In [2]:
from basepair.imports import *
from basepair.plot.profiles import extract_signal
from basepair.math import softmax
from basepair.plot.heatmaps import heatmap_stranded_profile, multiple_heatmap_stranded_profile
from basepair.plot.profiles import  plot_stranded_profile, multiple_plot_stranded_profile
from basepair.modisco.results import Seqlet, resize_seqlets
from basepair.modisco.core import dfi2seqlets, annotate_profile
from basepair.cli.modisco import load_profiles
from basepair.BPNet import BPNetPredictor
from basepair.plot.tracks import plot_tracks, filter_tracks
from basepair.preproc import rc_seq
from basepair.exp.chipnexus.simulate import (insert_motif, generate_sim, plot_sim, generate_seq, 
                                             model2tasks, motif_coords, interactive_tracks, plot_motif_table,
                                             plot_sim_motif_col)

from scipy.fftpack import fft, ifft
import warnings
warnings.filterwarnings("ignore")
hv.extension('bokeh', 'matplotlib')

/users/avsec/bin/anaconda3/envs/chipnexus/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
Using TensorFlow backend.

2018-10-24 05:45:56,182 [WARNING] doc empty for the `info:` field

Load the required data

In [3]:
model_dir = Path(f"{ddir}/processed/chipnexus/exp/models/oct-sox-nanog-klf/models/n_dil_layers=9/")
modisco_dir = model_dir / "modisco/all/profile/"

mr = ModiscoResult(modisco_dir / "modisco.h5")
mr.open()

# Load the data
d = HDF5Reader(model_dir / "grad.all.h5")
d.open()
In [5]:
# specify motifs to use in the analysis
motifs = OrderedDict([
    ("Oct4-Sox2", "m0_p0"),
    ("Oct4-Sox2-deg", "m6_p8"),
    ("Oct4", "m0_p18"),
    ("Sox2", "m0_p1"),
    ("Essrb", "m0_p2"),
    ("Nanog", "m0_p3"),
    ("Nanog-periodic", "m0_p9"),
    ("Klf4", "m2_p0"),
])

side_motifs = OrderedDict([
    ("Oct4-Sox2", ("m0_p0", "TTTGCATAACAA")),
    ("Sox2", ("m0_p1", "CCATTGTT")),
    ("Essrb", ("m0_p2", "TCAAGGTCA")),  # TODO - update
    ("Nanog", ("m0_p3", "TTGATGGC")),
    ("Klf4", ("m2_p0", "GGGTGTGG")),
])

# get also the rc motifs
rc_side_motifs = OrderedDict([
    (m + "/rc", (v[0], rc_seq(v[1])))
    for m,v in side_motifs.items()
])

all_side_motifs = OrderedDict(list(side_motifs.items()) + list(rc_side_motifs.items()))
In [6]:
create_tf_session(1)
Out[6]:
<tensorflow.python.client.session.Session at 0x7f9c54689470>
In [7]:
model_dir = Path(f"{ddir}/processed/chipnexus/exp/models/oct-sox-nanog-klf/models/n_dil_layers=9/")
In [8]:
model = BPNetPredictor.from_mdir(model_dir)

WARNING:tensorflow:From /users/avsec/bin/anaconda3/envs/chipnexus/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:497: calling conv1d (from tensorflow.python.ops.nn_ops) with data_format=NHWC is deprecated and will be removed in a future version.
Instructions for updating:
`NHWC` for data_format is deprecated, use `NWC` instead

2018-10-24 05:45:59,959 [WARNING] From /users/avsec/bin/anaconda3/envs/chipnexus/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:497: calling conv1d (from tensorflow.python.ops.nn_ops) with data_format=NHWC is deprecated and will be removed in a future version.
Instructions for updating:
`NHWC` for data_format is deprecated, use `NWC` instead

WARNING:tensorflow:From /users/avsec/bin/anaconda3/envs/chipnexus/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/base.py:198: retry (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.
Instructions for updating:
Use the retry module or similar alternatives.

2018-10-24 05:46:08,374 [WARNING] From /users/avsec/bin/anaconda3/envs/chipnexus/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/base.py:198: retry (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.
Instructions for updating:
Use the retry module or similar alternatives.
In [9]:
tasks = model.tasks
In [15]:
# find the right profile crop
center_coords = [485, 520]

# pre-compute all the values in all the simulations
cache_path = f"{ddir}/processed/chipnexus/simulation/spacing.pkl"
os.makedirs(os.path.dirname(cache_path), exist_ok=True)

df_d = {}
res_dict_d = {}
for central_motif_name, (p, central_motif) in tqdm(all_side_motifs.items()):
    print(central_motif_name)
    res_dict = OrderedDict([(motif, generate_sim(model, central_motif, side_motif, list(range(511, 511+150, 1)), 
                                                 center_coords=center_coords))
                           for motif, (pattern, side_motif) in all_side_motifs.items()])
    df = pd.concat([v[0].assign(motif=k) for k,v in res_dict.items()])  # stack the dataframes
    df_d[central_motif_name] = df
    res_dict_d[central_motif_name] = res_dict
write_pkl((df_d, res_dict_d), cache_path)

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100%|██████████| 150/150 [02:54<00:00,  1.16s/it]
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Sox2

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Essrb

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Nanog

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Klf4

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100%|██████████| 150/150 [02:54<00:00,  1.16s/it]
 70%|███████   | 7/10 [3:24:01<1:27:26, 1748.84s/it]
Essrb/rc

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Nanog/rc

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100%|██████████| 10/10 [4:51:42<00:00, 1750.29s/it]