from __future__ import division, print_function
%matplotlib inline
Yeast microarrays for genome wide parallel genetic and gene expression analysis
Two-color fluorescent scan of a yeast microarray containing 2,479 elements (ORFs). The center-to-center distance between elements is 345 μm. A probe mixture consisting of cDNA from yeast extract/peptone (YEP) galactose (green pseudocolor) and YEP glucose (red pseudocolor) grown yeast cultures was hybridized to the array. Intensity per element corresponds to ORF expression, and pseudocolor per element corresponds to relative ORF expression between the two cultures.
by Deval A. Lashkari, http://www.pnas.org/content/94/24/13057/F1.expansion
More example data:
import matplotlib.pyplot as plt
import numpy as np
from skimage import io, img_as_float
microarray = io.imread('../images/microarray.jpg')
# Scale between zero and one
microarray = img_as_float(microarray)
plt.figure(figsize=(10, 5))
plt.imshow(microarray[:500, :1000], cmap='gray', interpolation='nearest');
from skimage import color
f, (ax0, ax1) = plt.subplots(1, 2, figsize=(15, 10))
red = microarray[..., 0]
green = microarray[..., 1]
red_rgb = np.zeros_like(microarray)
red_rgb[..., 0] = red
green_rgb = np.zeros_like(microarray)
green_rgb[..., 1] = green
ax0.imshow(green_rgb, interpolation='nearest')
ax1.imshow(red_rgb, interpolation='nearest')
plt.suptitle('\n\nPseudocolor plots of red and green channels', fontsize=16);
from skimage import filter as filters
mask = (green > 0.1)
plt.imshow(mask[:1000, :1000], cmap='gray');
z = red.copy()
z /= green
z[~mask] = 0
print(z.min(), z.max())
plt.imshow(z[:500, :500], cmap=plt.cm.gray, vmin=0, vmax=2);
both = (green + red)
plt.imshow(both, cmap='gray');
from skimage import feature
sum_down_columns = both.sum(axis=0)
sum_across_rows = both.sum(axis=1)
dips_columns = feature.peak_local_max(sum_down_columns.max() - sum_down_columns)
dips_columns = dips_columns.ravel()
M = len(dips_columns)
column_distance = np.mean(np.diff(dips_columns))
dips_rows = feature.peak_local_max(sum_across_rows.max() - sum_across_rows)
dips_rows = dips_rows.ravel()
N = len(dips_rows)
row_distance = np.mean(np.diff(dips_rows))
print('Columns are a mean distance of %.2f apart' % column_distance)
print('Rows are a mean distance of %.2f apart' % row_distance)
f, (ax0, ax1) = plt.subplots(1, 2, figsize=(15, 5))
ax0.plot(sum_down_columns)
ax0.scatter(dips_columns, sum_down_columns[dips_columns])
ax0.set_xlim(0, 200)
ax0.set_title('Column gaps')
ax1.plot(sum_across_rows)
ax1.scatter(dips_rows, sum_across_rows[dips_rows])
ax1.set_xlim(0, 200)
ax0.set_title('Row gaps');
P, Q = 500, 500
plt.figure(figsize=(15, 10))
plt.imshow(microarray[:P, :Q])
for i in dips_rows[dips_rows < P]:
plt.plot([0, Q], [i, i], 'm')
for j in dips_columns[dips_columns < Q]:
plt.plot([j, j], [0, P], 'm')
plt.axis('image');
out = np.zeros(microarray.shape[:2])
for i in range(M - 1):
for j in range(N - 1):
row0, row1 = dips_rows[i], dips_rows[i + 1]
col0, col1 = dips_columns[j], dips_columns[j + 1]
r = microarray[row0:row1, col0:col1, 0]
g = microarray[row0:row1, col0:col1, 1]
ratio = r / g
mask = ~np.isinf(ratio)
mean_ratio = np.mean(ratio[mask])
if np.isnan(mean_ratio):
mean_ratio = 0
out[row0:row1, col0:col1] = mean_ratio
f, (ax0, ax1) = plt.subplots(1, 2, figsize=(15, 10))
ax0.imshow(microarray)
ax0.grid(color='magenta', linewidth=1)
ax1.imshow(out, cmap='gray', interpolation='nearest', vmin=0, vmax=3);
ax1.grid(color='magenta', linewidth=1)
f, (ax0, ax1) = plt.subplots(1, 2, figsize=(15, 10))
ax0.imshow(microarray)
ax0.grid(color='magenta', linewidth=1)
ax1.imshow(np.log(0.5 + out), cmap='gray', interpolation='nearest', vmin=0, vmax=3);
ax1.grid(color='magenta', linewidth=1)
%reload_ext load_style
%load_style ../themes/tutorial.css