def heatmap_cor(x, vec, minval, maxval):
# Compute and plots heatmap & dendrogram.
norm, corr, dist = analyse.all_corr(vec)
print 'statrting to cluster...'
fig = plt.figure(figsize=(8, 8))
ax1 = fig.add_axes([0.09, 0.1, 0.2, 0.6])
z = fastcluster.linkage(dist, method='complete')
#z=fastcluster.linkage(norm,metric='euclidean', method='ward')
print 'clustering done, drawing the dendogram'
Z1 = sch.dendrogram(z, labels=x, orientation='right')
del norm
del dist
plt.yticks(fontsize=8)
#ax1.set_yticks([])
ticks = ax1.get_xticks() #/ max(ax1.get_xticks())
ticks = map(float, ticks)
ticks = ['%.2f' % (a / 2.) for a in ticks]
ax1.set_xticklabels(ticks)
# Plot distance matrix.
axmatrix = fig.add_axes([0.4, 0.1, 0.5, 0.6])
axmatrix.set_xticks([])
axmatrix.set_yticks([])
axmatrix.xaxis.tick_top()
axmatrix.set_frame_on(False)
idx1 = Z1['leaves']
idx2 = Z1['leaves']
xx = []
for i in idx1:
xx.append(x[int(i)])
D = corr[idx1, :]
D = D[:, idx2]
print 'heatmap'
im = axmatrix.pcolor(
D,
cmap=plt.cm.RdYlBu,
edgecolor='k',
)
plt.xticks(fontsize=5)
plt.yticks([])
xx = []
for i in idx1:
xx.append(x[int(i)])
#plt.yticks(np.arange(len(x)),xx,fontsize = 12)
plt.xticks(np.arange(len(x)), xx)
plt.xticks(rotation=90)
plt.xticks(fontsize=8)
axcolor = fig.add_axes([0.91, 0.1, 0.02, 0.6])
plt.colorbar(im, cax=axcolor)
fig.show()
return xx
def heatmap_cor( x, vec, minval, maxval ):
# Compute and plots heatmap & dendrogram.
norm,corr,dist=analyse.all_corr(vec)
print 'statrting to cluster...'
fig = plt.figure(figsize=(8,8))
ax1 = fig.add_axes([0.09,0.1,0.2,0.6])
z=fastcluster.linkage(dist, method='complete')
#z=fastcluster.linkage(norm,metric='euclidean', method='ward')
print 'clustering done, drawing the dendogram'
Z1 = sch.dendrogram(z, labels=x,orientation='right')
del norm
del dist
plt.yticks(fontsize=8)
#ax1.set_yticks([])
ticks = ax1.get_xticks() #/ max(ax1.get_xticks())
ticks=map(float,ticks)
ticks = ['%.2f' % (a/2.) for a in ticks]
ax1.set_xticklabels(ticks)
# Plot distance matrix.
axmatrix = fig.add_axes([0.4,0.1,0.5,0.6])
axmatrix.set_xticks([])
axmatrix.set_yticks([])
axmatrix.xaxis.tick_top()
axmatrix.set_frame_on(False)
idx1 = Z1['leaves']
idx2 = Z1['leaves']
xx=[]
for i in idx1:
xx.append(x[int(i)])
D = corr[idx1,:]
D = D[:,idx2]
print 'heatmap'
im = axmatrix.pcolor(D, cmap=plt.cm.RdYlBu,edgecolor='k',)
plt.xticks(fontsize=5)
plt.yticks([])
xx=[]
for i in idx1:
xx.append(x[int(i)])
#plt.yticks(np.arange(len(x)),xx,fontsize = 12)
plt.xticks(np.arange(len(x)),xx)
plt.xticks(rotation=90)
plt.xticks(fontsize=8)
axcolor = fig.add_axes([0.91,0.1,0.02,0.6])
plt.colorbar(im, cax=axcolor)
fig.show()
return xx
def heatmap(rpkmls,hugo,txt):
rpkmls=data2arr(rpkmls)
norm,matrix,dist=analyse.all_corr(rpkmls.T)
plt.matshow((matrix),cmap='RdYlBu',vmax=1,vmin=-1)
plt.yticks(range(len(hugo)),hugo)
plt.xticks(range(len(hugo)),hugo)
plt.xticks(rotation=90)
plt.xlabel('Correlation-'+txt)
plt.colorbar()
def heatmap(rpkmls, hugo, txt):
rpkmls = data2arr(rpkmls)
norm, matrix, dist = analyse.all_corr(rpkmls.T)
plt.matshow((matrix), cmap='RdYlBu', vmax=1, vmin=-1)
plt.yticks(range(len(hugo)), hugo)
plt.xticks(range(len(hugo)), hugo)
plt.xticks(rotation=90)
plt.xlabel('Correlation-' + txt)
plt.colorbar()
def heatmap_vec( x, y , vec ):
# Compute and plot first dendrogram.
vec=np.array(vec)
mn=np.mean(vec,axis=1)
mat=[]
ylb=[]
for i in xrange(len(mn)):
if mn[i]>0.:
mat.append(vec[i])
ylb.append(y[i])
mat=np.array(mat)
norm,corr,dist=analyse.all_corr(mat.T)
del corr
fig = plt.figure(figsize=(8,8))
ax1 = fig.add_axes([0.09,0.1,0.25,0.6])
print 'fastcluster...'
z=fastcluster.linkage(dist , method='complete')
del dist
#print 'dendogramming...'
Z1 = sch.dendrogram(z,orientation='right')
ticks = ax1.get_xticks()
ticks=np.array(ticks)
ticks/=2.
ticks = ['%.1f' % a for a in ticks]
ax1.set_xticklabels(ticks)
idx1 = Z1['leaves']
yy=[]
for i in idx1:
yy.append(ylb[int(i)])
ax1.set_yticks(range(len(yy)),yy)
if len(yy)<20:
ax1.set_yticklabels(yy,fontsize=12)
elif len(yy)<50:
ax1.set_yticklabels(yy,fontsize=6)
elif len(yy)<150:
ax1.set_yticklabels(yy,fontsize=4)
elif len(yy)<250:
ax1.set_yticklabels(yy,fontsize=3)
elif len(yy)<500:
ax1.set_yticklabels(yy,fontsize=2)
elif len(yy)<1500:
ax1.set_yticklabels(yy,fontsize=1)
else:
ax1.set_yticklabels(yy,fontsize=.2)
# Plot distance matrix.
axmatrix = fig.add_axes([0.4,0.1,0.5,0.6])
D = norm[idx1,:]
D=D[::-1,:]
im = axmatrix.matshow(D, aspect='auto', origin='lower',cmap='RdYlBu', alpha=0.8,vmin=0)
plt.xticks(np.arange(len(x)),x)
plt.xticks(rotation=90)
mytemplate(D)
plt.xticks(fontsize=6)
axmatrix.set_yticks([])
#print x
# Plot colorbar.
axcolor = fig.add_axes([0.91,0.3,0.01,0.4])
plt.colorbar(im, cax=axcolor)
return yy
def heatmap_vec(x, y, vec):
# Compute and plot first dendrogram.
vec = np.array(vec)
mn = np.mean(vec, axis=1)
mat = []
ylb = []
for i in xrange(len(mn)):
if mn[i] > 0.:
mat.append(vec[i])
ylb.append(y[i])
mat = np.array(mat)
norm, corr, dist = analyse.all_corr(mat.T)
del corr
fig = plt.figure(figsize=(8, 8))
ax1 = fig.add_axes([0.09, 0.1, 0.25, 0.6])
print 'fastcluster...'
z = fastcluster.linkage(dist, method='complete')
del dist
#print 'dendogramming...'
Z1 = sch.dendrogram(z, orientation='right')
ticks = ax1.get_xticks()
ticks = np.array(ticks)
ticks /= 2.
ticks = ['%.1f' % a for a in ticks]
ax1.set_xticklabels(ticks)
idx1 = Z1['leaves']
yy = []
for i in idx1:
yy.append(ylb[int(i)])
ax1.set_yticks(range(len(yy)), yy)
if len(yy) < 20:
ax1.set_yticklabels(yy, fontsize=12)
elif len(yy) < 50:
ax1.set_yticklabels(yy, fontsize=6)
elif len(yy) < 150:
ax1.set_yticklabels(yy, fontsize=4)
elif len(yy) < 250:
ax1.set_yticklabels(yy, fontsize=3)
elif len(yy) < 500:
ax1.set_yticklabels(yy, fontsize=2)
elif len(yy) < 1500:
ax1.set_yticklabels(yy, fontsize=1)
else:
ax1.set_yticklabels(yy, fontsize=.2)
# Plot distance matrix.
axmatrix = fig.add_axes([0.4, 0.1, 0.5, 0.6])
D = norm[idx1, :]
D = D[::-1, :]
im = axmatrix.matshow(D,
aspect='auto',
origin='lower',
cmap='RdYlBu',
alpha=0.8,
vmin=0)
plt.xticks(np.arange(len(x)), x)
plt.xticks(rotation=90)
mytemplate(D)
plt.xticks(fontsize=6)
axmatrix.set_yticks([])
#print x
# Plot colorbar.
axcolor = fig.add_axes([0.91, 0.3, 0.01, 0.4])
plt.colorbar(im, cax=axcolor)
return yy
def heatmap_cor( x, vec, minval, maxval,*track ):
preprocessCore = importr('preprocessCore')
# Compute and plots heatmap & dendrogram. it shows the correlation in the right panel
#norm,corr,dist=analyse.all_corr(vec)
print 'statrting to cluster...'
fig = plt.figure(figsize=(8,8))
ax1 = fig.add_axes([0.09,0.1,0.2,0.8])
#z=fastcluster.linkage(dist, method='ward')
v = robjects.FloatVector([ element for col in vec.T for element in col ])
m = robjects.r['matrix'](v, ncol = len(vec.T), byrow=False)
Rnormalized_matrix = preprocessCore.normalize_quantiles(m)
normalized_matrix = numpy.array( Rnormalized_matrix)
norm,corr,dist=analyse.all_corr(vec)
z=fastcluster.linkage(norm,metric='correlation', method="ward")
#z=fastcluster.linkage(dist, method='complete')
print 'clustering done, drawing the dendogram'
Z1 = sch.dendrogram(z, labels=x,orientation='right')
plt.yticks(fontsize=8)
#ax1.set_yticks([])
ticks = ax1.get_xticks() #/ max(ax1.get_xticks())
ticks=map(float,ticks)
ticks = ['%.2f' % (a/2.) for a in ticks]
ax1.set_xticklabels(ticks)
# Plot distance matrix.
axmatrix = fig.add_axes([0.4,0.1,0.5,0.8])
axmatrix.set_xticks([])
axmatrix.set_yticks([])
axmatrix.xaxis.tick_top()
#axmatrix.set_frame_on(False)
idx1 = Z1['leaves']
idx2 = Z1['leaves']
xx=[]
for i in idx1:
xx.append(x[int(i)])
D = corr[idx1,:]
D = D[:,idx2]
im = axmatrix.pcolor(D, cmap=plt.cm.RdYlBu)
#im = axmatrix.pcolor(D, cmap=plt.cm.RdYlBu,edgecolor='w',)
plt.xticks(fontsize=5)
plt.yticks([])
del norm
del dist
del corr
del D
xx=[]
for i in idx1:
xx.append(x[int(i)])
#plt.yticks(np.arange(len(x)),xx,fontsize = 12)
plt.xticks(np.arange(len(x)),xx)
plt.xticks(rotation=90)
plt.xticks(fontsize=8)
axcolor = fig.add_axes([0.91,0.1,0.02,0.1])
plt.colorbar(im, cax=axcolor)
fig.show()
if len(track)>0:
track=track[0]
labels=track[0]
track=track[1]
for i in xrange(len(labels)):
labels[i]=labels[i].replace('_','')
category=[]
print len(xx), len(labels)
#print labels
for i in xx:
try:
ind=labels.index(i)
except:
print i, 'not in labels'
category.append(track[ind])
category=np.array(category)
category=np.array([category,category])
axmatrix = fig.add_axes([0.35,0.1,0.02,.87])
axmatrix.set_frame_on(False)
im=axmatrix.pcolor(category.T)
plt.xticks([])
plt.yticks([])
return xx , category[0]
return xx
