From 63a6ab2f565611bbe1464d718acff4398de12a19 Mon Sep 17 00:00:00 2001
From: Zachary Sloan
Date: Thu, 15 Aug 2013 17:08:20 -0500
Subject: Removed the markers with no variance (and thus no p-values) from the
 manhattan plot

---
 wqflask/base/data_set.py              | 12 +++++++-----
 wqflask/wqflask/my_pylmm/pyLMM/lmm.py |  4 ++--
 2 files changed, 9 insertions(+), 7 deletions(-)

(limited to 'wqflask')

diff --git a/wqflask/base/data_set.py b/wqflask/base/data_set.py
index 091433a6..0b9b1ce0 100755
--- a/wqflask/base/data_set.py
+++ b/wqflask/base/data_set.py
@@ -168,11 +168,13 @@ class Markers(object):
         
         for marker, p_value in itertools.izip(self.markers, p_values):
             marker['p_value'] = p_value
-            if math.isnan(marker['p_value']):
-                print("p_value is:", marker['p_value'])
-            marker['lod_score'] = -math.log10(marker['p_value'])
-            #Using -log(p) for the LRS; need to ask Rob how he wants to get LRS from p-values
-            marker['lrs_value'] = -math.log10(marker['p_value']) * 4.61
+            if marker['p_value'] == 0:
+                marker['lod_score'] = 0
+                marker['lrs_value'] = 0
+            else:
+                marker['lod_score'] = -math.log10(marker['p_value'])
+                #Using -log(p) for the LRS; need to ask Rob how he wants to get LRS from p-values
+                marker['lrs_value'] = -math.log10(marker['p_value']) * 4.61
         
         
 
diff --git a/wqflask/wqflask/my_pylmm/pyLMM/lmm.py b/wqflask/wqflask/my_pylmm/pyLMM/lmm.py
index 3743e77c..6ef1669b 100644
--- a/wqflask/wqflask/my_pylmm/pyLMM/lmm.py
+++ b/wqflask/wqflask/my_pylmm/pyLMM/lmm.py
@@ -396,7 +396,7 @@ def GWAS(pheno_vector,
             keep = True - v
             xs = x[keep,:]
             if xs.var() == 0:
-                p_values.append(np.nan)
+                p_values.append(0)
                 t_statistics.append(np.nan)
                 continue
 
@@ -413,7 +413,7 @@ def GWAS(pheno_vector,
             ts, ps, beta, betaVar = lmm_ob_2.association(xs, REML=restricted_max_likelihood)
         else:
             if x.var() == 0:
-                p_values.append(np.nan)
+                p_values.append(0)
                 t_statistics.append(np.nan)
                 continue
 
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