Changeset 936:5878bd4b8bfd in orange-bioinformatics for obiGsea.py

Timestamp:

07/28/09 14:13:25 (4 years ago)

Author:

markotoplak

Branch:

default

Convert:

1ef64a4c36f201cbd5edbe5c3a297b59e246326b

Message:

obiGsea: transposing of data sets works. widget and documentation are not done yet.

File:

• obiGsea.py (modified) (5 diffs)

obiGsea.py

@@ -673 +673 @@
     and names of genes as attribute names.
 
+    If phenVar is False, then we can work, then the input already
+    consists of scores of differential expressions
+
     If we have a single column, transpose it. 
     If phenVar is one of the groups, transpose the matrix.
     """
 
-    def transpose_data(data):
-        columns = [a for a in data.domain] +  [ data.domain.getmeta(a) for a in list(data.domain.getmetas()) ]
-        floatvars = [ a for a in columns if a.varType == orange.VarTypes.Continuous ]
-        if len(floatvars) == 1:
-            floatvar = floatvars[0]
-            stringvar = [ a for a in columns if a.varType == 6 ][0]
-
-            tup = [ (ex[stringvar].value, ex[floatvar].value) for ex in data ]
-            newdom = orange.Domain([orange.FloatVariable(name=a[0]) for a in tup ], False)
-            example = [ a[1] for a in tup ]
-            ndata = orange.ExampleTable(newdom, [example])
-            return ndata
-        return data
-
-    #transform every example table example tables
-
+    def prepare_data(data, phenVar=None, geneVar=None):
+
+        def rorq(a, name):
+            """ Group annatation or question mark. """
+            try: 
+                return a.attributes[name]
+            except: 
+                return '?'
+   
+        #use class as phenotype by default, if it is present,
+        #if not, do not use any phenotype!
+        if phenVar == None: 
+            if not data.domain.classVar:
+                phenVar = False
+            else:
+                phenVar = data.domain.classVar
+
+
+        #TODO validate phenVar and geneVar?
+        #TODO autodetection of groups?
+
+        #transpose is not needed if phenVar is classVar or phenVar is False
+        #and there is only one sample
+        if phenVar == data.domain.classVar or \
+            (phenVar == False and len(data) == 1):
+
+            if geneVar == None: #if not specified, set as true in this stage
+                geneVar = True
+
+            floatvars = [ a for a in data.domain.attributes \
+                if a.varType == orange.VarTypes.Continuous ]
+
+            #rename attributes without touching the original variable
+            if geneVar != True:
+                fl2 = []
+
+                for a in floatvars:
+                    na = orange.FloatVariable(name=rorq(a, geneVar))
+                    na.getValueFrom = lambda e, rw: e[a]
+                    fl2.append(na)
+
+                floatvars = fl2
+
+            dom = orange.Domain(floatvars, phenVar)
+            return orange.ExampleTable(dom, data)
+
+        elif phenVar == False or phenVar != data.domain.classVar:
+
+            cands = allgroups(data)
+            pv = False
+            if phenVar != False:
+                pv = orange.EnumVariable(name="phenotype", 
+                    values=list(cands[phenVar]))
+
+            #take the only string attribute as a gene name
+            gc = gene_cands(data, False)
+            if geneVar == None:
+                if len(gc) == 1:
+                    geneVar = gc[0]
+                else:
+                    geneNamesUnspecifiedError()
+           
+            latts = [ orange.FloatVariable(name=ex[geneVar].value) \
+                for ex in data ]
+
+            domain = orange.Domain(latts, pv)
+
+            examples = []
+            for at in data.domain.attributes:
+                if at.varType == orange.VarTypes.Continuous:
+                    vals = [ ex[at].value for ex in data ]
+                    if pv != False: #add class value
+                        vals.append(rorq(at, phenVar))
+                    examples.append(orange.Example(domain, vals))
+
+            return orange.ExampleTable(domain, examples)
+        else:
+            wrongInputsError()
+
+    #transform all example tables
     single = iset(data)
-    transposed = [ transpose_data(d) for d in wrap_in_list(data) ]
+    transposed = [ prepare_data(d, phenVar, geneVar) for d in wrap_in_list(data) ]
 
     if single:
@@ -725 +792 @@
     return filter(lambda x: len(x[1]) >= 2, cands)
 
-def gene_cands(data, phenVar):
+def gene_cands(data, correct):
     """
     Returns all valid gene descriptors with regards to the choosen
@@ -732 +799 @@
     descriptions in attr.attributes and True for the usage
     of attribute names.
-    """
-    if is_variable(phenVar[0]):
+    Correct is True, if the example table has genes as attributes.
+    """
+    if correct:
         #gene names could be in attributes or as gene names (marker True)
         return [True] + nth(sorted(allgroups(data)),0)
@@ -894 +962 @@
     """
     gso = GSEA(data, organism=organism, matcher=matcher, 
-        classValues=classValues, atLeast=atLeast, caseSensitive=caseSensitive)
+        classValues=classValues, atLeast=atLeast, caseSensitive=caseSensitive,
+        geneVar=geneVar, phenVar=phenVar)
     if geneSets == None:
         genesets = collections(default=True)
     gso.addGenesets(geneSets)
     res1 = gso.compute(n=n, permutation=permutation, minSize=minSize,
-        maxSize=maxSize, minPart=minPart, geneVar=geneVar, phenVar=phenVar,
-        **kwargs)
+        maxSize=maxSize, minPart=minPart, **kwargs)
     return res1
 
@@ -972 +1040 @@
 if  __name__=="__main__":
 
-    data = orange.ExampleTable("sterolTalkHepaM.tab")
-    print phenotype_cands(data)
-    print is_variable(phenotype_cands(data)[0][0])
-
-    """
+    #data = orange.ExampleTable("sterolTalkHepa.tab")
+
     data = orange.ExampleTable("gene_three_lines_log.tab")
-    print phenotype_cands(data)
-    print is_variable(phenotype_cands(data)[0][0])
-    """
 
     gen1 = collections(['steroltalk.gmt', ':kegg:hsa'], default=False)
-
-    gen1 = dict([ ('[KEGG] Complement and coagulation cascades', gen1['[KEGG] Complement and coagulation cascades'])])
+    #gen1 = dict([ ('[KEGG] Complement and coagulation cascades', gen1['[KEGG] Complement and coagulation cascades'])])
 
     rankingf = rankingFromOrangeMeas(MA_anova())
+
     matcher = obiGene.matcher([obiGene.GMKEGG('hsa')])
 
-    out = runGSEA(data, n=10, geneSets=gen1, permutation="gene", atLeast=3, matcher=matcher, rankingf=rankingf)
+    #out = runGSEA(data, n=10, geneSets=gen1, permutation="gene", atLeast=3, matcher=matcher, rankingf=rankingf)
+
+    geneVar = gene_cands(data, False)[1]
+    out = runGSEA(data, n=10, geneSets=gen1, permutation="gene", atLeast=3, matcher=matcher, rankingf=rankingf, phenVar="group", geneVar=geneVar)
+
     print "\n".join(map(str,sorted(out.items())))