from __future__ import division, print_function %matplotlib inline
Goal: we want to quantify the amount of a particular protein (red fluorescence) localized on the centromeres (green) versus the rest of the chromosome (blue).
The main challenge here is the uneven illumination, which makes isolating the chromosomes a struggle.
import numpy as np from matplotlib import cm, pyplot as plt import skdemo plt.rcParams['image.cmap'] = 'cubehelix' plt.rcParams['image.interpolation'] = 'none'
from skimage import io image = io.imread('../images/chromosomes.tif') skdemo.imshow_with_histogram(image);
Let's separate the channels so we can work on each individually.
protein, centromeres, chromosomes = image.transpose((2, 0, 1))
Getting the centromeres is easy because the signal is so clean:
from skimage.filter import threshold_otsu centromeres_binary = centromeres > threshold_otsu(centromeres) skdemo.imshow_all(centromeres, centromeres_binary)
But getting the chromosomes is not so easy:
chromosomes_binary = chromosomes > threshold_otsu(chromosomes) skdemo.imshow_all(chromosomes, chromosomes_binary, cmap='gray')
Let's try using an adaptive threshold:
from skimage.filter import threshold_adaptive chromosomes_adapt = threshold_adaptive(chromosomes, block_size=51) # Question: how did I choose this block size? skdemo.imshow_all(chromosomes, chromosomes_adapt)
Not only is the uneven illumination a problem, but there seem to be some artifacts due to the illumination pattern!
Exercise: Can you think of a way to fix this?
(Hint: in addition to everything you've learned so far, check out
Now that we have the centromeres and the chromosomes, it's time to do the science: get the distribution of intensities in the red channel using both centromere and chromosome locations.
# Replace "None" below with the right expressions! centromere_intensities = None chromosome_intensities = None all_intensities = np.concatenate((centromere_intensities, chromosome_intensities)) minint = np.min(all_intensities) maxint = np.max(all_intensities) bins = np.linspace(minint, maxint, 100) plt.hist(centromere_intensities, bins=bins, color='blue', alpha=0.5, label='centromeres') plt.hist(chromosome_intensities, bins=bins, color='orange', alpha=0.5, label='chromosomes') plt.legend(loc='upper right') plt.show()
%reload_ext load_style %load_style ../themes/tutorial.css