Goal¶
- implement modisco motif re-clustering
Here are notes on what you would need to do in order to perform clustering with a given affinity matrix and set of seqlets:
It would probably be easiest for you to implement this by adding a new method to TfMoDiscoSeqletsToPatterns.
1. Instantiate TfMoDiscoSeqletsToPatterns¶
- A
TfModiscoSeqletsToPatternsobject is generated by the__call__method ofTfModiscoSeqletsToPatternsFactory(TfModiscoSeqletsToPatternsFactoryis an argument to the mainTfModiscoWorkflowclass). - The
__call__method ofTfModiscoSeqletsToPatternsFactorytakes arguments oftrack_set,onehot_track_name,contrib_scores_track_names,hypothetical_contribs_track_namesandtrack_signs.- For
track_set, prepare a TrackSet object using the method workflow.py#L110-L133, which has arguments oftask_names,contrib_scores(which is the dict mapping task name-->numpy array of contrib scores), hypothetical_contribs (similarly a dict mapping task name-->hypothetical contrib scores), andonehot(a numpy array of the one-hot encoded sequence). - For
onehot_track_name, supply the string"sequence". - For
contrib_scores_track_names, supply[key+"_contrib_scores" for key in relevant_task_names], whererelevant_task_namesis a list of the tasks that you decide to care about for this group. - Similarly for
hypothetical_contribs_track_names(use[key+"_hypothetical_contribs" for key in relevant_task_names]). - For
track_signs, supply the corresponding signs associated with the tasks you picked forrelevant_task_names.
- For
2. Write the method¶
The method you add should take as arguments affinity_matrix and seqlets. Proceed as follows:
- Perform clustering on your affinity matrix:
- Obtain
density_adapted_affmatby callingself.density_adapted_affmat_transformeras in seqlets_to_patterns.py#L592-L593 (replacefiltered_affmatwithaffinity_matrix) - TfMoDISco supports the option of using slightly different clustering settings in different rounds, although by default they are the same. Get a clusterer using
clusterer = self.clusterer_per_round[-1]. - Create a
cluster_resultsobject by callingclustererondensity_adapted_affmatas in seqlets_to_patterns.py#L600 - Create a
cluster_to_seqletsdictionary (which maps the cluster index to the seqlets within that cluster) as in seqlets_to_patterns.py#L616-L620 (replacefiltered_seqletswithseqlets) - Create a
cluster_to_motifobject withcluster_to_motif = OrderedDict([(cluster_idx, self.seqlet_aggregator(cluster_seqlets) for (cluster_idx, cluster_seqlets) in cluster_to_seqlets.items()]) - The aggregation into motifs will end up editing the boundaries of the seqlets, and can also potentially cause originally distinct seqlets to overlap. Overwrite
seqletsto be a unique list of seqlets as in seqlets_to_patterns.py#L645-L647
- Obtain
- Perform postprocessing of the clusters:
- Perform spurious merging detection with
split_patterns = self.spurious_merge_detector(cluster_to_motif.values())as in seqlets_to_patterns.py#L654-L655 - Merge similar patterns with
merged_patterns, pattern_merge_hierarchy = self.similar_patterns_collapser(patterns=split_patterns, seqlets=seqlets), as in seqlets_to_patterns.py#L672-L675 - Reassign seqlets from the very small patterns to the larger patterns with
reassigned_patterns = self.seqlet_reassigner(merged_patterns), as in seqlets_to_patterns.py#L683 - Do a final editing of the seqlet boundaries with
final_patterns = self.final_postprocessor(reassigned_patterns), as in seqlets_to_patterns.py#L684. Returnfinal_patterns.
- Perform spurious merging detection with
Questions¶
Q: say we have 2 different affinity matrices: one for the profile and one for the contrib scores, what's your strategy on how to weight / merge them? Would you use the percentiles instead of actual affinity values and then add them up?
A: Honestly, I think you should set aside trying to do clustering with both the profile and the contrib scores for now because the if you want to include the profile, you would have to include it in the post-processing step as well - otherwise, the postprocessing step will just collapse clusters that don’t look different at the level of contrib scores. But to answer your question, yes, I would probably use percentiles to normalize as a first pass
Q: how do you decide whether to collapse them or not?
A: It’s a hacky strategy that I document in the technical note - I look at whether the aggregate profiles are similar in terms of cross-correlation (that part is less hacky) and a density-adaptive similarity metric (that part is hacky), and combine the two metrics (that part is the hackiest). I want to revise the “density adaptive” strategy to just look at what percentage of seqlets align more strongly with the other cluster than with their assigned cluster.
In [1]:
import basepair
import modisco
from basepair.utils import read_pkl
import numpy as np
import pandas as pd
Using TensorFlow backend. TF-MoDISco is using the TensorFlow backend.
In [2]:
%matplotlib inline
In [475]:
# # Load patterns
try:
from basepair.config import get_data_dir
ddir = get_data_dir()
modisco_subdir = "modisco/all/deeplift/profile/"
model_dir = f"{ddir}/processed/chipnexus/exp/models/oct-sox-nanog-klf/models/n_dil_layers=9/"
modisco_dir = os.path.join(model_dir, modisco_subdir)
patterns = read_pkl(modisco_dir / 'patterns.pkl')
except:
if not os.path.exists("patterns.pkl"):
!wget http://mitra.stanford.edu/kundaje/avsec/chipnexus/oct-sox-nanog-klf/models/n_dil_layers=9/modisco/all/deeplift/profile/patterns.pkl
patterns = read_pkl("patterns.pkl")
In [4]:
p = patterns[0]
In [5]:
p.attrs.keys()
Out[5]:
dict_keys(['features', 'align', 'cluster', 'stacked_seqlet_imp'])
In [477]:
clusters = pd.Series([p.attrs['cluster'] for p in patterns])
In [478]:
c1_patterns = [p for p in patterns if p.attrs['cluster'] == clusters[0]]
In [479]:
len(patterns)
Out[479]:
122
In [480]:
len(c1_patterns)
Out[480]:
14
In [481]:
for p in c1_patterns:
p.plot('seq_ic');
Create the similarity matrix¶
In [33]:
from basepair.exp.chipnexus.motif_clustering import similarity_matrix
In [34]:
sim_nte_seq = similarity_matrix(c1_patterns, track='seq_ic')
100%|██████████| 22/22 [00:01<00:00, 13.12it/s]
In [42]:
sim_nte_seq.shape
Out[42]:
(22, 22)
Run modisco¶
Here are notes on what you would need to do in order to perform clustering with a given affinity matrix and set of seqlets:
It would probably be easiest for you to implement this by adding a new method to TfMoDiscoSeqletsToPatterns.
1. Instantiate TfMoDiscoSeqletsToPatterns¶
- A
TfModiscoSeqletsToPatternsobject is generated by the__call__method ofTfModiscoSeqletsToPatternsFactory(TfModiscoSeqletsToPatternsFactoryis an argument to the mainTfModiscoWorkflowclass). - The
__call__method ofTfModiscoSeqletsToPatternsFactorytakes arguments oftrack_set,onehot_track_name,contrib_scores_track_names,hypothetical_contribs_track_namesandtrack_signs.- For
track_set, prepare a TrackSet object using the method workflow.py#L110-L133, which has arguments oftask_names,contrib_scores(which is the dict mapping task name-->numpy array of contrib scores), hypothetical_contribs (similarly a dict mapping task name-->hypothetical contrib scores), andonehot(a numpy array of the one-hot encoded sequence). - For
onehot_track_name, supply the string"sequence". - For
contrib_scores_track_names, supply[key+"_contrib_scores" for key in relevant_task_names], whererelevant_task_namesis a list of the tasks that you decide to care about for this group. - Similarly for
hypothetical_contribs_track_names(use[key+"_hypothetical_contribs" for key in relevant_task_names]). - For
track_signs, supply the corresponding signs associated with the tasks you picked forrelevant_task_names.
- For
In [430]:
from modisco.tfmodisco_workflow.workflow import prep_track_set
from modisco.tfmodisco_workflow.seqlets_to_patterns import TfModiscoSeqletsToPatternsFactory
In [431]:
tasks = p.tasks()
In [432]:
# Merge all the seqlets
all_seqlets = c1_patterns[0].attrs['stacked_seqlet_imp']
for p in c1_patterns[1:]:
all_seqlets = all_seqlets.append(p.attrs['stacked_seqlet_imp'])
In [433]:
len(all_seqlets)
Out[433]:
17011
In [434]:
all_seqlets = all_seqlets[np.arange(1000)]
In [435]:
track_set = prep_track_set(tasks, all_seqlets.contrib, all_seqlets.hyp_contrib, all_seqlets.seq)
In [436]:
s2p_factory = TfModiscoSeqletsToPatternsFactory()
In [437]:
self = s2p_factory(track_set, 'sequence',
contrib_scores_track_names=[key + "_contrib_scores" for key in tasks],
hypothetical_contribs_track_names=[key + "_hypothetical_contribs" for key in tasks],
track_signs=[+1 for k in tasks])
TfModiscoSeqletsToPatternsFactory: seed=1234
Get seqlets¶
In [438]:
from modisco.core import Seqlet, SeqletCoordinates
In [439]:
# generate seqlets
coords = [SeqletCoordinates(example_idx=i, start=0, end=all_seqlets.seq.shape[1], is_revcomp=False)
for i in range(len(all_seqlets))]
seqlets = track_set.create_seqlets(coords, track_names=list(track_set.track_name_to_data_track))
Create the affinity matrix¶
- this ignores the signs. Take also signs into account
In [440]:
from modisco.affinitymat.core import contin_jaccard_vec_mat_sim, ContinJaccardSimilarity, NumpyCosineSimilarity
In [441]:
import time
from joblib import Parallel, delayed
def contin_jaccard_vec_mat_sim(a_row, mat):
union = np.sum(np.maximum(np.abs(a_row[None,:]),
np.abs(mat)),axis=(1,2))
intersection = np.sum(np.minimum(np.abs(a_row[None,:]),
np.abs(mat))
*np.sign(a_row[None,:])
*np.sign(mat), axis=(1,2))
return intersection.astype("float")/union
class ContinJaccardSimilarity:
def __init__(self, verbose=True, n_cores=1, make_positive=False):
self.verbose = verbose
self.n_cores = n_cores
self.make_positive = make_positive
def __call__(self, vecs1, vecs2):
start_time = time.time()
normed_vecs1 = vecs1/np.sum(np.abs(vecs1), axis=1)[:,None]
normed_vecs2 = vecs2/np.sum(np.abs(vecs2), axis=1)[:,None]
if (self.verbose):
print("Normalization computed in",
round(time.time()-start_time,2),"s")
sys.stdout.flush()
similarity_rows = []
job_arguments = []
for idx in range(0,len(normed_vecs1)):
job_arguments.append(normed_vecs1[idx])
to_concat = (Parallel(n_jobs=self.n_cores)
(delayed(contin_jaccard_vec_mat_sim)(
job_arg, normed_vecs2)
for job_arg in job_arguments))
to_return = np.array(to_concat)
end_time = time.time()
if (self.verbose):
print("Contin jaccard similarity mat computed in",
round(end_time-start_time,2),"s")
sys.stdout.flush()
if (self.make_positive):
to_return = to_return + 1.0
return to_return
In [442]:
from basepair.functions import mean
In [443]:
metric = ContinJaccardSimilarity(n_cores=10)
affinity_matrix_jac = mean([metric(all_seqlets.contrib[t], all_seqlets.contrib[t])
for t in tasks])
Normalization computed in 0.01 s Contin jaccard similarity mat computed in 2.63 s Normalization computed in 0.01 s Contin jaccard similarity mat computed in 2.61 s Normalization computed in 0.01 s Contin jaccard similarity mat computed in 2.92 s Normalization computed in 0.01 s Contin jaccard similarity mat computed in 2.66 s
In [444]:
# TODO - what should we do with the sequence match?
In [445]:
affinity_matrix_jac.shape
Out[445]:
(1000, 1000)
In [446]:
metric = NumpyCosineSimilarity(verbose=True)
affinity_matrix_abs = metric(np.stack([all_seqlets.contrib[t].sum(axis=(1,2)) for t in tasks], axis=1),
np.stack([all_seqlets.contrib[t].sum(axis=(1,2)) for t in tasks], axis=1))
Normalization computed in 0.0 s Cosine similarity mat computed in 0.02 s
In [447]:
affinity_matrix = affinity_matrix_abs
In [448]:
%matplotlib inline
In [449]:
plt.hist(affinity_matrix_jac[np.tril_indices_from(affinity_matrix_jac, k=-1)], bins=50);
In [450]:
plt.hist(affinity_matrix_abs[np.tril_indices_from(affinity_matrix_abs, k=-1)], bins=50);
In [451]:
affinity_matrix.shape
Out[451]:
(1000, 1000)
In [452]:
affinity_matrix.shape
Out[452]:
(1000, 1000)
2. Write the method¶
The method you add should take as arguments affinity_matrix and seqlets. Proceed as follows:
- Perform clustering on your affinity matrix:
- Obtain
density_adapted_affmatby callingself.density_adapted_affmat_transformeras in seqlets_to_patterns.py#L592-L593 (replacefiltered_affmatwithaffinity_matrix) - TfMoDISco supports the option of using slightly different clustering settings in different rounds, although by default they are the same. Get a clusterer using
clusterer = self.clusterer_per_round[-1]. - Create a
cluster_resultsobject by callingclustererondensity_adapted_affmatas in seqlets_to_patterns.py#L600 - Create a
cluster_to_seqletsdictionary (which maps the cluster index to the seqlets within that cluster) as in seqlets_to_patterns.py#L616-L620 (replacefiltered_seqletswithseqlets) - Create a
cluster_to_motifobject withcluster_to_motif = OrderedDict([(cluster_idx, self.seqlet_aggregator(cluster_seqlets) for (cluster_idx, cluster_seqlets) in cluster_to_seqlets.items()]) - The aggregation into motifs will end up editing the boundaries of the seqlets, and can also potentially cause originally distinct seqlets to overlap. Overwrite
seqletsto be a unique list of seqlets as in seqlets_to_patterns.py#L645-L647
- Obtain
- Perform postprocessing of the clusters:
- Perform spurious merging detection with
split_patterns = self.spurious_merge_detector(cluster_to_motif.values())as in seqlets_to_patterns.py#L654-L655 - Merge similar patterns with
merged_patterns, pattern_merge_hierarchy = self.similar_patterns_collapser(patterns=split_patterns, seqlets=seqlets), as in seqlets_to_patterns.py#L672-L675 - Reassign seqlets from the very small patterns to the larger patterns with
reassigned_patterns = self.seqlet_reassigner(merged_patterns), as in seqlets_to_patterns.py#L683 - Do a final editing of the seqlet boundaries with
final_patterns = self.final_postprocessor(reassigned_patterns), as in seqlets_to_patterns.py#L684. Returnfinal_patterns.
- Perform spurious merging detection with
In [453]:
from collections import defaultdict, OrderedDict
In [454]:
def cluster(self, affinity_matrix, seqlets):
pass
In [455]:
affinity_matrix.shape
Out[455]:
(1000, 1000)
In [456]:
density_adapted_affmat = self.density_adapted_affmat_transformer(affinity_matrix)
[t-SNE] Computing 31 nearest neighbors... [t-SNE] Indexed 1000 samples in 0.001s... [t-SNE] Computed neighbors for 1000 samples in 0.013s... [t-SNE] Computed conditional probabilities for sample 1000 / 1000 [t-SNE] Mean sigma: 0.020629
In [457]:
clusterer = self.clusterer_per_round[-1]
In [458]:
cluster_results = clusterer(density_adapted_affmat)
Beginning preprocessing + Louvain Wrote graph to binary file in 0.08094334602355957 seconds Running Louvain modularity optimization
[Parallel(n_jobs=4)]: Done 42 tasks | elapsed: 2.3s [Parallel(n_jobs=4)]: Done 192 tasks | elapsed: 10.8s [Parallel(n_jobs=4)]: Done 200 out of 200 | elapsed: 11.2s finished
Louvain completed 200 runs in 12.275030851364136 seconds Wrote graph to binary file in 0.3876824378967285 seconds Running Louvain modularity optimization After 1 runs, maximum modularity is Q = 0.802969 After 2 runs, maximum modularity is Q = 0.84645 After 49 runs, maximum modularity is Q = 0.847327 After 62 runs, maximum modularity is Q = 0.848393 After 101 runs, maximum modularity is Q = 0.84982 Louvain completed 151 runs in 9.571075439453125 seconds Preproc + Louvain took 22.415324449539185 s
In [459]:
cluster_to_seqlets = defaultdict(list)
assert len(seqlets)==len(cluster_results.cluster_indices)
for seqlet,idx in zip(seqlets,
cluster_results.cluster_indices):
if len(seqlet) > 0:
cluster_to_seqlets[idx].append(seqlet)
In [462]:
self.seqlet_aggregator.postprocessor= None
In [ ]:
# TODO - seqlet aggregator postprocessing removes everything if we don't remove postprocessing
In [463]:
cluster_to_motif = OrderedDict([(cluster_idx, self.seqlet_aggregator(cluster_seqlets)[0]) for (cluster_idx, cluster_seqlets) in cluster_to_seqlets.items()])
cluster_to_motif
Out[463]:
OrderedDict([(1, <modisco.core.AggregatedSeqlet at 0x7f34e47074a8>),
(4, <modisco.core.AggregatedSeqlet at 0x7f34e39a1d68>),
(12, <modisco.core.AggregatedSeqlet at 0x7f34e39599b0>),
(5, <modisco.core.AggregatedSeqlet at 0x7f34e38a6128>),
(10, <modisco.core.AggregatedSeqlet at 0x7f34e3968c50>),
(9, <modisco.core.AggregatedSeqlet at 0x7f34e39eba90>),
(6, <modisco.core.AggregatedSeqlet at 0x7f34e3951d30>),
(3, <modisco.core.AggregatedSeqlet at 0x7f34e46161d0>),
(2, <modisco.core.AggregatedSeqlet at 0x7f34e39e8ac8>),
(13, <modisco.core.AggregatedSeqlet at 0x7f34e3935550>),
(0, <modisco.core.AggregatedSeqlet at 0x7f34e3913a90>),
(7, <modisco.core.AggregatedSeqlet at 0x7f34e3933f28>),
(15, <modisco.core.AggregatedSeqlet at 0x7f34e3941d30>),
(11, <modisco.core.AggregatedSeqlet at 0x7f34e385a7b8>),
(8, <modisco.core.AggregatedSeqlet at 0x7f34e38528d0>),
(14, <modisco.core.AggregatedSeqlet at 0x7f34e386b7f0>)])
In [464]:
[len(s.seqlets) for s in cluster_to_motif.values()]
Out[464]:
[122, 76, 35, 70, 46, 46, 59, 95, 99, 32, 134, 48, 23, 43, 47, 25]
In [427]:
c = cluster_to_motif[0]
In [465]:
split_patterns = self.spurious_merge_detector(cluster_to_motif.values())
Inspecting for spurious merging Wrote graph to binary file in 0.053403615951538086 seconds Running Louvain modularity optimization After 1 runs, maximum modularity is Q = 0.00500509 After 2 runs, maximum modularity is Q = 0.00681649 Louvain completed 22 runs in 1.3260712623596191 seconds Similarity is 0.8388699; is_dissimilar is False Inspecting for spurious merging Wrote graph to binary file in 0.012399911880493164 seconds Running Louvain modularity optimization After 1 runs, maximum modularity is Q = 0.00622418 After 4 runs, maximum modularity is Q = 0.00627151 After 5 runs, maximum modularity is Q = 0.00632882 After 7 runs, maximum modularity is Q = 0.00706251 After 9 runs, maximum modularity is Q = 0.00747216 Louvain completed 29 runs in 1.8788836002349854 seconds Similarity is 0.60510576; is_dissimilar is True Inspecting for spurious merging Wrote graph to binary file in 0.0068891048431396484 seconds Running Louvain modularity optimization After 1 runs, maximum modularity is Q = 0.00825874 After 2 runs, maximum modularity is Q = 0.0101393 After 19 runs, maximum modularity is Q = 0.0103746 Louvain completed 39 runs in 2.1216318607330322 seconds Similarity is 0.54664063; is_dissimilar is True Inspecting for spurious merging Wrote graph to binary file in 0.003186941146850586 seconds Running Louvain modularity optimization After 1 runs, maximum modularity is Q = 0.0103163 Louvain completed 21 runs in 1.1334924697875977 seconds Similarity is 0.4501366; is_dissimilar is True Got 3 subclusters
/home/avsec/workspace/kundajelab/tfmodisco/modisco/core.py:978: RuntimeWarning: invalid value encountered in true_divide /self.per_position_counts[:,None]), /home/avsec/workspace/kundajelab/tfmodisco/modisco/core.py:980: RuntimeWarning: invalid value encountered in true_divide /self.per_position_counts[::-1,None]),
Inspecting for spurious merging Wrote graph to binary file in 0.0027151107788085938 seconds Running Louvain modularity optimization After 1 runs, maximum modularity is Q = 0.00677569 After 2 runs, maximum modularity is Q = 0.00735883 Louvain completed 22 runs in 1.3657386302947998 seconds Similarity is 0.747187; is_dissimilar is True Got 2 subclusters
/home/avsec/workspace/kundajelab/tfmodisco/modisco/core.py:978: RuntimeWarning: invalid value encountered in true_divide /self.per_position_counts[:,None]), /home/avsec/workspace/kundajelab/tfmodisco/modisco/core.py:980: RuntimeWarning: invalid value encountered in true_divide /self.per_position_counts[::-1,None]),
Inspecting for spurious merging Wrote graph to binary file in 0.011326313018798828 seconds Running Louvain modularity optimization After 1 runs, maximum modularity is Q = 0.00480599 After 2 runs, maximum modularity is Q = 0.00540938 After 3 runs, maximum modularity is Q = 0.00590853 After 10 runs, maximum modularity is Q = 0.00639152 Louvain completed 30 runs in 1.852226972579956 seconds Similarity is 0.69306946; is_dissimilar is True Inspecting for spurious merging Wrote graph to binary file in 0.005899190902709961 seconds Running Louvain modularity optimization After 1 runs, maximum modularity is Q = 0.00735766 After 2 runs, maximum modularity is Q = 0.00752775 After 3 runs, maximum modularity is Q = 0.00892453 After 4 runs, maximum modularity is Q = 0.00892454 Louvain completed 24 runs in 1.487642526626587 seconds Similarity is 0.50477684; is_dissimilar is True Got 3 subclusters
/home/avsec/workspace/kundajelab/tfmodisco/modisco/core.py:978: RuntimeWarning: invalid value encountered in true_divide /self.per_position_counts[:,None]), /home/avsec/workspace/kundajelab/tfmodisco/modisco/core.py:980: RuntimeWarning: invalid value encountered in true_divide /self.per_position_counts[::-1,None]),
Inspecting for spurious merging Wrote graph to binary file in 0.004849433898925781 seconds Running Louvain modularity optimization After 1 runs, maximum modularity is Q = 0.0102085 After 6 runs, maximum modularity is Q = 0.0111803 Louvain completed 26 runs in 1.4202148914337158 seconds Similarity is 0.7545899; is_dissimilar is True Got 2 subclusters
/home/avsec/workspace/kundajelab/tfmodisco/modisco/core.py:978: RuntimeWarning: invalid value encountered in true_divide /self.per_position_counts[:,None]), /home/avsec/workspace/kundajelab/tfmodisco/modisco/core.py:980: RuntimeWarning: invalid value encountered in true_divide /self.per_position_counts[::-1,None]),
Inspecting for spurious merging Wrote graph to binary file in 0.008852481842041016 seconds Running Louvain modularity optimization After 1 runs, maximum modularity is Q = 0.0266079 Louvain completed 21 runs in 1.1975774765014648 seconds Similarity is 0.47929382; is_dissimilar is True Inspecting for spurious merging Wrote graph to binary file in 0.0028128623962402344 seconds Running Louvain modularity optimization After 1 runs, maximum modularity is Q = 0.0141128 Louvain completed 21 runs in 1.150632619857788 seconds Similarity is 0.5417262; is_dissimilar is True Got 3 subclusters
/home/avsec/workspace/kundajelab/tfmodisco/modisco/core.py:978: RuntimeWarning: invalid value encountered in true_divide /self.per_position_counts[:,None]), /home/avsec/workspace/kundajelab/tfmodisco/modisco/core.py:980: RuntimeWarning: invalid value encountered in true_divide /self.per_position_counts[::-1,None]),
Inspecting for spurious merging Wrote graph to binary file in 0.012821197509765625 seconds Running Louvain modularity optimization After 1 runs, maximum modularity is Q = 0.00758637 After 2 runs, maximum modularity is Q = 0.00768078 After 4 runs, maximum modularity is Q = 0.00821605 After 17 runs, maximum modularity is Q = 0.0084286 Louvain completed 37 runs in 2.189580202102661 seconds Similarity is 0.60788065; is_dissimilar is True Inspecting for spurious merging Wrote graph to binary file in 0.0036945343017578125 seconds Running Louvain modularity optimization After 1 runs, maximum modularity is Q = 0.00480804 After 2 runs, maximum modularity is Q = 0.00483495 After 3 runs, maximum modularity is Q = 0.00520456 After 4 runs, maximum modularity is Q = 0.00534839 After 10 runs, maximum modularity is Q = 0.00676739 After 22 runs, maximum modularity is Q = 0.00692205 Louvain completed 42 runs in 2.475128650665283 seconds Similarity is 0.7984518; is_dissimilar is True Got 3 subclusters
/home/avsec/workspace/kundajelab/tfmodisco/modisco/core.py:978: RuntimeWarning: invalid value encountered in true_divide /self.per_position_counts[:,None]), /home/avsec/workspace/kundajelab/tfmodisco/modisco/core.py:980: RuntimeWarning: invalid value encountered in true_divide /self.per_position_counts[::-1,None]),
Inspecting for spurious merging Wrote graph to binary file in 0.019110679626464844 seconds Running Louvain modularity optimization After 1 runs, maximum modularity is Q = 0.0036109 After 4 runs, maximum modularity is Q = 0.00479872 Louvain completed 24 runs in 1.455315351486206 seconds Similarity is 0.7974396; is_dissimilar is True Inspecting for spurious merging Wrote graph to binary file in 0.00614619255065918 seconds Running Louvain modularity optimization After 1 runs, maximum modularity is Q = -0.0187612 After 2 runs, maximum modularity is Q = 0.00251214 After 7 runs, maximum modularity is Q = 0.00275251 After 11 runs, maximum modularity is Q = 0.00313906 Louvain completed 31 runs in 2.1722259521484375 seconds Similarity is 0.83093435; is_dissimilar is False Inspecting for spurious merging Wrote graph to binary file in 0.004555463790893555 seconds Running Louvain modularity optimization After 1 runs, maximum modularity is Q = 0.0088396 Louvain completed 21 runs in 1.2350826263427734 seconds Similarity is 0.65235066; is_dissimilar is True Got 3 subclusters
/home/avsec/workspace/kundajelab/tfmodisco/modisco/core.py:978: RuntimeWarning: invalid value encountered in true_divide /self.per_position_counts[:,None]), /home/avsec/workspace/kundajelab/tfmodisco/modisco/core.py:980: RuntimeWarning: invalid value encountered in true_divide /self.per_position_counts[::-1,None]),
Inspecting for spurious merging Wrote graph to binary file in 0.03380751609802246 seconds Running Louvain modularity optimization After 1 runs, maximum modularity is Q = 0.00821254 After 3 runs, maximum modularity is Q = 0.00823722 After 5 runs, maximum modularity is Q = 0.00841656 After 6 runs, maximum modularity is Q = 0.00886854 After 8 runs, maximum modularity is Q = 0.00904084 After 28 runs, maximum modularity is Q = 0.00920853 Louvain completed 48 runs in 3.147193670272827 seconds Similarity is 0.6858583; is_dissimilar is True Inspecting for spurious merging Wrote graph to binary file in 0.006472110748291016 seconds Running Louvain modularity optimization After 1 runs, maximum modularity is Q = 0.0116761 After 2 runs, maximum modularity is Q = 0.015747 Louvain completed 22 runs in 1.2960717678070068 seconds Similarity is 0.11823471; is_dissimilar is True Inspecting for spurious merging Wrote graph to binary file in 0.0024657249450683594 seconds Running Louvain modularity optimization After 1 runs, maximum modularity is Q = 0.00490759 After 2 runs, maximum modularity is Q = 0.00570294 After 3 runs, maximum modularity is Q = 0.00671214 After 17 runs, maximum modularity is Q = 0.00726358 Louvain completed 37 runs in 2.1026127338409424 seconds Similarity is 0.64935577; is_dissimilar is True Inspecting for spurious merging Wrote graph to binary file in 0.003997325897216797 seconds Running Louvain modularity optimization After 1 runs, maximum modularity is Q = 0.00853262 After 5 runs, maximum modularity is Q = 0.00853263 After 8 runs, maximum modularity is Q = 0.00923337 Louvain completed 28 runs in 1.6638154983520508 seconds Similarity is 0.81683195; is_dissimilar is False Got 4 subclusters
/home/avsec/workspace/kundajelab/tfmodisco/modisco/core.py:978: RuntimeWarning: invalid value encountered in true_divide /self.per_position_counts[:,None]), /home/avsec/workspace/kundajelab/tfmodisco/modisco/core.py:980: RuntimeWarning: invalid value encountered in true_divide /self.per_position_counts[::-1,None]),
Inspecting for spurious merging Wrote graph to binary file in 0.002512216567993164 seconds Running Louvain modularity optimization After 1 runs, maximum modularity is Q = 0.00633429 After 2 runs, maximum modularity is Q = 0.00746705 Louvain completed 22 runs in 1.3320069313049316 seconds Similarity is 0.73380244; is_dissimilar is True Got 2 subclusters Inspecting for spurious merging Wrote graph to binary file in 0.03363800048828125 seconds Running Louvain modularity optimization After 1 runs, maximum modularity is Q = 0.00577793 After 2 runs, maximum modularity is Q = 0.00610051 After 3 runs, maximum modularity is Q = 0.00689855 After 16 runs, maximum modularity is Q = 0.00722914 Louvain completed 36 runs in 2.151035785675049 seconds Similarity is 0.79256374; is_dissimilar is True Inspecting for spurious merging Wrote graph to binary file in 0.015651941299438477 seconds Running Louvain modularity optimization After 1 runs, maximum modularity is Q = 0.00780434 After 2 runs, maximum modularity is Q = 0.00788126 After 4 runs, maximum modularity is Q = 0.00874318 After 5 runs, maximum modularity is Q = 0.00901862 After 11 runs, maximum modularity is Q = 0.00911596 Louvain completed 31 runs in 1.9844169616699219 seconds Similarity is 0.79388577; is_dissimilar is True Inspecting for spurious merging Wrote graph to binary file in 0.00478816032409668 seconds Running Louvain modularity optimization After 1 runs, maximum modularity is Q = 0.0083579 After 3 runs, maximum modularity is Q = 0.00942606 After 13 runs, maximum modularity is Q = 0.0102155 After 14 runs, maximum modularity is Q = 0.0103612 Louvain completed 34 runs in 2.0079009532928467 seconds Similarity is 0.7365298; is_dissimilar is True Inspecting for spurious merging Wrote graph to binary file in 0.007299900054931641 seconds Running Louvain modularity optimization After 1 runs, maximum modularity is Q = 0.0134341 After 2 runs, maximum modularity is Q = 0.0149271 After 4 runs, maximum modularity is Q = 0.0165031 After 7 runs, maximum modularity is Q = 0.0169396 After 19 runs, maximum modularity is Q = 0.0174288 Louvain completed 39 runs in 2.296250820159912 seconds Similarity is 0.5242937; is_dissimilar is True Inspecting for spurious merging Wrote graph to binary file in 0.005622386932373047 seconds Running Louvain modularity optimization After 1 runs, maximum modularity is Q = 0.0145549 After 5 runs, maximum modularity is Q = 0.0151181 After 10 runs, maximum modularity is Q = 0.0177686 Louvain completed 30 runs in 1.6809837818145752 seconds Similarity is 0.6020381; is_dissimilar is True Got 4 subclusters
/home/avsec/workspace/kundajelab/tfmodisco/modisco/core.py:978: RuntimeWarning: invalid value encountered in true_divide /self.per_position_counts[:,None]), /home/avsec/workspace/kundajelab/tfmodisco/modisco/core.py:980: RuntimeWarning: invalid value encountered in true_divide /self.per_position_counts[::-1,None]),
Inspecting for spurious merging Wrote graph to binary file in 0.004762887954711914 seconds Running Louvain modularity optimization After 1 runs, maximum modularity is Q = 0.0134972 After 18 runs, maximum modularity is Q = 0.0139388 Louvain completed 38 runs in 2.0018112659454346 seconds Similarity is 0.7066268; is_dissimilar is True Got 2 subclusters
/home/avsec/workspace/kundajelab/tfmodisco/modisco/core.py:978: RuntimeWarning: invalid value encountered in true_divide /self.per_position_counts[:,None]), /home/avsec/workspace/kundajelab/tfmodisco/modisco/core.py:980: RuntimeWarning: invalid value encountered in true_divide /self.per_position_counts[::-1,None]),
Inspecting for spurious merging Wrote graph to binary file in 0.005482912063598633 seconds Running Louvain modularity optimization After 1 runs, maximum modularity is Q = 0.0148056 After 2 runs, maximum modularity is Q = 0.017302 After 3 runs, maximum modularity is Q = 0.0174633 After 5 runs, maximum modularity is Q = 0.0185345 Louvain completed 25 runs in 1.510594367980957 seconds Similarity is 0.29461476; is_dissimilar is True Got 2 subclusters
/home/avsec/workspace/kundajelab/tfmodisco/modisco/core.py:978: RuntimeWarning: invalid value encountered in true_divide /self.per_position_counts[:,None]), /home/avsec/workspace/kundajelab/tfmodisco/modisco/core.py:980: RuntimeWarning: invalid value encountered in true_divide /self.per_position_counts[::-1,None]),
Inspecting for spurious merging Wrote graph to binary file in 0.0055637359619140625 seconds Running Louvain modularity optimization After 1 runs, maximum modularity is Q = 0.00747417 After 3 runs, maximum modularity is Q = 0.00783199 After 4 runs, maximum modularity is Q = 0.00877362 After 5 runs, maximum modularity is Q = 0.00904241 After 11 runs, maximum modularity is Q = 0.0101246 After 15 runs, maximum modularity is Q = 0.0102355 After 19 runs, maximum modularity is Q = 0.0103321 After 37 runs, maximum modularity is Q = 0.0108943 Louvain completed 57 runs in 3.430715322494507 seconds Similarity is 0.75532097; is_dissimilar is True Got 2 subclusters
In [466]:
merged_patterns, pattern_merge_hierarchy = self.similar_patterns_collapser(patterns=split_patterns, seqlets=seqlets)
/home/avsec/workspace/kundajelab/tfmodisco/modisco/core.py:978: RuntimeWarning: invalid value encountered in true_divide /self.per_position_counts[:,None]), /home/avsec/workspace/kundajelab/tfmodisco/modisco/core.py:980: RuntimeWarning: invalid value encountered in true_divide /self.per_position_counts[::-1,None]),
On merging iteration 1 Computing pattern to seqlet distances
--------------------------------------------------------------------------- AssertionError Traceback (most recent call last) <ipython-input-466-fbc73d390630> in <module>() ----> 1 merged_patterns, pattern_merge_hierarchy = self.similar_patterns_collapser(patterns=split_patterns, seqlets=seqlets) ~/workspace/kundajelab/tfmodisco/modisco/aggregator.py in __call__(self, patterns, seqlets) 846 self.aff_to_dist_mat(self.pattern_to_pattern_sim_computer( 847 seqlets=seqlets, --> 848 filter_seqlets=patterns)) 849 desired_perplexities = [len(pattern.seqlets_and_alnmts) 850 for pattern in patterns] ~/workspace/kundajelab/tfmodisco/modisco/affinitymat/core.py in __call__(self, seqlets, filter_seqlets, seqlet_neighbors) 437 filters=filters_all_fwd_data, 438 things_to_scan=all_fwd_data, --> 439 min_overlap=self.pattern_comparison_settings.min_overlap) 440 affmat_rev = self.sim_metric_on_nn_pairs( 441 neighbors_of_things_to_scan=seqlet_neighbors, ~/workspace/kundajelab/tfmodisco/modisco/affinitymat/core.py in __call__(self, filters, things_to_scan, min_overlap, neighbors_of_things_to_scan) 474 assert np.max(neighbors_of_things_to_scan) < filters.shape[0] 475 assert len(things_to_scan.shape)==3 --> 476 assert len(filters.shape)==3 477 478 filter_length = filters.shape[1] AssertionError:
In [425]:
%debug
> /home/avsec/workspace/kundajelab/tfmodisco/modisco/affinitymat/core.py(476)__call__()
474 assert np.max(neighbors_of_things_to_scan) < filters.shape[0]
475 assert len(things_to_scan.shape)==3
--> 476 assert len(filters.shape)==3
477
478 filter_length = filters.shape[1]
1
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(7,)
*** ValueError: all input arrays must have the same shape
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(80, 32)
(75, 32)
(70, 32)
(70, 32)
Debug¶
In [339]:
from modisco import core
In [387]:
sorted_seqlets = sorted(seqlets, key=self.seqlet_aggregator.seqlet_sort_metric)
aggregated_seqlet = core.AggregatedSeqlet.from_seqlet(sorted_seqlets[0])
In [389]:
aggregated_seqlet.
Out[389]:
1
In [359]:
seqlets = cluster_to_seqlets[0]
In [360]:
for seqlet in sorted_seqlets[1:]:
aggregated_seqlet.merge_aggregated_seqlet(
agg_seqlet=core.AggregatedSeqlet.from_seqlet(seqlet),
aligner=self.seqlet_aggregator.pattern_aligner)
In [361]:
to_return = [aggregated_seqlet]
In [363]:
self.seqlet_aggregator.postprocessor(to_return)
Trimmed 2 out of 100
/home/avsec/workspace/kundajelab/tfmodisco/modisco/core.py:978: RuntimeWarning: invalid value encountered in true_divide /self.per_position_counts[:,None]), /home/avsec/workspace/kundajelab/tfmodisco/modisco/core.py:980: RuntimeWarning: invalid value encountered in true_divide /self.per_position_counts[::-1,None]),
Skipped 98 seqlets
Out[363]:
[]
In [372]:
aggregated_seqlet.trim_to_positions_with_min_support(min_frac=0.1)
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-372-e0953938df3a> in <module>() ----> 1 aggregated_seqlet.trim_to_positions_with_min_support(min_frac=0.1) TypeError: trim_to_positions_with_min_support() missing 1 required positional argument: 'min_num'
In [383]:
# It seems to fail when calling this postprocessor
# https://github.com/kundajelab/tfmodisco/blob/5023bef4735677713f856e74445744d8f9d7a755/modisco/tfmodisco_workflow/seqlets_to_patterns.py#L259-L268
In [379]:
self.seqlet_aggregator.postprocessor.
Out[379]:
<modisco.aggregator.AdhocAggSeqletPostprocessor at 0x7f34e470f278>
In [362]:
self.seqlet_aggregator(cluster_to_seqlets[1])
Trimmed 0 out of 19 Skipped 19 seqlets
Out[362]:
[]
In [ ]:
%debug --breakpoint ~/workspace/kundajelab/tfmodisco/modisco/aggregator.py:
In [350]:
%debug self.seqlet_aggregator.__call__
NOTE: Enter 'c' at the ipdb> prompt to continue execution. > <string>(1)<module>()
--Return-- None > <string>(1)<module>()
In [395]:
[len(v) for v in cluster_to_seqlets.values()]
Out[395]:
[8, 19, 19, 20, 12, 13, 9, 0]
In [410]:
cluster_to_motif = OrderedDict([(cluster_idx, self.seqlet_aggregator(cluster_seqlets)) for (cluster_idx, cluster_seqlets) in cluster_to_seqlets.items()])
cluster_to_motif
In [ ]:
cluster_to_seqlets =
In [116]:
len(seqlets)
Out[116]:
100