Note
Click here to download the full example code
Normalizing a contact count matrixΒΆ
This example showcases some basic filtering and normalization.
Out:
/home/travis/build/hiclib/iced/iced/io/_io_pandas.py:56: UserWarning: Attempting to guess whether counts are 0 or 1 based
warnings.warn(
Text(0.5, 1.0, 'Normalized contact counts')
import matplotlib.pyplot as plt
from matplotlib import colors
from iced import datasets
from iced import filter
from iced import normalization
# Loading a sample dataset
counts, lengths = datasets.load_sample_yeast()
# Filtering and normalizing contact count data
normed = filter.filter_low_counts(counts, lengths=lengths, percentage=0.04)
normed = normalization.ICE_normalization(normed)
# Plotting the results using matplotlib
chromosomes = ["I", "II", "III", "IV", "V", "VI"]
fig, axes = plt.subplots(ncols=2, figsize=(12, 4))
axes[0].imshow(counts, cmap="RdBu_r", norm=colors.SymLogNorm(1),
extent=(0, len(counts), 0, len(counts)))
[axes[0].axhline(i, linewidth=1, color="#000000") for i in lengths.cumsum()]
[axes[0].axvline(i, linewidth=1, color="#000000") for i in lengths.cumsum()]
axes[0].set_title("Raw contact counts", fontweight="bold")
m = axes[1].imshow(normed, cmap="RdBu_r", norm=colors.SymLogNorm(1),
extent=(0, len(counts), 0, len(counts)))
[axes[1].axhline(i, linewidth=1, color="#000000") for i in lengths.cumsum()]
[axes[1].axvline(i, linewidth=1, color="#000000") for i in lengths.cumsum()]
cb = fig.colorbar(m)
axes[1].set_title("Normalized contact counts", fontweight="bold")
Total running time of the script: ( 0 minutes 0.513 seconds)