| Next changeset 1:5f589c91566e (2018-10-30) |
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Commit message:
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/heinz commit b0b2c64a46bdd9beebdfb7fc5312f75346483763 |
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added:
bum.R bum.xml heinz.xml heinz_scoring.py heinz_scoring.xml test-data/BUM_input.txt test-data/BUM_output.txt test-data/EdgesPCST.txt test-data/Heinz_output.txt test-data/Heinz_score_using_BUM_output.txt test-data/Heinz_score_using_manual_BUM_params.txt test-data/NodesPCST.txt test-data/genes_with_a_p_value.csv test-data/heinz_graph.pdf visualization.py visualization.xml |
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| 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 |
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| @@ -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$output,quote=FALSE, row.names=FALSE, col.names=FALSE) +message ("Parameters have been estimated successfully!") |
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| diff -r 000000000000 -r e41ec5af7472 bum.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/bum.xml Thu Aug 02 11:57:44 2018 -0400 |
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| @@ -0,0 +1,41 @@ +<tool id="heinz_bum" name="Fit a BUM model" version="1.0"> + <description>with p-values</description> + <requirements> + <requirement type="package" version="1.20.0">r-getopt</requirement> + <requirement type="package" version="1.34.0">bioconductor-bionet</requirement> + </requirements> + <command detect_errors="exit_code"><![CDATA[ +Rscript '$__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 ${on_string}: BUM params"/> + </outputs> + <tests> + <!--Ensure the stochastic result is similar enough--> + <test> + <param name="p_values" value="BUM_input.txt" /> + <output name="dist_params"> + <assert_contents> + <has_text text="0.684" /> + <has_text text="0.376" /> + </assert_contents> + </output> + </test> + </tests> + <help><![CDATA[ +BUM is the abbreviation for "Beta-Uniform Mixture (BUM) distribution." + +This tool (part of the package "Bionet") is used for fitting Beta-Uniform +mixture model to a P-value distribution, the output of which is two model +parameters: lambda and alpha, kept in a text file, the first line is +lambda; the second, alpha. + ]]></help> + <citations> + <citation type="doi">10.1093/bioinformatics/btq089</citation> + <citation type="doi">10.1093/bioinformatics/btn161</citation> + </citations> +</tool> |
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| diff -r 000000000000 -r e41ec5af7472 heinz.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/heinz.xml Thu Aug 02 11:57:44 2018 -0400 |
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| @@ -0,0 +1,76 @@ +<tool id="heinz" name="Identify optimal scoring subnetwork" version="1.0"> + <description>using Heinz</description> + <requirements> + <requirement type="package" version="2.0">heinz</requirement> + </requirements> + <command detect_errors="aggressive"><![CDATA[ +heinz -m \${GALAXY_SLOTS:-2} -n '$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> |
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| 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 |
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| @@ -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) |
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| 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 |
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| @@ -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> |
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| 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 |
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|
| b |
| 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 |
| b |
| @@ -0,0 +1,2 @@ +0.546281463286217 +0.453175526745403 |
| b |
| 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 |
| b |
| @@ -0,0 +1,10 @@ +a b +b c +a c +c d +b d +d f +c e +f h +f g +e f |
| b |
| 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 +} |
| b |
| 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 |
| b |
| @@ -0,0 +1,3 @@ +GRHPR -6.060224431226826 +B4GALT6 -7.230950844434514 +FGF17 -7.312812620557818 |
| b |
| 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 |
| b |
| @@ -0,0 +1,3 @@ +GRHPR -6.058859736554457 +B4GALT6 -7.229961944568557 +FGF17 -7.3118499977365765 |
| b |
| 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 |
| b |
| @@ -0,0 +1,8 @@ +a 5 +b -4 +c -5 +d 30 +e -5 +f -15 +g 10 +h 10 |
| b |
| 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 |
| b |
| @@ -0,0 +1,3 @@ +GRHPR 0.0843889413906522 +B4GALT6 0.717943620714012 +FGF17 0.833884563412679 |
| b |
| diff -r 000000000000 -r e41ec5af7472 test-data/heinz_graph.pdf |
| b |
| Binary file test-data/heinz_graph.pdf has changed |
| b |
| 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() |
| b |
| 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> |