def ds_ds2():
from singlet.dataset import Dataset
ds = Dataset(samplesheet='example_sheet_tsv',
counts_table='example_table_tsv')
ds2 = ds.copy()
ds.samplesheet = ds.samplesheet.iloc[:2]
ds2.samplesheet = ds2.samplesheet.iloc[2:]
return (ds, ds2)
content: Test Dataset class.
'''
# Script
if __name__ == '__main__':
# NOTE: an env variable for the config file needs to be set when
# calling this script
print('Instantiating Dataset')
from singlet.dataset import Dataset
ds = Dataset(samplesheet='example_sheet_tsv',
counts_table='example_table_tsv')
print('Done!')
print('Testing Dataset.__str__')
assert (str(ds) == 'Dataset with 4 samples and 60721 features')
print('Done!')
print('Testing Dataset.__repr__')
assert (ds.__repr__() == '<Dataset: 4 samples, 60721 features>')
print('Done!')
print('Testing Dataset.copy')
assert (ds.copy() == ds)
print('Done!')
print('Testing Dataset.copy with modifications')
dsp = ds.copy()
dsp._counts.iloc[0, 0] = -5
assert (dsp != ds)
print('Done!')
'''
author: Fabio Zanini
date: 07/08/17
content: Test Dataset class.
'''
import numpy as np
# Script
if __name__ == '__main__':
# NOTE: an env variable for the config file needs to be set when
# calling this script
from singlet.dataset import Dataset
ds = Dataset(samplesheet='example_sheet_tsv',
counts_table='example_table_tsv')
ds2 = ds.copy()
ds.samplesheet = ds.samplesheet.iloc[:2]
ds2.samplesheet = ds2.samplesheet.iloc[2:]
print('Bootstrap')
dsboot = ds.bootstrap()
assert ('--sampling_' in dsboot.samplenames[0])
print('Done!')
print('Test feature comparison (Mann-Whitney U)')
pvals = ds.compare(ds2, method='mann-whitney')
assert (np.isclose(pvals.values.min(), 0.193931))
print('Done!')
print('Test feature comparison (Kolmogorov-Smirnov)')
pvals = ds.compare(ds2, method='kolmogorov-smirnov')
'TNFRSF10B',
'EIF2A',
'DDIT3',
'PPP1R15A',
'ATF4',
'EDEM1',
'XBP1',
'ATF6',
'ATF3',
'ATG5',
'CASP3',
'CASP9',
'CASP4',
'CASP6',
]
dsd = ds.copy()
gids = [
ds.featuresheet.index[ds.featuresheet['GeneName'] == gname][0]
for gname in gnames
]
dsd.counts = dsd.counts.loc[gids]
dsh = dsd.copy()
if virus == 'dengue':
dsh.query_samples_by_metadata('virus_reads_per_million > 1e3',
inplace=True)
dsh.query_samples_by_metadata('MOI != "0"', inplace=True)
else:
dsh.query_samples_by_metadata('virus_reads_per_million > 1e1',
inplace=True)
dsh.query_samples_by_metadata('MOI == "1"', inplace=True)
n = ds.samplesheet['numberDengueReads'].astype(int)
ds.samplesheet['virus_reads_per_million'] = 1e6 * n / (cov + n)
ds.samplesheet['log_virus_reads_per_million'] = np.log10(
0.1 + ds.samplesheet['virus_reads_per_million'])
#print('Log counts')
#ds.counts.log(inplace=True)
print('Get cell cycle genes (Core 67)')
cc = load_cell_cycle_table()[[
'GeneName', 'Periodic Rank', 'Phase', 'Core'
]]
cc.query('Core != "No"', inplace=True)
print('Hierarchical clustering of cells based on cell cycle and virus')
dsn = ds.copy()
dsn.counts = dsn.counts.loc[cc.index]
for col in ['Periodic Rank', 'Phase']:
dsn.featuresheet.loc[:, col] = cc.loc[dsn.featuresheet.index, col]
dsn.rename(axis='features', column='GeneName', inplace=True)
print('Log counts')
dsn.counts.log(inplace=True)
dsn.featuresheet.loc[:, 'Mean'] = dsn.counts.mean(axis=1)
# Only keep decently expressed genes
dsn.query_features_by_metadata('Mean > 1', inplace=True)
hier = dsn.cluster.hierarchical(
axis='samples',
#phenotypes=['virus_reads_per_million'],
'tsne2_MOI1_10'
]:
ds.samplesheet[col] = metadata_felix[col]
ds.samplesheet['log_Dn'] = np.log10(1e-6 + ds.samplesheet['Dn'])
ds.samplesheet['log_Ds'] = np.log10(1e-6 + ds.samplesheet['Ds'])
cov = ds.samplesheet['coverage']
n = ds.samplesheet['numberDengueReads'].astype(int)
ds.samplesheet['virus_reads_per_million'] = 1e6 * n / (cov + n)
ds.samplesheet['log_virus_reads_per_million'] = np.log10(0.1 + 1e6 * n /
(cov + n))
vs = ds.samplesheet[['tsne1_MOI1_10', 'tsne2_MOI1_10']]
# Restrict to high variance SNVs
dsv = ds.copy()
ind = ds.counts.values.var(axis=1).argsort()[-200:]
dsv.counts = dsv.counts.iloc[ind]
vsg = dsv.dimensionality.tsne(perplexity=30)
# Cluster
# NOTE: the number is manually chosen but does not matter much atm
dsv.samplesheet['clusterN'] = ds.cluster.kmeans(axis='samples',
n_clusters=6)
dss = dsv.split('clusterN')
# Plot gene expression tSNE overlayed with viral genomics
fig, axs = plt.subplots(3, 3, figsize=(7, 6), sharex=True, sharey=True)
axs = axs.ravel()
plots = [
'log_virus_reads_per_million', 'coverage', 'depth', 'numSNV', 'log_Dn',
# Script
if __name__ == '__main__':
# NOTE: an env variable for the config file needs to be set when
# calling this script
from singlet.dataset import Dataset
ds = Dataset(samplesheet='example_sheet_tsv',
counts_table='example_table_tsv')
print('Test feature selection by expression')
res = ds.feature_selection.expressed(n_samples=1, exp_min=1)
assert (res[0] == 'TSPAN6')
print('Done!')
print('Test feature selection by expression, in place')
dsp = ds.copy()
dsp.feature_selection.expressed(n_samples=1, exp_min=1, inplace=True)
assert (dsp.featurenames[0] == 'TSPAN6')
print('Done!')
print('Test feature selection by overdispersed strata')
res = ds.feature_selection.overdispersed_strata()
assert (res[-1] == 'GLIPR2')
print('Done!')
print('Test feature selection by overdispersed strata, in place')
dsp = ds.copy()
dsp.feature_selection.overdispersed_strata(inplace=True)
assert (dsp.featurenames[-1] == 'GLIPR2')
print('Done!')
featuresheet='humanGC38',
)
ds.query_samples_by_counts('total >= 50000', inplace=True)
ds.samplesheet.rename(columns={'time [h]': 'time'}, inplace=True)
cov = ds.samplesheet['coverage'] = ds.counts.sum(axis=0)
ds.counts.normalize('counts_per_million', inplace=True)
ds.samplesheet['virus_reads_per_million'] = 0
for virus in ('dengue', 'zika'):
ind = ds.samplesheet['virus'] == virus
n = ds.samplesheet.loc[ind, 'number'+virus.capitalize()+'Reads'].astype(int)
ds.samplesheet.loc[ind, 'virus_reads_per_million'] = 1e6 * n / (cov.loc[ind] + n)
ds.counts.log(inplace=True)
# Select only some cells for comparison
dsc = ds.copy()
dsc.samplesheet = dsc.samplesheet.query('500 < virus_reads_per_million')
print('Get correlations')
dsv = dsc.split(phenotypes='virus')
vs = []
cos = []
for virus, dsvi in dsv.items():
co = dsvi.correlation.correlate_features_phenotypes(
phenotypes='virus_reads_per_million',
fillna=0).fillna(0)
cos.append(co)
vs.append(virus)
cos = pd.concat(cos, axis=1)
cos.columns = pd.Index(vs, name='virus')
from singlet.dataset import Dataset
ds = Dataset(counts_table='example_PBMC')
# Normalize
ds.counts.normalize(method='counts_per_million', inplace=True)
ds.counts.log(inplace=True)
# Select features
ds.feature_selection.expressed(n_samples=3, exp_min=1, inplace=True)
ds.feature_selection.overdispersed_strata(n_features_per_stratum=20,
inplace=True)
# Reduce dimensionality
vs = ds.dimensionality.tsne(n_dims=2, theta=0.5, perplexity=0.8)
dsr = ds.copy()
dsr.counts = vs.T
# Cluster
dsr.samplesheet['dbscan'] = dsr.cluster.dbscan(eps=5, axis='samples')
dsr.samplesheet['kmeans'] = dsr.cluster.kmeans(n_clusters=7,
axis='samples')
# Plot t-SNE
fig, axs = plt.subplots(nrows=1,
ncols=2,
sharex=True,
sharey=True,
figsize=(8, 4))
dsr.plot.scatter_reduced_samples(vs,
color_by='dbscan',
