Added escottfile and ranksorted args to prescott UI.

prescott/prescott.py

@@ -329,6 +329,15 @@ def main():
         help='Do not touch this if you don\'t know what you are doing! Default is true',
         required=False, default='true')
     
+    main_parser.add_argument('--ranksorted', dest='ranksorted', type=str, \
+        help='If your data is already ranksorted, change this argument to true. Default is false',
+        required=False, default='false')
+    
+    main_parser.add_argument('--escottformat', dest='escottformat', type=str, \
+        help='Main format of escott file. There are two possibilities: gemme or singleline. \n'+\
+            'gemme: a horizontal format of 20 rows and N columns.\n'+\
+            'singleline: each line contains a mutation and its value separated by a space.\n'+\
+            'M1A 0.378\n', required=False, default='gemme')
     # main_parser.add_argument('--colormap', dest='colormap', type=str, \
     #     help='A colormap as defined in matplotlib',
     #     required=False, default='coolwarm_r')
@@ -366,6 +375,9 @@ def main():
     usePopMaxOrNot = args.usepopmax.lower()
     version = args.equation
     if (os.path.exists(escottDataPath)):
+
+        if(args.escottformat=='gemme'):
+
             #Parse the file containing raw ESCOTT scores. 
             scanningMatrix = parseGEMMEoutput(args.escottfile, verbose=False)
             
@@ -382,6 +394,14 @@ def main():
             #Mostyl, I am using normPred_Combi_singleline as input file and it doesn't have a header.
             df = pd.read_table(protein+'_singleline.txt', sep="\s+", header=None)
             
+        elif(args.escottformat=='singleline'):
+            
+            df = pd.read_table(args.escottfile, sep="\s+", header=None)
+        else:
+            print('@> ERROR: Unknown escott format. It should be gemme or singleline!')
+            sys.exit(-1)
+
+        if(args.ranksorted == 'false'):
             #data = np.genfromtxt(args.input,dtype=None)
             data = df.to_numpy()
             rawData = data.T[1]
@@ -392,6 +412,8 @@ def main():
                     f.write("{:} {:6.2f}\n".format(data.T[0][i], processedData[i]))
             
             dfESCOTT = pd.read_table(protein+'_singleline_1-ranksort.txt', sep='\s+', header=None)
+        else:
+            dfESCOTT = df
 
         dfESCOTT.columns = ['mutant', 'ESCOTT']
         dfESCOTT['protein']=protein
@@ -416,10 +438,8 @@ def main():
             (row['ClinVar Clinical Significance']=='Pathogenic') or \
             (row['ClinVar Clinical Significance']=='Likely pathogenic')):
             gnomadDF.at[index,'labels'] = 1   
-
     print(gnomadDF.loc[(gnomadDF['labels']==0) | (gnomadDF['labels']==1)])
     # print(gnomadDF['ClinVar Clinical Significance'])
-
     # Add frequency column and a dummy frequency to each row in myBigMergedDF
     myBigMergedDF['frequency'] = 999.0
     myBigMergedDF['labels'] = np.nan

requirements.txt

@@ -5,5 +5,4 @@ scipy
 pandas
 biopython<=1.79
 biotite
-sklearn
-
+scikit-learn