Note
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Are Hi-C contact count data overdispersed?
import matplotlib.pyplot as plt
import numpy as np
from iced import datasets
import iced
from pastis import dispersion
Load a Yeast dataset, and a human dataset
counts, lengths = datasets.load_sample_yeast()
Out:
/usr/share/miniconda3/envs/pastis/lib/python3.9/site-packages/iced/io/_io_pandas.py:56: UserWarning: Attempting to guess whether counts are 0 or 1 based
warnings.warn(
Normalize the contact count data, but keep the biases to estimate the dispersion
counts = iced.filter.filter_low_counts(counts, percentage=0.06)
normed_counts, biases = iced.normalization.ICE_normalization(
counts,
output_bias=True)
Now, estimate the variance and mean for every genomic distance
Note that in order to have an unbiased estimation of the variance, you need to provide the bias vector.
_, mean, variance, _ = dispersion.compute_mean_variance(
counts, lengths, bias=biases)
And now plot the resulting mean versus variance
fig, ax = plt.subplots()
s = ax.scatter(mean, variance, linewidth=0, marker="o",
s=20)
ax.set_xscale("log")
ax.set_yscale("log")
ax.set_xlabel("Mean", fontweight="bold")
ax.set_ylabel("Variance", fontweight="bold")
xmin, xmax = ax.get_xlim()
ymin, ymax = ax.get_ylim()
ax.plot(np.arange(1e-1, 1e7, 1e6),
np.arange(1e-1, 1e7, 1e6),
linewidth=1,
linestyle="--", color=(0, 0, 0))
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.set_ylim((min(ymin, xmin), max(xmax, ymax)))
ax.set_xlim((min(ymin, xmin), max(xmax, ymax)))
ax.set_title("Are contat counts overdispersed?", fontweight="bold")
ax.grid("off")
Total running time of the script: ( 0 minutes 0.569 seconds)
