iups
This commit is contained in:
parent
9db5991108
commit
155dfada5c
@ -9,7 +9,7 @@ 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):
|
||||
def nipals_lpls(X, Y, Z, a_max, alpha=.7, mean_ctr=[2, 0, 1], scale='scores', verbose=True):
|
||||
""" L-shaped Partial Least Sqaures Regression by the nipals algorithm.
|
||||
|
||||
(X!Z)->Y
|
||||
@ -113,7 +113,11 @@ def nipals_lpls(X, Y, Z, a_max, alpha=.7, mean_ctr=[2, 0, 1], verbose=True):
|
||||
evx = 100.0*(1 - var_x/varX)
|
||||
evy = 100.0*(1 - var_y/varY)
|
||||
evz = 100.0*(1 - var_z/varZ)
|
||||
|
||||
if scale=='loads':
|
||||
tnorm = apply_along_axis(norm, 0, T)
|
||||
T = T/tnorm
|
||||
Q = Q*tnorm
|
||||
W = W*tnorm
|
||||
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):
|
||||
@ -307,7 +311,8 @@ def center(a, axis):
|
||||
# 0 = col center, 1 = row center, 2 = double center
|
||||
# -1 = nothing
|
||||
if axis==-1:
|
||||
return a
|
||||
mn = zeros((a.shape[1],))
|
||||
return a - mn, mn
|
||||
elif axis==0:
|
||||
mn = a.mean(0)
|
||||
return a - mn, mn
|
||||
@ -364,14 +369,14 @@ def correlation_loadings(D, T, P, test=True):
|
||||
|
||||
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)
|
||||
cv_iter = select_generators.pls_gen(X, Y, n_blocks=nsets,center=False,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],
|
||||
mean_ctr=[2,0,1],
|
||||
verbose=False)
|
||||
for a in range(a_max):
|
||||
Yhat[a,ind,:] = b0[a][0][0] + dot(xi, B[a])
|
||||
@ -385,7 +390,7 @@ def cv_lpls(X, Y, Z, a_max=2, nsets=None,alpha=.5):
|
||||
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)
|
||||
cv_iter = select_generators.pls_gen(X, Y, n_blocks=nsets,center=False,index_out=False)
|
||||
m, n = X.shape
|
||||
k, l = Y.shape
|
||||
o, p = Z.shape
|
||||
@ -398,7 +403,8 @@ def jk_lpls(X, Y, Z, a_max, nsets=None, alpha=.5):
|
||||
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],
|
||||
mean_ctr=[2,0,1],
|
||||
scale='loads',
|
||||
verbose=False)
|
||||
WWx[i,:,:] = W
|
||||
WWz[i,:,:] = L
|
||||
|
||||
@ -39,6 +39,7 @@ def plot_corrloads(R, pc1=0,pc2=1,s=20, c='b', zorder=5,expvar=None,ax=None,draw
|
||||
#pylab.show()
|
||||
|
||||
def plot_dag(edge_dict, node_color='b', node_size=30,labels=None,nodelist=None,pos=None):
|
||||
# networkx does not play well with colon in node names
|
||||
clean_edges = {}
|
||||
for head, neigb in edge_dict.items():
|
||||
head = head.replace(":", "_")
|
||||
|
||||
@ -1,6 +1,5 @@
|
||||
""" Module for Gene ontology related functions called in R"""
|
||||
import scipy
|
||||
|
||||
import rpy
|
||||
silent_eval = rpy.with_mode(rpy.NO_CONVERSION, rpy.r)
|
||||
|
||||
@ -126,3 +125,8 @@ def parents_dag(go_terms, ontology=['BP']):
|
||||
else:
|
||||
edge_dict[nn] = [head]
|
||||
return edge_dict
|
||||
|
||||
def gene_GO_hypergeo_test(genelist, universe, ontology = ['BP']):
|
||||
|
||||
pvals = geneGoHyperGeoTest(entrezGeneIds, lib=None, ontology=ontology[0], universe=universe)
|
||||
return pvals
|
||||
|
||||
@ -32,14 +32,14 @@ print "SAM done"
|
||||
qq = rpy.r('qobj<-qvalue(sam.out@p.value)')
|
||||
qvals = asarray(qq['qvalues'])
|
||||
# cut off
|
||||
co = 0.1
|
||||
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)
|
||||
|
||||
print "\nWorkiing on subset with %s genes " %len(gene_ids)
|
||||
### Build GO data ####
|
||||
|
||||
print "Go terms ..."
|
||||
@ -48,13 +48,15 @@ terms = set()
|
||||
for t in goterms.values():
|
||||
terms.update(t)
|
||||
terms = list(terms)
|
||||
print "Number of go-terms: %s" %len(terms)
|
||||
rpy.r.library("GOSim")
|
||||
# Go-term similarity matrix
|
||||
methods = ("JiangConrath","Resnik","Lin","CoutoEnriched","CoutoJiangConrath","CoutoResnik","CoutoLin")
|
||||
meth = methods[2]
|
||||
meth = methods[0]
|
||||
print "Term-term similarity matrix (method = %s)" %meth
|
||||
if meth=="CoutoEnriched":
|
||||
rpy.r('setEnrichmentFactors(alpha=0.1,beta=0.5)')
|
||||
print "Calculating term-term similarity matrix"
|
||||
tmat = rpy.r.getTermSim(terms, verbose=False, method=meth)
|
||||
# check if all terms where found
|
||||
nanindex = where(isnan(tmat[:,0]))[0]
|
||||
@ -93,20 +95,21 @@ gene_ids = asarray(gene_ids)[newind]
|
||||
print "LPLSR ..."
|
||||
a_max = 5
|
||||
aopt = 2
|
||||
alpha=.5
|
||||
alpha=.6
|
||||
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)
|
||||
dx,Rx,rssx = correlation_loadings(Xr, T, P)
|
||||
dx,Ry,rssy = correlation_loadings(Y, T, Q)
|
||||
cadz,Rz,rssz = correlation_loadings(Z.T, W, 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])
|
||||
Ws = W*apply_along_axis(norm, 0, T)
|
||||
tsqx = cx_stats.hotelling(Wx, Ws[:,:aopt])
|
||||
tsqz = cx_stats.hotelling(Wz, L[:,:aopt])
|
||||
|
||||
|
||||
## plots ##
|
||||
|
||||
Reference in New Issue
Block a user