pdf has changed
diff -r 000000000000 -r e41ec5af7472 visualization.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/visualization.py	Thu Aug 02 11:57:44 2018 -0400
@@ -0,0 +1,65 @@
+#!/usr/bin/env python
+"""Visualise the output of Heinz.
+
+This script is used to visualize the output of Heinz, which is in the form of
+DOT language:
+    1. Clear the output of Heinz, extract the DOT source code.
+    2. Visualize the DOT source code and save it into file.
+
+The function of this script is rather simple, for more advanced visualization,
+please adopt other solutions mentioned in the paper
+doi: 10.1093/bioinformatics/btv526
+
+This tool is only designed for visualizing the output of Heinz tool.
+"""
+
+# Author: Cico Zhang
+# Date: 2 Aug 2017
+# Version: 0.2
+
+import argparse
+import sys
+
+from graphviz import Source
+
+
+def get_args():
+    """Collect the inputs."""
+    parser = argparse.ArgumentParser(
+        description='Visualise the output of Heinz')
+    parser.add_argument('-i', '--input', required=True, dest='heinz',
+                        metavar='Heinz_output.txt', type=str,
+                        help='Output file of Heinz as the input')
+    parser.add_argument('-o', '--output', required=True, dest='output',
+                        metavar='graph.pdf', type=str,
+                        help='The output file that saves the visualisation')
+    args = parser.parse_args()
+
+    if args.heinz is None:
+        sys.exit('Input file must be designated.')
+
+    return args
+
+
+def main():
+    """Main function."""
+    args = get_args()
+    # Read the whole output file
+    with open(args.heinz) as r:
+        graph_dot = r.readlines()
+
+    # Remove the redundant lines
+    while not graph_dot[0].startswith('graph G {'):
+        graph_dot.pop(0)
+
+    src = Source(''.join(graph_dot))
+    data_pdf = src.pipe('pdf')
+    # Redirect the output (very important)
+    with open(args.output, 'wb') as w:
+        w.write(data_pdf)
+    print('The visualization is saved as PDF!')
+    sys.exit(0)
+
+
+if __name__ == "__main__":
+    main()
diff -r 000000000000 -r e41ec5af7472 visualization.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/visualization.xml	Thu Aug 02 11:57:44 2018 -0400
@@ -0,0 +1,33 @@
+<tool id="heinz_visualization" name="Visualize" version="0.1.0">
+    <description>the optimal scoring subnetwork</description>
+    <requirements>
+        <requirement type="package" version="2.38.0">graphviz</requirement>
+        <requirement type="package" version="0.4.10">py-graphviz</requirement>
+    </requirements>
+    <command detect_errors="aggressive"><![CDATA[
+python '$__tool_directory__/visualization.py' -i '$subnetwork' -o $visualization
+    ]]></command>
+    <inputs>
+        <param type="data" name="subnetwork" format="txt" label="Heinz output file"
+        help='Output file of the tool "Identify optimal scoring subnetwork"'/>
+    </inputs>
+    <outputs>
+        <data name="visualization" format="pdf" label="${tool.name} on ${on_string}: Heinz visualization" />
+    </outputs>
+    <tests>
+      <test>
+        <param name="subnetwork" value="Heinz_output.txt" />
+        <output name="visualization" file="heinz_graph.pdf" compare="sim_size" delta="2000" />
+      </test>
+    </tests>
+    <help><![CDATA[
+This tool provides a simple visualisation of the raw output of the Heinz Galaxy tool
+and saves the output as PDF.
+
+Heinz output file: output file of the tool "Identify optimal scoring subnetwork".
+    ]]></help>
+    <citations>
+        <citation type="doi">10.1093/bioinformatics/btn161</citation>
+        <citation type="doi">10.1093/bioinformatics/btg148</citation>
+    </citations>
+</tool>