| Previous changeset 15:d0e7f14b773f (2019-10-01) Next changeset 17:640f303d0cec (2019-10-01) |
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Commit message:
Uploaded |
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modified:
Marea/marea.py Marea/marea.xml Marea/marea_cluster.py Marea/marea_cluster.xml |
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added:
Marea/local/desktop.ini |
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removed:
marea-1.0.1/local/HMRcoreMap.svg marea-1.0.1/local/HMRcore_genes.p marea-1.0.1/local/HMRcore_rules.p marea-1.0.1/local/Recon_genes.p marea-1.0.1/local/Recon_rules.p marea-1.0.1/local/desktop.ini marea-1.0.1/marea.py marea-1.0.1/marea.xml marea-1.0.1/marea_cluster.py marea-1.0.1/marea_cluster.xml marea-1.0.1/marea_macros.xml |
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| diff -r d0e7f14b773f -r c71ac0bb12de Marea/local/desktop.ini --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Marea/local/desktop.ini Tue Oct 01 06:05:13 2019 -0400 |
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| @@ -0,0 +1,6 @@ +[.ShellClassInfo] +IconResource=C:\WINDOWS\System32\SHELL32.dll,4 +[ViewState] +Mode= +Vid= +FolderType=Generic |
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| diff -r d0e7f14b773f -r c71ac0bb12de Marea/marea.py --- a/Marea/marea.py Tue Oct 01 06:03:12 2019 -0400 +++ b/Marea/marea.py Tue Oct 01 06:05:13 2019 -0400 |
| [ |
| b'@@ -1,4 +1,3 @@\n-\n from __future__ import division\n import sys\n import pandas as pd\n@@ -6,6 +5,7 @@\n import scipy.stats as st\n import collections\n import lxml.etree as ET\n+import shutil\n import pickle as pk\n import math\n import os\n@@ -13,7 +13,7 @@\n from svglib.svglib import svg2rlg\n from reportlab.graphics import renderPDF\n \n-########################## argparse ###########################################\n+########################## argparse ##########################################\n \n def process_args(args):\n parser = argparse.ArgumentParser(usage = \'%(prog)s [options]\',\n@@ -71,6 +71,21 @@\n type = str,\n choices = [\'yes\', \'no\'],\n help = \'if make or not custom map\')\n+ parser.add_argument(\'-gs\', \'--generate_svg\',\n+ type = str,\n+ default = \'true\',\n+ choices = [\'true\', \'false\'], \n+ help = \'generate svg map\')\n+ parser.add_argument(\'-gp\', \'--generate_pdf\',\n+ type = str,\n+ default = \'true\',\n+ choices = [\'true\', \'false\'], \n+ help = \'generate pdf map\')\n+ parser.add_argument(\'-gr\', \'--generate_ras\',\n+ type = str,\n+ default = \'true\',\n+ choices = [\'true\', \'false\'],\n+ help = \'generate reaction activity score\')\n args = parser.parse_args()\n return args\n \n@@ -85,7 +100,7 @@\n \n def read_dataset(data, name):\n try:\n- dataset = pd.read_csv(data, sep = \'\\t\', header = 0)\n+ dataset = pd.read_csv(data, sep = \'\\t\', header = 0, engine=\'python\')\n except pd.errors.EmptyDataError:\n sys.exit(\'Execution aborted: wrong format of \' + name + \'\\n\')\n if len(dataset.columns) < 2:\n@@ -536,7 +551,7 @@\n ids = [react[i].id for i in range(len(react))]\n except cb.io.sbml3.CobraSBMLError:\n try:\n- data = (pd.read_csv(data, sep = \'\\t\', dtype = str)).fillna(\'\')\n+ data = (pd.read_csv(data, sep = \'\\t\', dtype = str, engine=\'python\')).fillna(\'\')\n if len(data.columns) < 2:\n sys.exit(\'Execution aborted: wrong format of \'+\n \'custom datarules\\n\')\n@@ -641,9 +656,28 @@\n \', the class has been disregarded\\n\')\n return class_pat\n \n+############################ create_ras #######################################\n+\n+def create_ras (resolve_rules, dataset_name):\n+\n+ if resolve_rules == None:\n+ warning("Couldn\'t generate RAS for current dataset: " + dataset_name)\n+\n+ for geni in resolve_rules.values():\n+ for i, valori in enumerate(geni):\n+ if valori == None:\n+ geni[i] = \'None\'\n+ \n+ output_ras = pd.DataFrame.from_dict(resolve_rules)\n+ output_to_csv = pd.DataFrame.to_csv(output_ras, sep = \'\\t\', index = False)\n+ \n+ text_file = open("ras/Reaction_Activity_Score_Of_" + dataset_name + ".tsv", "w")\n+ text_file.write(output_to_csv)\n+ text_file.close()\n+\n ############################ map ##############################################\n \n-def maps(core_map, class_pat, ids, threshold_P_V, threshold_F_C):\n+def maps(core_map, class_pat, ids, threshold_P_V, threshold_F_C, create_svg, create_pdf):\n args = process_args(sys.argv)\n if (not class_pat) or (len(class_pat.keys()) < 2):\n sys.exit(\'Execution aborted: classes provided for comparisons are \' +\n@@ -663,52 +697,81 @@\n count += 1\n except (TypeError, ZeroDivisionError):\n count += 1\n- tab = \'table_out/\' + i + \'_vs_\' + j + \'.tsv\'\n+ tab = \'result/\' + i + \'_vs_\' + j + \' (Tabular Result).tsv\'\n tmp_csv = pd.DataFrame.from_dict(tmp, orient = "index")\n tmp_csv = tmp_csv.reset_index()\n header = [\'ids\', \'P_Value\', \'Average\']\n tmp_csv.to_csv(tab, sep = \'\\t\','..b" fix_map(tmp, core_map, threshold_P_V, threshold_F_C, max_F_C)\n+ file_svg = 'result/' + i + '_vs_' + j + ' (SVG Map).svg'\n+ with open(file_svg, 'wb') as new_map:\n+ new_map.write(ET.tostring(core_map))\n+ \n+ \n+ if create_pdf:\n+ file_pdf = 'result/' + i + '_vs_' + j + ' (PDF Map).pdf'\n+ renderPDF.drawToFile(svg2rlg(file_svg), file_pdf)\n+ \n+ if not create_svg:\n+ #Ho utilizzato il file svg per generare il pdf, \n+ #ma l'utente non ne ha richiesto il ritorno, quindi\n+ #lo elimino\n+ os.remove('result/' + i + '_vs_' + j + ' (SVG Map).svg')\n+ \n return None\n \n ############################ MAIN #############################################\n \n def main():\n args = process_args(sys.argv)\n- os.makedirs('table_out')\n- if args.rules_selector == 'HMRcore':\n- os.makedirs('map_svg')\n- os.makedirs('map_pdf')\n+ \n+ create_svg = check_bool(args.generate_svg)\n+ create_pdf = check_bool(args.generate_pdf)\n+ generate_ras = check_bool(args.generate_ras)\n+ \n+ os.makedirs('result')\n+\n+ if generate_ras:\n+ os.makedirs('ras')\n+ \n+ if args.rules_selector == 'HMRcore': \n recon = pk.load(open(args.tool_dir + '/local/HMRcore_rules.p', 'rb'))\n elif args.rules_selector == 'Recon':\n recon = pk.load(open(args.tool_dir + '/local/Recon_rules.p', 'rb'))\n elif args.rules_selector == 'Custom':\n ids, rules, gene_in_rule = make_recon(args.custom)\n+ \n resolve_none = check_bool(args.none)\n+ \n class_pat = {}\n+ \n if args.option == 'datasets':\n num = 1\n- #if len(args.names) != len(set(args.names)):\n- # sys.exit('Execution aborted: datasets name duplicated')\n for i, j in zip(args.input_datas, args.names):\n+\n name = name_dataset(j, num)\n dataset = read_dataset(i, name)\n+\n dataset.iloc[:, 0] = (dataset.iloc[:, 0]).astype(str)\n- type_gene = gene_type(dataset.iloc[0, 0], name)\n+\n+ type_gene = gene_type(dataset.iloc[0, 0], name) \n+ \n if args.rules_selector != 'Custom':\n genes = data_gene(dataset, type_gene, name, None)\n ids, rules = load_id_rules(recon.get(type_gene))\n elif args.rules_selector == 'Custom':\n genes = data_gene(dataset, type_gene, name, gene_in_rule)\n+ \n resolve_rules, err = resolve(genes, rules, ids, resolve_none, name)\n+\n+ if generate_ras:\n+ create_ras(resolve_rules, name)\n+ \n+ \n if err != None and err:\n warning('Warning: gene\\n' + str(err) + '\\nnot found in class '\n + name + ', the expression level for this gene ' +\n@@ -738,10 +801,9 @@\n 'will be considered NaN\\n')\n if resolve_rules != None:\n class_pat = split_class(classes, resolve_rules)\n+ \n if args.rules_selector == 'Custom':\n if args.yes_no == 'yes':\n- os.makedirs('map_svg')\n- os.makedirs('map_pdf')\n try:\n core_map = ET.parse(args.custom_map)\n except (ET.XMLSyntaxError, ET.XMLSchemaParseError):\n@@ -750,8 +812,11 @@\n core_map = ET.parse(args.tool_dir + '/local/HMRcoreMap.svg')\n else: \n core_map = ET.parse(args.tool_dir+'/local/HMRcoreMap.svg')\n- maps(core_map, class_pat, ids, args.pValue, args.fChange)\n- warning('Execution succeeded')\n+ \n+ maps(core_map, class_pat, ids, args.pValue, args.fChange, create_svg, create_pdf)\n+ \n+ print('Execution succeded')\n+\n return None\n \n ###############################################################################\n" |
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| diff -r d0e7f14b773f -r c71ac0bb12de Marea/marea.xml --- a/Marea/marea.xml Tue Oct 01 06:03:12 2019 -0400 +++ b/Marea/marea.xml Tue Oct 01 06:05:13 2019 -0400 |
| [ |
| b'@@ -1,200 +1,223 @@\n-<tool id="MaREA" name="Metabolic Enrichment Analysis" version="1.0.0">\r\n- <description>for Galaxy</description>\r\n- <macros>\r\n- <import>marea_macros.xml</import>\r\n- </macros>\r\n- <requirements>\r\n- <requirement type="package" version="0.23.0">pandas</requirement>\r\n- <requirement type="package" version="1.1.0">scipy</requirement>\r\n- <requirement type="package" version="0.10.1">cobra</requirement>\r\n- <requirement type="package" version="4.2.1">lxml</requirement>\r\n- <requirement type="package" version="0.8.1">svglib</requirement>\r\n- <requirement type="package" version="3.4.0">reportlab</requirement>\r\n- </requirements>\r\n- <command detect_errors="exit_code">\r\n- <![CDATA[\r\n- \tpython $__tool_directory__/marea.py\r\n- --rules_selector $cond_rule.rules_selector\r\n- #if $cond_rule.rules_selector == \'Custom\':\r\n- --custom ${cond_rule.Custom_rules}\r\n- --yes_no ${cond_rule.cond_map.yes_no}\r\n- #if $cond_rule.cond_map.yes_no == \'yes\':\r\n- --custom_map $cond_rule.cond_map.Custom_map\r\n- #end if\r\n- #end if\r\n- \t--none $None\r\n- \t--pValue $pValue\r\n- \t--fChange $fChange\r\n- \t--tool_dir $__tool_directory__\r\n- \t--option $cond.type_selector\r\n- --out_log $log\r\n- #if $cond.type_selector == \'datasets\':\r\n- --input_datas\r\n- #for $data in $cond.input_Datasets:\r\n- ${data.input}\r\n- #end for\r\n- --names\r\n- #for $data in $cond.input_Datasets:\r\n- ${data.input_name}\r\n- #end for\r\n- #elif $cond.type_selector == \'dataset_class\':\r\n- --input_data ${input_data}\r\n- --input_class ${input_class}\r\n- #end if\r\n- ]]>\r\n- </command>\r\n-\r\n- <inputs>\r\n- <conditional name="cond_rule">\r\n- <expand macro="options"/>\r\n- <when value="HMRcore">\r\n- </when>\r\n- <when value="Recon">\r\n- </when>\r\n- <when value="Custom">\r\n- <param name="Custom_rules" type="data" format="tabular, csv, tsv, xml" label="Custom rules" />\r\n- <conditional name="cond_map">\r\n- <param name="yes_no" type="select" label="Custom map? (optional)">\r\n- <option value="no" selected="true">no</option>\r\n- <option value="yes">yes</option>\r\n- </param>\r\n- <when value="yes">\r\n- <param name="Custom_map" argument="--custom_map" type="data" format="xml, svg" label="custom-map.svg"/>\r\n- </when>\r\n- <when value="no">\r\n- </when>\r\n- </conditional>\r\n- </when>\r\n- </conditional>\r\n- <conditional name="cond">\r\n- <param name="type_selector" argument="--option" type="select" label="Input format:">\r\n- <option value="datasets" selected="true">RNAseq of group 1 + RNAseq of group 2 + ... + RNAseq of group N</option>\r\n- <option value="dataset_class">RNAseq of all samples + sample group specification</option>\r\n- </param>\r\n- <when value="datasets">\r\n- <repeat name="input_Datasets" title="RNAseq" min="2">\r\n- <param name="input" argument="--input_datas" type="data" format="tabular, csv, tsv" label="add dataset" />\t\r\n- <param name="input_name" argument="--names" type="text" label="Dataset\'s name:" value="Dataset" help="Defalut: Dataset" />\r\n- </repeat>\r\n- </when>\r\n- <when value="dataset_class">\r\n- <param name="input_data" argument="--input_data" type="data" format="tabular, csv, tsv" label="RNAseq of all samples" />\r\n- <param name="input_class" argument="--input_class" type="data" format="tabular, csv, tsv" lab'..b'ity Score for each table" help="Generate Reaction Activity Score for each table" />\t\t\n+\t\t</when>\n+ \t</conditional>\n+ </inputs>\n+\n+ <outputs>\n+ <data format="txt" name="log" label="${tool.name} - Log" />\n+ <collection name="results" type="list" label="${tool.name} - Results">\n+ <discover_datasets pattern="__name_and_ext__" directory="result"/>\n+ </collection>\n+\t<collection name="ras" type="list" label="${tool.name} - RAS" format_source="tabular">\n+\t <filter>advanced[\'choice\'] and advanced[\'generateRas\']</filter>\n+ \t <discover_datasets pattern="__name_and_ext__" directory="ras" format="tabular"/>\n+\t</collection>\n+ </outputs>\n+ <tests>\n+ <test>\n+ <param name="pValue" value="0.56"/>\n+ <output name="log" file="log.txt"/>\n+ </test>\n+ </tests>\n+ <help>\n+<![CDATA[\n+\n+What it does\n+-------------\n+\n+This tool analyzes RNA-seq dataset(s) as described in Graudenzi et al."`MaREA`_: Metabolic feature extraction, enrichment and visualization of RNAseq data" bioRxiv (2018): 248724.\n+\n+Accepted files are: \n+ - option 1) two or more RNA-seq datasets, each referring to samples in a given condition/class. The user can specify a label for each class (as e.g. "*classA*" and "*classB*");\n+ - option 2) one RNA dataset and one class-file specifying the class/condition each sample belongs to.\n+\n+Optional files:\n+ - custom GPR (Gene-Protein-Reaction) rules. Two accepted formats:\n+\n+\t* (Cobra Toolbox and CobraPy compliant) xml of metabolic model;\n+\t* .csv file specifyig for each reaction ID (column 1) the corresponding GPR rule (column 2).\n+ - custom svg map. Graphical elements must have the same IDs of reactions. See HmrCore svg map for an example.\n+\n+The tool generates:\n+ 1) a tab-separated file: reporting fold-change and p-values of reaction activity scores (RASs) between a pair of conditions/classes;\n+ 2) a metabolic map file (downlodable as .svg): visualizing up- and down-regulated reactions between a pair of conditions/classes;\n+ 3) a log file (.txt).\n+\n+RNA-seq datasets format: tab-separated text files, reporting the expression level (e.g., TPM, RPKM, ...) of each gene (row) for a given sample (column). Header: sample ID.\n+\n+Class-file format: each row of the class-file reports the sample ID (column1) and the label of the class/condition the sample belongs to (column 2).\n+\n+To calculate P-Values and Fold-Changes and to generate maps, comparisons are performed for each possible pair of classes.\n+\n+Output files will be named as classA_vs_classB. Reactions will conventionally be reported as up-regulated (down-regulated) if they are significantly more (less) active in class having label "classA".\n+\n+\n+Example input\n+-------------\n+\n+**"Custom Rules"** option:\n+\n+Custom Rules Dastaset:\n+\n+@CUSTOM_RULES_EXEMPLE@\n+\n+**"RNAseq of group 1 + RNAseq of group 2 + ... + RNAseq of group N"** option:\n+\n+RNA-seq Dataset 1:\t\t\t\t\t\t\n+\n+@DATASET_EXEMPLE1@\n+\n+RNA-seq Dataset 2:\n+\n+@DATASET_EXEMPLE2@\n+\n+**"RNAseq of all samples + sample group specification"** option:\n+\n+RNA-seq Dataset:\n+\n+@DATASET_EXEMPLE1@\n+\n+Class-file:\n+\n++------------+------------+ \n+| Patient_ID | class | \n++============+============+ \n+| TCGAAA3529 | MSI | \n++------------+------------+ \n+| TCGAA62671 | MSS | \n++------------+------------+ \n+| TCGAA62672 | MSI | \n++------------+------------+\n+\n+|\n+\n+.. class:: infomark\n+\n+**TIP**: If your data is not TAB delimited, use `Convert delimiters to TAB`_.\n+\n+.. class:: infomark\n+\n+**TIP**: If your dataset is not split into classes, use `MaREA cluster analysis`_.\n+\n+@REFERENCE@\n+\n+.. _MaREA: https://www.biorxiv.org/content/early/2018/01/16/248724\n+.. _Convert delimiters to TAB: https://usegalaxy.org/?tool_id=Convert+characters1&version=1.0.0&__identifer=6t22teyofhj\n+.. _MaREA cluster analysis: http://link del tool di cluster.org\n+\n+]]>\n+ </help>\n+ <expand macro="citations" />\n+</tool>\n+\t\n' |
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| diff -r d0e7f14b773f -r c71ac0bb12de Marea/marea_cluster.py --- a/Marea/marea_cluster.py Tue Oct 01 06:03:12 2019 -0400 +++ b/Marea/marea_cluster.py Tue Oct 01 06:05:13 2019 -0400 |
| [ |
| b'@@ -1,67 +1,84 @@\n-from __future__ import division\n-import os\n+# -*- coding: utf-8 -*-\n+"""\n+Created on Mon Jun 3 19:51:00 2019\n+\n+@author: Narger\n+"""\n+\n import sys\n-import pandas as pd\n-import collections\n-import pickle as pk\n import argparse\n-from sklearn.cluster import KMeans\n-import matplotlib\n-# Force matplotlib to not use any Xwindows backend.\n-matplotlib.use(\'Agg\')\n+import os\n+from sklearn.datasets import make_blobs\n+from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering\n+from sklearn.metrics import silhouette_samples, silhouette_score, davies_bouldin_score, cluster\n import matplotlib.pyplot as plt\n+import scipy.cluster.hierarchy as shc \n+import matplotlib.cm as cm\n+import numpy as np\n+import pandas as pd\n \n-########################## argparse ###########################################\n+################################# process args ###############################\n \n def process_args(args):\n parser = argparse.ArgumentParser(usage = \'%(prog)s [options]\',\n description = \'process some value\\\'s\' +\n \' genes to create class.\')\n- parser.add_argument(\'-rs\', \'--rules_selector\', \n+\n+ parser.add_argument(\'-ol\', \'--out_log\', \n+ help = "Output log")\n+ \n+ parser.add_argument(\'-in\', \'--input\',\n type = str,\n- default = \'HMRcore\',\n- choices = [\'HMRcore\', \'Recon\', \'Custom\'], \n- help = \'chose which type of dataset you want use\')\n- parser.add_argument(\'-cr\', \'--custom\',\n+ help = \'input dataset\')\n+ \n+ parser.add_argument(\'-cy\', \'--cluster_type\',\n type = str,\n- help=\'your dataset if you want custom rules\')\n- parser.add_argument(\'-ch\', \'--cond_hier\', \n- type = str,\n- default = \'no\',\n- choices = [\'no\', \'yes\'], \n- help = \'chose if you wanna hierical dendrogram\')\n- parser.add_argument(\'-lk\', \'--k_min\', \n+ choices = [\'kmeans\', \'meanshift\', \'dbscan\', \'hierarchy\'],\n+ default = \'kmeans\',\n+ help = \'choose clustering algorythm\')\n+ \n+ parser.add_argument(\'-k1\', \'--k_min\', \n+ type = int,\n+ default = 2,\n+ help = \'choose minimun cluster number to be generated\')\n+ \n+ parser.add_argument(\'-k2\', \'--k_max\', \n type = int,\n- help = \'min number of cluster\')\n- parser.add_argument(\'-uk\', \'--k_max\', \n- type = int,\n- help = \'max number of cluster\')\n- parser.add_argument(\'-li\', \'--linkage\', \n- type = str, \n- choices = [\'single\', \'complete\', \'average\'], \n- help=\'linkage hierarchical cluster\')\n- parser.add_argument(\'-d\', \'--data\',\n+ default = 7,\n+ help = \'choose maximum cluster number to be generated\')\n+ \n+ parser.add_argument(\'-el\', \'--elbow\', \n+ type = str,\n+ default = \'false\',\n+ choices = [\'true\', \'false\'],\n+ help = \'choose if you want to generate an elbow plot for kmeans\')\n+ \n+ parser.add_argument(\'-si\', \'--silhouette\', \n type = str,\n- required = True,\n- help = \'input dataset\')\n- parser.add_argument(\'-n\', \'--none\',\n+ default = \'false\',\n+ choices = [\'true\', \'false\'],\n+ help = \'choose if you want silhouette plots\')\n+ \n+ parser.add_argument(\'-db\', \'--davies\', \n type = str,\n- default = \'true\',\n- choi'..b'ries inverted.\\n\')\n- tmp = k_min\n- k_min = k_max\n- k_max = tmp\n- else: \n- warning(\'k range correct.\\n\')\n- cluster_data = pd.DataFrame.from_dict(resolve_rules, orient = \'index\')\n- for i in cluster_data.columns:\n- tmp = cluster_data[i][0]\n- if tmp == None:\n- cluster_data = cluster_data.drop(columns=[i])\n- distorsion = []\n- for i in range(k_min, k_max+1):\n- tmp_kmeans = KMeans(n_clusters = i,\n- n_init = 100, \n- max_iter = 300,\n- random_state = 0).fit(cluster_data)\n- distorsion.append(tmp_kmeans.inertia_)\n- predict = tmp_kmeans.predict(cluster_data)\n- predict = [x+1 for x in predict]\n- classe = (pd.DataFrame(list(zip(cluster_data.index, predict)))).astype(str)\n- dest = \'cluster_out/K=\' + str(i) + \'_\' + args.name+\'.tsv\'\n- classe.to_csv(dest, sep = \'\\t\', index = False,\n- header = [\'Patient_ID\', \'Class\'])\n- plt.figure(0)\n- plt.plot(range(k_min, k_max+1), distorsion, marker = \'o\')\n- plt.xlabel(\'Number of cluster\')\n- plt.ylabel(\'Distorsion\')\n- plt.savefig(args.elbow, dpi = 240, format = \'pdf\')\n- if args.cond_hier == \'yes\':\n- import scipy.cluster.hierarchy as hier\n- lin = hier.linkage(cluster_data, args.linkage)\n- plt.figure(1)\n- plt.figure(figsize=(10, 5))\n- hier.dendrogram(lin, leaf_font_size = 2, labels = cluster_data.index)\n- plt.savefig(args.dendro, dpi = 480, format = \'pdf\')\n- return None\n+ \n+############################# main ###########################################\n \n-################################# main ########################################\n \n def main():\n+ if not os.path.exists(\'clustering\'):\n+ os.makedirs(\'clustering\')\n+\n args = process_args(sys.argv)\n- if args.rules_selector == \'HMRcore\':\n- recon = pk.load(open(args.tool_dir + \'/local/HMRcore_rules.p\', \'rb\'))\n- elif args.rules_selector == \'Recon\':\n- recon = pk.load(open(args.tool_dir + \'/local/Recon_rules.p\', \'rb\'))\n- elif args.rules_selector == \'Custom\':\n- ids, rules, gene_in_rule = make_recon(args.custom)\n- resolve_none = check_bool(args.none)\n- dataset = read_dataset(args.data, args.name)\n- dataset.iloc[:, 0] = (dataset.iloc[:, 0]).astype(str)\n- type_gene = gene_type(dataset.iloc[0, 0], args.name)\n- if args.rules_selector != \'Custom\':\n- genes = data_gene(dataset, type_gene, args.name, None)\n- ids, rules = load_id_rules(recon.get(type_gene))\n- elif args.rules_selector == \'Custom\':\n- genes = data_gene(dataset, type_gene, args.name, gene_in_rule)\n- resolve_rules, err = resolve(genes, rules, ids, resolve_none, args.name)\n- if err:\n- warning(\'WARNING: gene\\n\' + str(err) + \'\\nnot found in class \' \n- + args.name + \', the expression level for this gene \' +\n- \'will be considered NaN\\n\')\n- f_cluster(resolve_rules)\n- warning(\'Execution succeeded\')\n- return None\n-\n-###############################################################################\n+ \n+ #Data read\n+ \n+ X = read_dataset(args.input)\n+ X = pd.DataFrame.to_dict(X, orient=\'list\')\n+ X = rewrite_input(X)\n+ X = pd.DataFrame.from_dict(X, orient = \'index\')\n+ \n+ for i in X.columns:\n+ tmp = X[i][0]\n+ if tmp == None:\n+ X = X.drop(columns=[i])\n+ \n+ X = pd.DataFrame.to_numpy(X)\n+ \n+ \n+ if args.cluster_type == \'kmeans\':\n+ kmeans(args.k_min, args.k_max, X, args.elbow, args.silhouette, args.davies)\n+ \n+ if args.cluster_type == \'dbscan\':\n+ dbscan(X, args.eps, args.min_samples)\n+ \n+ if args.cluster_type == \'hierarchy\':\n+ hierachical_agglomerative(X, args.k_min, args.k_max)\n+ \n+##############################################################################\n \n if __name__ == "__main__":\n main()\n' |
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| diff -r d0e7f14b773f -r c71ac0bb12de Marea/marea_cluster.xml --- a/Marea/marea_cluster.xml Tue Oct 01 06:03:12 2019 -0400 +++ b/Marea/marea_cluster.xml Tue Oct 01 06:05:13 2019 -0400 |
| [ |
| b'@@ -1,148 +1,92 @@\n-<tool id="MaREA_cluester" name="MaREA cluster analysis" version="1.0.0">\r\n- <description>of Reaction Activity Scores</description>\r\n- <macros>\r\n- <import>marea_macros.xml</import>\r\n- </macros>\r\n- <requirements>\r\n- <requirement type="package" version="0.23.0">pandas</requirement>\r\n- <requirement type="package" version="1.1.0">scipy</requirement>\r\n- <requirement type="package" version="0.10.1">cobra</requirement>\r\n- <requirement type="package" version="0.19.1">scikit-learn</requirement>\r\n- <requirement type="package" version="2.2.2">matplotlib</requirement>\r\n- </requirements>\r\n- <command detect_errors="exit_code">\r\n- <![CDATA[\r\n- \tpython $__tool_directory__/marea_cluster.py\r\n- --rules_selector $cond_rule.rules_selector\r\n- #if $cond_rule.rules_selector == \'Custom\':\r\n- --custom ${cond_rule.Custom_rules}\r\n- #end if\r\n- --cond_hier $cond_hier.hier\r\n- #if $cond_hier.hier == \'yes\':\r\n- --linkage ${cond_hier.linkage}\r\n- --dendro $dendrogram\r\n- #end if\r\n- --k_max $k_max\r\n- --k_min $k_min\r\n- --data $input\r\n- --name $name\r\n- \t--none $None\r\n- \t--tool_dir $__tool_directory__\r\n- --out_log $log\r\n- --elbow $elbow\r\n- ]]>\r\n- </command>\r\n- <inputs>\r\n- <conditional name="cond_rule">\r\n- <expand macro="options"/>\r\n- <when value="Custom">\r\n- <param name="Custom_rules" type="data" format="tabular, csv, tsv, xml" label="Custom rules" />\r\n- </when>\r\n- <when value="HMRcore">\r\n- </when>\r\n- <when value="Recon">\r\n- </when>\r\n- </conditional>\r\n- <param name="input" argument="--data" type="data" format="tabular, csv, tsv" label="RNAseq of all samples" />\r\n- <param name="name" argument="--name" type="text" label="Output name prefix" value="dataset" />\r\n- <param name="k_min" argument="--k_min" type="integer" size="20" value="3" min="2" max="30" label="Min number of clusters (k) to be tested (k-means)"/>\r\n- <param name="k_max" argument="--k_max" type="integer" size="20" value="3" min="2" max="30" label="Max number of clusters (k) to be tested (k-means)"/>\r\n- <param name="None" argument="--none" type="boolean" truevalue="true" falsevalue="false" checked="true" label="(A and NaN) solved as (A)?" help="If NO is selected, (A and NaN) is solved as (NaN)" />\r\n-\t<conditional name="cond_hier">\r\n- <param name="hier" argument="--cond_hier" type="select" label="Produce dendrogram (hierarchical clustering):">\r\n- <option value="no" selected="true">no</option>\r\n- <option value="yes">yes</option>\r\n- </param>\r\n- <when value="yes">\r\n- <param name="linkage" argument="--linkage" type="select" label="Linkage type:">\r\n- <option value="single" selected="true">Single: minimum distance between all observations of two sets</option>\r\n- <option value="complete">Complete: maximum distance between all observations of two sets</option>\r\n- <option value="average">Average: average distance between all observations of two sets</option>\r\n- </param>\r\n- </when>\r\n- <when value="no">\r\n- </when>\r\n- </conditional>\r\n- </inputs>\r\n-\r\n- <outputs>\r\n- <data format="txt" name="log" label="Log" />\r\n- <data format="pdf" name="dendrogram" label="$name dendrogram">\r\n- <filter>cond_hier[\'hier\'] == \'yes\'</filter>\r\n- </data>\r\n- <data format="pdf" name="elbow" label="$name elbow evaluation method" />\r\n- <collection name="cluster_out" type="list" label="Clusters $k_min - $k_max">\r\n- <discover_datasets pattern="__name_and_ext__" directory="cluster_out" />\r\n- </collection>\r\n- </outputs>\r\n- <tes'..b' <requirement type="package" version="1.1.0">scipy</requirement>\n+ <requirement type="package" version="0.10.1">cobra</requirement>\n+ <requirement type="package" version="0.21.3">scikit-learn</requirement>\n+ <requirement type="package" version="2.2.2">matplotlib</requirement>\n+\t<requirement type="package" version="1.17">numpy</requirement>\n+ </requirements>\n+ <command detect_errors="exit_code">\n+ <![CDATA[\n+ \tpython $__tool_directory__/marea_cluster.py\n+ --input $input\n+ \t--tool_dir $__tool_directory__\n+ --out_log $log\n+ #if $data.clust_type == \'kmeans\':\n+ \t--k_min ${data.k_min}\n+ \t--k_max ${data.k_max}\n+ \t--elbow ${data.elbow}\n+ \t--silhouette ${data.silhouette}\n+ #end if\n+ #if $data.clust_type == \'dbscan\':\n+ \t#if $data.dbscan_advanced.advanced == \'true\'\n+ \t\t--eps ${data.dbscan_advanced.eps}\n+ \t\t--min_samples ${data.dbscan_advanced.min_samples}\n+ \t#end if\n+ #end if\n+ #if $data.clust_type == \'hierarchy\':\n+ \t--k_min ${data.k_min}\n+ \t--k_max ${data.k_max}\n+ \t#end if\n+ ]]>\n+ </command>\n+ <inputs>\n+ <param name="input" argument="--input" type="data" format="tabular, csv, tsv" label="RNAseq of all samples" />\n+ \n+ <conditional name="data">\n+\t\t\t<param name="clust_type" argument="--cluster_type" type="select" label="Choose clustering type:">\n+ \t<option value="kmeans" selected="true">KMeans</option>\n+ \t<option value="dbscan">DBSCAN</option>\n+ \t<option value="hierarchy">Agglomerative Hierarchical</option>\n+ \t</param>\n+ \t<when value="kmeans">\n+ \t\t<param name="k_min" argument="--k_min" type="integer" min="2" max="99" value="3" label="Min number of clusters (k) to be tested" />\n+ \t\t<param name="k_max" argument="--k_max" type="integer" min="3" max="99" value="5" label="Max number of clusters (k) to be tested" />\n+ \t\t<param name="elbow" argument="--elbow" type="boolean" value="true" label="Draw the elbow plot from k-min to k-max"/>\n+ \t\t<param name="silhouette" argument="--silhouette" type="boolean" value="true" label="Draw the Silhouette plot from k-min to k-max"/>\n+ \t</when>\n+ \t<when value="dbscan">\n+ \t\t<conditional name="dbscan_advanced">\n+ \t\t\t<param name="advanced" type="boolean" value="false" label="Want to use custom params for DBSCAN? (if not optimal values will be used)">\n+ \t\t\t\t<option value="true">Yes</option>\n+ \t\t\t\t<option value="false">No</option>\n+ \t\t\t</param>\n+ \t\t\t<when value="false"></when>\n+ \t\t\t<when value="true">\n+ \t\t\t\t<param name="eps" argument="--eps" type="float" value="0.5" label="Epsilon - The maximum distance between two samples for one to be considered as in the neighborhood of the other" />\n+ \t\t\t\t<param name="min_samples" argument="min_samples" type="integer" value="5" label="Min samples - The number of samples in a neighborhood for a point to be considered as a core point (this includes the point itself)"/>\n+ \t\t\t\n+ \t\t\t</when>\n+ \t\t</conditional> \t\n+ \t</when>\n+ \t<when value="hierarchy">\n+ \t\t<param name="k_min" argument="--k_min" type="integer" min="2" max="99" value="3" label="Min number of clusters (k) to be tested" />\n+ \t\t<param name="k_max" argument="--k_max" type="integer" min="3" max="99" value="5" label="Max number of clusters (k) to be tested" />\n+ \t</when>\n+\t\t</conditional>\n+ </inputs>\n+\n+ <outputs>\n+ <data format="txt" name="log" label="${tool.name} - Log" />\n+ <collection name="results" type="list" label="${tool.name} - Results">\n+ <discover_datasets pattern="__name_and_ext__" directory="clustering"/>\n+ </collection>\n+ </outputs>\n+ <help>\n+<![CDATA[\n+\n+What it does\n+-------------\n+\n+\n+]]>\n+ </help>\n+ <expand macro="citations" />\n+</tool>\n+\t\n+\t\n' |
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| diff -r d0e7f14b773f -r c71ac0bb12de marea-1.0.1/local/HMRcoreMap.svg --- a/marea-1.0.1/local/HMRcoreMap.svg Tue Oct 01 06:03:12 2019 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
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| diff -r d0e7f14b773f -r c71ac0bb12de marea-1.0.1/local/desktop.ini --- a/marea-1.0.1/local/desktop.ini Tue Oct 01 06:03:12 2019 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
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| @@ -1,6 +0,0 @@ -[.ShellClassInfo] -IconResource=C:\WINDOWS\System32\SHELL32.dll,4 -[ViewState] -Mode= -Vid= -FolderType=Generic |
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| diff -r d0e7f14b773f -r c71ac0bb12de marea-1.0.1/marea.py --- a/marea-1.0.1/marea.py Tue Oct 01 06:03:12 2019 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| [ |
| b'@@ -1,825 +0,0 @@\n-from __future__ import division\n-import sys\n-import pandas as pd\n-import itertools as it\n-import scipy.stats as st\n-import collections\n-import lxml.etree as ET\n-import shutil\n-import pickle as pk\n-import math\n-import os\n-import argparse\n-from svglib.svglib import svg2rlg\n-from reportlab.graphics import renderPDF\n-\n-########################## argparse ##########################################\n-\n-def process_args(args):\n- parser = argparse.ArgumentParser(usage = \'%(prog)s [options]\',\n- description = \'process some value\\\'s\'+\n- \' genes to create a comparison\\\'s map.\')\n- parser.add_argument(\'-rs\', \'--rules_selector\', \n- type = str,\n- default = \'HMRcore\',\n- choices = [\'HMRcore\', \'Recon\', \'Custom\'], \n- help = \'chose which type of dataset you want use\')\n- parser.add_argument(\'-cr\', \'--custom\',\n- type = str,\n- help=\'your dataset if you want custom rules\')\n- parser.add_argument(\'-na\', \'--names\', \n- type = str,\n- nargs = \'+\', \n- help = \'input names\')\n- parser.add_argument(\'-n\', \'--none\',\n- type = str,\n- default = \'true\',\n- choices = [\'true\', \'false\'], \n- help = \'compute Nan values\')\n- parser.add_argument(\'-pv\' ,\'--pValue\', \n- type = float, \n- default = 0.05, \n- help = \'P-Value threshold (default: %(default)s)\')\n- parser.add_argument(\'-fc\', \'--fChange\', \n- type = float, \n- default = 1.5, \n- help = \'Fold-Change threshold (default: %(default)s)\')\n- parser.add_argument(\'-td\', \'--tool_dir\',\n- type = str,\n- required = True,\n- help = \'your tool directory\')\n- parser.add_argument(\'-op\', \'--option\', \n- type = str, \n- choices = [\'datasets\', \'dataset_class\'],\n- help=\'dataset or dataset and class\')\n- parser.add_argument(\'-ol\', \'--out_log\', \n- help = "Output log") \n- parser.add_argument(\'-ids\', \'--input_datas\', \n- type = str,\n- nargs = \'+\', \n- help = \'input datasets\')\n- parser.add_argument(\'-id\', \'--input_data\',\n- type = str,\n- help = \'input dataset\')\n- parser.add_argument(\'-ic\', \'--input_class\', \n- type = str, \n- help = \'sample group specification\')\n- parser.add_argument(\'-cm\', \'--custom_map\', \n- type = str, \n- help = \'custom map\')\n- parser.add_argument(\'-yn\', \'--yes_no\', \n- type = str,\n- choices = [\'yes\', \'no\'],\n- help = \'if make or not custom map\')\n- parser.add_argument(\'-gs\', \'--generate_svg\',\n- type = str,\n- default = \'true\',\n- choices = [\'true\', \'false\'], \n- help = \'generate svg map\')\n- parser.add_argument(\'-gp\', \'--generate_pdf\',\n- type = str,\n- default = \'true\',\n- choices = [\'true\', \'false\'], \n- help = \'generate pdf map\')\n- parser.add_argument(\'-gr\', \'--generate_ras\',\n- type = str,\n- default = \'true\',\n- choices = [\'true\', \'false\'],\n- help = \'generate reaction activity score\')\n- args = parser.parse_args()\n- return args\n-\n-########################### warning ######'..b' #############################################\n-\n-def main():\n- args = process_args(sys.argv)\n- \n- create_svg = check_bool(args.generate_svg)\n- create_pdf = check_bool(args.generate_pdf)\n- generate_ras = check_bool(args.generate_ras)\n- \n- os.makedirs(\'result\')\n-\n- if generate_ras:\n- os.makedirs(\'ras\')\n- \n- if args.rules_selector == \'HMRcore\': \n- recon = pk.load(open(args.tool_dir + \'/local/HMRcore_rules.p\', \'rb\'))\n- elif args.rules_selector == \'Recon\':\n- recon = pk.load(open(args.tool_dir + \'/local/Recon_rules.p\', \'rb\'))\n- elif args.rules_selector == \'Custom\':\n- ids, rules, gene_in_rule = make_recon(args.custom)\n- \n- resolve_none = check_bool(args.none)\n- \n- class_pat = {}\n- \n- if args.option == \'datasets\':\n- num = 1\n- for i, j in zip(args.input_datas, args.names):\n-\n- name = name_dataset(j, num)\n- dataset = read_dataset(i, name)\n-\n- dataset.iloc[:, 0] = (dataset.iloc[:, 0]).astype(str)\n-\n- type_gene = gene_type(dataset.iloc[0, 0], name) \n- \n- if args.rules_selector != \'Custom\':\n- genes = data_gene(dataset, type_gene, name, None)\n- ids, rules = load_id_rules(recon.get(type_gene))\n- elif args.rules_selector == \'Custom\':\n- genes = data_gene(dataset, type_gene, name, gene_in_rule)\n- \n- resolve_rules, err = resolve(genes, rules, ids, resolve_none, name)\n-\n- if generate_ras:\n- create_ras(resolve_rules, name)\n- \n- \n- if err != None and err:\n- warning(\'Warning: gene\\n\' + str(err) + \'\\nnot found in class \'\n- + name + \', the expression level for this gene \' +\n- \'will be considered NaN\\n\')\n- if resolve_rules != None:\n- class_pat[name] = list(map(list, zip(*resolve_rules.values())))\n- num += 1\n- elif args.option == \'dataset_class\':\n- name = \'RNAseq\'\n- dataset = read_dataset(args.input_data, name)\n- dataset.iloc[:, 0] = (dataset.iloc[:, 0]).astype(str)\n- type_gene = gene_type(dataset.iloc[0, 0], name)\n- classes = read_dataset(args.input_class, \'class\')\n- if not len(classes.columns) == 2:\n- warning(\'Warning: more than 2 columns in class file. Extra\' +\n- \'columns have been disregarded\\n\')\n- classes = classes.astype(str)\n- if args.rules_selector != \'Custom\':\n- genes = data_gene(dataset, type_gene, name, None)\n- ids, rules = load_id_rules(recon.get(type_gene))\n- elif args.rules_selector == \'Custom\':\n- genes = data_gene(dataset, type_gene, name, gene_in_rule)\n- resolve_rules, err = resolve(genes, rules, ids, resolve_none, name)\n- if err != None and err:\n- warning(\'Warning: gene\\n\'+str(err)+\'\\nnot found in class \'\n- + name + \', the expression level for this gene \' +\n- \'will be considered NaN\\n\')\n- if resolve_rules != None:\n- class_pat = split_class(classes, resolve_rules)\n- \n- if args.rules_selector == \'Custom\':\n- if args.yes_no == \'yes\':\n- try:\n- core_map = ET.parse(args.custom_map)\n- except (ET.XMLSyntaxError, ET.XMLSchemaParseError):\n- sys.exit(\'Execution aborted: custom map in wrong format\')\n- elif args.yes_no == \'no\':\n- core_map = ET.parse(args.tool_dir + \'/local/HMRcoreMap.svg\')\n- else: \n- core_map = ET.parse(args.tool_dir+\'/local/HMRcoreMap.svg\')\n- \n- maps(core_map, class_pat, ids, args.pValue, args.fChange, create_svg, create_pdf)\n- \n- print(\'Execution succeded\')\n-\n- return None\n-\n-###############################################################################\n-\n-if __name__ == "__main__":\n- main()\n' |
| b |
| diff -r d0e7f14b773f -r c71ac0bb12de marea-1.0.1/marea.xml --- a/marea-1.0.1/marea.xml Tue Oct 01 06:03:12 2019 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| [ |
| b'@@ -1,223 +0,0 @@\n-<tool id="MaREA" name="Metabolic Enrichment Analysis" version="1.0.1">\n- <description>for Galaxy - 1.0.1</description>\n- <macros>\n- <import>marea_macros.xml</import>\n- </macros>\n- <requirements>\n- <requirement type="package" version="0.23.0">pandas</requirement>\n- <requirement type="package" version="1.1.0">scipy</requirement>\n- <requirement type="package" version="0.10.1">cobra</requirement>\n- <requirement type="package" version="4.2.1">lxml</requirement>\n- <requirement type="package" version="0.8.1">svglib</requirement>\n- <requirement type="package" version="3.4.0">reportlab</requirement>\n- </requirements>\n- <command detect_errors="exit_code">\n- <![CDATA[\n- \tpython $__tool_directory__/marea.py\n- --rules_selector $cond_rule.rules_selector\n- #if $cond_rule.rules_selector == \'Custom\':\n- --custom ${cond_rule.Custom_rules}\n- --yes_no ${cond_rule.cond_map.yes_no}\n- #if $cond_rule.cond_map.yes_no == \'yes\':\n- --custom_map $cond_rule.cond_map.Custom_map\n- #end if\n- #end if\n-\t#if $advanced.choice == \'true\':\n- \t --none ${advanced.None}\n- \t --pValue ${advanced.pValue}\n- \t --fChange ${advanced.fChange}\n-\t --generate_svg ${advanced.generateSvg}\n-\t --generate_pdf ${advanced.generatePdf}\n-\t --generate_ras ${advanced.generateRas}\n-\t#else \n-\t --none true\n-\t --pValue 0.05\n-\t --fChange 1.5\n-\t --generate_svg false\n-\t --generate_pdf true\n-\t --generate_ras false\n-\t#end if\n- \t--tool_dir $__tool_directory__\n- \t--option $cond.type_selector\n- --out_log $log\t\t\n-\t\n- #if $cond.type_selector == \'datasets\':\n- --input_datas\n- #for $data in $cond.input_Datasets:\n- ${data.input}\n- #end for\n- --names\n- #for $data in $cond.input_Datasets:\n- ${data.input_name}\n- #end for\n- #elif $cond.type_selector == \'dataset_class\':\n- --input_data ${input_data}\n- --input_class ${input_class}\n- #end if\n- ]]>\n- </command>\n-\n- <inputs>\n- <conditional name="cond_rule">\n- <expand macro="options"/>\n- <when value="HMRcore">\n- </when>\n- <when value="Recon">\n- </when>\n- <when value="Custom">\n- <param name="Custom_rules" type="data" format="tabular, csv, tsv, xml" label="Custom rules" />\n- <conditional name="cond_map">\n- <param name="yes_no" type="select" label="Custom map? (optional)">\n- <option value="no" selected="true">no</option>\n- <option value="yes">yes</option>\n- </param>\n- <when value="yes">\n- <param name="Custom_map" argument="--custom_map" type="data" format="xml, svg" label="custom-map.svg"/>\n- </when>\n- <when value="no">\n- </when>\n- </conditional>\n- </when>\n- </conditional>\n- <conditional name="cond">\n- <param name="type_selector" argument="--option" type="select" label="Input format:">\n- <option value="datasets" selected="true">RNAseq of group 1 + RNAseq of group 2 + ... + RNAseq of group N</option>\n- <option value="dataset_class">RNAseq of all samples + sample group specification</option>\n- </param>\n- <when value="datasets">\n- <repeat name="input_Datasets" title="RNAseq" min="2">\n- <param name="input" argument="--input_datas" type="data" format="tabular, csv, tsv" label="add dataset" />\t\n- <param name="input_name" argument="--names" type="text" label="Dataset\'s name:" value="Dataset" help="Default: Dataset" />\n- </repeat>\n- '..b'ity Score for each table" help="Generate Reaction Activity Score for each table" />\t\t\n-\t\t</when>\n- \t</conditional>\n- </inputs>\n-\n- <outputs>\n- <data format="txt" name="log" label="${tool.name} - Log" />\n- <collection name="results" type="list" label="${tool.name} - Results">\n- <discover_datasets pattern="__name_and_ext__" directory="result"/>\n- </collection>\n-\t<collection name="ras" type="list" label="${tool.name} - RAS" format_source="tabular">\n-\t <filter>advanced[\'choice\'] and advanced[\'generateRas\']</filter>\n- \t <discover_datasets pattern="__name_and_ext__" directory="ras" format="tabular"/>\n-\t</collection>\n- </outputs>\n- <tests>\n- <test>\n- <param name="pValue" value="0.56"/>\n- <output name="log" file="log.txt"/>\n- </test>\n- </tests>\n- <help>\n-<![CDATA[\n-\n-What it does\n--------------\n-\n-This tool analyzes RNA-seq dataset(s) as described in Graudenzi et al."`MaREA`_: Metabolic feature extraction, enrichment and visualization of RNAseq data" bioRxiv (2018): 248724.\n-\n-Accepted files are: \n- - option 1) two or more RNA-seq datasets, each referring to samples in a given condition/class. The user can specify a label for each class (as e.g. "*classA*" and "*classB*");\n- - option 2) one RNA dataset and one class-file specifying the class/condition each sample belongs to.\n-\n-Optional files:\n- - custom GPR (Gene-Protein-Reaction) rules. Two accepted formats:\n-\n-\t* (Cobra Toolbox and CobraPy compliant) xml of metabolic model;\n-\t* .csv file specifyig for each reaction ID (column 1) the corresponding GPR rule (column 2).\n- - custom svg map. Graphical elements must have the same IDs of reactions. See HmrCore svg map for an example.\n-\n-The tool generates:\n- 1) a tab-separated file: reporting fold-change and p-values of reaction activity scores (RASs) between a pair of conditions/classes;\n- 2) a metabolic map file (downlodable as .svg): visualizing up- and down-regulated reactions between a pair of conditions/classes;\n- 3) a log file (.txt).\n-\n-RNA-seq datasets format: tab-separated text files, reporting the expression level (e.g., TPM, RPKM, ...) of each gene (row) for a given sample (column). Header: sample ID.\n-\n-Class-file format: each row of the class-file reports the sample ID (column1) and the label of the class/condition the sample belongs to (column 2).\n-\n-To calculate P-Values and Fold-Changes and to generate maps, comparisons are performed for each possible pair of classes.\n-\n-Output files will be named as classA_vs_classB. Reactions will conventionally be reported as up-regulated (down-regulated) if they are significantly more (less) active in class having label "classA".\n-\n-\n-Example input\n--------------\n-\n-**"Custom Rules"** option:\n-\n-Custom Rules Dastaset:\n-\n-@CUSTOM_RULES_EXEMPLE@\n-\n-**"RNAseq of group 1 + RNAseq of group 2 + ... + RNAseq of group N"** option:\n-\n-RNA-seq Dataset 1:\t\t\t\t\t\t\n-\n-@DATASET_EXEMPLE1@\n-\n-RNA-seq Dataset 2:\n-\n-@DATASET_EXEMPLE2@\n-\n-**"RNAseq of all samples + sample group specification"** option:\n-\n-RNA-seq Dataset:\n-\n-@DATASET_EXEMPLE1@\n-\n-Class-file:\n-\n-+------------+------------+ \n-| Patient_ID | class | \n-+============+============+ \n-| TCGAAA3529 | MSI | \n-+------------+------------+ \n-| TCGAA62671 | MSS | \n-+------------+------------+ \n-| TCGAA62672 | MSI | \n-+------------+------------+\n-\n-|\n-\n-.. class:: infomark\n-\n-**TIP**: If your data is not TAB delimited, use `Convert delimiters to TAB`_.\n-\n-.. class:: infomark\n-\n-**TIP**: If your dataset is not split into classes, use `MaREA cluster analysis`_.\n-\n-@REFERENCE@\n-\n-.. _MaREA: https://www.biorxiv.org/content/early/2018/01/16/248724\n-.. _Convert delimiters to TAB: https://usegalaxy.org/?tool_id=Convert+characters1&version=1.0.0&__identifer=6t22teyofhj\n-.. _MaREA cluster analysis: http://link del tool di cluster.org\n-\n-]]>\n- </help>\n- <expand macro="citations" />\n-</tool>\n-\t\n' |
| b |
| diff -r d0e7f14b773f -r c71ac0bb12de marea-1.0.1/marea_cluster.py --- a/marea-1.0.1/marea_cluster.py Tue Oct 01 06:03:12 2019 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| [ |
| b'@@ -1,417 +0,0 @@\n-# -*- coding: utf-8 -*-\n-"""\n-Created on Mon Jun 3 19:51:00 2019\n-\n-@author: Narger\n-"""\n-\n-import sys\n-import argparse\n-import os\n-from sklearn.datasets import make_blobs\n-from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering\n-from sklearn.metrics import silhouette_samples, silhouette_score, davies_bouldin_score, cluster\n-import matplotlib.pyplot as plt\n-import scipy.cluster.hierarchy as shc \n-import matplotlib.cm as cm\n-import numpy as np\n-import pandas as pd\n-\n-################################# process args ###############################\n-\n-def process_args(args):\n- parser = argparse.ArgumentParser(usage = \'%(prog)s [options]\',\n- description = \'process some value\\\'s\' +\n- \' genes to create class.\')\n-\n- parser.add_argument(\'-ol\', \'--out_log\', \n- help = "Output log")\n- \n- parser.add_argument(\'-in\', \'--input\',\n- type = str,\n- help = \'input dataset\')\n- \n- parser.add_argument(\'-cy\', \'--cluster_type\',\n- type = str,\n- choices = [\'kmeans\', \'meanshift\', \'dbscan\', \'hierarchy\'],\n- default = \'kmeans\',\n- help = \'choose clustering algorythm\')\n- \n- parser.add_argument(\'-k1\', \'--k_min\', \n- type = int,\n- default = 2,\n- help = \'choose minimun cluster number to be generated\')\n- \n- parser.add_argument(\'-k2\', \'--k_max\', \n- type = int,\n- default = 7,\n- help = \'choose maximum cluster number to be generated\')\n- \n- parser.add_argument(\'-el\', \'--elbow\', \n- type = str,\n- default = \'false\',\n- choices = [\'true\', \'false\'],\n- help = \'choose if you want to generate an elbow plot for kmeans\')\n- \n- parser.add_argument(\'-si\', \'--silhouette\', \n- type = str,\n- default = \'false\',\n- choices = [\'true\', \'false\'],\n- help = \'choose if you want silhouette plots\')\n- \n- parser.add_argument(\'-db\', \'--davies\', \n- type = str,\n- default = \'false\',\n- choices = [\'true\', \'false\'],\n- help = \'choose if you want davies bouldin scores\')\n- \n- parser.add_argument(\'-td\', \'--tool_dir\',\n- type = str,\n- required = True,\n- help = \'your tool directory\')\n- \n- parser.add_argument(\'-ms\', \'--min_samples\',\n- type = int,\n- help = \'min samples for dbscan (optional)\')\n- \n- parser.add_argument(\'-ep\', \'--eps\',\n- type = int,\n- help = \'eps for dbscan (optional)\')\n- \n- \n- args = parser.parse_args()\n- return args\n-\n-########################### warning ###########################################\n-\n-def warning(s):\n- args = process_args(sys.argv)\n- with open(args.out_log, \'a\') as log:\n- log.write(s + "\\n\\n")\n- print(s)\n-\n-########################## read dataset ######################################\n- \n-def read_dataset(dataset):\n- try:\n- dataset = pd.read_csv(dataset, sep = \'\\t\', header = 0)\n- except pd.errors.EmptyDataError:\n- sys.exit(\'Execution aborted: wrong format of dataset\\n\')\n- if len(dataset.columns) < 2:\n- sys.exit(\'Execution aborted: wrong format of dataset\\n\')\n- return dataset\n-\n-############################ rewrite_input ###################################\n- \n-def rewrite_input(dataset):\n- #Riscrivo il dataset come dizionario di liste, \n- #non come dizionario di dizionari\n- \n- for'..b' warning("For n_clusters =" + str(n_clusters_) + \n- "The average silhouette_score is :" + str(silhouette_avg))\n- \n- ##TODO: PLOT SU DBSCAN (no centers) e HIERARCHICAL\n-\n- # Black removed and is used for noise instead.\n- unique_labels = set(labels)\n- colors = [plt.cm.Spectral(each)\n- for each in np.linspace(0, 1, len(unique_labels))]\n- for k, col in zip(unique_labels, colors):\n- if k == -1:\n- # Black used for noise.\n- col = [0, 0, 0, 1]\n-\n- class_member_mask = (labels == k)\n- \n- xy = dataset[class_member_mask & core_samples_mask]\n- plt.plot(xy[:, 0], xy[:, 1], \'o\', markerfacecolor=tuple(col),\n- markeredgecolor=\'k\', markersize=14)\n- \n- xy = dataset[class_member_mask & ~core_samples_mask]\n- plt.plot(xy[:, 0], xy[:, 1], \'o\', markerfacecolor=tuple(col),\n- markeredgecolor=\'k\', markersize=6)\n-\n- plt.title(\'Estimated number of clusters: %d\' % n_clusters_)\n- s = \'clustering/dbscan_output/dbscan_plot.png\'\n- fig = plt.gcf()\n- fig.set_size_inches(18.5, 10.5, forward = True)\n- fig.savefig(s, dpi=100)\n- \n- \n- write_to_csv(dataset, labels, \'clustering/dbscan_output/dbscan_results.tsv\')\n- \n-########################## hierachical #######################################\n- \n-def hierachical_agglomerative(dataset, k_min, k_max):\n-\n- if not os.path.exists(\'clustering/agglomerative_output\'):\n- os.makedirs(\'clustering/agglomerative_output\')\n- \n- plt.figure(figsize=(10, 7)) \n- plt.title("Customer Dendograms") \n- shc.dendrogram(shc.linkage(dataset, method=\'ward\')) \n- fig = plt.gcf()\n- fig.savefig(\'clustering/agglomerative_output/dendogram.png\', dpi=200)\n- \n- range_n_clusters = [i for i in range(k_min, k_max+1)]\n-\n- for n_clusters in range_n_clusters:\n- \n- cluster = AgglomerativeClustering(n_clusters=n_clusters, affinity=\'euclidean\', linkage=\'ward\') \n- cluster.fit_predict(dataset) \n- cluster_labels = cluster.labels_\n- \n- silhouette_avg = silhouette_score(dataset, cluster_labels)\n- warning("For n_clusters =", n_clusters,\n- "The average silhouette_score is :", silhouette_avg)\n- \n- plt.clf()\n- plt.figure(figsize=(10, 7)) \n- plt.title("Agglomerative Hierarchical Clustering\\nwith " + str(n_clusters) + " clusters and " + str(silhouette_avg) + " silhouette score")\n- plt.scatter(dataset[:,0], dataset[:,1], c = cluster_labels, cmap=\'rainbow\') \n- s = \'clustering/agglomerative_output/hierachical_\' + str(n_clusters) + \'_clusters.png\'\n- fig = plt.gcf()\n- fig.set_size_inches(10, 7, forward = True)\n- fig.savefig(s, dpi=200)\n- \n- write_to_csv(dataset, cluster_labels, \'clustering/agglomerative_output/agglomerative_hierarchical_with_\' + str(n_clusters) + \'_clusters.tsv\')\n- \n- \n-\n- \n-############################# main ###########################################\n-\n-\n-def main():\n- if not os.path.exists(\'clustering\'):\n- os.makedirs(\'clustering\')\n-\n- args = process_args(sys.argv)\n- \n- #Data read\n- \n- X = read_dataset(args.input)\n- X = pd.DataFrame.to_dict(X, orient=\'list\')\n- X = rewrite_input(X)\n- X = pd.DataFrame.from_dict(X, orient = \'index\')\n- \n- for i in X.columns:\n- tmp = X[i][0]\n- if tmp == None:\n- X = X.drop(columns=[i])\n- \n- X = pd.DataFrame.to_numpy(X)\n- \n- \n- if args.cluster_type == \'kmeans\':\n- kmeans(args.k_min, args.k_max, X, args.elbow, args.silhouette, args.davies)\n- \n- if args.cluster_type == \'dbscan\':\n- dbscan(X, args.eps, args.min_samples)\n- \n- if args.cluster_type == \'hierarchy\':\n- hierachical_agglomerative(X, args.k_min, args.k_max)\n- \n-##############################################################################\n-\n-if __name__ == "__main__":\n- main()\n' |
| b |
| diff -r d0e7f14b773f -r c71ac0bb12de marea-1.0.1/marea_cluster.xml --- a/marea-1.0.1/marea_cluster.xml Tue Oct 01 06:03:12 2019 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| [ |
| @@ -1,92 +0,0 @@ -<tool id="MaREA_cluester" name="MaREA cluster analysis" version="1.0.1"> - <description>of Reaction Activity Scores - 1.0.1</description> - <macros> - <import>marea_macros.xml</import> - </macros> - <requirements> - <requirement type="package" version="0.23.0">pandas</requirement> - <requirement type="package" version="1.1.0">scipy</requirement> - <requirement type="package" version="0.10.1">cobra</requirement> - <requirement type="package" version="0.21.3">scikit-learn</requirement> - <requirement type="package" version="2.2.2">matplotlib</requirement> - <requirement type="package" version="1.17">numpy</requirement> - </requirements> - <command detect_errors="exit_code"> - <![CDATA[ - python $__tool_directory__/marea_cluster.py - --input $input - --tool_dir $__tool_directory__ - --out_log $log - #if $data.clust_type == 'kmeans': - --k_min ${data.k_min} - --k_max ${data.k_max} - --elbow ${data.elbow} - --silhouette ${data.silhouette} - #end if - #if $data.clust_type == 'dbscan': - #if $data.dbscan_advanced.advanced == 'true' - --eps ${data.dbscan_advanced.eps} - --min_samples ${data.dbscan_advanced.min_samples} - #end if - #end if - #if $data.clust_type == 'hierarchy': - --k_min ${data.k_min} - --k_max ${data.k_max} - #end if - ]]> - </command> - <inputs> - <param name="input" argument="--input" type="data" format="tabular, csv, tsv" label="RNAseq of all samples" /> - - <conditional name="data"> - <param name="clust_type" argument="--cluster_type" type="select" label="Choose clustering type:"> - <option value="kmeans" selected="true">KMeans</option> - <option value="dbscan">DBSCAN</option> - <option value="hierarchy">Agglomerative Hierarchical</option> - </param> - <when value="kmeans"> - <param name="k_min" argument="--k_min" type="integer" min="2" max="99" value="3" label="Min number of clusters (k) to be tested" /> - <param name="k_max" argument="--k_max" type="integer" min="3" max="99" value="5" label="Max number of clusters (k) to be tested" /> - <param name="elbow" argument="--elbow" type="boolean" value="true" label="Draw the elbow plot from k-min to k-max"/> - <param name="silhouette" argument="--silhouette" type="boolean" value="true" label="Draw the Silhouette plot from k-min to k-max"/> - </when> - <when value="dbscan"> - <conditional name="dbscan_advanced"> - <param name="advanced" type="boolean" value="false" label="Want to use custom params for DBSCAN? (if not optimal values will be used)"> - <option value="true">Yes</option> - <option value="false">No</option> - </param> - <when value="false"></when> - <when value="true"> - <param name="eps" argument="--eps" type="float" value="0.5" label="Epsilon - The maximum distance between two samples for one to be considered as in the neighborhood of the other" /> - <param name="min_samples" argument="min_samples" type="integer" value="5" label="Min samples - The number of samples in a neighborhood for a point to be considered as a core point (this includes the point itself)"/> - - </when> - </conditional> - </when> - <when value="hierarchy"> - <param name="k_min" argument="--k_min" type="integer" min="2" max="99" value="3" label="Min number of clusters (k) to be tested" /> - <param name="k_max" argument="--k_max" type="integer" min="3" max="99" value="5" label="Max number of clusters (k) to be tested" /> - </when> - </conditional> - </inputs> - - <outputs> - <data format="txt" name="log" label="${tool.name} - Log" /> - <collection name="results" type="list" label="${tool.name} - Results"> - <discover_datasets pattern="__name_and_ext__" directory="clustering"/> - </collection> - </outputs> - <help> -<![CDATA[ - -What it does -------------- - - -]]> - </help> - <expand macro="citations" /> -</tool> - - |
| b |
| diff -r d0e7f14b773f -r c71ac0bb12de marea-1.0.1/marea_macros.xml --- a/marea-1.0.1/marea_macros.xml Tue Oct 01 06:03:12 2019 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| @@ -1,92 +0,0 @@ -<macros> - - <xml name="options"> - <param name="rules_selector" argument="--rules_selector" type="select" label="Gene-Protein-Reaction rules:"> - <option value="HMRcore" selected="true">HMRcore rules</option> - <option value="Recon">Recon 2.2 rules</option> - <option value="Custom">Custom rules</option> - </param> - </xml> - - <token name="@CUSTOM_RULES_EXEMPLE@"> - -+--------------------+-------------------------------+ -| id | rule (with entrez-id) | -+====================+===============================+ -| SHMT1 | 155060 or 10357 | -+--------------------+-------------------------------+ -| NIT2 | 155060 or 100134869 | -+--------------------+-------------------------------+ -| GOT1_GOT2_GOT1L1_2 | 155060 and 100134869 or 10357 | -+--------------------+-------------------------------+ - -| - - </token> - - <token name="@DATASET_EXEMPLE1@"> - -+------------+------------+------------+------------+ -| Hugo_ID | TCGAA62670 | TCGAA62671 | TCGAA62672 | -+============+============+============+============+ -| HGNC:24086 | 0.523167 | 0.371355 | 0.925661 | -+------------+------------+------------+------------+ -| HGNC:24086 | 0.568765 | 0.765567 | 0.456789 | -+------------+------------+------------+------------+ -| HGNC:9876 | 0.876545 | 0.768933 | 0.987654 | -+------------+------------+------------+------------+ -| HGNC:9 | 0.456788 | 0.876543 | 0.876542 | -+------------+------------+------------+------------+ -| HGNC:23 | 0.876543 | 0.786543 | 0.897654 | -+------------+------------+------------+------------+ - -| - - </token> - - <token name="@DATASET_EXEMPLE2@"> - -+-------------+------------+------------+------------+ -| Hugo_Symbol | TCGAA62670 | TCGAA62671 | TCGAA62672 | -+=============+============+============+============+ -| A1BG | 0.523167 | 0.371355 | 0.925661 | -+-------------+------------+------------+------------+ -| A1CF | 0.568765 | 0.765567 | 0.456789 | -+-------------+------------+------------+------------+ -| A2M | 0.876545 | 0.768933 | 0.987654 | -+-------------+------------+------------+------------+ -| A4GALT | 0.456788 | 0.876543 | 0.876542 | -+-------------+------------+------------+------------+ -| M664Y65 | 0.876543 | 0.786543 | 0.897654 | -+-------------+------------+------------+------------+ - -| - - </token> - - <token name="@REFERENCE@"> - -This tool is developed by the `BIMIB`_ at the `Department of Informatics, Systems and Communications`_ of `University of Milan - Bicocca`_. - -.. _BIMIB: http://sito di bio.org -.. _Department of Informatics, Systems and Communications: http://www.disco.unimib.it/go/Home/English -.. _University of Milan - Bicocca: https://www.unimib.it/ - - </token> - - <xml name="citations"> - <citations> <!--esempio di citazione--> - <citation type="bibtex"> -@online{lh32017, - author = {Alex Graudenzi, Davide Maspero, Cluadio Isella, Marzia Di Filippo, Giancarlo Mauri, Enzo Medico, Marco Antoniotti, Chiara Damiani}, - year = {2018}, - title = {MaREA: Metabolic feature extraction, enrichment and visualization of RNAseq}, - publisher = {bioRxiv}, - journal = {bioRxiv}, - url = {https://www.biorxiv.org/content/early/2018/01/16/248724}, -} - </citation> - </citations> - </xml> - -</macros> |
