explicity argument naming in pd.dataframe.all , .any

jass_preprocessing/__main__.py

@@ -46,6 +46,10 @@ def launch_preprocessing(args):
 
         print("#SNPs in GWAS summary statistic file: {}".format(gw_df.shape[0]))
         ref = jp.map_reference.read_reference(args.ref_path, np.bool_(args.mask_MHC), np.double(args.minimum_MAF), region_to_mask=eval(args.additional_masked_region))
+
+        print("Unique chromosome in reference")
+        print(ref.chr.unique())
+
         mgwas = jp.map_reference.map_on_ref_panel(gw_df, ref, gwas_map.loc[tag, "index_type"])
 
         print("#SNPs mapped to reference panel: {}".format(mgwas.shape[0]))

jass_preprocessing/compute_score.py

@@ -32,7 +32,7 @@ def compute_sample_size(mgwas, diagnostic_folder, trait, perSS = 0.7):
     if 'n' in mgwas.columns:
         myN = mgwas.n
     #--- freq, case-cont N exist
-    elif(('ncas' in mgwas.columns) & ('ncont' in mgwas.columns)):
+    elif(('Ncase' in mgwas.columns) & ('Ncontrol' in mgwas.columns)):
         sumN = mgwas.ncas + mgwas.ncont
         perCase = mgwas.ncas / sumN
         myN = sumN * perCase * (1-perCase)

jass_preprocessing/map_gwas.py

@@ -146,12 +146,13 @@ def read_gwas( gwas_internal_link, column_map, imputation_treshold=None):
     fullGWAS = pd.read_csv(gwas_internal_link, delim_whitespace=True,
                                usecols = column_map.values,
                                compression=compression,
-                                #column_dict['label_position'].keys(),
-                               names= column_map.index,
-                                 header=0, na_values= ['', '#N/A', '#N/A', 'N/A','#NA', '-1.#IND', '-1.#QNAN',
-                                                 '-NaN', '-nan', '1.#IND', '1.#QNAN', 'N/A',
-                                                 'NA', 'NULL', 'NaN',
-                                                 'nan', 'na', '.', '-'], dtype={"snpid":str, "a1":str,"a2":str,"freq":float, "z":float,"se":float, "pval":float})
+                            #column_dict['label_position'].keys(),
+                            names= column_map.index,
+                            header=0, na_values= ['', '#N/A', '#N/A', 'N/A','#NA', '-1.#IND', '-1.#QNAN',
+                                                '-NaN', '-nan', '1.#IND', '1.#QNAN', 'N/A',
+                                                'NA', 'NULL', 'NaN',
+                                                'nan', 'na', '.', '-'],
+                                                dtype={"snpid":str, "a1":str,"a2":str,"freq":np.double, "z":np.double,"se":np.double, "pval":np.double})
     print(fullGWAS.head())
     #Ensure that allele are written in upper cases:
 

jass_preprocessing/map_reference.py

@@ -30,7 +30,8 @@ def read_reference(gwas_reference_panel, mask_MHC=False, minimum_MAF=None, regio
         return "".join(sorted(x))
     #Filter Strand ambiguous if biallelic
     ref = ref.loc[~(ref.ref+ref.alt).isin(["AT", "TA", 'CG','GC'])]
-
+    print("REFERENCE")
+    print(ref.head())
     ref["positional_index"] = ref.chr.apply(str)+ref.pos.apply(str)+(ref.ref+ref.alt).apply(sorted_alleles)
 
     if mask_MHC:
@@ -116,9 +117,9 @@ def compute_is_flipped(mgwas):
     flipped = pd.DataFrame({"ref_flipped" : (mgwas.ref == mgwas.a2), "alt_flipped" : (mgwas.alt == mgwas.a1)})
     flipped_complement = pd.DataFrame({"ref_flippedc" : (mgwas.ref == mgwas.a2c), "alt_flippedc" : (mgwas.alt == mgwas.a1c)})
 
-    is_flipped = pd.DataFrame({"flipped":flipped.all(1), # The allele of the
-                               "flipped_complement":flipped_complement.all(1)}
-                              ).any(1)
+    is_flipped = pd.DataFrame({"flipped":flipped.all(axis=1), # The allele of the
+                               "flipped_complement":flipped_complement.all(axis=1)}
+                              ).any(axis=1)
     return is_flipped
 
 def compute_is_aligned(mgwas):
@@ -132,9 +133,8 @@ def compute_is_aligned(mgwas):
     aligned = pd.DataFrame({"ref_ok" : (mgwas.ref == mgwas.a1), "alt_ok" : (mgwas.alt == mgwas.a2)})
     aligned_complement = pd.DataFrame({"ref_ok" : (mgwas.ref == mgwas.a1c), "alt_ok" : (mgwas.alt == mgwas.a2c)})
 
-    is_aligned = pd.DataFrame({"aligned":aligned.all(1), # The allele of the
-                               "aligned_complement":aligned_complement.all(1)}
-                              ).any(1)
+    is_aligned = pd.DataFrame({"aligned":aligned.all(axis=1), # The allele of the
+                               "aligned_complement":aligned_complement.all(axis=1)}).any(axis=1)
     return is_aligned
 
 def compute_snp_alignement(mgwas):
@@ -153,7 +153,7 @@ def compute_snp_alignement(mgwas):
 
     mgwas['a1c'] = dna_u.dna_complement(mgwas.a1)
     mgwas['a2c'] = dna_u.dna_complement(mgwas.a2)
-
+    print(mgwas)
     is_aligned = compute_is_aligned(mgwas)
     is_flipped = compute_is_flipped(mgwas)