diff build_protein_interaction_maps.py @ 0:b0ac71686b99 draft

planemo upload commit 968cd5b4f78f0a1da86fc3bc29f8159f86e199aa-dirty

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/build_protein_interaction_maps.py	Tue Mar 12 05:55:54 2019 -0400
@@ -0,0 +1,276 @@
+# -*- coding: utf-8 -*-
+import csv, json, argparse, re
+
+def get_args() :
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--species")
+    parser.add_argument("--database", help="Humap, Bioplex or Biogrid", required=True)
+    parser.add_argument("--dict_path", required=True)
+    parser.add_argument("--input_type", help="type of input (list of id or filename)",required=True)
+    parser.add_argument("--input", required=True)
+    parser.add_argument("--header")
+    parser.add_argument("--ncol")
+    parser.add_argument("--id_type")
+    parser.add_argument("--network_output")
+    parser.add_argument("--nodes_output")
+    args = parser.parse_args()
+
+    if args.input_type=="file" :
+        args.ncol = nb_col_to_int(args.ncol)
+        args.header = str2bool(args.header)
+
+    return args
+
+#Turn string into boolean
+def str2bool(v):
+    if v.lower() in ('yes', 'true', 't', 'y', '1'):
+        return True
+    elif v.lower() in ('no', 'false', 'f', 'n', '0'):
+        return False
+    else:
+        raise argparse.ArgumentTypeError('Boolean value expected.')
+
+#return the column number in int format
+def nb_col_to_int(nb_col):
+    try :
+        nb_col = int(nb_col.replace("c", "")) - 1
+        return nb_col
+    except :
+        sys.exit("Please specify the column where you would like to apply the filter with valid format")
+
+#return list of (unique) ids from string
+def get_input_ids_from_string(input) :
+    ids_list = list(set(re.split(r'\s+',input.replace(";"," ").replace("\r","").replace("\n"," ").replace("\t"," "))))
+    if "" in ids_list : ids_list.remove("")
+    #if "NA" in ids_list : ids_list.remove("NA")
+    return ids_list
+
+#return input_file and list of unique ids from input file path
+def get_input_ids_from_file(input,nb_col,header) :
+    with open(input, "r") as csv_file :
+        input_file= list(csv.reader(csv_file, delimiter='\t'))
+
+    input_file, ids_list = one_id_one_line(input_file,nb_col,header)
+    if "" in ids_list : ids_list.remove("")
+    #if "NA" in ids_list : ids_list.remove("NA")
+
+    return input_file, ids_list
+
+#return input file by adding lines when there are more than one id per line
+def one_id_one_line(input_file,nb_col,header) :
+
+    if header : 
+        new_file = [input_file[0]]
+        input_file = input_file[1:]
+    else : 
+        new_file=[]
+    ids_list=[]
+
+    for line in input_file :
+        if line != [] and set(line) != {''}: 
+            line[nb_col] = re.sub(r"\s+","",line[nb_col])
+            if ";" in line[nb_col] :
+                ids = line[nb_col].split(";")
+                for id in ids :
+                    new_file.append(line[:nb_col]+[id]+line[nb_col+1:])
+                    ids_list.append(id)
+            else : 
+                new_file.append(line)
+                ids_list.append(line[nb_col])
+
+    ids_list= list(set(ids_list))
+
+    return new_file, ids_list
+
+#replace all blank cells to NA
+def blank_to_NA(csv_file) :
+    tmp=[]
+    for line in csv_file :
+        line = ["NA" if cell=="" or cell==" " or cell=="NaN" or cell=="-" else cell for cell in line]
+        tmp.append(line)
+    
+    return tmp
+
+def biogrid_output_files(ids,species) :
+    network_file=[["Entrez Gene Interactor A","Entrez Gene Interactor B","Gene symbol Interactor A","Gene symbol Interactor B","Experimental System","Experimental Type","Pubmed ID","Interaction Score","Phenotypes"]]
+    ids_set= set(ids)
+    ids_not_found=set([])
+    for id in ids :
+        if id in ppi_dict['network'] :
+            network_file.extend(ppi_dict['network'][id])
+            ids_set.update([interact[1] for interact in ppi_dict['network'][id]])
+        else : 
+            ids_not_found.add(id)
+    
+    nodes_file = [["Entrez gene ID","Official Symbol Interactor","Present in user input ids","ID present in Biogrid "+species,"Pathway"]]
+    for id in ids_set:
+        #get pathway
+        if id in ppi_dict['nodes']:
+            description_pathway=";".join(ppi_dict['nodes'][id])
+        else :
+            description_pathway="NA"
+        
+        #get gene name
+        if id in ppi_dict['network']: gene_name = ppi_dict['network'][id][0][2]
+        else : gene_name="NA"
+
+        #make line
+        nodes_file.append([id]+[gene_name]+[id in ids]+[id not in ids_not_found]+[description_pathway])   
+    
+    return network_file,nodes_file
+
+def bioplex_output_files(ids,id_type,species) :
+    network_file=[[id_type+" Interactor A",id_type+" Interactor B","Gene symbol Interactor A","Gene symbol Interactor B","Interaction Score"]]
+    ids_set= set(ids)
+    ids_not_found=set([])
+    for id in ids :
+        if id in ppi_dict['network'][id_type] :
+            network_file.extend(ppi_dict['network'][id_type][id])
+            ids_set.update([interact[1] for interact in ppi_dict['network'][id_type][id]])
+        else :
+            ids_not_found.add(id)
+
+    if id_type=="UniProt-AC" : nodes_file=[[id_type,"Present in user input ids","ID present in Human Bioplex","Pathway"]]
+    else: nodes_file=[[id_type,"Official symbol Interactor","Present in user input ids","Present in interactome","Pathway"]]
+    for id in ids_set:
+
+        if id in ppi_dict['nodes'][id_type]:
+            description_pathway=";".join(ppi_dict['nodes'][id_type][id])
+        else :
+            description_pathway="NA"
+
+        #make line
+        if id_type=="UniProt-AC":
+            nodes_file.append([id]+[id in ids]+[id not in ids_not_found]+[description_pathway])  
+        elif id_type=="GeneID":
+            #get gene_name
+            if id in ppi_dict['network'][id_type]: gene_name = ppi_dict['network'][id_type][id][0][2]
+            else : gene_name="NA"
+            nodes_file.append([id]+[gene_name]+[id in ids]+[id not in ids_not_found]+[description_pathway])
+    
+    return network_file,nodes_file
+
+def humap_output_files(ids,species) :
+    network_file=[["Entrez Gene Interactor A","Entrez Gene Interactor B","Gene symbol Interactor A","Gene symbol Interactor B","Interaction Score"]]
+    ids_set= set(ids)
+    ids_not_found=set([])
+    for id in ids :
+        if id in ppi_dict['network'] :
+            network_file.extend(ppi_dict['network'][id])
+            ids_set.update([interact[1] for interact in ppi_dict['network'][id]])
+        else : 
+            ids_not_found.add(id)
+    
+    nodes_file = [["Entrez gene ID","Official Symbol Interactor","Present in user input ids","ID present in Hu.MAP","Pathway"]]
+    for id in ids_set:
+        if id in ppi_dict['nodes']:
+            description_pathway=";".join(ppi_dict['nodes'][id])
+        else :
+            description_pathway="NA"
+
+        #get gene name
+        if id in ppi_dict['gene_name']:
+            gene_name = ppi_dict['gene_name'][id]
+        else : 
+            gene_name = "NA"
+
+        #make line
+        nodes_file.append([id]+[gene_name]+[id in ids]+[id not in ids_not_found]+[description_pathway])   
+    
+    return network_file,nodes_file
+
+#function to sort the csv_file by value in a specific column
+def sort_by_column(tab,sort_col,reverse,header):
+    
+    if len(tab) > 1 : #if there's more than just a header or 1 row
+        if header :
+            head=tab[0]
+            tab=tab[1:]
+
+        #list of empty cells in the column to sort
+        unsortable_lines = [i for i,line in enumerate(tab) if (line[sort_col]=='' or line[sort_col] == 'NA')]
+        unsorted_tab=[ tab[i] for i in unsortable_lines]
+        tab= [line for i,line in enumerate(tab) if i not in unsortable_lines]
+
+        if only_number(tab,sort_col) and any_float(tab,sort_col)  : 
+            tab = sorted(tab, key=lambda row: float(row[sort_col]), reverse=reverse)
+        elif only_number(tab,sort_col):
+            tab = sorted(tab, key=lambda row: int(row[sort_col]), reverse=reverse)      
+        else :
+            tab = sorted(tab, key=lambda row: row[sort_col], reverse=reverse)
+        
+        tab.extend(unsorted_tab)
+        if header is True : tab = [head]+tab
+
+    return tab
+
+def only_number(tab,col) :
+
+    for line in tab :
+        if not (is_number("float",line[col].replace(",",".")) or is_number("int",line[col].replace(",","."))) :
+            return False
+    return True
+
+#Check if a variable is a float or an integer
+def is_number(number_format, n):
+    float_format = re.compile(r"^[-]?[0-9][0-9]*.?[0-9]+$")
+    int_format = re.compile(r"^[-]?[0-9][0-9]*$")
+    test = ""
+    if number_format == "int":
+        test = re.match(int_format, n)
+    elif number_format == "float":
+        test = re.match(float_format, n)
+    if test:
+        return True
+
+#return True is there is at least one float in the column
+def any_float(tab,col) :
+    
+    for line in tab :
+        if is_number("float",line[col].replace(",",".")) :
+            return True
+
+    return False
+
+def main() :
+
+    #Get args from command line
+    global args
+    args = get_args()
+
+    #get PPI dictionary
+    with open(args.dict_path, 'r') as handle:
+        global ppi_dict
+        ppi_dict = json.load(handle)
+
+    #Get file and/or ids from input 
+    if args.input_type == "text" :
+        ids = get_input_ids_from_string(args.input)
+    elif args.input_type == "file" :
+        input_file, ids = get_input_ids_from_file(args.input,args.ncol,args.header)
+
+    #create output files
+    if args.database=="biogrid":
+        network_file, nodes_file = biogrid_output_files(ids,args.species)
+    elif args.database=="bioplex":
+        network_file, nodes_file = bioplex_output_files(ids,args.id_type,args.species)
+    elif args.database=="humap":
+        network_file, nodes_file = humap_output_files(ids,args.species)
+
+    #convert blank to NA and sort files
+    network_file = blank_to_NA(network_file)
+    network_file = sort_by_column(network_file,0,False,True)
+    nodes_file = sort_by_column(nodes_file,0,False,True)
+
+    #write output files
+    with open(args.network_output,"w") as output :
+        writer = csv.writer(output,delimiter="\t")
+        writer.writerows(network_file)
+
+    with open(args.nodes_output,"w") as output :
+        writer = csv.writer(output,delimiter="\t")
+        for row in nodes_file:
+            writer.writerow([unicode(s).encode("utf-8") for s in row])
+
+if __name__ == "__main__":
+    main()
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