def test_embark_supplemental(self, study, tmpdir):
import flotilla
study_name = 'test_save_supplemental'
study.supplemental.expression_corr = study.expression.data.corr()
study.save(study_name, flotilla_dir=tmpdir)
study2 = flotilla.embark(study_name, flotilla_dir=tmpdir)
pdt.assert_frame_equal(study2.supplemental.expression_corr,
study.supplemental.expression_corr)
def test_embark_supplemental(self, study, tmpdir):
import flotilla
study_name = 'test_save_supplemental'
study.supplemental.expression_corr = study.expression.data.corr()
study.save(study_name, flotilla_dir=tmpdir)
study2 = flotilla.embark(study_name, flotilla_dir=tmpdir)
pdt.assert_frame_equal(study2.supplemental.expression_corr,
study.supplemental.expression_corr)
def begin(
flotilla_project="http://sauron.ucsd.edu/flotilla_projects/"
"neural_diff_chr22"):
import flotilla
study = flotilla.embark(flotilla_project)
rpkms = study.expression.data
psi = study.splicing.data
rbp_genes = study.expression.feature_sets['rbp']
rbpRpkms = rpkms[rbp_genes].fillna(0)
project_id = flotilla_project.split("/")[-1]
mongo_con, mongodb = get_mongo_db(project_id)
sys.stderr.write("finished loading study_data\n")
return (psi, rbpRpkms, mongodb)
import pandas as pd
import flotilla
import os
import sys
sns.set(style='ticks', context='paper', rc={'font.sans-serif':'Arial', 'pdf.fonttype': 42})
# Ensure the iteration is always at least 1
iteration = max(int(sys.argv[1]), 1)
iteration_str = str(iteration).zfill(4)
base_folder = '/home/obotvinnik/Dropbox/figures2/singlecell_pnm/figure2_modalities/bayesian/permutations'
seed = (sum(ord(c) for c in 'randomly_permute_modalities')/i) % 5437
np.random.seed(seed)
study = flotilla.embark('singlecell_pnm_figure1_supplementary_post_splicing_filtering')
not_outliers = study.splicing.singles.index.difference(study.splicing.outliers.index)
print splicing_singles_no_outliers.shape
splicing_singles_no_outliers = splicing_singles_no_outliers.groupby(
study.sample_id_to_phenotype).apply(lambda x: x.dropna(thresh=20, axis=1))
print splicing_singles_no_outliers.shape
permuted_psi = splicing_singles_no_outliers.groupby(study.sample_id_to_phenotype).apply(
lambda x: pd.DataFrame(np.random.permutation(x), index=x.index, columns=x.columns))
bayesian = anchor.BayesianModalities()
modality_assignments = permuted_psi.groupby(study.sample_id_to_phenotype).apply(bayesian.fit_predict)
"""
Plot the modality log-likelihoods and barplots during estimation
================================================================
See also
--------
:py:func:`Study.plot_event_modality_estimation`
"""
import flotilla
study = flotilla.embark('shalek2013')
study.plot_event_modality_estimation('chr8:97356415:97356600:-@chr8:97355689:97355825:-@chr8:97353054:97353130:-@chr8:97352177:97352339:-')
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
sns.set(style='ticks',
context='paper',
rc={
'font.sans-serif': 'Arial',
'pdf.fonttype': 42
})
import flotilla
flotilla_dir = '/projects/ps-yeolab/obotvinnik/flotilla_projects'
study = flotilla.embark('singlecell_pnm_figure2_modalities_bayesian_kmers',
flotilla_dir=flotilla_dir)
corr = study.supplemental.kmer_zscores.fillna(0).T.corr()
print corr.shape
corr = corr.dropna(how='all', axis=1).dropna(how='all', axis=0)
print corr.shape
folder = '/home/obotvinnik/Dropbox/figures2/singlecell_pnm/figure2_modalities/bayesian'
figure_folder = '{}/kmer_counting'.format(folder)
g = sns.clustermap(corr)
g.savefig(
'{}/modality_kmer_scores_pearson_correlated_clustermap_featurewise.pdf'.
format(figure_folder))
def study(example_datapackage_path):
import flotilla
return flotilla.embark(example_datapackage_path)
""" Plot bar graphs of percentage of splicing events in each modality ================================================================= See also -------- :py:func:`Study.plot_modalities_bars` """ import flotilla study = flotilla.embark(flotilla._shalek2013) study.plot_modalities_lavalamps()
"""
Compare gene expression in two features
======================================
"""
import flotilla
study = flotilla.embark(flotilla._brainspan)
study.plot_two_features('FOXP1', 'FOXJ1')
def test_save(self, example_datapackage_path, tmpdir, monkeypatch):
import flotilla
from flotilla.external import get_resource_from_name
study = flotilla.embark(example_datapackage_path,
load_species_data=False)
study_name = 'test_save'
study.save(study_name, flotilla_dir=tmpdir)
assert len(tmpdir.listdir()) == 1
save_dir = tmpdir.listdir()[0]
with open('{}/datapackage.json'.format(save_dir)) as f:
test_datapackage = json.load(f)
with open(example_datapackage_path) as f:
true_datapackage = json.load(f)
assert study_name == save_dir.purebasename
resource_keys_to_ignore = ('compression', 'format', 'path')
keys_from_study = {
'splicing': ['feature_rename_col'],
'expression': ['feature_rename_col', 'log_base'],
'metadata':
['phenotype_order', 'phenotype_to_color', 'phenotype_col'],
'mapping_stats': [u'number_mapped_col']
}
resource_names = ('metadata', 'expression', 'splicing',
'mapping_stats', 'spikein')
# Add auto-generated attributes into the true datapackage
for name, keys in keys_from_study.iteritems():
resource = get_resource_from_name(true_datapackage, name)
for key in keys:
monkeypatch.setitem(resource, key,
eval('study.{}.{}'.format(name, key)))
version = semantic_version.Version(study.version)
version.patch += 1
assert str(version) == test_datapackage['datapackage_version']
assert study_name == test_datapackage['name']
datapackage_keys_to_ignore = [
'name', 'datapackage_version', 'resources'
]
datapackages = (true_datapackage, test_datapackage)
for name in resource_names:
for datapackage in datapackages:
resource = get_resource_from_name(datapackage, name)
for key in resource_keys_to_ignore:
monkeypatch.delitem(resource, key, raising=False)
# Have to check for resources separately because they could be in any
# order, it just matters that the contents are equal
assert sorted(true_datapackage['resources']) == sorted(
test_datapackage['resources'])
for key in datapackage_keys_to_ignore:
for datapackage in datapackages:
monkeypatch.delitem(datapackage, key)
pdt.assert_dict_equal(test_datapackage, true_datapackage)
def test_embark(example_datapackage_path):
test_study = flotilla.embark(example_datapackage_path)
def test_real_init(self, example_datapackage_path):
import flotilla
flotilla.embark(example_datapackage_path, load_species_data=False)
def scrambled_study():
import flotilla
return flotilla.embark(flotilla._test_data)
def study(example_datapackage_path):
import flotilla
return flotilla.embark(example_datapackage_path)
def shalek2013():
import flotilla
return flotilla.embark(flotilla._shalek2013)
def test_embark(shalek2013_datapackage_path):
test_study = flotilla.embark(shalek2013_datapackage_path)
def test_real_init(self, shalek2013_datapackage_path):
import flotilla
flotilla.embark(shalek2013_datapackage_path, load_species_data=False)
"""
Plot the modality log-likelihoods and barplots during estimation
================================================================
See also
--------
:py:func:`Study.plot_event_modality_estimation`
"""
import flotilla
study = flotilla.embark('shalek2013')
study.plot_event_modality_estimation(
'chr8:97356415:97356600:-@chr8:97355689:97355825:-@chr8:97353054:97353130:-@chr8:97352177:97352339:-'
)
def test_real_init(self, example_datapackage_path):
import flotilla
flotilla.embark(example_datapackage_path, load_species_data=False)
"""
Perform classification on categorical traits
============================================
See also
--------
:py:func:`Study.interactive_classifier`
"""
import flotilla
study = flotilla.embark(flotilla._shalek2013)
study.plot_classifier('phenotype: Immature BDMC')
def test_save(self, example_datapackage_path, example_datapackage, tmpdir,
monkeypatch):
import flotilla
from flotilla.datapackage import get_resource_from_name
study = flotilla.embark(example_datapackage_path,
load_species_data=False)
study_name = 'test_save'
study.save(study_name, flotilla_dir=tmpdir)
assert len(tmpdir.listdir()) == 1
save_dir = tmpdir.listdir()[0]
with open('{}/datapackage.json'.format(save_dir)) as f:
test_datapackage = json.load(f)
true_datapackage = example_datapackage.copy()
assert study_name == save_dir.purebasename
resource_keys_to_ignore = ('compression', 'format', 'path', 'url')
keys_from_study = {'splicing': [],
'expression': ['thresh',
'log_base'],
'metadata': ['phenotype_order',
'phenotype_to_color',
'phenotype_col',
'phenotype_to_marker',
'pooled_col',
'minimum_samples'],
'mapping_stats': ['number_mapped_col'],
'expression_feature': ['rename_col',
'ignore_subset_cols'],
'splicing_feature': ['rename_col',
'ignore_subset_cols']}
resource_names = keys_from_study.keys()
# Add auto-generated attributes into the true datapackage
for name, keys in keys_from_study.iteritems():
resource = get_resource_from_name(true_datapackage, name)
for key in keys:
if 'feature' in name:
command = 'study.{}.feature_{}'.format(name.rstrip(
'_feature'), key)
else:
command = 'study.{}.{}'.format(name, key)
monkeypatch.setitem(resource, key, eval(command))
version = semantic_version.Version(study.version)
version.patch += 1
assert str(version) == test_datapackage['datapackage_version']
assert study_name == test_datapackage['name']
datapackage_keys_to_ignore = ['name', 'datapackage_version',
'resources']
datapackages = (true_datapackage, test_datapackage)
for name in resource_names:
for datapackage in datapackages:
resource = get_resource_from_name(datapackage, name)
for key in resource_keys_to_ignore:
monkeypatch.delitem(resource, key, raising=False)
# Have to check for resources separately because they could be in any
# order, it just matters that the contents are equal
assert sorted(true_datapackage['resources']) == sorted(
test_datapackage['resources'])
for key in datapackage_keys_to_ignore:
for datapackage in datapackages:
monkeypatch.delitem(datapackage, key)
pdt.assert_dict_equal(test_datapackage,
true_datapackage)
