diff --git a/scripts/lpls/lpls.py b/scripts/lpls/lpls.py
new file mode 100644
index 0000000..b32a91c
--- /dev/null
+++ b/scripts/lpls/lpls.py
@@ -0,0 +1,409 @@
+import sys
+from pylab import *
+import matplotlib
+from scipy import *
+from scipy.linalg import inv,norm
+
+sys.path.append("/home/flatberg/fluents/fluents/lib")
+import select_generators
+sys.path.remove("/home/flatberg/fluents/fluents/lib")
+
+
+def nipals_lpls(X, Y, Z, a_max, alpha=.7, mean_ctr=[2, 0, 1], verbose=True):
+ """ L-shaped Partial Least Sqaures Regression by the nipals algorithm.
+
+ (X!Z)->Y
+ :input:
+ X : data matrix (m, n)
+ Y : data matrix (m, l)
+ Z : data matrix (n, o)
+
+ :output:
+ T : X-scores
+ W : X-weights/Z-weights
+ P : X-loadings
+ Q : Y-loadings
+ U : X-Y relation
+ L : Z-scores
+ K : Z-loads
+ B : Regression coefficients X->Y
+ b0: Regression coefficient intercept
+ evx : X-explained variance
+ evy : Y-explained variance
+ evz : Z-explained variance
+
+ :Notes:
+
+ """
+ if mean_ctr:
+ xctr, yctr, zctr = mean_ctr
+ X, mnX = center(X, xctr)
+ Y, mnY = center(Y, xctr)
+ Z, mnZ = center(Z, zctr)
+
+ varX = pow(X, 2).sum()
+ varY = pow(Y, 2).sum()
+ varZ = pow(Z, 2).sum()
+
+ m, n = X.shape
+ k, l = Y.shape
+ u, o = Z.shape
+
+ # initialize
+ U = empty((k, a_max))
+ Q = empty((l, a_max))
+ T = empty((m, a_max))
+ W = empty((n, a_max))
+ P = empty((n, a_max))
+ K = empty((o, a_max))
+ L = empty((u, a_max))
+ B = empty((a_max, n, l))
+ b0 = empty((a_max, m, l))
+ var_x = empty((a_max,))
+ var_y = empty((a_max,))
+ var_z = empty((a_max,))
+
+ for a in range(a_max):
+ if verbose:
+ print "\n Working on comp. %s" %a
+ u = Y[:,:1]
+ diff = 1
+ MAX_ITER = 100
+ lim = 1e-7
+ niter = 0
+ while (diff>lim and niter<MAX_ITER):
+ niter += 1
+ u1 = u.copy()
+ w = dot(X.T, u)
+ w = w/sqrt(dot(w.T, w))
+ l = dot(Z, w)
+ k = dot(Z.T, l)
+ k = k/sqrt(dot(k.T, k))
+ w = alpha*k + (1-alpha)*w
+ w = w/sqrt(dot(w.T, w))
+ t = dot(X, w)
+ c = dot(Y.T, t)
+ c = c/sqrt(dot(c.T, c))
+ u = dot(Y, c)
+ diff = abs(u1 - u).max()
+ if verbose:
+ print "Converged after %s iterations" %niter
+ tt = dot(t.T, t)
+ p = dot(X.T, t)/tt
+ q = dot(Y.T, t)/tt
+ l = dot(Z, w)
+ U[:,a] = u.ravel()
+ W[:,a] = w.ravel()
+ P[:,a] = p.ravel()
+ T[:,a] = t.ravel()
+ Q[:,a] = q.ravel()
+ L[:,a] = l.ravel()
+ K[:,a] = k.ravel()
+
+ X = X - dot(t, p.T)
+ Y = Y - dot(t, q.T)
+ Z = (Z.T - dot(w, l.T)).T
+
+ var_x[a] = pow(X, 2).sum()
+ var_y[a] = pow(Y, 2).sum()
+ var_z[a] = pow(Z, 2).sum()
+ B[a] = dot(dot(W[:,:a+1], inv(dot(P[:,:a+1].T, W[:,:a+1]))), Q[:,:a+1].T)
+ b0[a] = mnY - dot(mnX, B[a])
+ # variance explained
+ evx = 100.0*(1 - var_x/varX)
+ evy = 100.0*(1 - var_y/varY)
+ evz = 100.0*(1 - var_z/varZ)
+
+ return T, W, P, Q, U, L, K, B, b0, evx, evy, evz
+
+def svd_lpls(X, Y, Z, a_max, alpha=.7, mean_ctr=[2, 0, 1], verbose=True):
+ """
+ NB: In the works ...
+ L-shaped Partial Least Sqaures Regression by the svd algorithm.
+
+ (X!Z)->Y
+ :input:
+ X : data matrix (m, n)
+ Y : data matrix (m, l)
+ Z : data matrix (n, o)
+
+ :output:
+ T : X-scores
+ W : X-weights/Z-weights
+ P : X-loadings
+ Q : Y-loadings
+ U : X-Y relation
+ L : Z-scores
+ K : Z-loads
+ B : Regression coefficients X->Y
+ b0: Regression coefficient intercept
+ evx : X-explained variance
+ evy : Y-explained variance
+ evz : Z-explained variance
+
+ :Notes:
+ Not quite there ,,,,,,,,,,,,,,
+
+ """
+ if mean_ctr:
+ xctr, yctr, zctr = mean_ctr
+ X, mnX = center(X, xctr)
+ Y, mnY = center(Y, xctr)
+ Z, mnZ = center(Z, zctr)
+
+ varX = pow(X, 2).sum()
+ varY = pow(Y, 2).sum()
+ varZ = pow(Z, 2).sum()
+
+ m, n = X.shape
+ k, l = Y.shape
+ u, o = Z.shape
+
+ # initialize
+ U = empty((k, a_max))
+ Q = empty((l, a_max))
+ T = empty((m, a_max))
+ W = empty((n, a_max))
+ P = empty((n, a_max))
+ K = empty((o, a_max))
+ L = empty((u, a_max))
+ var_x = empty((a_max,))
+ var_y = empty((a_max,))
+ var_z = empty((a_max,))
+
+ for a in range(a_max):
+ if verbose:
+ print "\n Working on comp. %s" %a
+ xyz = dot(dot(Z,X.T),Y)
+ u,s,vt = linalg.svd(xyz, 0)
+ w = u[:,o]
+ t = dot(X, w)
+ tt = dot(t.T, t)
+ p = dot(X.T, t)/tt
+ q = dot(Y.T, t)/tt
+ l = dot(Z.T, w)
+ W[:,a] = w.ravel()
+ P[:,a] = p.ravel()
+ T[:,a] = t.ravel()
+ Q[:,a] = q.ravel()
+ L[:,a] = l.ravel()
+ K[:,a] = k.ravel()
+ X = X - dot(t, p.T)
+ Y = Y - dot(t, q.T)
+ Z = (Z.T - dot(w, l.T)).T
+ var_x[a] = pow(X, 2).sum()
+ var_y[a] = pow(Y, 2).sum()
+ var_z[a] = pow(Z, 2).sum()
+ B = dot(dot(W, inv(dot(P.T, W))), Q.T)
+ b0 = mnY - dot(mnX, B)
+ # variance explained
+ evx = 100.0*(1 - var_x/varX)
+ evy = 100.0*(1 - var_y/varY)
+ evz = 100.0*(1 - var_z/varZ)
+ return T, W, P, Q, U, L, K, B, b0, evx, evy, evz
+
+
+def lplsr(X, Y, Z, a_max, mean_ctr=[2,0,1]):
+ """ Haralds LPLS.
+ """
+ if mean_ctr!=None:
+ xctr, yctr, zctr = mean_ctr
+ X, mnX = center(X, xctr)
+ Y, mnY = center(Y, yctr)
+ Z, mnZ = center(Z, zctr)
+
+ varX = pow(X, 2).sum()
+ varY = pow(Y, 2).sum()
+ varZ = pow(Z, 2).sum()
+ m, n = X.shape
+ k, l = Y.shape
+ u, o = Z.shape
+
+ # initialize
+ Wy = empty((l, a_max))
+ Py = empty((l, a_max))
+ Ty = empty((m, a_max))
+ Tz = empty((o, a_max))
+ Wz = empty((u, a_max))
+ Pz = empty((u, a_max))
+ var_x = empty((a_max,))
+ var_y = empty((a_max,))
+ var_z = empty((a_max,))
+
+ # residuals
+ Ey = Y.copy()
+ Ez = Z.copy()
+ Ex = X.copy()
+ for i in range(a_max):
+ YtXZ = dot(Ey.T, dot(X, Ez.T))
+ U, S, V = linalg.svd(YtXZ)
+ wy = U[:,0]
+ print wy
+ wz = V[0,:]
+ ty = dot(Ey, wy)
+ tz = dot(Ez.T, wz)
+ py = dot(Ey.T, ty)/dot(ty.T,ty)
+ pz = dot(Ez, tz)/dot(tz.T,tz)
+ Wy[:,i] = wy
+ Wz[:,i] = wz
+ Ty[:,i] = ty
+ Tz[:,i] = tz
+ Py[:,i] = py
+ Pz[:,i] = pz
+ Ey = Ey - outer(ty, py.T)
+ Ez = (Ez.T - outer(tz, pz.T)).T
+ var_y[i] = pow(Ey, 2).sum()
+ var_z[i] = pow(Ez, 2).sum()
+
+ tyd = apply_along_axis(norm, 0, Ty)
+ tzd = apply_along_axis(norm, 0, Tz)
+ Tyu = Ty/tyd
+ Tzu = Tz/tzd
+ C = dot(dot(Tyu.T, X), Tzu)
+ for i in range(a_max):
+ Ex = Ex - dot(dot(Ty[:,:i+1],C[:i+1,:i+1]), Tz[:,:i+1].T)
+ var_x[i] = pow(Ex,2).sum()
+ # variance explained
+ print "var_x:"
+ print var_x
+ print "varX total:"
+ print varX
+
+ evx = 100.0*(1 - var_x/varX)
+ evy = 100.0*(1 - var_y/varY)
+ evz = 100.0*(1 - var_z/varZ)
+
+ return Ty, Tz, Wy, Wz, Py, Pz, C, Ey, Ez, Ex, evx, evy, evz
+
+def bifpls(X, Y, Z, a_max, alpha):
+ """Swedssihsh LPLS by nipals.
+ """
+ u = X[:,0]
+ Ey = Y.copy()
+ Ez = Z.copy()
+ for i in range(100):
+ w = dot(X.T,u)
+ w = w/vnorm(w)
+ t = dot(X, w)
+ q = dot(Ey, t.T)/dot(t.T,t)
+ qnorm = vnorm(q)
+ q = q/qnorm
+ v = dot(Ez, q)
+ s = dot(Ez.T, v)/dot(v.T,v)
+ v = v*vnorm(s)
+ s = s/vnorm(s)
+ c = qnorm*(alpha*q + (1-alpha)*s)
+ u = dot(Ey, c)/dot(s.T,s)
+ p = dot(X.T, t)/dot(t.T,t)
+ v2 = dot(Ez, s)/dot(s.T,s)
+ Ey = Ey - dot(t, p.T)
+ Ez = Ez - dot(v2, c.T)
+ # variance explained
+ evx = 100.0*(1 - var_x/varX)
+ evy = 100.0*(1 - var_y/varY)
+ evz = 100.0*(1 - var_z/varZ)
+
+def center(a, axis):
+ # 0 = col center, 1 = row center, 2 = double center
+ # -1 = nothing
+ if axis==-1:
+ return a
+ elif axis==0:
+ mn = a.mean(0)
+ return a - mn, mn
+ elif axis==1:
+ mn = a.mean(1)[:,newaxis]
+ return a - mn , mn
+ elif axis==2:
+ mn = a.mean(0) + a.mean(1)[:,newaxis] - a.mean()
+ return a - mn, mn
+ else:
+ raise IOError("input error: axis must be in [-1,0,1,2]")
+
+def correlation_loadings(D, T, P, test=True):
+ """ Returns correlation loadings.
+
+ :input:
+ - D: [nsamps, nvars], data (non-centered data)
+ - T: [nsamps, a_max], Scores
+ - P: [nvars, a_max], Loadings
+ :ouput:
+ - Rloads: [nvars, a_max], Correlation loadings
+ - rmseVars: [nvars], scaling coeff. for each var in D
+
+ :notes:
+ - FIXME: Calculation is not valid .... using corrceof instead
+ """
+ nsamps, nvars = D.shape
+ nsampsT, a_max = T.shape
+ nvarsP, a_maxP = P.shape
+ if nsamps!=nsampsT: raise IOError("D/T mismatch")
+ if a_max!=a_maxP: raise IOError("a_max mismatch")
+ if nvars!=nvarsP: raise IOError("D/P mismatch")
+
+ #init
+ Rloads = empty((nvars, a_max), 'd')
+ stdvar = stats.std(D, 0)
+ rmseVars = sqrt(nsamps-1)*stdvar
+
+ # center
+ D = D - D.mean(0)
+ TT = diag(dot(T.T, T))
+ sTT = sqrt(TT)
+ for a in range(a_max):
+ Rloads[:,a] = sTT[a]*P[:,a]/rmseVars
+ R = empty_like(Rloads)
+ for a in range(a_max):
+ for k in range(nvars):
+ r = corrcoef(D[:,k], T[:,a])
+ R[k,a] = r[0,1]
+ #Rloads = R
+ return Rloads, R, rmseVars
+
+
+
+def cv_lpls(X, Y, Z, a_max=2, nsets=None,alpha=.5):
+ """Performs crossvalidation to get generalisation error in lpls"""
+ cv_iter = select_generators.pls_gen(X, Y, n_blocks=nsets,center=True,index_out=True)
+ k, l = Y.shape
+ Yhat = empty((a_max,k,l), 'd')
+ for i, (xcal,xi,ycal,yi,ind) in enumerate(cv_iter):
+ T, W, P, Q, U, L, K, B, b0, evx, evy, evz = nipals_lpls(xcal,ycal,Z,
+ a_max=a_max,
+ alpha=alpha,
+ mean_ctr=[0,0,1],
+ verbose=False)
+ for a in range(a_max):
+ Yhat[a,ind,:] = b0[a][0][0] + dot(xi, B[a])
+ Yhat_class = zeros_like(Yhat)
+ for a in range(a_max):
+ for i in range(k):
+ Yhat_class[a,i,argmax(Yhat[a,i,:])]=1.0
+ class_err = 100*((Yhat_class+Y)==2).sum(1)/Y.sum(0).astype('d')
+ sep = (Y - Yhat)**2
+ rmsep = sqrt(sep.mean(1))
+ return rmsep, Yhat, class_err
+
+def jk_lpls(X, Y, Z, a_max, nsets=None, alpha=.5):
+ cv_iter = select_generators.pls_gen(X, Y, n_blocks=nsets,center=True,index_out=False)
+ m, n = X.shape
+ k, l = Y.shape
+ o, p = Z.shape
+ if nsets==None:
+ nsets = m
+ WWx = empty((nsets, n, a_max), 'd')
+ WWz = empty((nsets, o, a_max), 'd')
+ WWy = empty((nsets, l, a_max), 'd')
+ for i, (xcal,xi,ycal,yi) in enumerate(cv_iter):
+ T, W, P, Q, U, L, K, B, b0, evx, evy, evz = nipals_lpls(xcal,ycal,Z,
+ a_max=a_max,
+ alpha=alpha,
+ mean_ctr=[0,0,1],
+ verbose=False)
+ WWx[i,:,:] = W
+ WWz[i,:,:] = L
+ WWy[i,:,:] = Q
+ print "Q"
+ print Q
+
+ return WWx, WWz, WWy
diff --git a/scripts/lpls/plots_lpls.py b/scripts/lpls/plots_lpls.py
new file mode 100644
index 0000000..1438ded
--- /dev/null
+++ b/scripts/lpls/plots_lpls.py
@@ -0,0 +1,38 @@
+import pylab
+import matplotlib
+
+def plot_corrloads(R, pc1=0,pc2=1,s=20, c='b', zorder=5,expvar=None,ax=None,drawback=True, labels=None):
+ """ Correlation loading plot."""
+
+ # backgorund
+ if ax==None or drawback==True:
+ radius = 1
+ center = (0,0)
+ c100 = matplotlib.patches.Circle(center,radius=radius,
+ facecolor='gray',
+ alpha=.1,
+ zorder=1)
+ c50 = matplotlib.patches.Circle(center, radius=radius/2.0,
+ facecolor='gray',
+ alpha=.1,
+ zorder=2)
+ ax = pylab.gca()
+ ax.add_patch(c100)
+ ax.add_patch(c50)
+ ax.axhline(lw=1.5,color='k')
+ ax.axvline(lw=1.5,color='k')
+
+ # corrloads
+ ax.scatter(R[:,pc1], R[:,pc2], s=s, c=c,zorder=zorder)
+ ax.set_xlim([-1,1])
+ ax.set_ylim([-1,1])
+ if expvar!=None:
+ xstring = "Comp: %d expl.var: %.1f " %(pc1+1, expvar[pc1])
+ pylab.xlabel(xstring)
+ ystring = "Comp: %d expl.var.: %.1f " %(pc2+1, expvar[pc2])
+ pylab.ylabel(ystring)
+ if labels:
+ assert(len(labels)==R.shape[0])
+ for name, r in zip(labels, R):
+ ax.text(r[pc1], r[pc2], " " + name)
+ #pylab.show()
diff --git a/scripts/lpls/rpy_go.py b/scripts/lpls/rpy_go.py
new file mode 100644
index 0000000..38f54d5
--- /dev/null
+++ b/scripts/lpls/rpy_go.py
@@ -0,0 +1,110 @@
+""" Module for Gene ontology related functions called in R"""
+import scipy
+
+import rpy
+silent_eval = rpy.with_mode(rpy.NO_CONVERSION, rpy.r)
+
+def get_term_sim(termlist, method = "JiangConrath", verbose=False):
+ """Returns the similariy matrix between go-terms.
+
+ Arguments:
+ termlist: character vector of GO terms
+ method: one of
+ ("JiangConrath","Resnik","Lin","CoutoEnriched","CoutoJiangConrath","CoutoResnik","CoutoLin")
+ verbose: print out various information or not
+ """
+ _methods = ("JiangConrath","Resnik","Lin","CoutoEnriched","CoutoJiangConrath","CoutoResnik","CoutoLin")
+ assert(method in _methods)
+ assert(termlist[0][:2]=='GO')
+ rpy.r.library("GOSim")
+ return rpy.r.getTermSim(termlist, method = method, verbose = verbose)
+
+def get_gene_sim(genelist, similarity='OA',
+ distance="Resnick"):
+ rpy.r.library("GOSim")
+ rpy.r.assign("ids", genelist)
+ silent_eval('a<-getGeneSim(ids)', verbose=FALSE)
+
+def goterms_from_gene(genelist, ontology=['BP'], garbage = ['IEA', 'ISS', 'ND']):
+ """ Returns the go-terms from a specified genelist (Entrez id).
+
+ """
+ rpy.r.library("GO")
+ _CODES = {"IMP" : "inferred from mutant phenotype",
+ "IGI" : "inferred from genetic interaction",
+ "IPI" :"inferred from physical interaction",
+ "ISS" : "inferred from sequence similarity",
+ "IDA" : "inferred from direct assay",
+ "IEP" : "inferred from expression pattern",
+ "IEA" : "inferred from electronic annotation",
+ "TAS" : "traceable author statement",
+ "NAS" : "non-traceable author statement",
+ "ND" : "no biological data available",
+ "IC" : "inferred by curator"
+ }
+ _ONTOLOGIES = ['BP', 'CC', 'MF']
+ assert(scipy.all([(code in _CODES) for code in garbage]))
+ assert(scipy.all([(ont in _ONTOLOGIES) for ont in ontology]))
+
+ goterms = {}
+ for gene in genelist:
+ goterms[gene] = []
+ info = rpy.r('GOENTREZID2GO[["' + str(gene) + '"]]')
+ #print info
+ if info:
+ for term, desc in info.items():
+ if desc['Ontology'] in ontology and desc['Evidence'] not in garbage:
+ goterms[gene].append(term)
+ return goterms
+
+def genego_matrix(goterms, tmat, gene_ids, term_ids, func=min):
+ ngenes = len(gene_ids)
+ nterms = len(term_ids)
+ gene2indx = {}
+ for i,id in enumerate(gene_ids):
+ gene2indx[id]=i
+ term2indx = {}
+ for i,id in enumerate(term_ids):
+ term2indx[id]=i
+ #G = scipy.empty((nterms, ngenes),'d')
+ G = []
+ newindex = []
+ for gene, terms in goterms.items():
+ g_ind = gene2indx[gene]
+ if len(terms)>0:
+ t_ind = []
+ newindex.append(g_ind)
+ for term in terms:
+ if term2indx.has_key(term): t_ind.append(term2indx[term])
+ print t_ind
+ subsim = tmat[t_ind, :]
+ gene_vec = scipy.apply_along_axis(func, 0, subsim)
+ G.append(gene_vec)
+
+ return scipy.asarray(G), newindex
+
+def goterm2desc(gotermlist):
+ """Returns the go-terms description keyed by go-term
+ """
+ rpy.r.library("GO")
+ term2desc = {}
+ for term in gotermlist:
+ try:
+ desc = rpy.r('Term(GOTERM[["' +str(term)+ '"]])')
+ term2desc[str(term)] = desc
+ except:
+ raise Warning("Description not found for %s\n Mapping incomplete" %term)
+ return term2desc
+
+def parents_dag(go_terms, ontology=['BP']):
+ """ Returns a list of lists representation of a GO DAG parents of goterms."""
+ try:
+ rpy.r.library("GOstats")
+ except:
+ raise ImportError, "Gostats"
+ assert(go_terms[0][:3]=='GO:')
+
+ # go valid namespace
+ go_env = {'BP':rpy.r.BPPARENTS, 'MF':rpy.r.MFPARENTS, 'CC': rpy.r.CCPARENTS}
+
+
diff --git a/scripts/lpls/run_smoker.py b/scripts/lpls/run_smoker.py
new file mode 100644
index 0000000..1c6036f
--- /dev/null
+++ b/scripts/lpls/run_smoker.py
@@ -0,0 +1,141 @@
+import sys
+import rpy
+from pylab import gca, figure, subplot
+from scipy import *
+from lpls import *
+import rpy_go
+sys.path.append("../../fluents") # home of dataset
+sys.path.append("../../fluents/lib") # home of cx_stats
+import dataset
+import cx_stats
+from plots_lpls import plot_corrloads
+
+######## DATA ##########
+# full smoker data
+DX = dataset.read_ftsv(open("../../data/smokers-full/Xfull.ftsv"))
+DY = dataset.read_ftsv(open("../../data/smokers-full/Yg.ftsv"))
+Y = DY.asarray()
+# select subset genes by SAM
+rpy.r.library("siggenes")
+rpy.r.library("qvalue")
+data = DX.asarray().T
+# data = data[:100,:]
+rpy.r.assign("data", data)
+cl = dot(DY.asarray(), diag([1,2,3])).sum(1)
+rpy.r.assign("cl", cl)
+rpy.r.assign("B", 100)
+# Perform a SAM analysis.
+print "Starting SAM"
+sam = rpy.r('sam.out<-sam(data=data,cl=cl,B=B,rand=123)')
+print "SAM done"
+# Compute the q-values of the genes.
+qq = rpy.r('qobj<-qvalue(sam.out@p.value)')
+qvals = asarray(qq['qvalues'])
+# cut off
+co = 0.001
+index = where(qvals<0.01)[0]
+
+# Subset data
+X = DX.asarray()
+Xr = X[:,index]
+gene_ids = DX.get_identifiers('gene_ids', index)
+
+### Build GO data ####
+
+print "Go terms ..."
+goterms = rpy_go.goterms_from_gene(gene_ids)
+terms = set()
+for t in goterms.values():
+ terms.update(t)
+terms = list(terms)
+rpy.r.library("GOSim")
+# Go-term similarity matrix
+methods = ("JiangConrath","Resnik","Lin","CoutoEnriched","CoutoJiangConrath","CoutoResnik","CoutoLin")
+meth = methods[0]
+print "Term-term similarity matrix (method = %s)" %meth
+if meth=="CoutoEnriched":
+ rpy.r('setEnrichmentFactors(alpha=0.1,beta=0.5)')
+tmat = rpy.r.getTermSim(terms, verbose=False, method=meth)
+# check if all terms where found
+nanindex = where(isnan(tmat[:,0]))[0]
+keep=[]
+has_miss = False
+if len(nanindex)>0:
+ has_miss = True
+ print "Some terms missing in similarity matrix"
+ keep = where(isnan(tmat[:,0])!=True)[0]
+ print "Number of nans: %d" %len(nanindex)
+ tmat_new = tmat[:,keep][keep,:]
+ new_terms = [i for ind,i in enumerate(terms) if ind in keep]
+ bad_terms = [i for ind,i in enumerate(terms) if ind not in keep]
+ # update go-term dict
+ for gene,trm in goterms.items():
+ for t in trm:
+ if t in bad_terms:
+ trm.remove(t)
+ if len(trm)==0:
+ print "Removing gene: %s" %gene
+ goterms[gene]=trm
+ terms = new_terms
+ tmat = tmat_new
+# Z-matrix
+# func (min, max, median, mean, etc),
+# func decides on the representation of gene-> goterm when multiple
+# goterms exist for one gene
+Z, newind = rpy_go.genego_matrix(goterms, tmat, gene_ids, terms,func=mean)
+Z = Z.T
+# update X matrix (no go-terms available)
+Xr = Xr[:,newind]
+gene_ids = asarray(gene_ids)[newind]
+
+
+######## LPLSR ########
+print "LPLSR ..."
+a_max = 5
+aopt = 2
+alpha=.5
+T, W, P, Q, U, L, K, B, b0, evx, evy, evz = nipals_lpls(Xr,Y,Z, a_max, alpha)
+
+# Correlation loadings
+dx,Rx,ssx= correlation_loadings(Xr, T, P)
+dx,Ry,ssx= correlation_loadings(Y, T, Q)
+cadx,Rz,ssx= correlation_loadings(Z.T, K, L)
+
+# Prediction error
+rmsep , yhat, class_error = cv_lpls(Xr, Y, Z, a_max, alpha=alpha)
+
+# Significance Hotellings T
+Wx, Wz, Wy, = jk_lpls(Xr, Y, Z, aopt)
+tsqx = cx_stats.hotelling(Wx,W[:,:aopt])
+tsqz = cx_stats.hotelling(Wz,L[:,:aopt])
+
+
+## plots ##
+figure(1) #rmsep
+#bar()
+figure(2) # Hypoid correlations
+plot_corrloads(Rz, pc1=0, pc2=1, s=tsqz/10.0, c='b', zorder=5, expvar=evz, ax=None)
+ax = gca()
+plot_corrloads(Ry, pc1=0, pc2=1, s=150, c='g', zorder=5, expvar=evy, ax=ax)
+
+figure(3)
+subplot(221)
+ax = gca()
+plot_corrloads(Rx, pc1=0, pc2=1, s=tsqx/2.0, c='b', zorder=5, expvar=evx, ax=ax)
+# title('X correlation')
+subplot(222)
+ax = gca()
+plot_corrloads(Ry, pc1=0, pc2=1, s=150, c='g', zorder=5, expvar=evy, ax=ax)
+#title('Y correlation')
+subplot(223)
+ax = gca()
+plot_corrloads(Rz, pc1=0, pc2=1, s=tsqz/10.0, c='r', zorder=5, expvar=evz, ax=ax)
+#title('Z correlation')
+subplot(224)
+plot(arange(len(evx)), evx, 'b', label='X', linewidth=2)
+plot(evy, 'g', label='Y', linewidth=2)
+plot(evz, 'r', label='Z', linewidth=2)
+legend(loc=2)
+ylabel('Explained variance')
+xlabel('Component')
+show()
diff --git a/scripts/lpls/yeast_annot.py b/scripts/lpls/yeast_annot.py
new file mode 100644
index 0000000..c7abc3b
--- /dev/null
+++ b/scripts/lpls/yeast_annot.py
@@ -0,0 +1,44 @@
+
+def smdb_annot(orflist=None, input_fname='registry.genenames.tab', output_fname='yeast.annot'):
+
+ """Reads registry.genenames.tab from the Stanford yeast
+ microarray database.
+
+ Available from:
+ ftp://genome-ftp.stanford.edu/pub/yeast/data_download/gene_registry/registry.genenames.tab
+
+ input: orf -- list of orfs (open reading frames)
+ file -- (optional) file to fetch info from
+
+ registry.genames contains:
+
+ 0 = Locus name
+ 1 = Other name
+ 2 = Description
+ 3 = Gene product
+ 4 = Phenotype
+ 5 = ORF name
+ 6 = SGDID
+
+ NB! Other name, Gene product and Phenotype may have more
+ than one mapping. These are separated by |
+
+ Output: writes an annotation file
+
+ """
+ outfile = open(output_fname, 'w')
+ header = "Orf\tLocus_id\tOther_name\tDescription\tGene_product\tPhenotype\tSGD_ID\n"
+ outfile.write(header)
+ text = open(input_fname, 'r').read().splitlines()
+ for line in text:
+ els = line.split('\t')
+ orf_name = els.pop(5)
+ if orf_name!='': # we dont care about non-named orfs
+ if orflist and orf_name not in orflist:
+ break
+ for e in els:
+ if e !='':
+ outfile.write(str(e) + "\t")
+ else:
+ outfile.write("NA")
+ f.write("\n")