On 2011-9-26 16:02, Angus McMorland wrote:
On 25 September 2011 06:59, fdu.xia...@gmail.com<fdu.xia...@gmail.com> wrote:
Dear all,
Heatmap (like those on the page
http://www2.warwick.ac.uk/fac/sci/moac/students/peter_cock/r/heatmap/)
is a frequently used type of image in microarray data analysis. However,
it seems there are no convenient functions in matplotlib to plot heatmap
(please correct me if I was wrong), so I'm planning to write my own.
Let me take the heatmap by the link
http://www2.warwick.ac.uk/fac/sci/moac/students/peter_cock/r/heatmap/scaled_color_key.png
as an example, which is produced by R.
With my limited knowledge and expertise of matplotlib, I have the
following questions and I hope you guys could help me.
1) I tend to use pcolor to draw the colormap in the central area.
However, I've seen a lot of examples draw colormap with imshow.
What's the difference between pcolor and imshow?
Shall I use pcolor or imshow to produce the heatmap in the link above?
One difference between pcolor and imshow is that pcolor lines up the
bottom-left corner of each square with the co-ordinates given, whereas
imshow puts the grid center at those co-ordinates. My guess would be
(not having implemented a heatmap) that pcolor would be the more
useful to you to try first for this reason.
2) How to draw the dendrograms on the top and left of the colormap?
I got hints from
http://matplotlib.sourceforge.net/examples/axes_grid/scatter_hist.html
on how to append axes to current plot, but I still have now idea how to
draw the dengrograms.
A quick google search suggests that scipy-cluster might help you out
with the dendrogram drawing. I think I would try using separate axes
objects for the heatmap and the dendrograms, and switch off the frames
of the dendrogram axes.
3) How to draw the column side colormap (the smaller one) between the
top dendrogram and the big colormap?
This could another axes object too. You can use the sharex keyword to
add_axes to get it to have the same x scale as the big axes.
4) I can use colorbar to draw a colorbar, but how to place the colorbar
on the topleft of the image just as the R heatmap does?
Again, the answer is to manually create the axes for your colorbar,
and then specify those axes in the call to colorbar using the `cax`
keyword.
5) Any other suggestions on how to draw the heatmap?
I suggest that you give it a try and post your efforts here if there
are any problems. We'll also be keen to see the final result, if you'd
like to share it.
I hope that helps at least a little. Happy coding,
Angus
Hi all,
Thanks to Angus, and thanks to those guys who contributed to the
internet. I wrote a prototype of a heatmap function with their help.
Could some one help to review my code? Any comments will be greatly
appreciated. I would like to continue to refine the code, and hope it
can be of any help to the community someday.
I still have some questions:
1) The number of rows and columns could vary significantly and the
heatmap may look ugly if the parameters are not set right. How to set
the appropriate positions of all axes and the spaces between those axes?
And how to set up appropriate font size?
2) If there are too many rows or columns, it may not be efficient
to draw the labels of each row or column one bye one with the text()
function. Is there a better way to draw many text at once at different
locations?
Thanks a lot.
Regards,
Jianfeng
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 26 20:42:25 2011
http://stackoverflow.com/questions/5089030/how-do-i-create-a-radial-cluster-like-the-following-code-example-in-python
colorbar:
http://matplotlib.sourceforge.net/api/colorbar_api.html
http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.colorbar
http://matplotlib.sourceforge.net/examples/api/colorbar_only.html
@author: Xiao Jianfeng
"""
import scipy
import pylab
import scipy.cluster.hierarchy as sch
import scipy.spatial.distance as dist
from matplotlib import mpl
#def heatmap(x, colside_colors=None, rowside_colors=None):
def heatmap():
"""
x is a m by n ndarray, m observations, n genes
"""
x = scipy.rand(20, 30)
#x = scipy.loadtxt("f:/learn/heatmap/tst.data")
x = x*10
cmap=pylab.cm.YlGnBu
norm = mpl.colors.Normalize(vmin=x.min(), vmax=x.max())
fig = pylab.figure(figsize=(8,8))
## calculate positions for all elements
# ax1, dendrogram 1, on the left of the heatmap
[ax1_x, ax1_y, ax1_w, ax1_h] = [0.05,0.1,0.2,0.6]
width_between_ax1_axr = 0.01
height_between_ax1_axc = 0.01
# axr, row side colorbar
[axr_x, axr_y, axr_w, axr_h] = [0.31,0.1,0.02,0.6]
axr_x = ax1_x + ax1_w + width_between_ax1_axr
axr_y = ax1_y; axr_h = ax1_h
width_between_axr_axm = 0.01
# axc, column side colorbar
[axc_x, axc_y, axc_w, axc_h] = [0.4,0.63,0.5,0.02]
axc_x = axr_x + axr_w + width_between_axr_axm
axc_y = ax1_y + ax1_h + height_between_ax1_axc
height_between_axc_ax2 = 0.01
# axm, heatmap for the data matrix
[axm_x, axm_y, axm_w, axm_h] = [0.4,0.1,0.5,0.5]
axm_x = axr_x + axr_w + width_between_axr_axm
axm_y = ax1_y; axm_h = ax1_h
axm_w = axc_w
# ax2, dendrogram 2, on the top of the heatmap
[ax2_x, ax2_y, ax2_w, ax2_h] = [0.3,0.72,0.6,0.2]
ax2_x = axr_x + axr_w + width_between_axr_axm
ax2_y = ax1_y + ax1_h + height_between_ax1_axc + axc_h +
height_between_axc_ax2
ax2_w = axc_w
# axcb
[axcb_x, axcb_y, axcb_w, axcb_h] = [0.10,0.75,0.15,0.1]
# Compute and plot left dendrogram.
d1 = dist.pdist(x)
D1 = dist.squareform(d1) # full matrix
# postion = [left(x), bottom(y), width, height]
ax1 = fig.add_axes([ax1_x, ax1_y, ax1_w, ax1_h], frame_on=True) # frame_on
may be False
Y1 = sch.linkage(D1, method='single')
#Y = sch.linkage(D1, method='centroid')
Z1 = sch.dendrogram(Y1, orientation='right')
#ax1.set_xticks([])
#ax1.set_yticks([])
# Plot rowside colors
# axr --> axes for row side colorbar
axr = fig.add_axes([axr_x, axr_y, axr_w, axr_h]) # axes for column side
colorbar
dr = scipy.random.randint(low=0, high=5, size=(x.shape[0], 1))
# there are two methods to generate discrete colormap:
# matplotlib.cm.get_cmap() and mpl.colros.ListedColormap()
#cmap_r = mpl.cm.get_cmap('hot', lut=5)
cmap_r = mpl.colors.ListedColormap(['r', 'g', 'b', 'y', 'w'])
im_c = axr.pcolor(dr, cmap=cmap_r)
axr.set_xticks([])
axr.set_yticks([])
# Compute and plot top dendrogram.
d2 = dist.pdist(x.T)
D2 = dist.squareform(d2)
ax2 = fig.add_axes([ax2_x, ax2_y, ax2_w, ax2_h], frame_on=True)
Y2 = sch.linkage(D2, method='single')
Z2 = sch.dendrogram(Y2)
#ax2.set_xticks([])
#ax2.set_yticks([])
# Plot distance matrix.
axm = fig.add_axes([axm_x, axm_y, axm_w, axm_h]) # axes for the data matrix
idx1 = Z1['leaves']
idx2 = Z2['leaves']
xt = x[idx1,:] # xt is transformed x
xt = xt[:,idx2]
im = axm.pcolor(xt, cmap=cmap)
axm.set_xticks([])
#fix_verts(ax1,1)
#fix_verts(ax2,0)
# Add text
texts_row = [("%s_" % i)*4 for i in range(x.shape[0])]
texts_col = [("%s_" % i)*4 for i in range(x.shape[1])]
for i in range(x.shape[0]):
axm.text(x.shape[1]+0.5, i, texts_row[idx1[i]])
for i in range(x.shape[1]):
axm.text(i, -0.5, texts_col[idx2[i]], rotation=270,
verticalalignment="top") # rotation could also be degrees
# Plot colside colors
# axc --> axes for column side colorbar
axc = fig.add_axes([axc_x, axc_y, axc_w, axc_h]) # axes for column side
colorbar
dc = scipy.random.randint(low=0, high=5, size=(1, x.shape[1]))
# there are two methods to generate discrete colormap:
# matplotlib.cm.get_cmap() and mpl.colros.ListedColormap()
#cmap_c = mpl.cm.get_cmap('hot', lut=5)
cmap_c = mpl.colors.ListedColormap(['r', 'g', 'b', 'y', 'w'])
im_c = axc.pcolor(dc, cmap=cmap_c)
axc.set_yticks([])
# Plot colorbar
axcb = fig.add_axes([axcb_x, axcb_y, axcb_w, axcb_h], frame_on=False) #
axes for colorbar
cb = mpl.colorbar.ColorbarBase(axcb, cmap=cmap, norm=norm,
orientation='horizontal')
axcb.set_title("colorkey")
cb.set_label("add label here")
#axcb.set_xticks([])
#axcb.set_yticks([])
pylab.show()
#-----------------------------------------
if __name__ == '__main__':
heatmap()
------------------------------------------------------------------------------
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