V0.0.4

python/PascalX/pathway.py

@@ -202,7 +202,7 @@ def _genefusion_fuse(self,modules,chrs=None):
 
         return COMPUTE_SET, FUSION_SET
 
-    def _genefusion(self,modules,method='auto',mode='auto',reqacc=1e-100,parallel=1,nobar=False,chrs=None):
+    def _genefusion(self,modules,method='auto',mode='auto',reqacc=1e-100,parallel=1,nobar=False,chrs=None,autorescore=False):
 
         COMPUTE_SET, FUSION_SET = self._genefusion_fuse(modules,chrs)
 
@@ -214,7 +214,7 @@ def _genefusion(self,modules,method='auto',mode='auto',reqacc=1e-100,parallel=1,
         print("Scoring",len(SET),"missing (meta)-genes")
 
         # Compute missing (meta)-genes
-        R = self._genescorer.score(SET,method=method,mode=mode,reqacc=reqacc,parallel=parallel,nobar=nobar,autorescore=True)
+        R = self._genescorer.score(SET,method=method,mode=mode,reqacc=reqacc,parallel=parallel,nobar=nobar,autorescore=autorescore)
 
         #print(R)
         #print(FUSION_SET)
@@ -263,7 +263,7 @@ class chi2rank(pathwayscorer):
 
     """
 
-    def score(self,modules,method='saddle',mode='auto',reqacc=1e-100,parallel=1,nobar=False,genes_only=False,chrs_only=None):
+    def score(self,modules,method='saddle',mode='auto',reqacc=1e-100,parallel=1,nobar=False,genes_only=False,chrs_only=None,autorescore=True):
         """
         Scores a set of pathways/modules
 
@@ -274,15 +274,17 @@ def score(self,modules,method='saddle',mode='auto',reqacc=1e-100,parallel=1,noba
             method(string): Method to use to evaluate tail probability ('auto','davies','ruben','satterthwaite','pearson','saddle')
             mode(string): Precision mode to use ('','128b','100d','auto')
             reqacc(float): requested accuracy 
+            autorescore(bool): Automatically try to re-score failed genes
             nobar(bool): Show progress bar
             genes_only(bool): Compute only (fused)-genescores (accessible via genescorer method)
-            chrs_only(list): Only consider genes on listed chromosomes. None for all.
+            chrs_only(list): Only consider genes on listed chromosomes. None for all
         """
         tic = time.time()
 
+
         # Compute fusion sets
         if self._fuse:
-            COMPUTE_SET,FUSION_SET,R = self._genefusion(modules,method=method,mode=mode,reqacc=reqacc,parallel=parallel,nobar=nobar,chrs=chrs_only)
+            COMPUTE_SET,FUSION_SET,R = self._genefusion(modules,method=method,mode=mode,reqacc=reqacc,parallel=parallel,nobar=nobar,chrs=chrs_only,autorescore=autorescore)
         else:
             COMPUTE_SET,FUSION_SET,R = self._nogenefusion(modules)
 
@@ -400,7 +402,7 @@ class chi2perm(pathwayscorer):
         Genes in the background gene sets are NOT fused.
     """
 
-    def score(self,modules,samples=100000,method='saddle',mode='auto',reqacc=1e-100,parallel=1,nobar=False):
+    def score(self,modules,samples=100000,method='saddle',mode='auto',reqacc=1e-100,parallel=1,nobar=False,autorescore=True):
         """
         Scores a set of pathways/modules
 
@@ -412,11 +414,12 @@ def score(self,modules,samples=100000,method='saddle',mode='auto',reqacc=1e-100,
             mode(string): Precision mode to use ('','128b','100d','auto')
             reqacc(float): requested accuracy 
             nobar(bool): Show progress bar
-
+            autorescore(bool): Automatically try to re-score failed genes
+
         """
         # Compute fusion sets
         if self._fuse:
-            COMPUTE_SET,FUSION_SET,R = self._genefusion(modules,method=method,mode=mode,reqacc=reqacc,parallel=parallel,nobar=nobar)
+            COMPUTE_SET,FUSION_SET,R = self._genefusion(modules,method=method,mode=mode,reqacc=reqacc,parallel=parallel,nobar=nobar,autorescore=autorescore)
         else:
             COMPUTE_SET,FUSION_SET,R = self._nogenefusion(modules)
 

python/docs/source/index.rst

@@ -6,7 +6,7 @@
 Welcome to PascalX's documentation!
 ===================================
 
-PascalX is a python3 library (`source <https://github.com/BergmannLab/PascalX>`_) for high precision gene and pathway scoring for GWAS summary statistics. Aggregation of SNP p-values to gene and pathway scores follows the `Pascal <https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004714>`_ methodology, which is based on :math:`\chi^2` statistics. The cummulative distribution function of the weighted :math:`\chi^2` distribution can be calculated approximately or exactly via a multi-precision C++ implementation of Ruben's and Davies algorithm. This allows to apply the Pascal methodology to modern UK BioBank scale GWAS. In addition, PascalX offers a novel coherence test between two different GWAS on the level of genes, based on the product-normal distribution, as described `here <https://doi.org/10.1101/2021.05.16.21257289>`_.    
+PascalX is a python3 library (`source <https://github.com/BergmannLab/PascalX>`_) for high precision gene and pathway scoring for GWAS summary statistics. Aggregation of SNP p-values to gene and pathway scores follows the `Pascal <https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004714>`_ methodology, which is based on :math:`\chi^2` statistics. The cumulative distribution function of the weighted :math:`\chi^2` distribution can be calculated approximately or exactly via a multi-precision C++ implementation of Ruben's and Davies algorithm. This allows to apply the Pascal methodology to modern UK BioBank scale GWAS. In addition, PascalX offers a novel coherence test between two different GWAS on the level of genes, based on the product-normal distribution, as described `here <https://doi.org/10.1101/2021.05.16.21257289>`_.    
 
 **Highlights:**
 

python/setup.py

@@ -42,11 +42,11 @@ def find_version(*file_paths):
 	"numpy>=1.18.0",
 	"seaborn>=0.11.0",
 	"progressbar>=2.5",
-	"docutils<0.18",
-	"sphinx<6,>=1.6",
-	"sphinx-rtd-theme>=0.5.0",
-	"fastnumbers>=3.1.0"
-    ],
+	"fastnumbers>=3.1.0",
+    #"docutils<0.18",
+	#"sphinx<6,>=1.6",
+	#"sphinx-rtd-theme>=0.5.0",
+	],
     setup_requires=["cffi","path.py"],
     cffi_modules=["hpstats.py:ffibuilder","wchissum.py:ffibuilder"],
     zip_safe=False,