0xe3

setup.py

@@ -5,7 +5,7 @@ with open("README.md", "r") as fh:
 
 setuptools.setup(
     name = "impetuous-gfa",
-    version = "0.14.0",
+    version = "0.15.0",
     author = "Richard Tjörnhammar",
     author_email = "richard.tjornhammar@gmail.com",
     description = "Impetuous Quantification, Enrichment and Group Variation Analysis",

src/impetuous/hierarchal.py

@@ -46,9 +46,9 @@ def create_dag_representation_df ( pathway_file = '../data/GROUPDEFINITIONS.gmt'
 
 def HierarchalEnrichment (
             analyte_df , dag_df , dag_level_label = 'DAG,l' ,
-            ancestors_id_label = 'aid' , id_name = None , threshold=0.05 ,
-            p_label = 'C(Status),p', analyte_name_label = 'analytes',
-            item_delimiter = ',' , alexa_elim=False
+            ancestors_id_label = 'aid' , id_name = None , threshold = 0.05 ,
+            p_label = 'C(Status),p', analyte_name_label = 'analytes' ,
+            item_delimiter = ',' , alexa_elim=False , alternative = 'two-sided'
         ) :
     #
     # NEEDS AN ANALYTE SIGNIFICANCE FRAME:
@@ -83,7 +83,7 @@ def HierarchalEnrichment (
             L_ = len( group ) ; str_analytes=','.join(group.index.values)
             if L_ > 0 :
                 used_analytes[node] = ','.join( group.index.values )
-                pv,odds = group_significance( group , AllAnalytes=AllAnalytes, SigAnalytes=SigAnalytes , tolerance = threshold )
+                pv,odds = group_significance( group , AllAnalytes=AllAnalytes, SigAnalytes=SigAnalytes , tolerance = threshold , alternative=alternative )
                 node_sig[node] = pv ; marked_ = set( group.index.values )
                 ancestors = df.loc[node,ancestors_id_label].replace('\n','').replace(' ','').split(item_delimiter)
                 if alexa_elim and pv > threshold : # USE ALEXAS ELIM ALGORITHM : IS NOT DEFAULT

src/impetuous/quantification.py

 """
-Copyright 2019 RICHARD TJÖRNHAMMAR
+Copyright 2020 RICHARD TJÖRNHAMMAR
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
@@ -584,16 +584,16 @@ def quantify_groups_by_analyte_pvalues( analyte_df, grouping_file, delimiter='\t
             edf.loc[l] = q
     return ( edf.T )
 
-class sPCA( object ) :
+class APCA ( object ) :
     #
     # THIS CLASS PERFORMS A SPARSE PCA
     # IT USES THE SPARSE SVD ALGORITHM
     # FOUND IN SCIPY
     #
-    def __init__ ( self,X,k=-1,fillna=None ) :
+    def __init__ ( self , X=None , k=-1 , fillna=None , transcending=True) :
         from scipy.sparse import csc_matrix
         from scipy.sparse.linalg import svds
-        self.svds_,self.smatrix_ = svds,csc_matrix
+        self.svds_ , self.smatrix_ = svds , csc_matrix
         self.components_ = None
         self.F_ = None
         self.U_ , self.S_, self.V_ = None,None,None
@@ -602,6 +602,7 @@ class sPCA( object ) :
         self.fillna_ = fillna
         self.X_   = self.interpret_input(X)
         self.k_   = k
+        self.transcending_ = transcending
 
     def interpret_input ( self,X ) :
         if 'pandas' in str(type(X)) :
@@ -612,29 +613,43 @@ class sPCA( object ) :
             self.X_ = X.values
         else :
             self.X_ = X
-        return( self.X_ )
+        return ( self.X_ )
 
     def fit ( self , X=None ) :
         self.fit_transform( X=X )
 
     def fit_transform ( self , X=None ) :
-        X = self.X_
-        if not X is None : # DID THE USER SUPPLY NEW DATA
+        if X is None:
+            X = self.X_
+        if not X is None :
             X = self.interpret_input(X)
         Xc = X - np.mean( X , 0 )
-        if self.k_<=0:
-            k_ = np.min(np.shape(X))-1
+        if self.k_<=0 :
+            k_ = np.min( np.shape(Xc) ) - 1
         else:
             k_ = self.k_
         u, s, v = self.svds_ ( self.smatrix_(Xc, dtype=float) , k=k_ )
+        if self.transcending_ :
+            u, s, v = self.transcending_order(u,s,v)
         S = np.diag( s )
         self.F_   = np.dot(u,S)
         self.var_ = s ** 2 / Xc.shape[0]
         self.explained_variance_ratio_ = self.var_/self.var_.sum()
         self.U_ , self.S_ , self.V_ = u,s,v
-        self.components_ = self.V_        
+        self.components_ = self.V_
         return ( self.F_ )
 
+    def transcending_order(self,u,s,v) :
+        return ( u[:,::-1],s[::-1],v[::-1,:] )
+
+    def apply_matrix( self , R ) :
+        self.U_ = np.dot( self.U_,R.T  )
+        self.V_ = np.dot( self.V_.T,R.T ).T
+        self.F_ = np.dot( self.F_,R.T )
+        self.components_ = self.V_
+        return ( self.F_ )
+
+
 dimred = PCA()
 
 def quantify_groups ( analyte_df , journal_df , formula , grouping_file , synonyms = None ,
@@ -685,10 +700,10 @@ def quantify_by_dictionary ( analyte_df , journal_df , formula , split_id=None,
                     grouping_dictionary = dict() , synonyms = None ,
                     delimiter = ':' ,test_type = 'random', tolerance = 0.05,
                     supress_q = False , analyte_formula = None,
-                    use_sparse_pca=False , k=-1 ) :
+                    use_loc_pca=False , k=-1 ) :
 
-    if use_sparse_pca :
-        dimred = sPCA(X=analyte_df,k=k)
+    if use_loc_pca :
+        dimred = APCA(X=analyte_df,k=k)
 
     if not 'dict' in str(type(grouping_dictionary)) :
         print ( 'INVALID GROUPING' )