Add files via upload

Author: FH96

FOR PAPER GITHUB.py

@@ -0,0 +1,152 @@
+import csv
+import statistics
+import cobra
+import pandas
+import sklearn.linear_model
+from matplotlib import pyplot as plt
+from sklearn.model_selection import LeaveOneOut
+
+model=cobra.io.read_sbml_model("iJO1366.xml")
+
+expression_values=pandas.read_csv("expression values.csv",index_col='genes')
+
+flux=pandas.read_csv("Rnx-Flux relationship modified for MLR for each reaction.csv")
+
+data=pandas.read_csv("D:/semesters/11/3/list of genes added-Rnx-Flux relationship modified for MLR for each reaction .csv")
+
+
+rxns_f_list=pandas.read_csv("D:/semesters/11/first data ref 17/reactions.csv")
+
+list_of_systematic_names_of_needed_genes=[]
+
+for item in rxns_f_list:
+    g_r_rule=model.reactions.get_by_id(item).gene_reaction_rule
+    if 'or' in g_r_rule and 'and' in g_r_rule:
+        or_splitted = g_r_rule.split(' or ')
+        for i in or_splitted:
+            if not 'and' in i:
+                list_of_systematic_names_of_needed_genes.append(i)
+
+            else:
+                new_i = i.replace("( ", '')
+                newer_i = new_i.replace(" )", '')
+
+                list_of_systematic_names_of_needed_genes.extend(newer_i.split(' and '))
+
+    elif g_r_rule=="":
+        pass
+        #print(item ,'has no gene and',g_r_rule)
+    elif 'or' in g_r_rule:
+        list_of_systematic_names_of_needed_genes.extend(g_r_rule.split(' or '))
+
+
+    elif 'and' in g_r_rule:
+       # print(item,'=',g_r_rule)
+        list_of_systematic_names_of_needed_genes.extend(g_r_rule.split(' and '))
+
+
+    else:
+
+        list_of_systematic_names_of_needed_genes.append(g_r_rule)
+
+'length= 126 '
+
+#removing repetetive files:
+list_of_systematic_names_of_needed_genes=list(dict.fromkeys
+                                              (list_of_systematic_names_of_needed_genes))
+
+
+
+list_of_systematic_names_of_needed_genes.to_csv("genes needed.csv")
+
+column_of_reactions=[]
+
+column_of_genes=[]
+for n in range(flux.shape[0]):
+    rxns=flux.iloc[n]["Rnx-Flux relationship"]
+    rxns = rxns.replace("(", '')
+    rxns = rxns.replace(")", '')
+    rxns = rxns.replace(" ", '')
+
+    list_of_rxns=[]
+    if "+" in rxns:
+        list_of_rxns=rxns.split("+")
+    elif '-' in rxns:
+        list_of_rxns=rxns.split("-")
+    elif ',' in rxns:
+        list_of_rxns=rxns.split(",")
+    else:
+        list_of_rxns.append(rxns)
+
+    column_of_reactions.append(list_of_rxns)
+
+    genes=[]
+    for rxn in list_of_rxns:
+        #print("list_of_rxns: ",list_of_rxns)
+        #print("rxn: ",rxn)
+        gene_rule=model.reactions.get_by_id(rxn).gene_reaction_rule
+
+        if "or" in gene_rule:
+            genes.extend(gene_rule.split(" or "))
+        elif "and" in gene_rule:
+            genes.extend(gene_rule.split(" and "))
+        else:
+            genes.append(gene_rule)
+        genes=list(set(genes))
+
+    column_of_genes.append(genes)
+
+flux_and_genes=flux
+flux_and_genes["list of reactions"]=column_of_reactions
+flux_and_genes["list of genes"]=column_of_genes
+
+#flux_and_genes.to_csv("list of genes added-Rnx-Flux relationship modified for MLR for each reaction .csv")
+flux_and_genes.set_index("Flux Module Name (short)",inplace=True)
+
+conditions=['Acetate','Fructose','Galactose','Glucose','Glycerol','Gluconate',
+           'Pyruvate','Succinate']
+
+pearson_for_reactions= {}
+for reaction in flux_and_genes.index:
+
+    Y = flux_and_genes.loc[reaction,conditions]
+    X = expression_values.loc[flux_and_genes.loc[reaction,"list of genes"],conditions]
+    X=X.T
+
+    loocv = LeaveOneOut()
+
+    y_test_for_pearson = []
+    y_predicted_for_pearson = []
+
+    for train_index, test_index in loocv.split(X):
+        x_train = X.iloc[train_index]
+        y_train = Y.iloc[train_index]
+
+        x_test = X.iloc[test_index]
+        y_test = Y.iloc[test_index]
+        MLR_model = sklearn.linear_model.LinearRegression()
+
+
+        MLR_model.fit(x_train, y_train)
+
+
+        predicted = MLR_model.predict(x_test)
+        y_test_for_pearson.append(y_test.iloc[0])
+        y_predicted_for_pearson.append(predicted)
+
+    y_test_for_pearson_df = pandas.DataFrame(y_test_for_pearson)
+    y_predicted_for_pearson_df = pandas.DataFrame(y_predicted_for_pearson)
+
+    #print('model score: ' , MLR_model.score(X,Y))
+    #print("y_test_for_pearson_df: ",y_test_for_pearson_df)
+    pearson = y_test_for_pearson_df.corrwith(y_predicted_for_pearson_df, axis=0)
+    pearson_for_reactions[reaction] = pearson.iloc[0]
+
+
+pearson_for_reactions_df=pandas.DataFrame(pearson_for_reactions,index=["pearson"])
+pearson_for_reactions_df=pearson_for_reactions_df.T
+
+#pearson_for_reactions_df.columns=["Flux Module Name (short)"]
+
+#print("pearson_for_reactions as df: ",pearson_for_reactions_df)
+