Merge pull request #59 from CenterForMedicalGeneticsGhent/v1.1.6

fixed #58

setup.py

@@ -1,7 +1,7 @@
 #! /usr/bin/env python
 from setuptools import setup, find_packages
 
-version = '1.1.5'
+version = '1.1.6'
 dl_version = 'master' if 'dev' in version else '{}'.format(version)
 
 setup(

wisecondorX/include/plotter.R

@@ -168,12 +168,12 @@ for (ab in input$results_c){
       dot.cols[start:end] = color.C
     }
   } else {
-    if (z < -zcutoff){
-      dot.cols[start:end] = color.B
-    }
-    if (z > zcutoff){
-      dot.cols[start:end] = color.C
+    if (is.na(z)){
+      dot.cols[start:end] = 'grey'
+      next
     }
+    if (z < -zcutoff) dot.cols[start:end] = color.B
+    if (z > zcutoff) dot.cols[start:end] = color.C
   }
 }
 

wisecondorX/overall_tools.py

@@ -5,6 +5,7 @@
 import os
 import subprocess
 import sys
+import math
 
 import numpy as np
 
@@ -69,7 +70,6 @@ def exec_R(json_dict):
     except subprocess.CalledProcessError as e:
         logging.critical('Rscript failed: {}'.format(e))
         sys.exit()
-
     os.remove(json_dict['infile'])
     if 'outfile' in json_dict.keys():
         json_out = json.load(open(json_dict['outfile']))
@@ -89,13 +89,19 @@ def get_z_score(results_c, results):
         segment_nr = results_nr[segment[0]][segment[1]:segment[2]]
         segment_rr = results_r[segment[0]][segment[1]:segment[2]]
         segment_nr = [segment_nr[i] for i in range(len(segment_nr)) if segment_rr[i] != 0]
+        for i in range(len(segment_nr)):
+            for ii in range(len(segment_nr[i])):
+                if not np.isfinite(segment_nr[i][ii]):
+                    segment_nr[i][ii] = np.nan
         segment_w = results_w[segment[0]][segment[1]:segment[2]]
         segment_w = [segment_w[i] for i in range(len(segment_w)) if segment_rr[i] != 0]
-        null_segments = [np.ma.average(x, weights=segment_w) for x in np.transpose(segment_nr)]
+        null_segments = [np.ma.average(np.ma.masked_array(x, np.isnan(x)), weights=segment_w) for x in np.transpose(segment_nr)]
         null_mean = np.ma.mean([x for x in null_segments if np.isfinite(x)])
         null_sd = np.ma.std([x for x in null_segments if np.isfinite(x)])
         z = (segment[3] - null_mean) / null_sd
         z = min(z, 1000) ; z = max(z, -1000)
+        if math.isnan(null_mean) or math.isnan(null_sd):
+            z = 'NaN'
         zs.append(z)
     return zs
 

wisecondorX/predict_output.py

@@ -97,6 +97,8 @@ def _generate_segments_and_aberrations_bed(rem_input, results):
                 abberations_file.write('{}\tgain\n'.format('\t'.join([str(x) for x in row])))
             elif float(segment[4]) < __get_aberration_cutoff(rem_input['args'].beta, ploidy)[0]:
                 abberations_file.write('{}\tloss\n'.format('\t'.join([str(x) for x in row])))
+        elif isinstance(segment[3], str):
+            continue
         else:
             if float(segment[3]) > rem_input['args'].zscore:
                 abberations_file.write('{}\tgain\n'.format('\t'.join([str(x) for x in row])))