modify main

jass_preprocessing/__main__.py

@@ -29,45 +29,46 @@ def launch_preprocessing(args):
     """
     gwas_map = pd.read_csv(args.gwas_info, sep="\t", index_col=0)
 
-    tag = "{0}_{1}".format(gwas_map.loc[args.gwas_filename, 'consortia'],
-                           gwas_map.loc[args.gwas_filename, 'outcome'])
+    for gwas_filename in gwas_map[['filename']]:
+        tag = "{0}_{1}".format(gwas_map.loc[gwas_filename, 'consortia'],
+                               gwas_map.loc[gwas_filename, 'outcome'])
 
-    print('processing GWAS: {}'.format(tag))
-    start = time.time()
-    GWAS_link = jp.map_gwas.walkfs(args.gwas_folder, args.gwas_filename)[2]
-    mapgw = jp.map_gwas.map_columns_position(GWAS_link, args.gwas_info)
+        print('processing GWAS: {}'.format(tag))
+        start = time.time()
+        GWAS_link = jp.map_gwas.walkfs(args.gwas_folder, gwas_filename)[2]
+        mapgw = jp.map_gwas.map_columns_position(GWAS_link, args.gwas_info)
 
-    gw_df = jp.map_gwas.read_gwas(GWAS_link, mapgw)
+        gw_df = jp.map_gwas.read_gwas(GWAS_link, mapgw)
 
-    ref = pd.read_csv(args.ref_path, header=None, sep= "\t",
-                      names =['chr', "pos", "snp_id", "ref", "alt", "MAF"],
-                       index_col="snp_id")
+        ref = pd.read_csv(args.ref_path, header=None, sep= "\t",
+                          names =['chr', "pos", "snp_id", "ref", "alt", "MAF"],
+                           index_col="snp_id")
 
-    mgwas = jp.map_reference.map_on_ref_panel(gw_df, ref)
-    mgwas = jp.map_reference.compute_snp_alignement(mgwas)
-    mgwas = jp.compute_score.compute_z_score(mgwas)
-    mgwas = jp.compute_score.compute_sample_size(mgwas, args.diagnostic_folder, tag, args.percent_sample_size)
+        mgwas = jp.map_reference.map_on_ref_panel(gw_df, ref)
+        mgwas = jp.map_reference.compute_snp_alignement(mgwas)
+        mgwas = jp.compute_score.compute_z_score(mgwas)
+        mgwas = jp.compute_score.compute_sample_size(mgwas, args.diagnostic_folder, tag, args.percent_sample_size)
 
-    end = time.time()
+        end = time.time()
 
-    print("Preprocessing of {0} in {1}s".format(tag, end-start))
-    jp.save_output.save_output_by_chromosome(mgwas, args.output_folder, tag)
+        print("Preprocessing of {0} in {1}s".format(tag, end-start))
+        jp.save_output.save_output_by_chromosome(mgwas, args.output_folder, tag)
 
-    if(args.output_folder_1_file):
-        jp.save_output.save_output(mgwas, args.output_folder_1_file, tag)
+        if(args.output_folder_1_file):
+            jp.save_output.save_output(mgwas, args.output_folder_1_file, tag)
 
 
 def add_preprocessing_argument():
 
     parser = argparse.ArgumentParser()
-    parser.add_argument('--gwas-info', required=True, help= "Path to the file describing the format of the individual GWASs files")
-    parser.add_argument('--gwas-filename', required=True, help= "Name of the raw GWAS file to standardize")
+    parser.add_argument('--gwas-info', required=True, help= "Path to the file describing the format of the individual GWASs files with correct header")
+    #parser.add_argument('--gwas-filename', required=True, help= "Name of the raw GWAS file to standardize")
     parser.add_argument('--ref-path', required=True, help= "reference panel location (used to determine which snp to impute)")
-    parser.add_argument('--gwas-folder', required=True, help= "Path to the folder containing the GWASs summ stat files, must end by '/'")
+    parser.add_argument('--input-folder', required=True, help= "Path to the folder containing the Raw GWASs summary statistic files, must end by '/'")
     parser.add_argument('--diagnostic-folder', required=True, help= "Path to the reporting information on the PreProcessing such as the SNPs sample size distribution")
 
     parser.add_argument('--output-folder', required=True, help= "Location of main ouput folder for preprocessed GWAS files (splitted by chromosome)")
-    parser.add_argument('--output-folder-1-file', required=False, help= "optional location to store the preprocessing in one tabular file with one chromosome columns")
+    parser.add_argument('--output-folder-1-file', required=False, help= "optional location to store the preprocessing in one tabular file with one chromosome columns (useful to compute LDSC correlation for instance)")
     parser.add_argument('--percent-sample-size', required=False, help= "the proportion (between 0 and 1) of the 90th percentile of the sample size used to filter the SNPs", default=0.7)
     parser.set_defaults(func=launch_preprocessing)