# HG changeset patch
# User bimib
# Date 1582127092 18000
# Node ID 3af9d394367c9fc83fa17541659510e32864ab12
# Parent  5d5d01ef1d68cf66de0c303bfe9693a51e4be842
Uploaded

diff -r 5d5d01ef1d68 -r 3af9d394367c Marea/marea.py
--- a/Marea/marea.py	Wed Jan 22 11:50:54 2020 -0500
+++ b/Marea/marea.py	Wed Feb 19 10:44:52 2020 -0500
@@ -26,10 +26,6 @@
     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',
@@ -37,7 +33,7 @@
                         help = 'compute Nan values')
     parser.add_argument('-pv' ,'--pValue', 
                         type = float, 
-                        default = 0.05, 
+                        default = 0.1, 
                         help = 'P-Value threshold (default: %(default)s)')
     parser.add_argument('-fc', '--fChange', 
                         type = float, 
@@ -49,14 +45,10 @@
                         help = 'your tool directory')
     parser.add_argument('-op', '--option', 
                         type = str, 
-                        choices = ['datasets', 'dataset_class', 'datasets_rasonly'],
+                        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')
@@ -80,15 +72,21 @@
                         default = 'true',
                         choices = ['true', 'false'], 
                         help = 'generate pdf map')
-    parser.add_argument('-gr', '--generate_ras',
+    parser.add_argument('-on', '--control',
+                        type = str)
+    parser.add_argument('-co', '--comparison',
+                        type = str, 
+                        default = '1vs1',
+                        choices = ['manyvsmany', 'onevsrest', 'onevsmany'])
+    parser.add_argument('-ids', '--input_datas', 
                         type = str,
-                        default = 'true',
-                        choices = ['true', 'false'],
-                        help = 'generate reaction activity score')
-    parser.add_argument('-sr', '--single_ras_file',  
-                         type = str,              
-                         help = 'file that will contain ras')
-    					
+                        nargs = '+', 
+                        help = 'input datasets')
+    parser.add_argument('-na', '--names', 
+                        type = str,
+                        nargs = '+', 
+                        help = 'input names')
+                                    
     args = parser.parse_args()
     return args
 
@@ -615,7 +613,6 @@
 
 def resolve(genes, rules, ids, resolve_none, name):
     resolve_rules = {}
-    names_array = []
     not_found = []
     flag = False
     for key, value in genes.items():
@@ -663,79 +660,157 @@
                         ', the class has been disregarded\n')
     return class_pat
 
-############################ create_ras #######################################
-
-def create_ras (resolve_rules, dataset_name, single_ras, rules, ids):
-
-    if resolve_rules == None:
-        warning("Couldn't generate RAS for current dataset: " + dataset_name)
-
-    for geni in resolve_rules.values():
-        for i, valori in enumerate(geni):
-            if valori == None:
-                geni[i] = 'None'
-                
-    output_ras = pd.DataFrame.from_dict(resolve_rules)
-    
-    output_ras.insert(0, 'Reactions', ids)
-    output_to_csv = pd.DataFrame.to_csv(output_ras, sep = '\t', index = False)
-    
-    if (single_ras):
-        args = process_args(sys.argv)
-        text_file = open(args.single_ras_file, "w")
-    else:
-        text_file = open("ras/Reaction_Activity_Score_Of_" + dataset_name + ".tsv", "w")
-    
-    text_file.write(output_to_csv)
-    text_file.close()
-
 ############################ map ##############################################
 
-def maps(core_map, class_pat, ids, threshold_P_V, threshold_F_C, create_svg, create_pdf):
+def maps(core_map, class_pat, ids, threshold_P_V, threshold_F_C, create_svg, create_pdf, comparison, control):
     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 = 'result/' + i + '_vs_' + j + ' (Tabular Result).tsv'
-        tmp_csv = pd.DataFrame.from_dict(tmp, orient = "index")
-        tmp_csv = tmp_csv.reset_index()
-        header = ['ids', 'P_Value', 'Log2(fold change)']
-        tmp_csv.to_csv(tab, sep = '\t', index = False, header = header)
+
+    if comparison == "manyvsmany":
+        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)
+                    #sum(l1) da errore secondo me perchè ha null
+                    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 = 'result/' + i + '_vs_' + j + ' (Tabular Result).tsv'
+            tmp_csv = pd.DataFrame.from_dict(tmp, orient = "index")
+            tmp_csv = tmp_csv.reset_index()
+            header = ['ids', 'P_Value', 'Log2(fold change)']
+            tmp_csv.to_csv(tab, sep = '\t', index = False, header = header)
+            
+            if create_svg or create_pdf:
+                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 = 'result/' + i + '_vs_' + j + ' (SVG Map).svg'
+                    with open(file_svg, 'wb') as new_map:
+                        new_map.write(ET.tostring(core_map))
+                        
+                    
+                    if create_pdf:
+                        file_pdf = 'result/' + i + '_vs_' + j + ' (PDF Map).pdf'
+                        renderPDF.drawToFile(svg2rlg(file_svg), file_pdf)
+                    
+                    if not create_svg:
+                        #Ho utilizzato il file svg per generare il pdf, 
+                        #ma l'utente non ne ha richiesto il ritorno, quindi
+                        #lo elimino
+                        
+                        os.remove('result/' + i + '_vs_' + j + ' (SVG Map).svg')
+    elif comparison == "onevsrest":
+        for single_cluster in class_pat.keys():
+            t = []
+            for k in class_pat.keys():
+                if k != single_cluster:
+                   t.append(class_pat.get(k))
+            rest = []
+            for i in t:
+                rest = rest + i
+            
+            tmp = {}
+            count = 0
+            max_F_C = 0
+
+            for l1, l2 in zip(rest, class_pat.get(single_cluster)):
+                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 = 'result/' + single_cluster + '_vs_rest (Tabular Result).tsv'
+            tmp_csv = pd.DataFrame.from_dict(tmp, orient = "index")
+            tmp_csv = tmp_csv.reset_index()
+            header = ['ids', 'P_Value', 'Log2(fold change)']
+            tmp_csv.to_csv(tab, sep = '\t', index = False, header = header)
+            
+            if create_svg or create_pdf:
+                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 = 'result/' + single_cluster + '_vs_ rest (SVG Map).svg'
+                    with open(file_svg, 'wb') as new_map:
+                        new_map.write(ET.tostring(core_map))
+                        
+                    
+                    if create_pdf:
+                        file_pdf = 'result/' + single_cluster + '_vs_ rest (PDF Map).pdf'
+                        renderPDF.drawToFile(svg2rlg(file_svg), file_pdf)
+                    
+                    if not create_svg:
+                        os.remove('result/' + single_cluster + '_vs_ rest (SVG Map).svg') 
+                        
+    elif comparison == "onevsmany":
+        for i, j in it.combinations(class_pat.keys(), 2):
+
+            if i != control and j != control:
+                print(str(control) + " " + str(i) + " " + str(j))
+                #Se è un confronto fra elementi diversi dal controllo, skippo
+                continue
+            
+            print('vado')
+            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)
+                    #sum(l1) da errore secondo me perchè ha null
+                    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 = 'result/' + i + '_vs_' + j + ' (Tabular Result).tsv'
+            tmp_csv = pd.DataFrame.from_dict(tmp, orient = "index")
+            tmp_csv = tmp_csv.reset_index()
+            header = ['ids', 'P_Value', 'Log2(fold change)']
+            tmp_csv.to_csv(tab, sep = '\t', index = False, header = header)
+            
+            if create_svg or create_pdf:
+                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 = 'result/' + i + '_vs_' + j + ' (SVG Map).svg'
+                    with open(file_svg, 'wb') as new_map:
+                        new_map.write(ET.tostring(core_map))
+                        
+                    
+                    if create_pdf:
+                        file_pdf = 'result/' + i + '_vs_' + j + ' (PDF Map).pdf'
+                        renderPDF.drawToFile(svg2rlg(file_svg), file_pdf)
+                    
+                    if not create_svg:
+                        #Ho utilizzato il file svg per generare il pdf, 
+                        #ma l'utente non ne ha richiesto il ritorno, quindi
+                        #lo elimino
+                        
+                        os.remove('result/' + i + '_vs_' + j + ' (SVG Map).svg')
         
-        if create_svg or create_pdf:
-            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 = 'result/' + i + '_vs_' + j + ' (SVG Map).svg'
-                with open(file_svg, 'wb') as new_map:
-                    new_map.write(ET.tostring(core_map))
-                    
-                
-                if create_pdf:
-                    file_pdf = 'result/' + i + '_vs_' + j + ' (PDF Map).pdf'
-                    renderPDF.drawToFile(svg2rlg(file_svg), file_pdf)
-                
-                if not create_svg:
-                    #Ho utilizzato il file svg per generare il pdf, 
-                    #ma l'utente non ne ha richiesto il ritorno, quindi
-                    #lo elimino
-                    os.remove('result/' + i + '_vs_' + j + ' (SVG Map).svg')
-                    
+        
+        
+
     return None
 
 ############################ MAIN #############################################
@@ -745,12 +820,9 @@
     
     create_svg = check_bool(args.generate_svg)
     create_pdf = check_bool(args.generate_pdf)
-    generate_ras = check_bool(args.generate_ras)
 
-    os.makedirs('result')
-
-    if generate_ras:
-        os.makedirs('ras')
+    if os.path.isdir('result') == False:
+        os.makedirs('result')
     
     if args.rules_selector == 'HMRcore':        
         recon = pk.load(open(args.tool_dir + '/local/HMRcore_rules.p', 'rb'))
@@ -758,93 +830,60 @@
         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_rasonly':
-        name = "RAS Dataset"
-        dataset = read_dataset(args.input_datas[0],"dataset")
-
-        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)
-
-        create_ras(resolve_rules, name, True, rules, ids)
-          
-        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')
-        
-        print('execution succeded')
-        return None
-    
-    
-    elif args.option == 'datasets':
+    if args.option == 'datasets':
         num = 1
         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) 
+            resolve_rules = read_dataset(i, name)
+            
+            resolve_rules.iloc[:, 0] = (resolve_rules.iloc[:, 0]).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)
+            ids =  pd.Series.tolist(resolve_rules.iloc[:, 0])
 
-            if generate_ras:
-                create_ras(resolve_rules, name, False, rules, ids)
+            resolve_rules = resolve_rules.drop(resolve_rules.columns[[0]], axis=1)
+            resolve_rules = resolve_rules.replace({'None': None})
+            resolve_rules = resolve_rules.to_dict('list')
             
-            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')
+            #Converto i valori da str a float
+            to_float = lambda x: float(x) if (x != None) else None
+            
+            resolve_rules_float = {}
+           
+            for k in resolve_rules:
+                resolve_rules_float[k] = list(map(to_float, resolve_rules[k])); resolve_rules_float
+            
             if resolve_rules != None:
-                class_pat[name] = list(map(list, zip(*resolve_rules.values())))
+                class_pat[name] = list(map(list, zip(*resolve_rules_float.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)
+            
+    if args.option == 'dataset_class':
+        name = 'RAS'
+        resolve_rules = read_dataset(args.input_data, name)
+        resolve_rules.iloc[:, 0] = (resolve_rules.iloc[:, 0]).astype(str)
+            
+        ids =  pd.Series.tolist(resolve_rules.iloc[:, 0])
+
+        resolve_rules = resolve_rules.drop(resolve_rules.columns[[0]], axis=1)
+        resolve_rules = resolve_rules.replace({'None': None})
+        resolve_rules = resolve_rules.to_dict('list')
+            
+        #Converto i valori da str a float
+        to_float = lambda x: float(x) if (x != None) else None
+            
+        resolve_rules_float = {}
+           
+        for k in resolve_rules:
+            resolve_rules_float[k] = list(map(to_float, resolve_rules[k])); resolve_rules_float
+
         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 generate_ras:
-                create_ras(resolve_rules, name, False, rules, ids)
-    
+        
+        if resolve_rules_float != None:
+            class_pat = split_class(classes, resolve_rules_float)
     	
     if args.rules_selector == 'Custom':
         if args.yes_no == 'yes':
@@ -857,11 +896,12 @@
     else:       
         core_map = ET.parse(args.tool_dir+'/local/HMRcoreMap.svg')
         
-    maps(core_map, class_pat, ids, args.pValue, args.fChange, create_svg, create_pdf)
+    maps(core_map, class_pat, ids, args.pValue, args.fChange, create_svg, create_pdf, args.comparison, args.control)
         
     print('Execution succeded')
+    
+    return None
 
-    return None
 
 ###############################################################################
 
diff -r 5d5d01ef1d68 -r 3af9d394367c Marea/marea.xml
--- a/Marea/marea.xml	Wed Jan 22 11:50:54 2020 -0500
+++ b/Marea/marea.xml	Wed Feb 19 10:44:52 2020 -0500
@@ -1,28 +1,19 @@
-<tool id="MaREA" name="Metabolic Reaction Enrichment Analysis" version="1.0.6">
-    <description></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="4.2.1">lxml</requirement>
-        <requirement type="package" version="0.8.1">svglib</requirement>
-        <requirement type="package" version="3.4.0">reportlab</requirement>
-    </requirements>
-    <command detect_errors="exit_code">
-        <![CDATA[
+<tool id="MaREA" name="Metabolic Reaction Enrichment Analysis" version="1.0.7">
+	<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="4.2.1">lxml</requirement>
+		<requirement type="package" version="0.8.1">svglib</requirement>
+		<requirement type="package" version="3.4.0">reportlab</requirement>
+	</requirements>
+	<command detect_errors="exit_code">
+		<![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
-	
+
       	--tool_dir $__tool_directory__
       	--option $cond.type_selector
         --out_log $log		
@@ -36,153 +27,130 @@
             #for $data in $cond.input_Datasets:
                 ${data.input_name}
             #end for
+            --comparison ${cond.comparis.comparison}
             #if $cond.advanced.choice == 'true':
-      	    --none ${cond.advanced.None}
-      	    --pValue ${cond.advanced.pValue}
-      	    --fChange ${cond.advanced.fChange}
-	    	--generate_svg ${cond.advanced.generateSvg}
-	    	--generate_pdf ${cond.advanced.generatePdf}
-	    --generate_ras ${cond.advanced.generateRas}
-	#else 
-	    --none true
-	    --pValue 0.05
-	    --fChange 1.5
-	    --generate_svg false
-	    --generate_pdf true
-	    --generate_ras false
-	#end if
+      	       --pValue ${cond.advanced.pValue}
+      	       --fChange ${cond.advanced.fChange}
+	           --generate_svg ${cond.advanced.generateSvg}
+	    	   --generate_pdf ${cond.advanced.generatePdf}
+     	    #else 
+	            --pValue 0.05
+	            --fChange 1.5
+	            --generate_svg false
+	            --generate_pdf true
+	            --generate_ras false
+	        #end if
         #elif $cond.type_selector == 'dataset_class':
             --input_data ${input_data}
             --input_class ${input_class}
+            --comparison ${cond.comparis.comparison}
+            #if $cond.comparis.comparison == 'onevsmany'
+            	--control ${cond.comparis.controlgroup}
+            #end if
             #if $cond.advanced.choice == 'true':
-      	    --none ${cond.advanced.None}
-      	    --pValue ${cond.advanced.pValue}
-      	    --fChange ${cond.advanced.fChange}
-	    --generate_svg ${cond.advanced.generateSvg}
-	    --generate_pdf ${cond.advanced.generatePdf}
-	    --generate_ras ${cond.advanced.generateRas}
-	#else 
-	    --none true
-	    --pValue 0.05
-	    --fChange 1.5
-	    --generate_svg false
-	    --generate_pdf true
-	    --generate_ras false
-	#end if
-        #end if
-        #if $cond.type_selector == 'datasets_rasonly':
-            --input_datas ${input_Datasets}
-            --single_ras_file $ras_single
-            --none ${cond.None}
+      	        --pValue ${cond.advanced.pValue}
+      	        --fChange ${cond.advanced.fChange}
+	            --generate_svg ${cond.advanced.generateSvg}
+	            --generate_pdf ${cond.advanced.generatePdf}
+	        #else 
+	            --pValue 0.05
+	            --fChange 1.5
+	            --generate_svg false
+	            --generate_pdf true
+	        #end if
         #end if
         ]]>
-    </command>
-
-    <inputs>
-        <conditional name="cond_rule">
-            <expand macro="options"/>
-            <when value="HMRcore">
-            </when>
-            <when value="Recon">
-            </when>
-            <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" argument="--custom_map" type="data" format="xml, svg" label="custom-map.svg"/>
-                    </when>
-                    <when value="no">
-                    </when>
-                </conditional>
-            </when>
-        </conditional>
-        <conditional name="cond">
-            <param name="type_selector" argument="--option" 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>
-                <option value="datasets_rasonly" selected="true">RNAseq dataset</option>
-            </param>
-            <when value="datasets">
-                <repeat name="input_Datasets" title="RNAseq" min="2">
-                    <param name="input" argument="--input_datas" type="data" format="tabular, csv, tsv" label="add dataset" />	
-                    <param name="input_name" argument="--names" type="text" label="Dataset's name:" value="Dataset" help="Default: Dataset" />
-                </repeat>
-                <conditional name="advanced">
+	</command>
+	<inputs>
+		<conditional name="cond">
+			<param name="type_selector" argument="--option" type="select" label="Input format:">
+				<option value="datasets" selected="true">RAS of group 1 + RAS of group 2 + ... + RAS of group N</option>
+				<option value="dataset_class">RAS of all samples + sample group specification</option>
+			</param>
+			<when value="datasets">
+				<repeat name="input_Datasets" title="RAS dataset" min="2">
+					<param name="input" argument="--input_datas" type="data" format="tabular, csv, tsv" label="add dataset" />
+					<param name="input_name" argument="--names" type="text" label="Dataset's name:" value="Dataset" help="Default: Dataset" />
+				</repeat>
+				<conditional name="comparis">
+					<param name="comparison" argument="--comparison" type="select" label="Groups comparison:">
+						<option value="manyvsmany" selected="true">One vs One</option>
+						<option value="onevsrest">One vs All</option>
+						<option value="onevsmany">One vs Control</option>
+					</param>
+					<when value="onevsmany">
+						<param name="controlgroup" argument="--controlgroup" type="text" label="Control group label:" value="0" help="Name of group label to be compared to others"/>
+					</when>
+				</conditional>
+				<conditional name="advanced">
 					<param name="choice" type="boolean" checked="false" label="Use advanced options?" help="Use this options to choose custom parameters for evaluation: pValue, Fold-Change threshold, how to solve (A and NaN) and specify output maps.">
-		    			<option value="true" selected="true">No</option>
-		    			<option value="false">Yes</option>
+						<option value="true" selected="true">No</option>
+						<option value="false">Yes</option>
 					</param>
-					<when value="false">
-					</when>
+					<when value="false"></when>
 					<when value="true">
-		    			<param name="None" argument="--none" type="boolean" truevalue="true" falsevalue="false" checked="true" label="(A and NaN) solved as (A)?" /> 
-		    			<param name="pValue" argument="--pValue" type="float" size="20" value="0.01" max="1" min="0" label="P-value threshold:" help="min value 0" />
-		    			<param name="fChange" argument="--fChange" type="float" size="20" value="1.2" min="1" label="Fold-Change threshold:" help="min value 1" />
-		    			<param name="generateSvg" argument="--generateSvg" type="boolean" checked="false" label="Generate SVG map" help="should the program generate an editable svg map of the processes?" />
-		    			<param name="generatePdf" argument="--generatePdf" type="boolean" checked="true" label="Generate PDF map" help="should the program return a non editable (but displayble) pdf map of the processes?" />	
-		    			<param name="generateRas" argument="--generateRas" type="boolean" checked="false" label="Generate Reaction Activity Score for each table" help="Generate Reaction Activity Score for each table" />		
+						<conditional name="cond_map">
+						<param name="choice" type="boolean" checked="false" label="Use custom map?" help="Use this option only if you have generated RAS using a custom set of rules">
+							<option value="false" selected="true">No</option>
+							<option value="true">Yes</option>
+						</param>
+						<when value="true">							
+							<param name="Custom_map" argument="--custom_map" type="data" format="xml, svg" label="custom-map.svg"/>
+						</when>
+					</conditional>
+						<param name="pValue" argument="--pValue" type="float" size="20" value="0.01" max="1" min="0" label="P-value threshold:" help="min value 0" />
+						<param name="fChange" argument="--fChange" type="float" size="20" value="1.2" min="1" label="Fold-Change threshold:" help="min value 1" />
+						<param name="generateSvg" argument="--generateSvg" type="boolean" checked="false" label="Generate SVG map" help="should the program generate an editable svg map of the processes?" />
+						<param name="generatePdf" argument="--generatePdf" type="boolean" checked="true" label="Generate PDF map" help="should the program return a non editable (but displayble) pdf map of the processes?" />
 					</when>
-    	</conditional>
-            </when>
-            <when value="datasets_rasonly">
-                <param name="input_Datasets" argument="--input_datas" type="data" format="tabular, csv, tsv" label="add dataset" />
-                <param name="input_name" argument="--names" type="text" label="Dataset's name:" value="Dataset" help="Default: Dataset" />
-                <param name="None" argument="--none" type="boolean" truevalue="true" falsevalue="false" checked="true" label="(A and NaN) solved as (A)?" /> 
-            </when>
-            <when value="dataset_class">
-                <param name="input_data" argument="--input_data" type="data" format="tabular, csv, tsv" label="RNAseq of all samples" />
+			</conditional>
+			</when>
+			<when value="dataset_class">
+                <param name="input_data" argument="--input_data" type="data" format="tabular, csv, tsv" label="RAS of all samples" />
                 <param name="input_class" argument="--input_class" type="data" format="tabular, csv, tsv" label="Sample group specification" />
-                <conditional name="advanced">
-					<param name="choice" type="boolean" checked="false" label="Use advanced options?" help="Use this options to choose custom parameters for evaluation: pValue, Fold-Change threshold, how to solve (A and NaN) and specify output maps.">
-		    			<option value="true" selected="true">No</option>
-		    			<option value="false">Yes</option>
+                <conditional name="comparis">
+					<param name="comparison" argument="--comparison" type="select" label="Groups comparison:">
+						<option value="manyvsmany" selected="true">One vs One</option>
+						<option value="onevsrest">One vs All</option>
+						<option value="onevsmany">One vs Control</option>
 					</param>
-					<when value="false">
-					</when>
-					<when value="true">
-		    			<param name="None" argument="--none" type="boolean" truevalue="true" falsevalue="false" checked="true" label="(A and NaN) solved as (A)?" /> 
-		    			<param name="pValue" argument="--pValue" type="float" size="20" value="0.01" max="1" min="0" label="P-value threshold:" help="min value 0" />
-		    			<param name="fChange" argument="--fChange" type="float" size="20" value="1.2" min="1" label="Fold-Change threshold:" help="min value 1" />
-		    			<param name="generateSvg" argument="--generateSvg" type="boolean" checked="false" label="Generate SVG map" help="should the program generate an editable svg map of the processes?" />
-		    			<param name="generatePdf" argument="--generatePdf" type="boolean" checked="true" label="Generate PDF map" help="should the program return a non editable (but displayble) pdf map of the processes?" />	
-		    			<param name="generateRas" argument="--generateRas" type="boolean" checked="false" label="Generate Reaction Activity Score for each table" help="Generate Reaction Activity Score for each table" />		
+					<when value="onevsmany">
+						<param name="controlgroup" argument="--controlgroup" type="text" label="Control group label:" value="0" help="Name of group label to be compared to others"/>
 					</when>
-    	</conditional>
-            </when>
-        </conditional>
-       
-      
-       
-	
-    </inputs>
-
-    <outputs>
-        <data format="txt" name="log" label="MaREA - Log" />
-        <data format="tabular" name="ras_single" label="MaREA - RAS - ${cond.input_name}">
-        	<filter>cond['type_selector'] == "datasets_rasonly"</filter>
-        </data>
-        <collection name="results" type="list" label="MaREA - Results">
-        <filter>cond['type_selector'] == "datasets" or cond['type_selector'] == "dataset_class"</filter>
-            <discover_datasets pattern="__name_and_ext__" directory="result"/>
-        </collection>
-	<collection name="ras" type="list" label="MaREA - RAS list" format_source="tabular">
-	    <filter>cond['type_selector'] != "datasets_rasonly" and cond['advanced']['choice'] and cond['advanced']['generateRas']</filter>
-    	    <discover_datasets pattern="__name_and_ext__" directory="ras" format="tabular"/>
+				</conditional>
+				<conditional name="advanced">
+				<param name="choice" type="boolean" checked="false" label="Use advanced options?" help="Use this options to choose custom parameters for evaluation: pValue, Fold-Change threshold, how to solve (A and NaN) and specify output maps.">
+					<option value="true" selected="true">No</option>
+					<option value="false">Yes</option>
+				</param>
+				<when value="false"></when>
+				<when value="true">
+					<conditional name="cond_map">
+						<param name="choice" type="boolean" checked="false" label="Use custom map?" help="Use this option only if you have generated RAS using a custom set of rules">
+							<option value="false" selected="true">No</option>
+							<option value="true">Yes</option>
+						</param>
+						<when value="true">							
+							<param name="Custom_map" argument="--custom_map" type="data" format="xml, svg" label="custom-map.svg"/>
+						</when>
+					</conditional>
+					<param name="pValue" argument="--pValue" type="float" size="20" value="0.01" max="1" min="0" label="P-value threshold:" help="min value 0" />
+					<param name="fChange" argument="--fChange" type="float" size="20" value="1.2" min="1" label="Fold-Change threshold:" help="min value 1" />
+					<param name="generateSvg" argument="--generateSvg" type="boolean" checked="false" label="Generate SVG map" help="should the program generate an editable svg map of the processes?" />
+					<param name="generatePdf" argument="--generatePdf" type="boolean" checked="true" label="Generate PDF map" help="should the program return a non editable (but displayble) pdf map of the processes?" />
+				</when>
+			</conditional>
+		</when>
+	</conditional>
+</inputs>
+<outputs>
+	<data format="txt" name="log" label="MaREA - Log" />
+	<collection name="results" type="list" label="MaREA - Results">
+		<discover_datasets pattern="__name_and_ext__" directory="result"/>
 	</collection>
-	
-    </outputs>
-    <tests>
-        <test>
-            <param name="pValue" value="0.56"/>
-            <output name="log" file="log.txt"/>
-        </test>
-    </tests>
-    <help>
-<![CDATA[
+</outputs>
+<help>
+	<![CDATA[
 
 What it does
 -------------
@@ -268,7 +236,6 @@
 .. _MaREA cluster analysis: http://link del tool di cluster.org
 
 ]]>
-    </help>
-    <expand macro="citations" />
-</tool>
+</help>
+<expand macro="citations" />undefined</tool>
 	
diff -r 5d5d01ef1d68 -r 3af9d394367c Marea/ras_generator.py
--- a/Marea/ras_generator.py	Wed Jan 22 11:50:54 2020 -0500
+++ b/Marea/ras_generator.py	Wed Feb 19 10:44:52 2020 -0500
@@ -1,16 +1,10 @@
 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 ##########################################
 
@@ -26,69 +20,25 @@
     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', 'datasets_rasonly'],
-                        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',
+    parser.add_argument('-id', '--input',
                         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')
-    parser.add_argument('-gs', '--generate_svg',
+    parser.add_argument('-ra', '--ras_output',
                         type = str,
-                        default = 'true',
-                        choices = ['true', 'false'], 
-                        help = 'generate svg map')
-    parser.add_argument('-gp', '--generate_pdf',
-                        type = str,
-                        default = 'true',
-                        choices = ['true', 'false'], 
-                        help = 'generate pdf map')
-    parser.add_argument('-gr', '--generate_ras',
-                        type = str,
-                        default = 'true',
-                        choices = ['true', 'false'],
-                        help = 'generate reaction activity score')
-    parser.add_argument('-sr', '--single_ras_file',  
-                         type = str,              
-                         help = 'file that will contain ras')
-    					
+                        required = True,
+                        help = 'ras output')
+    
     args = parser.parse_args()
     return args
 
@@ -297,79 +247,6 @@
             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):
@@ -615,7 +492,6 @@
 
 def resolve(genes, rules, ids, resolve_none, name):
     resolve_rules = {}
-    names_array = []
     not_found = []
     flag = False
     for key, value in genes.items():
@@ -652,7 +528,6 @@
             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
@@ -665,7 +540,7 @@
 
 ############################ create_ras #######################################
 
-def create_ras (resolve_rules, dataset_name, single_ras, rules, ids):
+def create_ras (resolve_rules, dataset_name, rules, ids, file):
 
     if resolve_rules == None:
         warning("Couldn't generate RAS for current dataset: " + dataset_name)
@@ -680,78 +555,16 @@
     output_ras.insert(0, 'Reactions', ids)
     output_to_csv = pd.DataFrame.to_csv(output_ras, sep = '\t', index = False)
     
-    if (single_ras):
-        args = process_args(sys.argv)
-        text_file = open(args.single_ras_file, "w")
-    else:
-        text_file = open("ras/Reaction_Activity_Score_Of_" + dataset_name + ".tsv", "w")
+    text_file = open(file, "w")
     
     text_file.write(output_to_csv)
     text_file.close()
 
-############################ map ##############################################
-
-def maps(core_map, class_pat, ids, threshold_P_V, threshold_F_C, create_svg, create_pdf):
-    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 = 'result/' + i + '_vs_' + j + ' (Tabular Result).tsv'
-        tmp_csv = pd.DataFrame.from_dict(tmp, orient = "index")
-        tmp_csv = tmp_csv.reset_index()
-        header = ['ids', 'P_Value', 'Log2(fold change)']
-        tmp_csv.to_csv(tab, sep = '\t', index = False, header = header)
-        
-        if create_svg or create_pdf:
-            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 = 'result/' + i + '_vs_' + j + ' (SVG Map).svg'
-                with open(file_svg, 'wb') as new_map:
-                    new_map.write(ET.tostring(core_map))
-                    
-                
-                if create_pdf:
-                    file_pdf = 'result/' + i + '_vs_' + j + ' (PDF Map).pdf'
-                    renderPDF.drawToFile(svg2rlg(file_svg), file_pdf)
-                
-                if not create_svg:
-                    #Ho utilizzato il file svg per generare il pdf, 
-                    #ma l'utente non ne ha richiesto il ritorno, quindi
-                    #lo elimino
-                    os.remove('result/' + i + '_vs_' + j + ' (SVG Map).svg')
-                    
-    return None
-
 ############################ MAIN #############################################
 
 def main():
     args = process_args(sys.argv)
-    
-    create_svg = check_bool(args.generate_svg)
-    create_pdf = check_bool(args.generate_pdf)
-    generate_ras = check_bool(args.generate_ras)
 
-    os.makedirs('result')
-
-    if generate_ras:
-        os.makedirs('ras')
-    
     if args.rules_selector == 'HMRcore':        
         recon = pk.load(open(args.tool_dir + '/local/HMRcore_rules.p', 'rb'))
     elif args.rules_selector == 'Recon':
@@ -761,104 +574,30 @@
         
     resolve_none = check_bool(args.none)
     
-    class_pat = {}
     
-    if args.option == 'datasets_rasonly':
-        name = "RAS Dataset"
-        dataset = read_dataset(args.input_datas[0],"dataset")
-
-        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)
+    name = "RAS Dataset"
+    dataset = read_dataset(args.input, "dataset")
 
-        create_ras(resolve_rules, name, True, rules, ids)
-          
-        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')
-        
-        print('execution succeded')
-        return None
-    
-    
-    elif args.option == 'datasets':
-        num = 1
-        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)
+    dataset.iloc[:, 0] = (dataset.iloc[:, 0]).astype(str)
 
-            if generate_ras:
-                create_ras(resolve_rules, name, False, rules, ids)
-            
-            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 generate_ras:
-                create_ras(resolve_rules, name, False, rules, ids)
+    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)
     
-    	
-    if args.rules_selector == 'Custom':
-        if args.yes_no == 'yes':
-            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, create_svg, create_pdf)
-        
+    resolve_rules, err = resolve(genes, rules, ids, resolve_none, name)
+
+    create_ras(resolve_rules, name, rules, ids, args.ras_output)
+      
+    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')
+
+    
     print('Execution succeded')
 
     return None
diff -r 5d5d01ef1d68 -r 3af9d394367c Marea/ras_generator.xml
--- a/Marea/ras_generator.xml	Wed Jan 22 11:50:54 2020 -0500
+++ b/Marea/ras_generator.xml	Wed Feb 19 10:44:52 2020 -0500
@@ -1,5 +1,5 @@
-<tool id="MaREA RAS Generator" name="RAS Generator" version="1.0.0">
-    <description></description>
+<tool id="MaREA RAS Generator" name="Expression2RAS" version="1.0.2">
+    <description>- Reaction Activity Scores computation</description>
     <macros>
         <import>marea_macros.xml</import>
     </macros>
@@ -13,18 +13,15 @@
     </requirements>
     <command detect_errors="exit_code">
         <![CDATA[
-      	python $__tool_directory__/marea.py
-        --rules_selector $cond_rule.rules_selector	
-      	--tool_dir $__tool_directory__
-      	--option $cond.type_selector
-        --out_log $log		
-        --input_datas ${input_Datasets}
-        --single_ras_file $ras_single
-        --none ${cond.None}
-        #end if
+      	python $__tool_directory__/ras_generator.py
+      	--rules_selector $cond_rule.rules_selector	
+        --input $input
+        --none $none
+        --tool_dir $__tool_directory__
+        --out_log $log	
+        --ras_output $ras_output
         ]]>
     </command>
-
     <inputs>
         <conditional name="cond_rule">
             <expand macro="options"/>
@@ -47,124 +44,21 @@
                 </conditional>
             </when>
         </conditional>
-        <conditional name="cond">
-            
-             <param name="input_Datasets" argument="--input_datas" type="data" format="tabular, csv, tsv" label="add dataset" />
-                <param name="input_name" argument="--names" type="text" label="Dataset's name:" value="Dataset" help="Default: Dataset" />
-                <param name="None" argument="--none" type="boolean" truevalue="true" falsevalue="false" checked="true" label="(A and NaN) solved as (A)?" /> 
-
-                       
-            </when>
-        </conditional>
+        <param name="input" argument="--input" type="data" format="tabular, csv, tsv" label="Gene Expression dataset:" />
+        <param name="name" argument="--name" type="text" label="Dataset's name:" value="Dataset" help="Default: Dataset" />
+        <param name="none" argument="--none" type="boolean" truevalue="true" falsevalue="false" checked="true" label="(A and NaN) solved as (A)?" /> 
     </inputs>
 
     <outputs>
-        <data format="txt" name="log" label="MaREA - Log" />
-        <data format="tabular" name="ras_single" label="MaREA - RAS - ${cond.input_name}">
-        	<filter>cond['type_selector'] == "datasets_rasonly"</filter>
-        </data>
-        <collection name="results" type="list" label="MaREA - Results">
-        <filter>cond['type_selector'] == "datasets" or cond['type_selector'] == "dataset_class"</filter>
-            <discover_datasets pattern="__name_and_ext__" directory="result"/>
-        </collection>
-	<collection name="ras" type="list" label="MaREA - RAS list" format_source="tabular">
-	    <filter>cond['type_selector'] != "datasets_rasonly" and cond['advanced']['choice'] and cond['advanced']['generateRas']</filter>
-    	    <discover_datasets pattern="__name_and_ext__" directory="ras" format="tabular"/>
-	</collection>
-	
+        <data format="txt" name="log" label="Expression2RAS - $name - Log" />
+        <data format="tabular" name="ras_output" label="$name RAS"/>
     </outputs>
-    <tests>
-        <test>
-            <param name="pValue" value="0.56"/>
-            <output name="log" file="log.txt"/>
-        </test>
-    </tests>
+
     <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.
-
-Accepted files are: 
-    - 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*");
-    - option 2) one RNA dataset and one class-file specifying the class/condition each sample belongs to.
-
-Optional files:
-    - custom GPR (Gene-Protein-Reaction) rules. Two accepted formats:
-
-	* (Cobra Toolbox and CobraPy compliant) xml of metabolic model;
-	* .csv file specifyig for each reaction ID (column 1) the corresponding GPR rule (column 2).
-    - custom svg map. Graphical elements must have the same IDs of reactions. See HmrCore svg map for an example.
-
-The tool generates:
-    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).
-
-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".
-
-
-Example input
--------------
-
-**"Custom Rules"** option:
-
-Custom Rules Dastaset:
-
-@CUSTOM_RULES_EXEMPLE@
-
-**"RNAseq of group 1 + RNAseq of group 2 + ... + RNAseq of group N"** option:
-
-RNA-seq Dataset 1:						
-
-@DATASET_EXEMPLE1@
-
-RNA-seq Dataset 2:
-
-@DATASET_EXEMPLE2@
-
-**"RNAseq of all samples + sample group specification"** option:
-
-RNA-seq Dataset:
-
-@DATASET_EXEMPLE1@
-
-Class-file:
-
-+------------+------------+   
-| Patient_ID |    class   |   
-+============+============+   
-| TCGAAA3529 |     MSI    |   
-+------------+------------+    
-| TCGAA62671 |     MSS    |    
-+------------+------------+    
-| TCGAA62672 |     MSI    |   
-+------------+------------+
-
-|
-
-.. 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 `MaREA cluster analysis`_.
-
-@REFERENCE@
-
-.. _MaREA: https://www.biorxiv.org/content/early/2018/01/16/248724
-.. _Convert delimiters to TAB: https://usegalaxy.org/?tool_id=Convert+characters1&version=1.0.0&__identifer=6t22teyofhj
-.. _MaREA cluster analysis: http://link del tool di cluster.org
-
 ]]>
     </help>
     <expand macro="citations" />