Mercurial > repos > bimib > marea
changeset 0:23ac9cf12788 draft
Uploaded
author | bimib |
---|---|
date | Tue, 06 Nov 2018 03:16:21 -0500 |
parents | |
children | 9e63d5f02d62 |
files | Marea/local/HMRcoreMap.svg Marea/local/HMRcore_genes.p Marea/local/HMRcore_rules.p Marea/local/Recon_genes.p Marea/local/Recon_rules.p Marea/marea.py Marea/marea.xml Marea/marea_cluster.py Marea/marea_cluster.xml |
diffstat | 9 files changed, 9407 insertions(+), 0 deletions(-) [+] |
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class="st17 st18" + x="901.06671" + y="208.81564">4 ADP</text> +<text + style="font-size:10px;font-family:Calibri" + id="text187" + class="st17 st18" + x="836.94171" + y="209.48065">Fum</text> +<text + style="font-size:16px;font-family:Calibri-Bold" + id="text189" + class="st15 st16" + x="1001.889" + y="227.94115">IMP</text> +<text + style="font-size:10px;font-family:Calibri" + id="text191" + class="st17 st18" + x="802.77081" + y="243.09105">2 Gln</text> +<text + style="font-size:10px;font-family:Calibri" + id="text193" + class="st17 st18" + x="812.37921" + y="209.35135">2 Glu</text> +<text + style="font-size:10px;font-family:Calibri" + id="text195" + class="st17 st18" + x="890.34705" + y="243.16335">4 ATP</text> +<text + style="font-size:10px;font-family:Calibri" + id="text197" + class="st17 st18" + x="868.02661" + y="242.86446">H</text> +<text + style="font-size:6.09369993px;font-family:Calibri" + id="text199" + class="st17 st26" + x="874.25714" + y="244.86446">2</text> +<text + style="font-size:10px;font-family:Calibri" + id="text201" + class="st17 st18" + x="877.34601" + y="242.86446">O</text> +<text + style="font-size:10px;font-family:Calibri" + id="text203" + class="st17 st18" + x="964.69855" + y="243.09105">CO</text> +<text + style="font-size:6.09369993px;font-family:Calibri" + id="text205" + class="st17 st26" + x="976.56384" + y="245.09105">2</text> +<text + style="font-size:16px;font-family:Calibri-Bold" + id="text207" + class="st15 st16" + x="1268.7963" + y="274.35965">GDP</text> +<text + style="font-size:10px;font-family:Calibri" + id="text209" + class="st17 st18" + x="1227.5297" + y="295.81955">ATP</text> +<text + style="font-size:10px;font-family:Calibri" + id="text211" + class="st17 st18" + x="1245.308" + y="295.39575">ADP</text> +<text + style="font-size:10px;font-family:Calibri" + id="text213" + class="st17 st18" + x="588.70343" + y="538.1087">ATP</text> +<text + style="font-size:10px;font-family:Calibri" + id="text215" + class="st17 st18" + 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st18" + x="882.31775" + y="413.45636">CO</text> +<text + style="font-size:6.09369993px;font-family:Calibri" + id="text247" + class="st17 st26" + x="894.18292" + y="415.45636">2</text> +<text + style="font-size:16px;font-family:Calibri-Bold" + id="text249" + class="st15 st16" + x="914.74835" + y="432.96216">AcAcACP</text> +<text + style="font-size:10px;font-family:Calibri" + id="text251" + class="st17 st18" + x="999.9397" + y="447.29025">14NADPH</text> +<text + style="font-size:10px;font-family:Calibri" + id="text253" + class="st17 st18" + x="1008.5541" + y="412.44064">14NADP</text> +<text + style="font-size:10px;font-family:Calibri" + id="text255" + class="st17 st18" + x="1071.347" + y="412.67795">6 H</text> +<text + style="font-size:6.09369993px;font-family:Calibri" + id="text257" + class="st17 st26" + x="1084.9066" + y="414.67795">2</text> +<text + style="font-size:10px;font-family:Calibri" + id="text259" + class="st17 st18" + x="1087.9945" + y="412.67795">O</text> +<text + 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-1.7,2.2 1.7,2.2 z m 61,-14.2 c 2.1,-5.8 2.1,-5.8 2.1,-5.8 l 2.2,5.7 -2.2,-1.4 -2.1,1.5 z" + class="st37" + inkscape:label="#F_SLC25A5_SLC25A4_SLC25A6" + inkscape:connector-curvature="0" + id="F_SLC25A5_SLC25A4_SLC25A6" /><path + style="fill:none;stroke:#000000;stroke-width:2.44919991" + d="m 1501.4,1114.8186 -73.8,0 0,0 m 21.8,0 c -5.9,-1.4 -4.4,-13.5 -4.4,-13.5 m 41.7,-1 c 3.2,6.1 -5.5,14.6 -5.5,14.6" + class="st32" + inkscape:connector-curvature="0" + id="R_ALDH3B1_ALDH3A1_ALDH3B2_ALDH1A3" /><text + style="font-size:14px;font-family:Calibri-Bold" + id="text310" + class="st15 st38" + x="1473.5541" + y="1092.8215">GTP</text> +<text + style="font-size:16px;font-family:Calibri-Bold" + id="text312" + class="st15 st16" + x="1509.8929" + y="1117.5793">GDP</text> +<text + style="font-size:14px;font-family:Calibri-Bold" + id="text314" + class="st15 st38" + x="1428.8754" + y="1092.8958">GTP</text> +<text + style="font-size:16px;font-family:Calibri-Bold" + id="text316" + class="st15 st16" + 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+<text + style="font-size:16px;font-family:Calibri-Bold" + id="text350" + class="st15 st16" + x="1500.932" + y="879.84888">CoA</text> +<text + style="font-size:16px;font-family:Calibri-Bold" + id="text352" + class="st15 st16" + x="1400.6957" + y="879.69458">CoA</text> +<path + style="fill:none;stroke:#000000;stroke-width:2.44919991" + d="m 1441.9,917.71855 c -6.9,-2.1 -6.9,-2.1 -6.9,-2.1 l 6.9,-2.3 -1.7,2.2 1.7,2.2 z" + class="st32" + inkscape:label="TransportOxygen F" + inkscape:connector-curvature="0" + id="F_TransportOxygen" /><path + style="fill:none;stroke:#000000;stroke-width:1.88160002" + d="m 1497.8,915.91855 -56.7,0 0,0" + class="st41" + inkscape:label="TransportOxygen" + inkscape:connector-curvature="0" + id="R_TransportOxygen" /><path + style="fill:none;stroke:#000000;stroke-width:2.44919991" + d="m 1440.8,854.71855 c -6.9,-2.1 -6.9,-2.1 -6.9,-2.1 l 6.9,-2.3 -1.7,2.2 1.7,2.2 z" + class="st32" + inkscape:label="forward Transport_CO2 " + inkscape:connector-curvature="0" + 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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Marea/marea.py Tue Nov 06 03:16:21 2018 -0500 @@ -0,0 +1,760 @@ + +from __future__ import division +import sys +import pandas as pd +import itertools as it +import scipy.stats as st +import collections +import lxml.etree as ET +import pickle as pk +import math +import os +import argparse +from svglib.svglib import svg2rlg +from reportlab.graphics import renderPDF + +########################## argparse ########################################### + +def process_args(args): + parser = argparse.ArgumentParser(usage = '%(prog)s [options]', + description = 'process some value\'s'+ + ' genes to create a comparison\'s map.') + parser.add_argument('-rs', '--rules_selector', + type = str, + default = 'HMRcore', + choices = ['HMRcore', 'Recon', 'Custom'], + help = 'chose which type of dataset you want use') + parser.add_argument('-cr', '--custom', + type = str, + help='your dataset if you want custom rules') + parser.add_argument('-na', '--names', + type = str, + nargs = '+', + help = 'input names') + parser.add_argument('-n', '--none', + type = str, + default = 'true', + choices = ['true', 'false'], + help = 'compute Nan values') + parser.add_argument('-pv' ,'--pValue', + type = float, + default = 0.05, + help = 'P-Value threshold (default: %(default)s)') + parser.add_argument('-fc', '--fChange', + type = float, + default = 1.5, + help = 'Fold-Change threshold (default: %(default)s)') + parser.add_argument('-td', '--tool_dir', + type = str, + required = True, + help = 'your tool directory') + parser.add_argument('-op', '--option', + type = str, + choices = ['datasets', 'dataset_class'], + help='dataset or dataset and class') + parser.add_argument('-ol', '--out_log', + help = "Output log") + parser.add_argument('-ids', '--input_datas', + type = str, + nargs = '+', + help = 'input datasets') + parser.add_argument('-id', '--input_data', + type = str, + help = 'input dataset') + parser.add_argument('-ic', '--input_class', + type = str, + help = 'sample group specification') + parser.add_argument('-cm', '--custom_map', + type = str, + help = 'custom map') + parser.add_argument('-yn', '--yes_no', + type = str, + choices = ['yes', 'no'], + help = 'if make or not custom map') + args = parser.parse_args() + return args + +########################### warning ########################################### + +def warning(s): + args = process_args(sys.argv) + with open(args.out_log, 'a') as log: + log.write(s) + +############################ dataset input #################################### + +def read_dataset(data, name): + try: + dataset = pd.read_csv(data, sep = '\t', header = 0) + except pd.errors.EmptyDataError: + sys.exit('Execution aborted: wrong format of ' + name + '\n') + if len(dataset.columns) < 2: + sys.exit('Execution aborted: wrong format of ' + name + '\n') + return dataset + +############################ dataset name ##################################### + +def name_dataset(name_data, count): + if str(name_data) == 'Dataset': + return str(name_data) + '_' + str(count) + else: + return str(name_data) + +############################ load id e rules ################################## + +def load_id_rules(reactions): + ids, rules = [], [] + for key, value in reactions.items(): + ids.append(key) + rules.append(value) + return (ids, rules) + +############################ check_methods #################################### + +def gene_type(l, name): + if check_hgnc(l): + return 'hugo_id' + elif check_ensembl(l): + return 'ensembl_gene_id' + elif check_symbol(l): + return 'symbol' + elif check_entrez(l): + return 'entrez_id' + else: + sys.exit('Execution aborted:\n' + + 'gene ID type in ' + name + ' not supported. Supported ID'+ + 'types are: HUGO ID, Ensemble ID, HUGO symbol, Entrez ID\n') + +def check_hgnc(l): + if len(l) > 5: + if (l.upper()).startswith('HGNC:'): + return l[5:].isdigit() + else: + return False + else: + return False + +def check_ensembl(l): + if len(l) == 15: + if (l.upper()).startswith('ENS'): + return l[4:].isdigit() + else: + return False + else: + return False + +def check_symbol(l): + if len(l) > 0: + if l[0].isalpha() and l[1:].isalnum(): + return True + else: + return False + else: + return False + +def check_entrez(l): + if len(l) > 0: + return l.isdigit() + else: + return False + +def check_bool(b): + if b == 'true': + return True + elif b == 'false': + return False + +############################ resolve_methods ################################## + +def replace_gene_value(l, d): + tmp = [] + err = [] + while l: + if isinstance(l[0], list): + tmp_rules, tmp_err = replace_gene_value(l[0], d) + tmp.append(tmp_rules) + err.extend(tmp_err) + else: + value = replace_gene(l[0], d) + tmp.append(value) + if value == None: + err.append(l[0]) + l = l[1:] + return (tmp, err) + +def replace_gene(l, d): + if l =='and' or l == 'or': + return l + else: + value = d.get(l, None) + if not(value == None or isinstance(value, (int, float))): + sys.exit('Execution aborted: ' + value + ' value not valid\n') + return value + +def computes(val1, op, val2, cn): + if val1 != None and val2 != None: + if op == 'and': + return min(val1, val2) + else: + return val1 + val2 + elif op == 'and': + if cn is True: + if val1 != None: + return val1 + elif val2 != None: + return val2 + else: + return None + else: + return None + else: + if val1 != None: + return val1 + elif val2 != None: + return val2 + else: + return None + +def control(ris, l, cn): + if len(l) == 1: + if isinstance(l[0], (float, int)) or l[0] == None: + return l[0] + elif isinstance(l[0], list): + return control(None, l[0], cn) + else: + return False + elif len(l) > 2: + return control_list(ris, l, cn) + else: + return False + +def control_list(ris, l, cn): + while l: + if len(l) == 1: + return False + elif (isinstance(l[0], (float, int)) or + l[0] == None) and l[1] in ['and', 'or']: + if isinstance(l[2], (float, int)) or l[2] == None: + ris = computes(l[0], l[1], l[2], cn) + elif isinstance(l[2], list): + tmp = control(None, l[2], cn) + if tmp is False: + return False + else: + ris = computes(l[0], l[1], tmp, cn) + else: + return False + l = l[3:] + elif l[0] in ['and', 'or']: + if isinstance(l[1], (float, int)) or l[1] == None: + ris = computes(ris, l[0], l[1], cn) + elif isinstance(l[1], list): + tmp = control(None,l[1], cn) + if tmp is False: + return False + else: + ris = computes(ris, l[0], tmp, cn) + else: + return False + l = l[2:] + elif isinstance(l[0], list) and l[1] in ['and', 'or']: + if isinstance(l[2], (float, int)) or l[2] == None: + tmp = control(None, l[0], cn) + if tmp is False: + return False + else: + ris = computes(tmp, l[1], l[2], cn) + elif isinstance(l[2], list): + tmp = control(None, l[0], cn) + tmp2 = control(None, l[2], cn) + if tmp is False or tmp2 is False: + return False + else: + ris = computes(tmp, l[1], tmp2, cn) + else: + return False + l = l[3:] + else: + return False + return ris + +############################ map_methods ###################################### + +def fold_change(avg1, avg2): + if avg1 == 0 and avg2 == 0: + return 0 + elif avg1 == 0: + return '-INF' + elif avg2 == 0: + return 'INF' + else: + return math.log(avg1 / avg2, 2) + +def fix_style(l, col, width, dash): + tmp = l.split(';') + flag_col = False + flag_width = False + flag_dash = False + for i in range(len(tmp)): + if tmp[i].startswith('stroke:'): + tmp[i] = 'stroke:' + col + flag_col = True + if tmp[i].startswith('stroke-width:'): + tmp[i] = 'stroke-width:' + width + flag_width = True + if tmp[i].startswith('stroke-dasharray:'): + tmp[i] = 'stroke-dasharray:' + dash + flag_dash = True + if not flag_col: + tmp.append('stroke:' + col) + if not flag_width: + tmp.append('stroke-width:' + width) + if not flag_dash: + tmp.append('stroke-dasharray:' + dash) + return ';'.join(tmp) + +def fix_map(d, core_map, threshold_P_V, threshold_F_C, max_F_C): + maxT = 12 + minT = 2 + grey = '#BEBEBE' + blue = '#0000FF' + red = '#E41A1C' + for el in core_map.iter(): + el_id = str(el.get('id')) + if el_id.startswith('R_'): + tmp = d.get(el_id[2:]) + if tmp != None: + p_val = tmp[0] + f_c = tmp[1] + if p_val < threshold_P_V: + if not isinstance(f_c, str): + if abs(f_c) < math.log(threshold_F_C, 2): + col = grey + width = str(minT) + else: + if f_c < 0: + col = blue + elif f_c > 0: + col = red + width = str(max((abs(f_c) * maxT) / max_F_C, minT)) + else: + if f_c == '-INF': + col = blue + elif f_c == 'INF': + col = red + width = str(maxT) + dash = 'none' + else: + dash = '5,5' + col = grey + width = str(minT) + el.set('style', fix_style(el.get('style'), col, width, dash)) + return core_map + +############################ make recon ####################################### + +def check_and_doWord(l): + tmp = [] + tmp_genes = [] + count = 0 + while l: + if count >= 0: + if l[0] == '(': + count += 1 + tmp.append(l[0]) + l.pop(0) + elif l[0] == ')': + count -= 1 + tmp.append(l[0]) + l.pop(0) + elif l[0] == ' ': + l.pop(0) + else: + word = [] + while l: + if l[0] in [' ', '(', ')']: + break + else: + word.append(l[0]) + l.pop(0) + word = ''.join(word) + tmp.append(word) + if not(word in ['or', 'and']): + tmp_genes.append(word) + else: + return False + if count == 0: + return (tmp, tmp_genes) + else: + return False + +def brackets_to_list(l): + tmp = [] + while l: + if l[0] == '(': + l.pop(0) + tmp.append(resolve_brackets(l)) + else: + tmp.append(l[0]) + l.pop(0) + return tmp + +def resolve_brackets(l): + tmp = [] + while l[0] != ')': + if l[0] == '(': + l.pop(0) + tmp.append(resolve_brackets(l)) + else: + tmp.append(l[0]) + l.pop(0) + l.pop(0) + return tmp + +def priorityAND(l): + tmp = [] + flag = True + while l: + if len(l) == 1: + if isinstance(l[0], list): + tmp.append(priorityAND(l[0])) + else: + tmp.append(l[0]) + l = l[1:] + elif l[0] == 'or': + tmp.append(l[0]) + flag = False + l = l[1:] + elif l[1] == 'or': + if isinstance(l[0], list): + tmp.append(priorityAND(l[0])) + else: + tmp.append(l[0]) + tmp.append(l[1]) + flag = False + l = l[2:] + elif l[1] == 'and': + tmpAnd = [] + if isinstance(l[0], list): + tmpAnd.append(priorityAND(l[0])) + else: + tmpAnd.append(l[0]) + tmpAnd.append(l[1]) + if isinstance(l[2], list): + tmpAnd.append(priorityAND(l[2])) + else: + tmpAnd.append(l[2]) + l = l[3:] + while l: + if l[0] == 'and': + tmpAnd.append(l[0]) + if isinstance(l[1], list): + tmpAnd.append(priorityAND(l[1])) + else: + tmpAnd.append(l[1]) + l = l[2:] + elif l[0] == 'or': + flag = False + break + if flag == True: #se ci sono solo AND nella lista + tmp.extend(tmpAnd) + elif flag == False: + tmp.append(tmpAnd) + return tmp + +def checkRule(l): + if len(l) == 1: + if isinstance(l[0], list): + if checkRule(l[0]) is False: + return False + elif len(l) > 2: + if checkRule2(l) is False: + return False + else: + return False + return True + +def checkRule2(l): + while l: + if len(l) == 1: + return False + elif isinstance(l[0], list) and l[1] in ['and', 'or']: + if checkRule(l[0]) is False: + return False + if isinstance(l[2], list): + if checkRule(l[2]) is False: + return False + l = l[3:] + elif l[1] in ['and', 'or']: + if isinstance(l[2], list): + if checkRule(l[2]) is False: + return False + l = l[3:] + elif l[0] in ['and', 'or']: + if isinstance(l[1], list): + if checkRule(l[1]) is False: + return False + l = l[2:] + else: + return False + return True + +def do_rules(rules): + split_rules = [] + err_rules = [] + tmp_gene_in_rule = [] + for i in range(len(rules)): + tmp = list(rules[i]) + if tmp: + tmp, tmp_genes = check_and_doWord(tmp) + tmp_gene_in_rule.extend(tmp_genes) + if tmp is False: + split_rules.append([]) + err_rules.append(rules[i]) + else: + tmp = brackets_to_list(tmp) + if checkRule(tmp): + split_rules.append(priorityAND(tmp)) + else: + split_rules.append([]) + err_rules.append(rules[i]) + else: + split_rules.append([]) + if err_rules: + warning('Warning: wrong format rule in ' + str(err_rules) + '\n') + return (split_rules, list(set(tmp_gene_in_rule))) + +def make_recon(data): + try: + import cobra as cb + import warnings + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + recon = cb.io.read_sbml_model(data) + react = recon.reactions + rules = [react[i].gene_reaction_rule for i in range(len(react))] + ids = [react[i].id for i in range(len(react))] + except cb.io.sbml3.CobraSBMLError: + try: + data = (pd.read_csv(data, sep = '\t', dtype = str)).fillna('') + if len(data.columns) < 2: + sys.exit('Execution aborted: wrong format of '+ + 'custom datarules\n') + if not len(data.columns) == 2: + warning('Warning: more than 2 columns in custom datarules.\n' + + 'Extra columns have been disregarded\n') + ids = list(data.iloc[:, 0]) + rules = list(data.iloc[:, 1]) + except pd.errors.EmptyDataError: + sys.exit('Execution aborted: wrong format of custom datarules\n') + except pd.errors.ParserError: + sys.exit('Execution aborted: wrong format of custom datarules\n') + split_rules, tmp_genes = do_rules(rules) + gene_in_rule = {} + for i in tmp_genes: + gene_in_rule[i] = 'ok' + return (ids, split_rules, gene_in_rule) + +############################ gene ############################################# + +def data_gene(gene, type_gene, name, gene_custom): + args = process_args(sys.argv) + for i in range(len(gene)): + tmp = gene.iloc[i, 0] + if tmp.startswith(' ') or tmp.endswith(' '): + gene.iloc[i, 0] = (tmp.lstrip()).rstrip() + gene_dup = [item for item, count in + collections.Counter(gene[gene.columns[0]]).items() if count > 1] + pat_dup = [item for item, count in + collections.Counter(list(gene.columns)).items() if count > 1] + if gene_dup: + if gene_custom == None: + if args.rules_selector == 'HMRcore': + gene_in_rule = pk.load(open(args.tool_dir + + '/local/HMRcore_genes.p', 'rb')) + elif args.rules_selector == 'Recon': + gene_in_rule = pk.load(open(args.tool_dir + + '/local/Recon_genes.p', 'rb')) + gene_in_rule = gene_in_rule.get(type_gene) + else: + gene_in_rule = gene_custom + tmp = [] + for i in gene_dup: + if gene_in_rule.get(i) == 'ok': + tmp.append(i) + if tmp: + sys.exit('Execution aborted because gene ID ' + +str(tmp)+' in '+name+' is duplicated\n') + if pat_dup: + warning('Warning: duplicated label\n' + str(pat_dup) + 'in ' + name + + '\n') + return (gene.set_index(gene.columns[0])).to_dict() + +############################ resolve ########################################## + +def resolve(genes, rules, ids, resolve_none, name): + resolve_rules = {} + not_found = [] + flag = False + for key, value in genes.items(): + tmp_resolve = [] + for i in range(len(rules)): + tmp = rules[i] + if tmp: + tmp, err = replace_gene_value(tmp, value) + if err: + not_found.extend(err) + ris = control(None, tmp, resolve_none) + if ris is False or ris == None: + tmp_resolve.append(None) + else: + tmp_resolve.append(ris) + flag = True + else: + tmp_resolve.append(None) + resolve_rules[key] = tmp_resolve + if flag is False: + warning('Warning: no computable score (due to missing gene values)' + + 'for class ' + name + ', the class has been disregarded\n') + return (None, None) + return (resolve_rules, list(set(not_found))) + +############################ split class ###################################### + +def split_class(classes, resolve_rules): + class_pat = {} + for i in range(len(classes)): + classe = classes.iloc[i, 1] + if not pd.isnull(classe): + l = [] + for j in range(i, len(classes)): + if classes.iloc[j, 1] == classe: + pat_id = classes.iloc[j, 0] + tmp = resolve_rules.get(pat_id, None) + if tmp != None: + l.append(tmp) + classes.iloc[j, 1] = None + if l: + class_pat[classe] = list(map(list, zip(*l))) + else: + warning('Warning: no sample found in class ' + classe + + ', the class has been disregarded\n') + return class_pat + +############################ map ############################################## + +def maps(core_map, class_pat, ids, threshold_P_V, threshold_F_C): + args = process_args(sys.argv) + if (not class_pat) or (len(class_pat.keys()) < 2): + sys.exit('Execution aborted: classes provided for comparisons are ' + + 'less than two\n') + for i, j in it.combinations(class_pat.keys(), 2): + tmp = {} + count = 0 + max_F_C = 0 + for l1, l2 in zip(class_pat.get(i), class_pat.get(j)): + try: + stat_D, p_value = st.ks_2samp(l1, l2) + avg = fold_change(sum(l1) / len(l1), sum(l2) / len(l2)) + if not isinstance(avg, str): + if max_F_C < abs(avg): + max_F_C = abs(avg) + tmp[ids[count]] = [float(p_value), avg] + count += 1 + except (TypeError, ZeroDivisionError): + count += 1 + tab = 'table_out/' + i + '_vs_' + j + '.tsv' + tmp_csv = pd.DataFrame.from_dict(tmp, orient = "index") + tmp_csv = tmp_csv.reset_index() + header = ['ids', 'P_Value', 'Average'] + tmp_csv.to_csv(tab, sep = '\t', index = False, header = header) + if args.rules_selector == 'HMRcore' or (args.rules_selector == 'Custom' + and args.yes_no == 'yes'): + fix_map(tmp, core_map, threshold_P_V, threshold_F_C, max_F_C) + file_svg = 'map_svg/' + i + '_vs_' + j + '.svg' + with open(file_svg, 'wb') as new_map: + new_map.write(ET.tostring(core_map, encoding='UTF-8', + method='xml')) + file_pdf = 'map_pdf/' + i + '_vs_' + j + '.pdf' + renderPDF.drawToFile(svg2rlg(file_svg), file_pdf) + return None + +############################ MAIN ############################################# + +def main(): + args = process_args(sys.argv) + os.makedirs('table_out') + if args.rules_selector == 'HMRcore': + os.makedirs('map_svg') + os.makedirs('map_pdf') + recon = pk.load(open(args.tool_dir + '/local/HMRcore_rules.p', 'rb')) + elif args.rules_selector == 'Recon': + recon = pk.load(open(args.tool_dir + '/local/Recon_rules.p', 'rb')) + elif args.rules_selector == 'Custom': + ids, rules, gene_in_rule = make_recon(args.custom) + resolve_none = check_bool(args.none) + class_pat = {} + if args.option == 'datasets': + num = 1 + #if len(args.names) != len(set(args.names)): + # sys.exit('Execution aborted: datasets name duplicated') + for i, j in zip(args.input_datas, args.names): + name = name_dataset(j, num) + dataset = read_dataset(i, name) + dataset.iloc[:, 0] = (dataset.iloc[:, 0]).astype(str) + type_gene = gene_type(dataset.iloc[0, 0], name) + if args.rules_selector != 'Custom': + genes = data_gene(dataset, type_gene, name, None) + ids, rules = load_id_rules(recon.get(type_gene)) + elif args.rules_selector == 'Custom': + genes = data_gene(dataset, type_gene, name, gene_in_rule) + resolve_rules, err = resolve(genes, rules, ids, resolve_none, name) + if err != None and err: + warning('Warning: gene\n' + str(err) + '\nnot found in class ' + + name + ', the expression level for this gene ' + + 'will be considered NaN\n') + if resolve_rules != None: + class_pat[name] = list(map(list, zip(*resolve_rules.values()))) + num += 1 + elif args.option == 'dataset_class': + name = 'RNAseq' + dataset = read_dataset(args.input_data, name) + dataset.iloc[:, 0] = (dataset.iloc[:, 0]).astype(str) + type_gene = gene_type(dataset.iloc[0, 0], name) + classes = read_dataset(args.input_class, 'class') + if not len(classes.columns) == 2: + warning('Warning: more than 2 columns in class file. Extra' + + 'columns have been disregarded\n') + classes = classes.astype(str) + if args.rules_selector != 'Custom': + genes = data_gene(dataset, type_gene, name, None) + ids, rules = load_id_rules(recon.get(type_gene)) + elif args.rules_selector == 'Custom': + genes = data_gene(dataset, type_gene, name, gene_in_rule) + resolve_rules, err = resolve(genes, rules, ids, resolve_none, name) + if err != None and err: + warning('Warning: gene\n'+str(err)+'\nnot found in class ' + + name + ', the expression level for this gene ' + + 'will be considered NaN\n') + if resolve_rules != None: + class_pat = split_class(classes, resolve_rules) + if args.rules_selector == 'Custom': + if args.yes_no == 'yes': + os.makedirs('map_svg') + os.makedirs('map_pdf') + try: + core_map = ET.parse(args.custom_map) + except (ET.XMLSyntaxError, ET.XMLSchemaParseError): + sys.exit('Execution aborted: custom map in wrong format') + elif args.yes_no == 'no': + core_map = ET.parse(args.tool_dir + '/local/HMRcoreMap.svg') + else: + core_map = ET.parse(args.tool_dir+'/local/HMRcoreMap.svg') + maps(core_map, class_pat, ids, args.pValue, args.fChange) + warning('Execution succeeded') + return None + +############################################################################### + +if __name__ == "__main__": + main()
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Marea/marea.xml Tue Nov 06 03:16:21 2018 -0500 @@ -0,0 +1,240 @@ +<tool id="MaREA" name="Metabolic Enrichment Analysis"> + <description>for Galaxy</description> + <requirements> + <requirement type="package">pandas</requirement> + <requirement type="package">scipy</requirement> + <requirement type="package">lxml</requirement> + <requirement type="package">svglib</requirement> + <requirement type="package">reportlab</requirement> + <requirement type="package">cobrapy</requirement> + <requirement type="package">python-libsbml</requirement> + </requirements> + <command> + <![CDATA[ + python $__tool_directory__/marea.py + --rules_selector $cond_rule.rules_selector + #if $cond_rule.rules_selector == 'Custom': + --custom ${cond_rule.Custom_rules} + --yes_no ${cond_rule.cond_map.yes_no} + #if $cond_rule.cond_map.yes_no == 'yes': + --custom_map $cond_rule.cond_map.Custom_map + #end if + #end if + --none $None + --pValue $pValue + --fChange $fChange + --tool_dir $__tool_directory__ + --option $cond.type_selector + --out_log $log + #if $cond.type_selector == 'datasets': + --input_datas + #for $data in $cond.input_Datasets: + ${data.input} + #end for + --names + #for $data in $cond.input_Datasets: + ${data.input_name} + #end for + #elif $cond.type_selector == 'dataset_class': + --input_data ${input_data} + --input_class ${input_class} + #end if + ]]> + </command> + <inputs> + <conditional name="cond_rule"> + <param name="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> + <when value="Custom"> + <param name="Custom_rules" type="data" format="tabular, csv, tsv, xml" label="Custom rules"/> + <conditional name="cond_map"> + <param name="yes_no" type="select" label="Custom map? (optional)"> + <option value="no" selected="true">no</option> + <option value="yes">yes</option> + </param> + <when value="yes"> + <param name="Custom_map" type="data" format="xml, svg" label="custom-map.svg"/> + </when> + </conditional> + </when> + </conditional> + <conditional name="cond"> + <param name="type_selector" type="select" label="Input format:"> + <option value="datasets" selected="true">RNAseq of group 1 + RNAseq of group 2 + … + RNAseq of group N</option> + <option value="dataset_class">RNAseq of all samples + sample group specification</option> + </param> + <when value="datasets"> + <repeat name="input_Datasets" title="RNAseq" type="data" min="2"> + <param name="input" type="data" format="tabular, csv, tsv" label="add dataset"/> + <param name="input_name" type="text" label="Dataset's name:" value="Dataset" help="Defalut: Dataset"/> + </repeat> + </when> + <when value="dataset_class"> + <param name="input_data" type="data" format="tabular, csv, tsv" label="RNAseq of all samples"/> + <param name="input_class" type="data" format="tabular, csv, tsv" label="Sample group specification"/> + </when> + </conditional> + <param name="None" type="boolean" truevalue="true" falsevalue="false" checked="true" label="(A and NaN) solved as (A)?"/> + <param name="pValue" type="float" size="20" value="0.05" max="1" min="0" label="P-value threshold" help="min value 0"/> + <param name="fChange" type="float" size="20" value="1.5" min="1" label="Fold-Cahnge threshold" help="min value 1"/> + </inputs> + <outputs> + <data format="txt" name="log" label="Log"/> + <collection name="map_svg" type="list" label="file svg"> + <filter>(cond_rule['rules_selector'] == 'HMRcore') or ((cond_rule['rules_selector'] == 'Custom') and (cond_rule['cond_map']['yes_no'] == 'yes'))</filter> + <discover_datasets pattern="__name_and_ext__" directory="map_svg"/> + </collection> + <collection name="map_pdf" type="list" label="file pdf"> + <filter>(cond_rule['rules_selector'] == 'HMRcore') or ((cond_rule['rules_selector'] == 'Custom') and (cond_rule['cond_map']['yes_no'] == 'yes'))</filter> + <discover_datasets pattern="__name_and_ext__" directory="map_pdf"/> + </collection> + <collection name="table_out" type="list" label="file table"> + <discover_datasets pattern="__name_and_ext__" directory="table_out"/> + </collection> + </outputs> + <help> +<![CDATA[ + +What it does +------------- + +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. + +The tool can be used to generate: + 1) a tab-separated file: reporting fold-change and p-values of reaction activity scores (RASs) between a pair of conditions/classes + 2) a metabolic map file (downlodable as .svg): visualizing up- and down-regulated reactions between a pair of conditions/classes + 3) a log file (.txt) + +Accepted files are: + 1) or 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”). + 2) or one RNA dataset and one class-file specifying the class/condition each sample belongs to. + + +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. + +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). + +To calculate P-Values and Fold-Changes and to generate maps, comparisons are performed for each possible pair of classes. + +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”. + +.. _MaREA: https://www.biorxiv.org/content/early/2018/01/16/248724 + + +Example input +------------- + +**"RNAseq of group 1 + RNAseq of group 2 + ... + RNAseq of group N" exemple input"** option: + +Dataset 1: + ++------------+------------+------------+------------+ +| 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 | ++------------+------------+------------+------------+ + +| + +Dataset 2: + ++-------------+------------+------------+------------+ +| 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 | ++-------------+------------+------------+------------+ + +| + +**"RNAseq of all samples + sample group specification"** option: + +Dataset: + ++------------+------------+------------+------------+ +| 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 | ++------------+------------+------------+------------+ + +| + +Class-file: + ++------------+------------+ +| Patient_ID | class | ++============+============+ +| TCGAAA3529 | MSI | ++------------+------------+ +| TCGAA62671 | MSS | ++------------+------------+ +| TCGAA62672 | MSI | ++------------+------------+ + +| + + + +.. class:: warningmark + +This tool expects input datasets consisting of tab-delimited columns. + + +.. class:: infomark + +TIP: If your data is not TAB delimited, use `Convert delimiters to TAB`_. + +.. class:: infomark + +TIP: If your dataset is not split into classes, use `Cluster for MaREA`_. + +This tool is developed by the `nome del gruppo di bioinformatica`_ at the `dipartimento di informatica disco`_. + + +.. _Convert delimiters to TAB: https://usegalaxy.org/?tool_id=Convert+characters1&version=1.0.0&__identifer=6t22teyofhj +.. _Cluster for MaREA: http://link del tool di cluster.org/ +.. _nome del gruppo di bioinformatica: http://sito di bio.org +.. _dipartimento di informatica disco : http://www.disco.unimib.it/go/Home/English + +]]> + </help> +</tool> + + + + + + + + + + + + +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Marea/marea_cluster.py Tue Nov 06 03:16:21 2018 -0500 @@ -0,0 +1,608 @@ + +from __future__ import division +import os +import sys +import pandas as pd +import collections +import pickle as pk +import argparse +from sklearn.cluster import KMeans +import matplotlib.pyplot as plt + +########################## argparse ########################################### + +def process_args(args): + parser = argparse.ArgumentParser(usage = '%(prog)s [options]', + description = 'process some value\'s' + + ' genes to create class.') + parser.add_argument('-rs', '--rules_selector', + type = str, + default = 'HMRcore', + choices = ['HMRcore', 'Recon', 'Custom'], + help = 'chose which type of dataset you want use') + parser.add_argument('-cr', '--custom', + type = str, + help='your dataset if you want custom rules') + parser.add_argument('-ch', '--cond_hier', + type = str, + default = 'no', + choices = ['no', 'yes'], + help = 'chose if you wanna hierical dendrogram') + parser.add_argument('-lk', '--k_min', + type = int, + help = 'min number of cluster') + parser.add_argument('-uk', '--k_max', + type = int, + help = 'max number of cluster') + parser.add_argument('-li', '--linkage', + type = str, + choices = ['single', 'complete', 'average'], + help='linkage hierarchical cluster') + parser.add_argument('-d', '--data', + type = str, + required = True, + help = 'input dataset') + parser.add_argument('-n', '--none', + type = str, + default = 'true', + choices = ['true', 'false'], + help = 'compute Nan values') + parser.add_argument('-td', '--tool_dir', + type = str, + required = True, + help = 'your tool directory') + parser.add_argument('-na', '--name', + type = str, + help = 'name of dataset') + parser.add_argument('-de', '--dendro', + help = "Dendrogram out") + parser.add_argument('-ol', '--out_log', + help = "Output log") + parser.add_argument('-el', '--elbow', + help = "Out elbow") + args = parser.parse_args() + return args + +########################### warning ########################################### + +def warning(s): + args = process_args(sys.argv) + with open(args.out_log, 'a') as log: + log.write(s) + +############################ dataset input #################################### + +def read_dataset(data, name): + try: + dataset = pd.read_csv(data, sep = '\t', header = 0) + except pd.errors.EmptyDataError: + sys.exit('Execution aborted: wrong format of '+name+'\n') + if len(dataset.columns) < 2: + sys.exit('Execution aborted: wrong format of '+name+'\n') + return dataset + +############################ dataset name ##################################### + +def name_dataset(name_data, count): + if str(name_data) == 'Dataset': + return str(name_data) + '_' + str(count) + else: + return str(name_data) + +############################ load id e rules ################################## + +def load_id_rules(reactions): + ids, rules = [], [] + for key, value in reactions.items(): + ids.append(key) + rules.append(value) + return (ids, rules) + +############################ check_methods #################################### + +def gene_type(l, name): + if check_hgnc(l): + return 'hugo_id' + elif check_ensembl(l): + return 'ensembl_gene_id' + elif check_symbol(l): + return 'symbol' + elif check_entrez(l): + return 'entrez_id' + else: + sys.exit('Execution aborted:\n' + + 'gene ID type in ' + name + ' not supported. Supported ID' + + 'types are: HUGO ID, Ensemble ID, HUGO symbol, Entrez ID\n') + +def check_hgnc(l): + if len(l) > 5: + if (l.upper()).startswith('HGNC:'): + return l[5:].isdigit() + else: + return False + else: + return False + +def check_ensembl(l): + if len(l) == 15: + if (l.upper()).startswith('ENS'): + return l[4:].isdigit() + else: + return False + else: + return False + +def check_symbol(l): + if len(l) > 0: + if l[0].isalpha() and l[1:].isalnum(): + return True + else: + return False + else: + return False + +def check_entrez(l): + if len(l) > 0: + return l.isdigit() + else: + return False + +def check_bool(b): + if b == 'true': + return True + elif b == 'false': + return False + +############################ make recon ####################################### + +def check_and_doWord(l): + tmp = [] + tmp_genes = [] + count = 0 + while l: + if count >= 0: + if l[0] == '(': + count += 1 + tmp.append(l[0]) + l.pop(0) + elif l[0] == ')': + count -= 1 + tmp.append(l[0]) + l.pop(0) + elif l[0] == ' ': + l.pop(0) + else: + word = [] + while l: + if l[0] in [' ', '(', ')']: + break + else: + word.append(l[0]) + l.pop(0) + word = ''.join(word) + tmp.append(word) + if not(word in ['or', 'and']): + tmp_genes.append(word) + else: + return False + if count == 0: + return (tmp, tmp_genes) + else: + return False + +def brackets_to_list(l): + tmp = [] + while l: + if l[0] == '(': + l.pop(0) + tmp.append(resolve_brackets(l)) + else: + tmp.append(l[0]) + l.pop(0) + return tmp + +def resolve_brackets(l): + tmp = [] + while l[0] != ')': + if l[0] == '(': + l.pop(0) + tmp.append(resolve_brackets(l)) + else: + tmp.append(l[0]) + l.pop(0) + l.pop(0) + return tmp + +def priorityAND(l): + tmp = [] + flag = True + while l: + if len(l) == 1: + if isinstance(l[0], list): + tmp.append(priorityAND(l[0])) + else: + tmp.append(l[0]) + l = l[1:] + elif l[0] == 'or': + tmp.append(l[0]) + flag = False + l = l[1:] + elif l[1] == 'or': + if isinstance(l[0], list): + tmp.append(priorityAND(l[0])) + else: + tmp.append(l[0]) + tmp.append(l[1]) + flag = False + l = l[2:] + elif l[1] == 'and': + tmpAnd = [] + if isinstance(l[0], list): + tmpAnd.append(priorityAND(l[0])) + else: + tmpAnd.append(l[0]) + tmpAnd.append(l[1]) + if isinstance(l[2], list): + tmpAnd.append(priorityAND(l[2])) + else: + tmpAnd.append(l[2]) + l = l[3:] + while l: + if l[0] == 'and': + tmpAnd.append(l[0]) + if isinstance(l[1], list): + tmpAnd.append(priorityAND(l[1])) + else: + tmpAnd.append(l[1]) + l = l[2:] + elif l[0] == 'or': + flag = False + break + if flag == True: #se ci sono solo AND nella lista + tmp.extend(tmpAnd) + elif flag == False: + tmp.append(tmpAnd) + return tmp + +def checkRule(l): + if len(l) == 1: + if isinstance(l[0], list): + if checkRule(l[0]) is False: + return False + elif len(l) > 2: + if checkRule2(l) is False: + return False + else: + return False + return True + +def checkRule2(l): + while l: + if len(l) == 1: + return False + elif isinstance(l[0], list) and l[1] in ['and', 'or']: + if checkRule(l[0]) is False: + return False + if isinstance(l[2], list): + if checkRule(l[2]) is False: + return False + l = l[3:] + elif l[1] in ['and', 'or']: + if isinstance(l[2], list): + if checkRule(l[2]) is False: + return False + l = l[3:] + elif l[0] in ['and', 'or']: + if isinstance(l[1], list): + if checkRule(l[1]) is False: + return False + l = l[2:] + else: + return False + return True + +def do_rules(rules): + split_rules = [] + err_rules = [] + tmp_gene_in_rule = [] + for i in range(len(rules)): + tmp = list(rules[i]) + if tmp: + tmp, tmp_genes = check_and_doWord(tmp) + tmp_gene_in_rule.extend(tmp_genes) + if tmp is False: + split_rules.append([]) + err_rules.append(rules[i]) + else: + tmp = brackets_to_list(tmp) + if checkRule(tmp): + split_rules.append(priorityAND(tmp)) + else: + split_rules.append([]) + err_rules.append(rules[i]) + else: + split_rules.append([]) + if err_rules: + warning('Warning: wrong format rule in ' + str(err_rules) + '\n') + return (split_rules, list(set(tmp_gene_in_rule))) + +def make_recon(data): + try: + import cobra as cb + import warnings + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + recon = cb.io.read_sbml_model(data) + react = recon.reactions + rules = [react[i].gene_reaction_rule for i in range(len(react))] + ids = [react[i].id for i in range(len(react))] + except cb.io.sbml3.CobraSBMLError: + try: + data = (pd.read_csv(data, sep = '\t', dtype = str)).fillna('') + if len(data.columns) < 2: + sys.exit('Execution aborted: wrong format of ' + + 'custom GPR rules\n') + if not len(data.columns) == 2: + warning('WARNING: more than 2 columns in custom GPR rules.\n' + + 'Extra columns have been disregarded\n') + ids = list(data.iloc[:, 0]) + rules = list(data.iloc[:, 1]) + except pd.errors.EmptyDataError: + sys.exit('Execution aborted: wrong format of custom GPR rules\n') + except pd.errors.ParserError: + sys.exit('Execution aborted: wrong format of custom GPR rules\n') + split_rules, tmp_genes = do_rules(rules) + gene_in_rule = {} + for i in tmp_genes: + gene_in_rule[i] = 'ok' + return (ids, split_rules, gene_in_rule) + +############################ resolve_methods ################################## + +def replace_gene_value(l, d): + tmp = [] + err = [] + while l: + if isinstance(l[0], list): + tmp_rules, tmp_err = replace_gene_value(l[0], d) + tmp.append(tmp_rules) + err.extend(tmp_err) + else: + value = replace_gene(l[0],d) + tmp.append(value) + if value == None: + err.append(l[0]) + l = l[1:] + return (tmp, err) + +def replace_gene(l, d): + if l =='and' or l == 'or': + return l + else: + value = d.get(l, None) + if not(value == None or isinstance(value, (int, float))): + sys.exit('Execution aborted: ' + value + ' value not valid\n') + return value + +def compute(val1, op, val2, cn): + if val1 != None and val2 != None: + if op == 'and': + return min(val1, val2) + else: + return val1 + val2 + elif op == 'and': + if cn is True: + if val1 != None: + return val1 + elif val2 != None: + return val2 + else: + return None + else: + return None + else: + if val1 != None: + return val1 + elif val2 != None: + return val2 + else: + return None + +def control(ris, l, cn): + if len(l) == 1: + if isinstance(l[0], (float, int)) or l[0] == None: + return l[0] + elif isinstance(l[0], list): + return control(None, l[0], cn) + else: + return False + elif len(l) > 2: + return control_list(ris, l, cn) + else: + return False + +def control_list(ris, l, cn): + while l: + if len(l) == 1: + return False + elif (isinstance(l[0], (float, int)) or + l[0] == None) and l[1] in ['and', 'or']: + if isinstance(l[2], (float, int)) or l[2] == None: + ris = compute(l[0], l[1], l[2], cn) + elif isinstance(l[2], list): + tmp = control(None, l[2], cn) + if tmp is False: + return False + else: + ris = compute(l[0], l[1], tmp, cn) + else: + return False + l = l[3:] + elif l[0] in ['and', 'or']: + if isinstance(l[1], (float, int)) or l[1] == None: + ris = compute(ris, l[0], l[1], cn) + elif isinstance(l[1], list): + tmp = control(None,l[1], cn) + if tmp is False: + return False + else: + ris = compute(ris, l[0], tmp, cn) + else: + return False + l = l[2:] + elif isinstance(l[0], list) and l[1] in ['and', 'or']: + if isinstance(l[2], (float, int)) or l[2] == None: + tmp = control(None, l[0], cn) + if tmp is False: + return False + else: + ris = compute(tmp, l[1], l[2], cn) + elif isinstance(l[2], list): + tmp = control(None, l[0], cn) + tmp2 = control(None, l[2], cn) + if tmp is False or tmp2 is False: + return False + else: + ris = compute(tmp, l[1], tmp2, cn) + else: + return False + l = l[3:] + else: + return False + return ris + +############################ gene ############################################# + +def data_gene(gene, type_gene, name, gene_custom): + args = process_args(sys.argv) + for i in range(len(gene)): + tmp = gene.iloc[i, 0] + if tmp.startswith(' ') or tmp.endswith(' '): + gene.iloc[i, 0] = (tmp.lstrip()).rstrip() + gene_dup = [item for item, count in + collections.Counter(gene[gene.columns[0]]).items() if count > 1] + pat_dup = [item for item, count in + collections.Counter(list(gene.columns)).items() if count > 1] + if gene_dup: + if gene_custom == None: + if args.rules_selector == 'HMRcore': + gene_in_rule = pk.load(open(args.tool_dir + + '/local/HMRcore_genes.p', 'rb')) + elif args.rules_selector == 'Recon': + gene_in_rule = pk.load(open(args.tool_dir + + '/local/Recon_genes.p', 'rb')) + gene_in_rule = gene_in_rule.get(type_gene) + else: + gene_in_rule = gene_custom + tmp = [] + for i in gene_dup: + if gene_in_rule.get(i) == 'ok': + tmp.append(i) + if tmp: + sys.exit('Execution aborted because gene ID ' + + str(tmp) + ' in ' + name + ' is duplicated\n') + if pat_dup: + sys.exit('Execution aborted: duplicated label\n' + + str(pat_dup) + 'in ' + name + '\n') + return (gene.set_index(gene.columns[0])).to_dict() + +############################ resolve ########################################## + +def resolve(genes, rules, ids, resolve_none, name): + resolve_rules = {} + not_found = [] + flag = False + for key, value in genes.items(): + tmp_resolve = [] + for i in range(len(rules)): + tmp = rules[i] + if tmp: + tmp, err = replace_gene_value(tmp, value) + if err: + not_found.extend(err) + ris = control(None, tmp, resolve_none) + if ris is False or ris == None: + tmp_resolve.append(None) + else: + tmp_resolve.append(ris) + flag = True + else: + tmp_resolve.append(None) + resolve_rules[key] = tmp_resolve + if flag is False: + sys.exit('Execution aborted: no computable score' + + ' (due to missing gene values) for class ' + + name + ', the class has been disregarded\n') + return (resolve_rules, list(set(not_found))) + +################################# clustering ################################## + +def f_cluster(resolve_rules): + os.makedirs('cluster_out') + args = process_args(sys.argv) + cluster_data = pd.DataFrame.from_dict(resolve_rules, orient = 'index') + for i in cluster_data.columns: + tmp = cluster_data[i][0] + if tmp == None: + cluster_data = cluster_data.drop(columns=[i]) + distorsion = [] + for i in range(args.k_min, args.k_max+1): + tmp_kmeans = KMeans(n_clusters = i, + n_init = 100, + max_iter = 300, + random_state = 0).fit(cluster_data) + distorsion.append(tmp_kmeans.inertia_) + predict = tmp_kmeans.predict(cluster_data) + predict = [x+1 for x in predict] + classe = (pd.DataFrame(zip(cluster_data.index, predict))).astype(str) + dest = 'cluster_out/K=' + str(i) + '_' + args.name+'.tsv' + classe.to_csv(dest, sep = '\t', index = False, + header = ['Patient_ID', 'Class']) + plt.figure(0) + plt.plot(range(args.k_min, args.k_max+1), distorsion, marker = 'o') + plt.xlabel('Number of cluster') + plt.ylabel('Distorsion') + plt.savefig(args.elbow, dpi = 240, format = 'pdf') + if args.cond_hier == 'yes': + import scipy.cluster.hierarchy as hier + lin = hier.linkage(cluster_data, args.linkage) + plt.figure(1) + plt.figure(figsize=(10, 5)) + hier.dendrogram(lin, leaf_font_size = 2, labels = cluster_data.index) + plt.savefig(args.dendro, dpi = 480, format = 'pdf') + return None + +################################# main ######################################## + +def main(): + args = process_args(sys.argv) + if args.k_min > args.k_max: + sys.exit('Execution aborted: max cluster > min cluster') + if args.rules_selector == 'HMRcore': + recon = pk.load(open(args.tool_dir + '/local/HMRcore_rules.p', 'rb')) + elif args.rules_selector == 'Recon': + recon = pk.load(open(args.tool_dir + '/local/Recon_rules.p', 'rb')) + elif args.rules_selector == 'Custom': + ids, rules, gene_in_rule = make_recon(args.custom) + resolve_none = check_bool(args.none) + dataset = read_dataset(args.data, args.name) + dataset.iloc[:, 0] = (dataset.iloc[:, 0]).astype(str) + type_gene = gene_type(dataset.iloc[0, 0], args.name) + if args.rules_selector != 'Custom': + genes = data_gene(dataset, type_gene, args.name, None) + ids, rules = load_id_rules(recon.get(type_gene)) + elif args.rules_selector == 'Custom': + genes = data_gene(dataset, type_gene, args.name, gene_in_rule) + resolve_rules, err = resolve(genes, rules, ids, resolve_none, args.name) + if err: + warning('WARNING: gene\n' + str(err) + '\nnot found in class ' + + args.name + ', the expression level for this gene ' + + 'will be considered NaN\n') + f_cluster(resolve_rules) + warning('Execution succeeded') + return None + +############################################################################### + +if __name__ == "__main__": + main() \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Marea/marea_cluster.xml Tue Nov 06 03:16:21 2018 -0500 @@ -0,0 +1,97 @@ +<tool id="MaREA_cluester" name="MaREA cluster analysis"> + <description>of Reaction Activity Scores</description> + <requirements> + <requirement type="package">pandas</requirement> + <requirement type="package">scikit-learn</requirement> + <requirement type="package">scipy</requirement> + <requirement type="package">matplotlib</requirement> + <requirement type="package">cobrapy</requirement> + <requirement type="package">python-libsbml</requirement> + </requirements> + <command> + <![CDATA[ + python $__tool_directory__/marea_cluster.py + --rules_selector $cond_rule.rules_selector + #if $cond_rule.rules_selector == 'Custom': + --custom ${cond_rule.Custom_rules} + #end if + --cond_hier $cond_hier.hier + #if $cond_hier.hier == 'yes': + --linkage ${cond_hier.linkage} + --dendro $dendrogram + #end if + --k_max $k_max + --k_min $k_min + --data $input + --name $name + --none $None + --tool_dir $__tool_directory__ + --out_log $log + --elbow $elbow + ]]> + </command> + <inputs> + <conditional name="cond_rule"> + <param name="rules_selector" type="select" label="Gene-Protein-Reaction rules:"> + <option value="HMRcore" selected="true">HMRcore rules</option> + <option value="Recon">Recon 2.2 rules</option> + <option value="Custom">Custom rules</option> + </param> + <when value="Custom"> + <param name="Custom_rules" type="data" format="tabular, csv, tsv, xml" label="Custom rules"/> + </when> + </conditional> + <param name="input" type="data" format="tabular, csv, tsv" label="RNAseq of all samples"/> + <param name="name" type="text" label="Output name prefix" value = "dataset"/> + <param name="k_min" type="integer" size="20" value="3" min="2" max="30" label="min number of clusters (k) to be tested (k-means)"/> + <param name="k_max" type="integer" size="20" value="3" min="2" max="30" label="max number of clusters (k) to be tested (k-means)"/> + <param name="None" type="boolean" truevalue="true" falsevalue="false" checked="true" label="(A and NaN) solved as (A)?" help="if NO is selected (A and NaN) is solved as (NaN)"/> + <conditional name="cond_hier"> + <param name="hier" type="select" label="Produce dendrogram (hierarchical clustering):"> + <option value="no" selected="true">no</option> + <option value="yes">yes</option> + </param> + <when value="yes"> + <param name="linkage" type="select" label="Linkage type:"> + <option value="single" selected="true">Single: minimum distance between all observations of two sets</option> + <option value="complete">Complete: maximum distance between all observations of two sets</option> + <option value="average">Average: average distance between all observations of two sets</option> + </param> + </when> + </conditional> + </inputs> + <outputs> + <data format="txt" name="log" label="Log"/> + <data format="pdf" name="dendrogram" label="$name dendrogram"> + <filter>cond_hier['hier'] == 'yes'</filter> + </data> + <data format="pdf" name="elbow" label="$name elbow evaluation method"/> + <collection name="cluster_out" type="list" label="Clusters $k_min - $k_max"> + <discover_datasets pattern="__name_and_ext__" directory="cluster_out"/> + </collection> + </outputs> + <help> + +.. class:: warningmark + +This tool expects input datasets consisting of tab-delimited columns. + +.. class:: infomark + +**TIP:** If your data is not TAB delimited, use *Text Manipulation > Convert delimiters to TAB* + + </help> +</tool> + + + + + + + + + + + + +