modisco_dir = "/users/avsec/workspace/basepair/data/processed/chipnexus/exp/models/oct-sox-nanog-klf/models/n_dil_layers=9/modisco/valid/new-hparams"
# Parameters
modisco_dir = "."
from basepair.modisco.results import ModiscoResult
from basepair.config import get_data_dir
from basepair.utils import read_json
from basepair.plot.vdom import vdom_modisco
from kipoi.readers import HDF5Reader
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
from plotnine import *
Using TensorFlow backend.
mr = ModiscoResult(f"{modisco_dir}/modisco.h5")
mr.open()
# load the data
modisco_kwargs = read_json(os.path.join(modisco_dir, "kwargs.json"))
d = HDF5Reader(modisco_kwargs['imp_scores'])
d.open()
strand_dist_file = f"{modisco_dir}/strand_distances.h5"
if modisco_kwargs.get("ignore_strand_dist", False) and os.path.exists(strand_dist_file):
included_samples = HDF5Reader.load(strand_dist_file)['included_samples']
else:
included_samples = np.ones(d.f['inputs'].shape[:1], dtype=bool)
if modisco_kwargs.get("filter_npy", None) is not None:
included_samples = np.load(modisco_kwargs['filter_npy']) * included_samples
id_hash = pd.DataFrame({"peak_id": d.f['/metadata/interval_from_task'][:][included_samples],
"example_idx": np.arange(d.f['/metadata/interval_from_task'][included_samples].shape[0])})
tasks = list(d.f["targets"]["profile"].keys())
# get all seqlet instances
dfp = mr.seqlet_df_instances().rename(columns=dict(seqname="example_idx"))
dfp = pd.merge(dfp, id_hash, on="example_idx")
TF-MoDISco is using the TensorFlow backend.
# row = example_idx
total_counts = pd.DataFrame({task: d.f[f"/targets/profile/{task}"][:][included_samples].sum(axis=-1).sum(axis=-1)
for task in tasks
})
len(mr.patterns())
112
# total number of seqlets
len(dfp)
48825
# Number of metaclusters
len(mr.metaclusters())
15
mc_stat = mr.metacluster_stats()
ggplot(aes(x="pattern", y='n'), mc_stat) + geom_bar(stat='identity') + \
facet_wrap("~metacluster", ncol=4, labeller='label_both') + \
ylab("Number of seqlets") + theme_classic()
<ggplot: (-9223363246194832661)>
ggplot(aes(x="pattern", y='n'), mc_stat) + geom_bar(stat='identity') + \
facet_wrap("~metacluster", ncol=4, labeller='label_both') + \
ylab("Number of seqlets") + theme_classic() + coord_cartesian(ylim=[0, 500])
<ggplot: (8790660168417)>
mcs_grouped = mc_stat.groupby("metacluster").n.agg(["count", "sum"]).reset_index()
fig, ax = plt.subplots(2, 1, sharex=False, figsize=(18,6),
gridspec_kw={'height_ratios': [2,1]})
mcs_grouped.plot("metacluster", "count",
label="# patterns per metacluster", style="o--",
ax=ax[0],
yticks=range(mcs_grouped['count'].max()+1),
xticks=range(38),
fontsize='large',
xlim=(-.5, len(mr.metaclusters()) - .5 ))
mcs_grouped.plot("metacluster", "sum",
label="# seqlets per metacluster",
style="o--", ax=ax[0], secondary_y=True)
ax[0].grid(linewidth=0.2)
mr.plot_metacluster_activity(ax[1], cbar=False)
ax[1].set_title("Importance score activity: Red = positive, Blue = negative");
vdom_modisco(mr, "plots", total_counts, dfp, is_open=True, trim_frac=0.08, letter_width=0.15, height=0.5)







Pattern occurs in 2521 / 98428 regions (2.6%)







Pattern occurs in 257 / 98428 regions (0.3%)







Pattern occurs in 202 / 98428 regions (0.2%)







Pattern occurs in 107 / 98428 regions (0.1%)







Pattern occurs in 96 / 98428 regions (0.1%)







Pattern occurs in 96 / 98428 regions (0.1%)







Pattern occurs in 93 / 98428 regions (0.1%)







Pattern occurs in 7618 / 98428 regions (7.7%)







Pattern occurs in 875 / 98428 regions (0.9%)







Pattern occurs in 536 / 98428 regions (0.5%)







Pattern occurs in 537 / 98428 regions (0.5%)







Pattern occurs in 488 / 98428 regions (0.5%)







Pattern occurs in 416 / 98428 regions (0.4%)







Pattern occurs in 337 / 98428 regions (0.3%)







Pattern occurs in 310 / 98428 regions (0.3%)







Pattern occurs in 209 / 98428 regions (0.2%)







Pattern occurs in 193 / 98428 regions (0.2%)







Pattern occurs in 218 / 98428 regions (0.2%)







Pattern occurs in 180 / 98428 regions (0.2%)







Pattern occurs in 170 / 98428 regions (0.2%)







Pattern occurs in 128 / 98428 regions (0.1%)







Pattern occurs in 120 / 98428 regions (0.1%)







Pattern occurs in 153 / 98428 regions (0.2%)







Pattern occurs in 67 / 98428 regions (0.1%)







Pattern occurs in 3464 / 98428 regions (3.5%)







Pattern occurs in 1007 / 98428 regions (1.0%)







Pattern occurs in 849 / 98428 regions (0.9%)







Pattern occurs in 298 / 98428 regions (0.3%)







Pattern occurs in 246 / 98428 regions (0.2%)







Pattern occurs in 220 / 98428 regions (0.2%)







Pattern occurs in 145 / 98428 regions (0.1%)







Pattern occurs in 170 / 98428 regions (0.2%)







Pattern occurs in 156 / 98428 regions (0.2%)







Pattern occurs in 102 / 98428 regions (0.1%)







Pattern occurs in 84 / 98428 regions (0.1%)







Pattern occurs in 64 / 98428 regions (0.1%)







Pattern occurs in 1767 / 98428 regions (1.8%)







Pattern occurs in 345 / 98428 regions (0.4%)







Pattern occurs in 318 / 98428 regions (0.3%)







Pattern occurs in 281 / 98428 regions (0.3%)







Pattern occurs in 204 / 98428 regions (0.2%)







Pattern occurs in 223 / 98428 regions (0.2%)







Pattern occurs in 115 / 98428 regions (0.1%)







Pattern occurs in 112 / 98428 regions (0.1%)







Pattern occurs in 78 / 98428 regions (0.1%)







Pattern occurs in 101 / 98428 regions (0.1%)







Pattern occurs in 2011 / 98428 regions (2.0%)







Pattern occurs in 322 / 98428 regions (0.3%)







Pattern occurs in 244 / 98428 regions (0.2%)







Pattern occurs in 283 / 98428 regions (0.3%)







Pattern occurs in 156 / 98428 regions (0.2%)







Pattern occurs in 122 / 98428 regions (0.1%)







Pattern occurs in 113 / 98428 regions (0.1%)







Pattern occurs in 153 / 98428 regions (0.2%)







Pattern occurs in 1614 / 98428 regions (1.6%)







Pattern occurs in 308 / 98428 regions (0.3%)







Pattern occurs in 254 / 98428 regions (0.3%)







Pattern occurs in 204 / 98428 regions (0.2%)







Pattern occurs in 149 / 98428 regions (0.2%)







Pattern occurs in 226 / 98428 regions (0.2%)







Pattern occurs in 141 / 98428 regions (0.1%)







Pattern occurs in 88 / 98428 regions (0.1%)







Pattern occurs in 759 / 98428 regions (0.8%)







Pattern occurs in 504 / 98428 regions (0.5%)







Pattern occurs in 378 / 98428 regions (0.4%)







Pattern occurs in 307 / 98428 regions (0.3%)







Pattern occurs in 261 / 98428 regions (0.3%)







Pattern occurs in 216 / 98428 regions (0.2%)







Pattern occurs in 1986 / 98428 regions (2.0%)







Pattern occurs in 633 / 98428 regions (0.6%)







Pattern occurs in 326 / 98428 regions (0.3%)







Pattern occurs in 190 / 98428 regions (0.2%)







Pattern occurs in 78 / 98428 regions (0.1%)







Pattern occurs in 105 / 98428 regions (0.1%)







Pattern occurs in 67 / 98428 regions (0.1%)







Pattern occurs in 379 / 98428 regions (0.4%)







Pattern occurs in 339 / 98428 regions (0.3%)







Pattern occurs in 229 / 98428 regions (0.2%)







Pattern occurs in 255 / 98428 regions (0.3%)







Pattern occurs in 171 / 98428 regions (0.2%)







Pattern occurs in 1110 / 98428 regions (1.1%)







Pattern occurs in 231 / 98428 regions (0.2%)







Pattern occurs in 180 / 98428 regions (0.2%)







Pattern occurs in 164 / 98428 regions (0.2%)







Pattern occurs in 112 / 98428 regions (0.1%)







Pattern occurs in 151 / 98428 regions (0.2%)







Pattern occurs in 431 / 98428 regions (0.4%)







Pattern occurs in 502 / 98428 regions (0.5%)







Pattern occurs in 92 / 98428 regions (0.1%)







Pattern occurs in 112 / 98428 regions (0.1%)







Pattern occurs in 113 / 98428 regions (0.1%)







Pattern occurs in 91 / 98428 regions (0.1%)







Pattern occurs in 73 / 98428 regions (0.1%)







Pattern occurs in 292 / 98428 regions (0.3%)







Pattern occurs in 220 / 98428 regions (0.2%)







Pattern occurs in 258 / 98428 regions (0.3%)







Pattern occurs in 1506 / 98428 regions (1.5%)







Pattern occurs in 240 / 98428 regions (0.2%)







Pattern occurs in 122 / 98428 regions (0.1%)







Pattern occurs in 255 / 98428 regions (0.3%)







Pattern occurs in 137 / 98428 regions (0.1%)







Pattern occurs in 187 / 98428 regions (0.2%)







Pattern occurs in 181 / 98428 regions (0.2%)







Pattern occurs in 111 / 98428 regions (0.1%)







Pattern occurs in 131 / 98428 regions (0.1%)







Pattern occurs in 146 / 98428 regions (0.1%)







Pattern occurs in 422 / 98428 regions (0.4%)







Pattern occurs in 257 / 98428 regions (0.3%)







Pattern occurs in 220 / 98428 regions (0.2%)







Pattern occurs in 94 / 98428 regions (0.1%)







Pattern occurs in 103 / 98428 regions (0.1%)







Pattern occurs in 132 / 98428 regions (0.1%)
print("Metaclusters heatmap")
import seaborn as sns
activity_patterns = np.array(mr.f.f['metaclustering_results']['attribute_vectors'])[
np.array(
[x[0] for x in sorted(
enumerate(mr.f.f['metaclustering_results']['metacluster_indices']),
key=lambda x: x[1])])]
sns.heatmap(activity_patterns, center=0);
Metaclusters heatmap