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@ -5,7 +5,7 @@ import networkx as nx
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def plot_corrloads(R, pc1=0,pc2=1,s=20, c='b', zorder=5,expvar=None,ax=None,drawback=True, labels=None):
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""" Correlation loading plot."""
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# backgorund
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# background
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if ax==None or drawback==True:
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radius = 1
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center = (0,0)
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@ -2,7 +2,7 @@
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import scipy
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import rpy
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silent_eval = rpy.with_mode(rpy.NO_CONVERSION, rpy.r)
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import collections
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def goterms_from_gene(genelist, ontology='BP', garbage=None):
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""" Returns the go-terms from a specified genelist (Entrez id).
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@ -18,7 +18,7 @@ def goterms_from_gene(genelist, ontology='BP', garbage=None):
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"IDA" : "inferred from direct assay",
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"IEP" : "inferred from expression pattern",
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"IEA" : "inferred from electronic annotation",
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"TAS" : "traceable author statement",
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"TAS" : "traceable author statement",
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"NAS" : "non-traceable author statement",
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"ND" : "no biological data available",
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"IC" : "inferred by curator"
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@ -167,3 +167,47 @@ def gene_GO_hypergeo_test(genelist,universe="entrezUniverse",ontology="BP",chip
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result = rpy.r.summary(rpy.r.hyperGTest(params))
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return rpy.r.summary(result), params
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def data_aff2loc_hgu133a(X, aff_ids, verbose=False):
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aff_ids = scipy.asarray(aff_ids)
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if verbose:
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print "\nNumber of probesets in affy list: %s" %len(aff_ids)
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import rpy
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rpy.r.library("hgu133a")
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trans_table = rpy.r.as_list(rpy.r.hgu133aENTREZID)
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if verbose:
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print "Number of entrez ids: %d" %(scipy.asarray(trans_table.values())>0).sum()
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enz2aff = collections.defaultdict(list)
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#aff2enz = collections.defaultdict(list)
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for aff, enz in trans_table.items():
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if int(enz)>0 and (aff in aff_ids):
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enz2aff[enz].append(aff)
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#aff2enz[aff].append(enz)
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if verbose:
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print "\nNumber of translated entrez ids: %d" %len(enz2aff)
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aff2ind = dict(zip(aff_ids, scipy.arange(len(aff_ids))))
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var_x = X.var(0)
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new_data = []
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new_ids = []
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m = 0
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s = 0
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for enz, aff_id_list in enz2aff.items():
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index = [aff2ind[aff_id] for aff_id in aff_id_list]
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if len(index)>1:
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m+=1
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if verbose:
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pass
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#print "\nEntrez id: %s has %d probesets" %(enz, len(index))
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#print index
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xsub = X[:,index]
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choose_this = scipy.argmax(xsub.var(0))
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new_data.append(xsub[:,choose_this].ravel())
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else:
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s+=1
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new_data.append(X[:,index].ravel())
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new_ids.append(enz)
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if verbose:
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print "Ids with multiple probesets: %d" %m
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print "Ids with unique probeset: %d" %s
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X = scipy.asarray(new_data).T
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return X, new_ids
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@ -11,41 +11,30 @@ import cx_stats
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from plots_lpls import plot_corrloads
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######## DATA ##########
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# full smoker data
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DX = dataset.read_ftsv(open("../../data/smokers-full/Smokers.ftsv"))
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DY = dataset.read_ftsv(open("../../data/smokers-full/Yg.ftsv"))
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Y = DY.asarray().astype('d')
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# select subset genes by SAM
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rpy.r.library("siggenes")
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rpy.r.library("qvalue")
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data = DX.asarray().T
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# data = data[:100,:]
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rpy.r.assign("data", data)
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cl = dot(DY.asarray(), diag([1,2,3])).sum(1)
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rpy.r.assign("cl", cl)
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rpy.r.assign("B", 20)
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# Perform a SAM analysis.
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print "Starting SAM"
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sam = rpy.r('sam.out<-sam(data=data,cl=cl,B=B,rand=123)')
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print "SAM done"
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# Compute the q-values of the genes.
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qq = rpy.r('qobj<-qvalue(sam.out@p.value)')
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qvals = asarray(qq['qvalues'])
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# cut off
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cutoff = 0.05
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index = where(qvals<cutoff)[0]
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use_data='uma'
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if use_data=='smoker':
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# full smoker data
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DX = dataset.read_ftsv(open("../../data/smokers-full/Smokers.ftsv"))
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DY = dataset.read_ftsv(open("../../data/smokers-full/Yg.ftsv"))
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Y = DY.asarray().astype('d')
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gene_ids = DX.get_identifiers('gene_ids', sorted=True)
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elif use_data=='scherf':
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DX = dataset.read_ftsv(open("../../data/scherf/Scherf.ftsv"))
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DY = dataset.read_ftsv(open("../../data/scherf/Yd.ftsv"))
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Y = DY.asarray().astype('d')
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gene_ids = DX.get_identifiers('gene_ids', sorted=True)
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elif use_data=='staunton':
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pass
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elif use_data=='uma':
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DX = dataset.read_ftsv(open("../../data/uma/X133.ftsv"))
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DY = dataset.read_ftsv(open("../../data/uma/Yg133.ftsv"))
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Y = DY.asarray().astype('d')
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gene_ids = DX.get_identifiers('gene_ids', sorted=True)
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# Subset data
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X = DX.asarray()
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Xr = X[:,index]
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gene_ids = DX.get_identifiers('gene_ids', index)
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print "\nWorking on subset with %s genes " %len(gene_ids)
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#gene2ind = {}
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#for i, gene in enumerate(gene_ids):
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# gene2ind[gene] = i
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### Build GO data ####
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# Use only subset defined on GO
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ontology = 'BP'
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print "\n\nFiltering genes by Go terms "
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gene2goterms = rpy_go.goterms_from_gene(gene_ids)
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all_terms = set()
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for t in gene2goterms.values():
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@ -55,61 +44,72 @@ print "\nNumber of go-terms: %s" %len(terms)
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# update genelist
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gene_ids = gene2goterms.keys()
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print "\nNumber of genes: %s" %len(gene_ids)
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# use subset based on SAM or IQR
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subset = 'm'
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if subset=='sam':
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# select subset genes by SAM
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rpy.r.library("siggenes")
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rpy.r.library("qvalue")
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data = DX.asarray().T
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# data = data[:100,:]
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rpy.r.assign("data", data)
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cl = dot(DY.asarray(), diag([1,2,3])).sum(1)
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rpy.r.assign("cl", cl)
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rpy.r.assign("B", 20)
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# Perform a SAM analysis.
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print "Starting SAM"
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sam = rpy.r('sam.out<-sam(data=data,cl=cl,B=B,rand=123)')
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print "SAM done"
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# Compute the q-values of the genes.
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qq = rpy.r('qobj<-qvalue(sam.out@p.value)')
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qvals = asarray(qq['qvalues'])
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# cut off
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cutoff = 0.001
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index = where(qvals<cutoff)[0]
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# Subset data
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X = DX.asarray()
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#Xr = X[:,index]
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gene_ids = DX.get_identifiers('gene_ids', index)
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print "\nWorking on subset with %s genes " %len(gene_ids)
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else:
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# noimp (smoker data is prefiltered)
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pass
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rpy.r.library("GOSim")
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# Go-term similarity matrix
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methods = ("JiangConrath","Resnik","Lin","CoutoEnriched","CoutoJiangConrath","CoutoResnik","CoutoLin")
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meth = methods[0]
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meth = methods[2]
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print "Term-term similarity matrix (method = %s)" %meth
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if meth=="CoutoEnriched":
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rpy.r('setEnrichmentFactors(alpha=0.1,beta=0.5)')
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print "\nCalculating term-term similarity matrix"
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rpytmat1 = rpy.with_mode(rpy.NO_CONVERSION, rpy.r.getTermSim)(terms, method=meth,verbose=False)
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tmat1 = rpy.r.assign("haha", rpytmat1)
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# check if all terms where found
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nanindex = where(isnan(tmat1[:,0]))[0]
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if len(nanindex)>0:
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raise valueError("NANs in tmat")
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# Z-matrix
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#Z, newind = rpy_go.genego_matrix(terms, tmat, gene_ids, terms,func=mean)
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#Z = Z.T
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Z = rpy_go.genego_sim(gene2goterms,gene_ids,terms,rpytmat1,go_term_sim="OA",term_sim=meth)
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#### do another
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#meth = methods[4]
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#rpytmat = rpy.with_mode(rpy.NO_CONVERSION, rpy.r.getTermSim)(terms, method=meth,verbose=False)
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#tmat = rpy.r.assign("haha", rpytmat)
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# check if all terms where found
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#nanindex = where(isnan(tmat[:,0]))[0]
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#if len(nanindex)>0:
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# raise valueError("NANs in tmat")
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# Z-matrix
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#Z, newind = rpy_go.genego_matrix(terms, tmat, gene_ids, terms,func=mean)
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#Z = Z.T
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#Z = rpy_go.genego_sim(gene2goterms,gene_ids,terms,rpytmat,go_term_sim="OA",term_sim=meth)
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rpytmat = rpy.with_mode(rpy.NO_CONVERSION, rpy.r.getTermSim)(terms, method=meth,verbose=False)
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tmat = rpy.r.assign("haha", rpytmat)
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print "\n Calculating Z matrix"
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Z = rpy_go.genego_sim(gene2goterms,gene_ids,terms,rpytmat,go_term_sim="OA",term_sim=meth)
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# update data (X) matrix
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#newind = [gene2ind[gene] for gene in gene_ids]
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newind = DX.get_indices('gene_ids', gene_ids)
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Xr = X[:,newind]
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#new_gene_ids = asarray(gene_ids)[newind]
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Xr = DX.asarray()[:,newind]
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######## LPLSR ########
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print "LPLSR ..."
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a_max = 5
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aopt = 3
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alpha=.4
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mean_ctr = [2, 0, 1]
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xz_alpha = .5
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w_alpha = .1
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mean_ctr = [2, 0, 2]
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# standardize Z?
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sdtz = False
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if sdtz:
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Z = Z/Z.std(0)
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T, W, P, Q, U, L, K, B, b0, evx, evy, evz = nipals_lpls(Xr,Y,Z, a_max,
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alpha=alpha,
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alpha=xz_alpha,
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mean_ctr=mean_ctr)
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# Correlation loadings
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@ -117,24 +117,22 @@ dx,Rx,rssx = correlation_loadings(Xr, T, P)
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dx,Ry,rssy = correlation_loadings(Y, T, Q)
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cadz,Rz,rssz = correlation_loadings(Z.T, W, L)
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# Prediction error
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rmsep , yhat, class_error = cv_lpls(Xr, Y, Z, a_max, alpha=alpha,mean_ctr=mean_ctr)
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alpha_check=True
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rmsep , yhat, class_error = cv_lpls(Xr, Y, Z, a_max, alpha=xz_alpha,mean_ctr=mean_ctr)
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alpha_check=False
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if alpha_check:
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Alpha = arange(0.01, 1, .1)
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Rmsep,Yhat, CE = [],[],[]
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for a in Alpha:
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rmsep , yhat, ce = cv_lpls(Xr, Y, Z, a_max, alpha=alpha)
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rmsep , yhat, ce = cv_lpls(Xr, Y, Z, a_max, alpha=xz_alpha,mean_ctr=mean_ctr)
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Rmsep.append(rmsep)
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Yhat.append(yhat)
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CE.append(ce)
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Rmsep = asarray(Rmsep)
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Yhat = asarray(Yhat)
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CE = asarray(CE)
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# Significance Hotellings T
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Wx, Wz, Wy, = jk_lpls(Xr, Y, Z, aopt)
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Wx, Wz, Wy, = jk_lpls(Xr, Y, Z, aopt, mean_ctr=mean_ctr,alpha=w_alpha)
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Ws = W*apply_along_axis(norm, 0, T)
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tsqx = cx_stats.hotelling(Wx, Ws[:,:aopt])
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tsqz = cx_stats.hotelling(Wz, L[:,:aopt])
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@ -157,7 +155,8 @@ ylim([class_error.min()-.05, class_error.max()+.05])
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title('Classification accuracy')
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figure(3) # Hypoid correlations
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plot_corrloads(Rz, pc1=0, pc2=1, s=tsqz/10.0, c='b', zorder=5, expvar=evz, ax=None)
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tsqz_s = 250*tsqz/tsqz.max()
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plot_corrloads(Rz, pc1=0, pc2=1, s=tsqz_s, c='b', zorder=5, expvar=evz, ax=None)
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ax = gca()
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ylabels = DY.get_identifiers('_status', sorted=True)
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plot_corrloads(Ry, pc1=0, pc2=1, s=150, c='g', zorder=5, expvar=evy, ax=ax,labels=ylabels)
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