Goal¶
- develop the CSI plot
TODO¶
- weight each sequence
In [2]:
from basepair.imports import *
import matplotlib.pyplot as plt
import numpy as np
from basepair.BPNet import BPNetPredictor
from basepair.utils import read_pkl, write_pkl
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.plot.tracks import plot_tracks, filter_tracks
from basepair.exp.chipnexus.simulate import (insert_motif, generate_sim, plot_sim, generate_seq,
single_motif_sim,
model2tasks, motif_coords, interactive_tracks, plot_motif_table)
from collections import defaultdict
from basepair.exp.chipnexus.simulate import *
from basepair.utils import unflatten
from basepair.plot.csi import SeqMutationTree, SeqNode, plot_csi, heatmap_csi_tasks, heatmap_csi
from basepair.modisco.table import longer_pattern
/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.
In [3]:
create_tf_session(1)
Out[3]:
<tensorflow.python.client.session.Session at 0x7fd5e0020518>
In [4]:
model_dir = Path(f"{ddir}/processed/chipnexus/exp/models/oct-sox-nanog-klf/models/n_dil_layers=9/")
In [5]:
# Load the model
model = load_model(model_dir / "model.h5")
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
2018-09-26 12:35:33,327 [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.
2018-09-26 12:35:39,903 [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 [6]:
bpnet = BPNetPredictor(model)
In [7]:
seq_by_depth = SeqMutationTree.create(SeqNode("CCATTGTT"), max_hamming_distance=2).get_seq_by_depth()
In [8]:
a = bpnet.sim_pred("CCATTGT", importance=['weighted', 'count'])
In [9]:
tasks = bpnet.tasks
repeat = 128
importance = ['count', 'weighted']
#importance = []
ref_pred = unflatten(bpnet.sim_pred(seq_by_depth[0][0], repeat=repeat, importance=importance), "/")
In [10]:
motif_sims = {}
In [ ]:
motifs = OrderedDict([
("Oct4-Sox2", ("m0_p0", "TTTGCATAACAA")),
("Sox2", ("m0_p2", "CCATTGTT")),
("Err2", ("m0_p1", "TCAAGGTCA")),
("Nanog", ("m0_p3", "TTGATGGC")),
("Klf4", ("m2_p0", "GGGTGTGG")),
])
In [12]:
model_dir = Path(f"{ddir}/processed/chipnexus/exp/models/oct-sox-nanog-klf/models/n_dil_layers=9/")
In [13]:
mr = ModiscoResult(model_dir / f"modisco/by_peak_tasks/weighted/Oct4/modisco.h5")
mr.open()
In [14]:
cache_path = f"{ddir}/cache/chipnexus/simulation/single-motif.pkl"
os.makedirs(os.path.dirname(cache_path), exist_ok=True)
In [15]:
# Load cached values
motif_sims, df = read_pkl(cache_path)
In [16]:
cached = True
In [17]:
df.head()
Out[17]:
| hamming_distance | i | motif | score | seq | task | value | |
|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | Oct4-Sox2 | imp/weighted | TTTGCATAACAA | Oct4 | 1.191465 |
| 1 | 0 | 0 | Oct4-Sox2 | imp/count | TTTGCATAACAA | Oct4 | 1.524797 |
| 2 | 0 | 0 | Oct4-Sox2 | profile/simmetric_kl | TTTGCATAACAA | Oct4 | 0.000000 |
| 3 | 0 | 0 | Oct4-Sox2 | profile/counts | TTTGCATAACAA | Oct4 | 514.134766 |
| 4 | 0 | 0 | Oct4-Sox2 | profile/counts_frac | TTTGCATAACAA | Oct4 | 1.000000 |
In [18]:
# write_pkl((motif_sims, motif_sims2df(motif_sims)), cache_path)
# !du -sh {cache_path}
Oct4¶
In [19]:
motif = 'Oct4-Sox2'
In [20]:
mr.plot_pssm(*longer_pattern(motifs[motif][0]).split("/"), trim_frac=0.08);
In [21]:
if not cached:
motif_sims[motif] = single_motif_sim(bpnet, motifs[motif][1], max_hamming_dist=2)
In [75]:
fig, axes = plt.subplots(1, len(tasks), figsize=(40, 10))
for i, task in enumerate(tasks):
plot_csi({h: [v['scores'][task]['imp/weighted']
for v in vlist]
for h,vlist in motif_sims[motif].items()}, ax=axes[i], title=task)
In [24]:
motif_sims[motif]
Out[24]:
{0: [{'seq': 'TTTGCATAACAA',
'scores': {'Oct4': {'imp/weighted': 1.1914652009286328,
'imp/count': 1.5247974377948594,
'profile/simmetric_kl': 0.0,
'profile/counts': 514.13477,
'profile/counts_frac': 1.0,
'profile/max': 6.164808,
'profile/max_frac': 1.0},
'Sox2': {'imp/weighted': 0.8371508540120658,
'imp/count': 0.9451945780756432,
'profile/simmetric_kl': 0.0,
'profile/counts': 223.09503,
'profile/counts_frac': 1.0,
'profile/max': 2.796452,
'profile/max_frac': 1.0},
'Nanog': {'imp/weighted': 0.4679825676512337,
'imp/count': 1.0467601170521448,
'profile/simmetric_kl': 0.0,
'profile/counts': 311.26038,
'profile/counts_frac': 1.0,
'profile/max': 0.9304994,
'profile/max_frac': 1.0},
'Klf4': {'imp/weighted': 0.4760468199328898,
'imp/count': 0.4334542248469688,
'profile/simmetric_kl': 0.0,
'profile/counts': 194.065,
'profile/counts_frac': 1.0,
'profile/max': 0.54090506,
'profile/max_frac': 1.0}}}],
1: [{'seq': 'ATTGCATAACAA',
'scores': {'Oct4': {'imp/weighted': 0.7776341132462221,
'imp/count': 1.12574354335311,
'profile/simmetric_kl': 0.03718494,
'profile/counts': 373.81824,
'profile/counts_frac': 0.7270822,
'profile/max': 1.7019966,
'profile/max_frac': 0.27608266},
'Sox2': {'imp/weighted': 0.4880392170351797,
'imp/count': 0.6980301403651765,
'profile/simmetric_kl': 0.033822313,
'profile/counts': 183.14153,
'profile/counts_frac': 0.8209126,
'profile/max': 0.9465379,
'profile/max_frac': 0.33847815},
'Nanog': {'imp/weighted': 0.3414787384576812,
'imp/count': 0.7986748352964241,
'profile/simmetric_kl': 0.014996553,
'profile/counts': 246.28574,
'profile/counts_frac': 0.7912531,
'profile/max': 0.7429214,
'profile/max_frac': 0.7984115},
'Klf4': {'imp/weighted': 0.3198473366446706,
'imp/count': 0.2850052206970304,
'profile/simmetric_kl': 0.0074583343,
'profile/counts': 184.0658,
'profile/counts_frac': 0.94847494,
'profile/max': 0.4383298,
'profile/max_frac': 0.81036365}}},
{'seq': 'CTTGCATAACAA',
'scores': {'Oct4': {'imp/weighted': 0.9508954736220403,
'imp/count': 1.2633177068030363,
'profile/simmetric_kl': 0.01833331,
'profile/counts': 404.37842,
'profile/counts_frac': 0.7865222,
'profile/max': 2.7244914,
'profile/max_frac': 0.44194263},
'Sox2': {'imp/weighted': 0.5991115046851974,
'imp/count': 0.7708444762145064,
'profile/simmetric_kl': 0.018009115,
'profile/counts': 190.72282,
'profile/counts_frac': 0.854895,
'profile/max': 1.3383172,
'profile/max_frac': 0.47857684},
'Nanog': {'imp/weighted': 0.3503733304291927,
'imp/count': 0.8518652500354165,
'profile/simmetric_kl': 0.011841467,
'profile/counts': 254.79475,
'profile/counts_frac': 0.8185904,
'profile/max': 0.7013642,
'profile/max_frac': 0.7537503},
'Klf4': {'imp/weighted': 0.3878005122014656,
'imp/count': 0.32985287776159566,
'profile/simmetric_kl': 0.0056017395,
'profile/counts': 185.36092,
'profile/counts_frac': 0.9551486,
'profile/max': 0.5213842,
'profile/max_frac': 0.9639107}}},
{'seq': 'GTTGCATAACAA',
'scores': {'Oct4': {'imp/weighted': 0.8576787908789356,
'imp/count': 1.222559187561501,
'profile/simmetric_kl': 0.0318526,
'profile/counts': 383.04626,
'profile/counts_frac': 0.7450309,
'profile/max': 1.9986503,
'profile/max_frac': 0.3242032},
'Sox2': {'imp/weighted': 0.5568849054798193,
'imp/count': 0.7593024585572721,
'profile/simmetric_kl': 0.024350965,
'profile/counts': 185.52036,
'profile/counts_frac': 0.83157545,
'profile/max': 1.1911677,
'profile/max_frac': 0.4259568},
'Nanog': {'imp/weighted': 0.33311615575007636,
'imp/count': 0.8609405958936804,
'profile/simmetric_kl': 0.011837876,
'profile/counts': 246.33435,
'profile/counts_frac': 0.79140925,
'profile/max': 0.7514371,
'profile/max_frac': 0.8075633},
'Klf4': {'imp/weighted': 0.37202506029689175,
'imp/count': 0.3238761981722291,
'profile/simmetric_kl': 0.0056892023,
'profile/counts': 183.7143,
'profile/counts_frac': 0.9466637,
'profile/max': 0.48243746,
'profile/max_frac': 0.8919078}}},
{'seq': 'TATGCATAACAA',
'scores': {'Oct4': {'imp/weighted': 1.129930074433105,
'imp/count': 1.4333278836793397,
'profile/simmetric_kl': 0.011088872,
'profile/counts': 446.01593,
'profile/counts_frac': 0.8675078,
'profile/max': 3.2289486,
'profile/max_frac': 0.52377117},
'Sox2': {'imp/weighted': 0.7239638613903026,
'imp/count': 0.8682684337400133,
'profile/simmetric_kl': 0.010174805,
'profile/counts': 203.16017,
'profile/counts_frac': 0.9106441,
'profile/max': 2.1760073,
'profile/max_frac': 0.7781314},
'Nanog': {'imp/weighted': 0.3862786334264001,
'imp/count': 0.9438052784327056,
'profile/simmetric_kl': 0.0065301843,
'profile/counts': 275.6278,
'profile/counts_frac': 0.88552165,
'profile/max': 1.1401671,
'profile/max_frac': 1.2253282},
'Klf4': {'imp/weighted': 0.4233667702940238,
'imp/count': 0.371122098362207,
'profile/simmetric_kl': 0.0046907715,
'profile/counts': 187.22772,
'profile/counts_frac': 0.9647681,
'profile/max': 0.69045746,
'profile/max_frac': 1.2764854}}},
{'seq': 'TCTGCATAACAA',
'scores': {'Oct4': {'imp/weighted': 1.1416377307145922,
'imp/count': 1.498007823081025,
'profile/simmetric_kl': 0.00973263,
'profile/counts': 456.6967,
'profile/counts_frac': 0.88828206,
'profile/max': 4.1473927,
'profile/max_frac': 0.672753},
'Sox2': {'imp/weighted': 0.7684682741862616,
'imp/count': 0.9374594015544062,
'profile/simmetric_kl': 0.008117387,
'profile/counts': 206.6318,
'profile/counts_frac': 0.92620534,
'profile/max': 2.0323112,
'profile/max_frac': 0.7267463},
'Nanog': {'imp/weighted': 0.47425049338517056,
'imp/count': 1.0495418335904105,
'profile/simmetric_kl': 0.0087598115,
'profile/counts': 280.8603,
'profile/counts_frac': 0.9023323,
'profile/max': 1.1343714,
'profile/max_frac': 1.2190996},
'Klf4': {'imp/weighted': 0.5092454263181025,
'imp/count': 0.43949505151294943,
'profile/simmetric_kl': 0.0050725318,
'profile/counts': 190.51123,
'profile/counts_frac': 0.9816877,
'profile/max': 0.6668419,
'profile/max_frac': 1.2328262}}},
{'seq': 'TGTGCATAACAA',
'scores': {'Oct4': {'imp/weighted': 0.9906921630342822,
'imp/count': 1.3137110356419726,
'profile/simmetric_kl': 0.01881171,
'profile/counts': 417.41516,
'profile/counts_frac': 0.81187886,
'profile/max': 2.408469,
'profile/max_frac': 0.3906803},
'Sox2': {'imp/weighted': 0.6679313424103839,
'imp/count': 0.8262433754239282,
'profile/simmetric_kl': 0.013786374,
'profile/counts': 196.55042,
'profile/counts_frac': 0.88101655,
'profile/max': 1.7250074,
'profile/max_frac': 0.61685574},
'Nanog': {'imp/weighted': 0.44780798912506725,
'imp/count': 0.9456229691429598,
'profile/simmetric_kl': 0.009988169,
'profile/counts': 270.9229,
'profile/counts_frac': 0.87040603,
'profile/max': 1.0827035,
'profile/max_frac': 1.1635724},
'Klf4': {'imp/weighted': 0.4367875979407927,
'imp/count': 0.3642280929335584,
'profile/simmetric_kl': 0.0057746507,
'profile/counts': 184.47018,
'profile/counts_frac': 0.9505587,
'profile/max': 0.7024944,
'profile/max_frac': 1.2987388}}},
{'seq': 'TTAGCATAACAA',
'scores': {'Oct4': {'imp/weighted': 1.0041212446576537,
'imp/count': 1.4203736796675912,
'profile/simmetric_kl': 0.018356679,
'profile/counts': 439.41876,
'profile/counts_frac': 0.85467625,
'profile/max': 2.8670895,
'profile/max_frac': 0.46507362},
'Sox2': {'imp/weighted': 0.6467345518747152,
'imp/count': 0.871707644793787,
'profile/simmetric_kl': 0.015667686,
'profile/counts': 204.27863,
'profile/counts_frac': 0.91565746,
'profile/max': 1.3614165,
'profile/max_frac': 0.48683706},
'Nanog': {'imp/weighted': 0.42480238906057366,
'imp/count': 0.9611616388128823,
'profile/simmetric_kl': 0.006651395,
'profile/counts': 286.63394,
'profile/counts_frac': 0.92088157,
'profile/max': 0.8658163,
'profile/max_frac': 0.9304856},
'Klf4': {'imp/weighted': 0.4276105817034477,
'imp/count': 0.38335565064983257,
'profile/simmetric_kl': 0.004785159,
'profile/counts': 185.02313,
'profile/counts_frac': 0.95340806,
'profile/max': 0.41104928,
'profile/max_frac': 0.7599287}}},
{'seq': 'TTCGCATAACAA',
'scores': {'Oct4': {'imp/weighted': 0.9117451447582425,
'imp/count': 1.277237748264497,
'profile/simmetric_kl': 0.021031054,
'profile/counts': 407.46136,
'profile/counts_frac': 0.7925186,
'profile/max': 2.146354,
'profile/max_frac': 0.34816235},
'Sox2': {'imp/weighted': 0.6206755485479789,
'imp/count': 0.8016084870535565,
'profile/simmetric_kl': 0.015676767,
'profile/counts': 194.48169,
'profile/counts_frac': 0.8717437,
'profile/max': 1.2798394,
'profile/max_frac': 0.4576654},
'Nanog': {'imp/weighted': 0.4227856165657613,
'imp/count': 0.919133626883422,
'profile/simmetric_kl': 0.007649229,
'profile/counts': 267.73688,
'profile/counts_frac': 0.8601701,
'profile/max': 0.7924074,
'profile/max_frac': 0.8515937},
'Klf4': {'imp/weighted': 0.4072819045111373,
'imp/count': 0.3452880354792569,
'profile/simmetric_kl': 0.0042049326,
'profile/counts': 187.29475,
'profile/counts_frac': 0.9651135,
'profile/max': 0.54072297,
'profile/max_frac': 0.99966335}}},
{'seq': 'TTGGCATAACAA',
'scores': {'Oct4': {'imp/weighted': 0.7168313917618434,
'imp/count': 1.042011490917048,
'profile/simmetric_kl': 0.047056906,
'profile/counts': 350.8537,
'profile/counts_frac': 0.68241584,
'profile/max': 1.3772871,
'profile/max_frac': 0.2234112},
'Sox2': {'imp/weighted': 0.4688173218000884,
'imp/count': 0.6551498525327588,
'profile/simmetric_kl': 0.034439333,
'profile/counts': 176.57782,
'profile/counts_frac': 0.7914915,
'profile/max': 0.9844416,
'profile/max_frac': 0.35203233},
'Nanog': {'imp/weighted': 0.3446315242304081,
'imp/count': 0.7744680985865671,
'profile/simmetric_kl': 0.013268987,
'profile/counts': 237.01332,
'profile/counts_frac': 0.7614632,
'profile/max': 0.65677875,
'profile/max_frac': 0.7058347},
'Klf4': {'imp/weighted': 0.2936079727178367,
'imp/count': 0.24961046591056402,
'profile/simmetric_kl': 0.0062010023,
'profile/counts': 179.97559,
'profile/counts_frac': 0.92739844,
'profile/max': 0.47553813,
'profile/max_frac': 0.87915266}}},
{'seq': 'TTTACATAACAA',
'scores': {'Oct4': {'imp/weighted': 0.8671464301201013,
'imp/count': 1.2789565847558606,
'profile/simmetric_kl': 0.02498693,
'profile/counts': 414.33826,
'profile/counts_frac': 0.80589426,
'profile/max': 2.1743398,
'profile/max_frac': 0.35270196},
'Sox2': {'imp/weighted': 0.5360295085229154,
'imp/count': 0.7907967930386803,
'profile/simmetric_kl': 0.02676012,
'profile/counts': 196.31985,
'profile/counts_frac': 0.87998307,
'profile/max': 1.0506954,
'profile/max_frac': 0.37572446},
'Nanog': {'imp/weighted': 0.4061644610892472,
'imp/count': 0.8906048993758304,
'profile/simmetric_kl': 0.007839594,
'profile/counts': 269.9087,
'profile/counts_frac': 0.8671476,
'profile/max': 0.84281737,
'profile/max_frac': 0.9057689},
'Klf4': {'imp/weighted': 0.3671719238722062,
'imp/count': 0.32804651270669183,
'profile/simmetric_kl': 0.0059227464,
'profile/counts': 182.91095,
'profile/counts_frac': 0.94252414,
'profile/max': 0.34149536,
'profile/max_frac': 0.6313407}}},
{'seq': 'TTTCCATAACAA',
'scores': {'Oct4': {'imp/weighted': 0.862287292709425,
'imp/count': 1.2414204900512686,
'profile/simmetric_kl': 0.029506732,
'profile/counts': 400.35358,
'profile/counts_frac': 0.77869385,
'profile/max': 2.004125,
'profile/max_frac': 0.32509124},
'Sox2': {'imp/weighted': 0.5503719683736108,
'imp/count': 0.7842707713637083,
'profile/simmetric_kl': 0.024007779,
'profile/counts': 193.17319,
'profile/counts_frac': 0.86587846,
'profile/max': 1.1148373,
'profile/max_frac': 0.39866132},
'Nanog': {'imp/weighted': 0.41166940489279114,
'imp/count': 0.9038197633874461,
'profile/simmetric_kl': 0.007995645,
'profile/counts': 266.87888,
'profile/counts_frac': 0.8574136,
'profile/max': 0.69302785,
'profile/max_frac': 0.7447913},
'Klf4': {'imp/weighted': 0.39490986703549424,
'imp/count': 0.3460943039863196,
'profile/simmetric_kl': 0.0057594134,
'profile/counts': 187.25505,
'profile/counts_frac': 0.9649089,
'profile/max': 0.44252336,
'profile/max_frac': 0.8181165}}},
{'seq': 'TTTTCATAACAA',
'scores': {'Oct4': {'imp/weighted': 0.6394015236970176,
'imp/count': 0.9983042730159468,
'profile/simmetric_kl': 0.0662785,
'profile/counts': 344.54205,
'profile/counts_frac': 0.67013955,
'profile/max': 1.1008968,
'profile/max_frac': 0.17857765},
'Sox2': {'imp/weighted': 0.39784182388279987,
'imp/count': 0.6423485540001366,
'profile/simmetric_kl': 0.059284624,
'profile/counts': 174.21875,
'profile/counts_frac': 0.7809172,
'profile/max': 0.53815997,
'profile/max_frac': 0.19244383},
'Nanog': {'imp/weighted': 0.3599677585516474,
'imp/count': 0.781870930847731,
'profile/simmetric_kl': 0.0123038925,
'profile/counts': 236.74469,
'profile/counts_frac': 0.76060015,
'profile/max': 0.6342366,
'profile/max_frac': 0.6816088},
'Klf4': {'imp/weighted': 0.27962950688424826,
'imp/count': 0.24510665080970284,
'profile/simmetric_kl': 0.009465007,
'profile/counts': 174.89095,
'profile/counts_frac': 0.90119773,
'profile/max': 0.2926935,
'profile/max_frac': 0.5411181}}},
{'seq': 'TTTGAATAACAA',
'scores': {'Oct4': {'imp/weighted': 0.732673592312949,
'imp/count': 1.148020139292612,
'profile/simmetric_kl': 0.03994238,
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'profile/max': 1.1448687,
'profile/max_frac': 0.18571037},
'Sox2': {'imp/weighted': 0.3597600778805372,
'imp/count': 0.5237087992741181,
'profile/simmetric_kl': 0.058705106,
'profile/counts': 170.42245,
'profile/counts_frac': 0.7639007,
'profile/max': 0.6267251,
'profile/max_frac': 0.22411437},
'Nanog': {'imp/weighted': 0.31156935462240654,
'imp/count': 0.6230631936198279,
'profile/simmetric_kl': 0.01298282,
'profile/counts': 229.51657,
'profile/counts_frac': 0.73737806,
'profile/max': 0.79341006,
'profile/max_frac': 0.85267127},
'Klf4': {'imp/weighted': 0.22721741604641466,
'imp/count': 0.19575909599552688,
'profile/simmetric_kl': 0.007144888,
'profile/counts': 173.18848,
'profile/counts_frac': 0.89242506,
'profile/max': 0.42381063,
'profile/max_frac': 0.7835213}}},
{'seq': 'TTTGCATAATAT',
'scores': {'Oct4': {'imp/weighted': 0.48212204313369966,
'imp/count': 0.7166224268510177,
'profile/simmetric_kl': 0.0747326,
'profile/counts': 317.1472,
'profile/counts_frac': 0.61685616,
'profile/max': 1.087645,
'profile/max_frac': 0.17642805},
'Sox2': {'imp/weighted': 0.2321422983662842,
'imp/count': 0.41167075621797267,
'profile/simmetric_kl': 0.08010062,
'profile/counts': 164.87361,
'profile/counts_frac': 0.73902863,
'profile/max': 0.49710596,
'profile/max_frac': 0.17776309},
'Nanog': {'imp/weighted': 0.1280872327074576,
'imp/count': 0.43016737188463594,
'profile/simmetric_kl': 0.024156708,
'profile/counts': 217.79712,
'profile/counts_frac': 0.69972646,
'profile/max': 0.47622144,
'profile/max_frac': 0.5117912},
'Klf4': {'imp/weighted': 0.14176940471384114,
'imp/count': 0.13604045377326202,
'profile/simmetric_kl': 0.011152508,
'profile/counts': 171.37392,
'profile/counts_frac': 0.8830748,
'profile/max': 0.28684244,
'profile/max_frac': 0.5303009}}},
{'seq': 'ATTGCATAACCA',
'scores': {'Oct4': {'imp/weighted': 0.37697269641557796,
'imp/count': 0.5250546431529983,
'profile/simmetric_kl': 0.10485977,
'profile/counts': 289.38806,
'profile/counts_frac': 0.56286424,
'profile/max': 1.3903091,
'profile/max_frac': 0.22552352},
'Sox2': {'imp/weighted': 0.1856029624287885,
'imp/count': 0.3261489917019844,
'profile/simmetric_kl': 0.10059987,
'profile/counts': 156.39871,
'profile/counts_frac': 0.70104074,
'profile/max': 0.52211916,
'profile/max_frac': 0.1867077},
'Nanog': {'imp/weighted': 0.15529920242670414,
'imp/count': 0.3930046694945304,
'profile/simmetric_kl': 0.029784996,
'profile/counts': 204.03912,
'profile/counts_frac': 0.6555255,
'profile/max': 0.5084149,
'profile/max_frac': 0.54638934},
'Klf4': {'imp/weighted': 0.10644061825666995,
'imp/count': 0.10243815743069717,
'profile/simmetric_kl': 0.0134825725,
'profile/counts': 171.83533,
'profile/counts_frac': 0.88545245,
'profile/max': 0.34519494,
'profile/max_frac': 0.63818026}}},
{'seq': 'CTTGCATAACCA',
'scores': {'Oct4': {'imp/weighted': 0.5211397146695163,
'imp/count': 0.6653828027147028,
'profile/simmetric_kl': 0.08158291,
'profile/counts': 304.1554,
'profile/counts_frac': 0.5915869,
'profile/max': 1.7626301,
'profile/max_frac': 0.2859181},
'Sox2': {'imp/weighted': 0.25210333124550743,
'imp/count': 0.39117250928211433,
'profile/simmetric_kl': 0.08585496,
'profile/counts': 160.61172,
'profile/counts_frac': 0.71992517,
'profile/max': 0.6613434,
'profile/max_frac': 0.23649374},
'Nanog': {'imp/weighted': 0.15485246910498063,
'imp/count': 0.4276592775622703,
'profile/simmetric_kl': 0.03281376,
'profile/counts': 210.33725,
'profile/counts_frac': 0.6757598,
'profile/max': 0.43763477,
'profile/max_frac': 0.47032246},
'Klf4': {'imp/weighted': 0.18176446937468427,
'imp/count': 0.14213971914682588,
'profile/simmetric_kl': 0.011336954,
'profile/counts': 172.44086,
'profile/counts_frac': 0.8885727,
'profile/max': 0.39589068,
'profile/max_frac': 0.7319042}}},
{'seq': 'GTTGCATAACCA',
'scores': {'Oct4': {'imp/weighted': 0.38218245676080187,
'imp/count': 0.5012108642320072,
'profile/simmetric_kl': 0.10549353,
'profile/counts': 282.96277,
'profile/counts_frac': 0.55036694,
'profile/max': 1.2610568,
'profile/max_frac': 0.20455736},
'Sox2': {'imp/weighted': 0.17897259542633037,
'imp/count': 0.29786757067563485,
'profile/simmetric_kl': 0.101152964,
'profile/counts': 153.72939,
'profile/counts_frac': 0.68907577,
'profile/max': 0.46138346,
'profile/max_frac': 0.16498888},
'Nanog': {'imp/weighted': 0.10672294827952555,
'imp/count': 0.3341181278158274,
'profile/simmetric_kl': 0.034206018,
'profile/counts': 197.3687,
'profile/counts_frac': 0.63409513,
'profile/max': 0.4770031,
'profile/max_frac': 0.5126313},
'Klf4': {'imp/weighted': 0.11706984107763674,
'imp/count': 0.10026568298019356,
'profile/simmetric_kl': 0.010852505,
'profile/counts': 171.18918,
'profile/counts_frac': 0.8821229,
'profile/max': 0.410221,
'profile/max_frac': 0.75839746}}},
{'seq': 'TATGCATAACCA',
'scores': {'Oct4': {'imp/weighted': 0.6078159326416426,
'imp/count': 0.7587621502487423,
'profile/simmetric_kl': 0.05999194,
'profile/counts': 325.66284,
'profile/counts_frac': 0.6334192,
'profile/max': 2.6846974,
'profile/max_frac': 0.4354876},
'Sox2': {'imp/weighted': 0.2879182470858268,
'imp/count': 0.4387920052441814,
'profile/simmetric_kl': 0.06964774,
'profile/counts': 166.7286,
'profile/counts_frac': 0.7473434,
'profile/max': 0.94035625,
'profile/max_frac': 0.33626762},
'Nanog': {'imp/weighted': 0.14107006913491205,
'imp/count': 0.45831903590215006,
'profile/simmetric_kl': 0.02813876,
'profile/counts': 217.71295,
'profile/counts_frac': 0.69945604,
'profile/max': 0.578196,
'profile/max_frac': 0.6213825},
'Klf4': {'imp/weighted': 0.18921607968832177,
'imp/count': 0.1649429967087599,
'profile/simmetric_kl': 0.008559841,
'profile/counts': 173.78557,
'profile/counts_frac': 0.89550185,
'profile/max': 0.48601043,
'profile/max_frac': 0.8985134}}},
{'seq': 'TCTGCATAACCA',
'scores': {'Oct4': {'imp/weighted': 0.5042809275323634,
'imp/count': 0.6491879226732635,
'profile/simmetric_kl': 0.076676786,
'profile/counts': 313.0779,
'profile/counts_frac': 0.6089413,
'profile/max': 2.0424247,
'profile/max_frac': 0.33130386},
'Sox2': {'imp/weighted': 0.22711596450290017,
'imp/count': 0.3701760717196976,
'profile/simmetric_kl': 0.08460903,
'profile/counts': 161.94818,
'profile/counts_frac': 0.7259157,
'profile/max': 0.7261225,
'profile/max_frac': 0.2596585},
'Nanog': {'imp/weighted': 0.12230297421251635,
'imp/count': 0.3788235312165398,
'profile/simmetric_kl': 0.03902524,
'profile/counts': 209.63504,
'profile/counts_frac': 0.67350376,
'profile/max': 0.51483977,
'profile/max_frac': 0.55329406},
'Klf4': {'imp/weighted': 0.1825203304142633,
'imp/count': 0.1470030338360857,
'profile/simmetric_kl': 0.012027343,
'profile/counts': 172.51355,
'profile/counts_frac': 0.88894725,
'profile/max': 0.42466992,
'profile/max_frac': 0.78510994}}},
{'seq': 'TGTGCATAACCA',
'scores': {'Oct4': {'imp/weighted': 0.4658824605994596,
'imp/count': 0.5849573776204124,
'profile/simmetric_kl': 0.08815164,
'profile/counts': 293.11792,
'profile/counts_frac': 0.57011884,
'profile/max': 1.8583909,
'profile/max_frac': 0.30145156},
'Sox2': {'imp/weighted': 0.22103844500384184,
'imp/count': 0.3445959295281682,
'profile/simmetric_kl': 0.08685388,
'profile/counts': 156.22076,
'profile/counts_frac': 0.7002431,
'profile/max': 0.6990571,
'profile/max_frac': 0.24998},
'Nanog': {'imp/weighted': 0.1505863913837402,
'imp/count': 0.3720835068112365,
'profile/simmetric_kl': 0.029988274,
'profile/counts': 201.4878,
'profile/counts_frac': 0.64732873,
'profile/max': 0.55882996,
'profile/max_frac': 0.60056996},
'Klf4': {'imp/weighted': 0.17678745800805018,
'imp/count': 0.1379658058053792,
'profile/simmetric_kl': 0.011133378,
'profile/counts': 170.56796,
'profile/counts_frac': 0.8789218,
'profile/max': 0.4759175,
'profile/max_frac': 0.879854}}},
{'seq': 'TTAGCATAACCA',
'scores': {'Oct4': {'imp/weighted': 0.5112594003198865,
'imp/count': 0.726093288077891,
'profile/simmetric_kl': 0.07977909,
'profile/counts': 312.50778,
'profile/counts_frac': 0.60783243,
'profile/max': 1.5760282,
'profile/max_frac': 0.2556492},
'Sox2': {'imp/weighted': 0.23487823487516835,
'imp/count': 0.42240883807073715,
'profile/simmetric_kl': 0.085495375,
'profile/counts': 162.37085,
'profile/counts_frac': 0.72781026,
'profile/max': 0.5633728,
'profile/max_frac': 0.20145984},
'Nanog': {'imp/weighted': 0.12041396209451705,
'imp/count': 0.4519701689946487,
'profile/simmetric_kl': 0.024652446,
'profile/counts': 209.14328,
'profile/counts_frac': 0.6719239,
'profile/max': 0.39918706,
'profile/max_frac': 0.42900303},
'Klf4': {'imp/weighted': 0.16040624944275805,
'imp/count': 0.1519135739106332,
'profile/simmetric_kl': 0.009651019,
'profile/counts': 176.96759,
'profile/counts_frac': 0.91189855,
'profile/max': 0.35953853,
'profile/max_frac': 0.664698}}},
{'seq': 'TTCGCATAACCA',
'scores': {'Oct4': {'imp/weighted': 0.3656257577300721,
'imp/count': 0.5068621855306219,
'profile/simmetric_kl': 0.10910646,
'profile/counts': 281.1426,
'profile/counts_frac': 0.54682666,
'profile/max': 1.3249168,
'profile/max_frac': 0.21491617},
'Sox2': {'imp/weighted': 0.1829762710954128,
'imp/count': 0.31257483421757115,
'profile/simmetric_kl': 0.09822515,
'profile/counts': 153.1417,
'profile/counts_frac': 0.68644154,
'profile/max': 0.53270775,
'profile/max_frac': 0.19049415},
'Nanog': {'imp/weighted': 0.13942904408509094,
'imp/count': 0.37337709685562004,
'profile/simmetric_kl': 0.028313465,
'profile/counts': 198.28302,
'profile/counts_frac': 0.6370326,
'profile/max': 0.43904814,
'profile/max_frac': 0.47184142},
'Klf4': {'imp/weighted': 0.10499247258258038,
'imp/count': 0.09527226925473542,
'profile/simmetric_kl': 0.010750488,
'profile/counts': 169.5527,
'profile/counts_frac': 0.87369025,
'profile/max': 0.40898892,
'profile/max_frac': 0.7561196}}},
{'seq': 'TTGGCATAACCA',
'scores': {'Oct4': {'imp/weighted': 0.25069997349464757,
'imp/count': 0.3284737630573318,
'profile/simmetric_kl': 0.12772956,
'profile/counts': 274.84924,
'profile/counts_frac': 0.534586,
'profile/max': 1.1146317,
'profile/max_frac': 0.18080558},
'Sox2': {'imp/weighted': 0.11992419651733144,
'imp/count': 0.20433187264040953,
'profile/simmetric_kl': 0.11486322,
'profile/counts': 151.37642,
'profile/counts_frac': 0.67852885,
'profile/max': 0.4300542,
'profile/max_frac': 0.15378565},
'Nanog': {'imp/weighted': 0.10175560694182195,
'imp/count': 0.2542816722323096,
'profile/simmetric_kl': 0.03484349,
'profile/counts': 197.71707,
'profile/counts_frac': 0.6352144,
'profile/max': 0.43792945,
'profile/max_frac': 0.47063917},
'Klf4': {'imp/weighted': 0.056995660153063454,
'imp/count': 0.05425246377859594,
'profile/simmetric_kl': 0.012779275,
'profile/counts': 167.41347,
'profile/counts_frac': 0.86266696,
'profile/max': 0.3955412,
'profile/max_frac': 0.7312581}}},
{'seq': 'TTTACATAACCA',
'scores': {'Oct4': {'imp/weighted': 0.4914763933377344,
'imp/count': 0.7468917025603616,
'profile/simmetric_kl': 0.07917684,
'profile/counts': 319.46332,
'profile/counts_frac': 0.6213611,
'profile/max': 1.3521419,
'profile/max_frac': 0.21933237},
'Sox2': {'imp/weighted': 0.22994093292766138,
'imp/count': 0.44233214637023366,
'profile/simmetric_kl': 0.08803074,
'profile/counts': 166.33781,
'profile/counts_frac': 0.74559176,
'profile/max': 0.4962779,
'profile/max_frac': 0.17746697},
'Nanog': {'imp/weighted': 0.14752316491012607,
'imp/count': 0.48380413769376673,
'profile/simmetric_kl': 0.022573538,
'profile/counts': 219.99188,
'profile/counts_frac': 0.7067777,
'profile/max': 0.47310266,
'profile/max_frac': 0.50843954},
'Klf4': {'imp/weighted': 0.16607581873404342,
'imp/count': 0.15848881548077998,
'profile/simmetric_kl': 0.009909147,
'profile/counts': 174.3188,
'profile/counts_frac': 0.89824957,
'profile/max': 0.264565,
'profile/max_frac': 0.4891154}}},
...]}
In [23]:
vlist
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-23-e5b4934eb791> in <module>() ----> 1 vlist NameError: name 'vlist' is not defined
In [22]:
v['scores'][task]['imp/weighted']
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-22-cfcf23b8b17f> in <module>() ----> 1 v['scores'][task]['imp/weighted'] NameError: name 'v' is not defined
In [ ]:
In [76]:
heatmap_csi_tasks(df, tasks, motif, 'imp/weighted', 'Blues');
In [77]:
heatmap_csi_tasks(df, tasks, motif, 'profile/max_frac', 'Blues');
Sox2¶
In [78]:
motif = 'Sox2'
In [79]:
mr.plot_pssm(*longer_pattern(motifs[motif][0]).split("/"), trim_frac=0.08);
In [80]:
if not cached:
motif_sims[motif] = single_motif_sim(bpnet, motifs[motif][1], max_hamming_dist=2)
In [81]:
fig, axes = plt.subplots(1, len(tasks), figsize=(40, 10))
for i, task in enumerate(tasks):
plot_csi({h: [v['scores'][task]['imp/weighted']
for v in vlist]
for h,vlist in motif_sims['Sox2'].items()}, ax=axes[i], title=task)
In [82]:
heatmap_csi_tasks(df, tasks, motif, 'imp/weighted', 'Blues');
In [83]:
heatmap_csi_tasks(df, tasks, motif, 'profile/max_frac', 'Blues');
Nanog¶
In [27]:
motif = 'Nanog'
In [28]:
mr.plot_pssm(*longer_pattern(motifs[motif][0]).split("/"), trim_frac=0.08);
In [86]:
if not cached:
motif_sims[motif] = single_motif_sim(bpnet, motifs[motif][1], max_hamming_dist=2)
In [87]:
fig, axes = plt.subplots(1, len(tasks), figsize=(40, 10))
for i, task in enumerate(tasks):
plot_csi({h: [v['scores'][task]['imp/weighted']
for v in vlist]
for h,vlist in motif_sims[motif].items()}, ax=axes[i], title=task)
In [88]:
heatmap_csi_tasks(df, tasks, motif, 'imp/weighted', 'Blues');
In [89]:
heatmap_csi_tasks(df, tasks, motif, 'profile/max_frac', 'Blues');
Klf4¶
In [90]:
motif = 'Klf4'
In [91]:
mr.plot_pssm(*longer_pattern(motifs[motif][0]).split("/"), trim_frac=0.08);
In [92]:
if not cached:
motif_sims[motif] = single_motif_sim(bpnet, motifs[motif][1], max_hamming_dist=2)
In [93]:
fig, axes = plt.subplots(1, len(tasks), figsize=(40, 10))
for i, task in enumerate(tasks):
plot_csi({h: [v['scores'][task]['imp/weighted']
for v in vlist]
for h,vlist in motif_sims[motif].items()}, ax=axes[i], title=task)
In [94]:
heatmap_csi_tasks(df, tasks, motif, 'imp/weighted', 'Blues');
In [95]:
heatmap_csi_tasks(df, tasks, motif, 'profile/max_frac', 'Blues');
Other¶
In [288]:
plot_tracks(filter_tracks(bpnet.sim_pred("CCATTGTT", importance=[]), xlim=[400, 600]),
fig_width=15, fig_height_per_track=1.5);
