Merge pull request #245 from AlexandrovLab/u65

U65

SigProfilerExtractor/__init__.py

@@ -1 +1 @@
-from .version import short_version as __version__
+from .version import short_version as __version__

SigProfilerExtractor/estimate_best_solution.py

@@ -13,97 +13,127 @@
 from SigProfilerExtractor import subroutines as sub
 
 
-def estimate_solution(base_csvfile="All_solutions_stat.csv", 
-                    All_solution="All_Solutions", 
-                    genomes="Samples.txt", 
-                    output="results", 
-                    title="Selection_Plot",
-                    stability=0.8, 
-                    min_stability=0.2, 
-                    combined_stability=1.0,
-                    statistics=True,
-                    select=None,
-                    exome=False,
-                    allow_stability_drop=False):
-
-
-
+def estimate_solution(
+    base_csvfile="All_solutions_stat.csv",
+    All_solution="All_Solutions",
+    genomes="Samples.txt",
+    output="results",
+    title="Selection_Plot",
+    stability=0.8,
+    min_stability=0.2,
+    combined_stability=1.0,
+    statistics=True,
+    select=None,
+    exome=False,
+    allow_stability_drop=False,
+):
     base_csvfile = pd.read_csv(base_csvfile, sep=",", index_col=0)
-    signatures=list(base_csvfile.index)
-    genomes=pd.read_csv(genomes, sep="\t", index_col=0)
-    colnames=genomes.columns
-    genomes=np.array(genomes)
-    all_similarities_list=[]
-    layer_directory=output
-    
-    if genomes.shape[0]==78:
-        mtype="DBS78"
-    elif genomes.shape[0]==83:
-        mtype="ID83"
-    elif genomes.shape[0]==48:
-        mtype="CNV48"
+    signatures = list(base_csvfile.index)
+    genomes = pd.read_csv(genomes, sep="\t", index_col=0)
+    colnames = genomes.columns
+    genomes = np.array(genomes)
+    all_similarities_list = []
+    layer_directory = output
+
+    if genomes.shape[0] == 78:
+        mtype = "DBS78"
+    elif genomes.shape[0] == 83:
+        mtype = "ID83"
+    elif genomes.shape[0] == 48:
+        mtype = "CNV48"
     else:
-        mtype="SBS"+str(genomes.shape[0])
+        mtype = "SBS" + str(genomes.shape[0])
 
-    #set the squence type ("genome" or "exome") for selection criteria
-    if exome==False:
-        sequence="genome"
-    if exome==True:
-        sequence="exome"
+    # set the squence type ("genome" or "exome") for selection criteria
+    if exome == False:
+        sequence = "genome"
+    if exome == True:
+        sequence = "exome"
 
-    #prepare the csvfile
-    csvfile=np.zeros([len(signatures),4])
-    csvfile=csvfile
+    # prepare the csvfile
+    csvfile = np.zeros([len(signatures), 4])
+    csvfile = csvfile
     for i in range(len(signatures)):
-        if base_csvfile.shape[1]!=3:
-            signatures[i]=signatures[i].rstrip("*")
-            fnorm=base_csvfile.iloc[i,4].rstrip("%")
-            csvfile[i,[1,3]]=base_csvfile.iloc[i,[1,0]]
-        elif base_csvfile.shape[1]==3:
-            signatures[i]=str(signatures[i])
-            fnorm=base_csvfile.iloc[i,1].astype(float)
-            fnorm=fnorm*100
-            csvfile[i,[1,3]]=base_csvfile.iloc[i,[0,2]]
-        csvfile[i,0]=signatures[i]    
-        csvfile[i,2]=fnorm
-        w=pd.read_csv(All_solution+"/"+mtype+"_"+signatures[i]+"_Signatures/Signatures/"+mtype+"_S"+signatures[i]+"_Signatures.txt", sep="\t", index_col=0)
-        h=pd.read_csv(All_solution+"/"+mtype+"_"+signatures[i]+"_Signatures/Activities/"+mtype+"_S"+signatures[i]+"_NMF_Activities.txt", sep="\t", index_col=0)
+        if base_csvfile.shape[1] != 3:
+            signatures[i] = signatures[i].rstrip("*")
+            fnorm = base_csvfile.iloc[i, 4].rstrip("%")
+            csvfile[i, [1, 3]] = base_csvfile.iloc[i, [1, 0]]
+        elif base_csvfile.shape[1] == 3:
+            signatures[i] = str(signatures[i])
+            fnorm = base_csvfile.iloc[i, 1].astype(float)
+            fnorm = fnorm * 100
+            csvfile[i, [1, 3]] = base_csvfile.iloc[i, [0, 2]]
+        csvfile[i, 0] = signatures[i]
+        csvfile[i, 2] = fnorm
+        w = pd.read_csv(
+            All_solution
+            + "/"
+            + mtype
+            + "_"
+            + signatures[i]
+            + "_Signatures/Signatures/"
+            + mtype
+            + "_S"
+            + signatures[i]
+            + "_Signatures.txt",
+            sep="\t",
+            index_col=0,
+        )
+        h = pd.read_csv(
+            All_solution
+            + "/"
+            + mtype
+            + "_"
+            + signatures[i]
+            + "_Signatures/Activities/"
+            + mtype
+            + "_S"
+            + signatures[i]
+            + "_NMF_Activities.txt",
+            sep="\t",
+            index_col=0,
+        )
         w = np.array(w)
         h = np.array(h).T
-        est_genomes=np.dot(w,h)
-        all_similarities, cosine_similarities = sub.calculate_similarities(genomes, est_genomes, colnames)
+        est_genomes = np.dot(w, h)
+        all_similarities, cosine_similarities = sub.calculate_similarities(
+            genomes, est_genomes, colnames
+        )
         all_similarities_list.append(all_similarities)
-
-    csvfile=pd.DataFrame(csvfile)
-    csvfile.columns=["Total Signatures",  "Stability",  "Matrix Frobenius%",  "avgStability"]
-
+
+    csvfile = pd.DataFrame(csvfile)
+    csvfile.columns = [
+        "Total Signatures",
+        "Stability",
+        "Matrix Frobenius%",
+        "avgStability",
+    ]
+
     try:
         if not os.path.exists(layer_directory):
             os.makedirs(layer_directory)
-    except: 
-        print ("The {} folder could not be created".format("output"))
-
-    solution, all_stats= sub.stabVsRError(csvfile, 
-                                          layer_directory, 
-                                          title, all_similarities_list, 
-                                          input_type="dataframe",  
-                                          stability=stability, 
-                                          min_stability=min_stability, 
-                                          combined_stability=combined_stability, 
-                                          mtype=mtype,
-                                          statistics=statistics,
-                                          select=select,
-                                          sequence=sequence,
-                                          allow_stability_drop=allow_stability_drop)
-
-    all_stats.insert(1, 'Stability (Avg Silhouette)', csvfile["avgStability"]) 
-    all_stats=all_stats.set_index(["Signatures"])
-    all_stats.to_csv(layer_directory+"/All_solutions_stat.csv", sep = ",")
-    #print("\nSelected Solution: ", solution)
-
-
-
-    return(solution)
+    except:
+        print("The {} folder could not be created".format("output"))
 
+    solution, all_stats = sub.stabVsRError(
+        csvfile,
+        layer_directory,
+        title,
+        all_similarities_list,
+        input_type="dataframe",
+        stability=stability,
+        min_stability=min_stability,
+        combined_stability=combined_stability,
+        mtype=mtype,
+        statistics=statistics,
+        select=select,
+        sequence=sequence,
+        allow_stability_drop=allow_stability_drop,
+    )
 
+    all_stats.insert(1, "Stability (Avg Silhouette)", csvfile["avgStability"])
+    all_stats = all_stats.set_index(["Signatures"])
+    all_stats.to_csv(layer_directory + "/All_solutions_stat.csv", sep=",")
+    # print("\nSelected Solution: ", solution)
 
+    return solution