# vim: fdm=indent
# author: Fabio Zanini
# date: 14/08/17
# content: Dataset that combines feature counts with metadata.
# Modules
import numpy as np
import pandas as pd
from ..samplesheet import SampleSheet
from ..counts_table import CountsTable
from ..counts_table import CountsTableSparse
from ..featuresheet import FeatureSheet
from .plugins import Plugin
# Classes / functions
[docs]class Dataset():
'''Collection of cells, with feature counts and metadata'''
def __init__(
self,
counts_table=None,
samplesheet=None,
featuresheet=None,
dataset=None,
plugins=None):
'''Collection of cells, with feature counts and metadata
Args:
counts_table (string): Name of the counts table (to load from a
config file) or instance of CountsTable or CountsTableSparse
samplesheet (string or None): Name of the samplesheet (to load from
a config file) or instance of SampleSheet
featuresheet (string or None): Name of the samplesheet (to load
from a config file) or instance of FeatureSheet
dataset (string or dict or None): Name of the Dataset (to load from
a config file) or dict with the config settings themselves.
plugins (dict): Dictionary of classes that take the Dataset
instance as only argument for __init__, to expand the
possibilities of Dataset operations.
NOTE: you can set *either* a dataset or a combination of counts_table,
samplesheet, and featuresheet. Setting both will raise an error.
NOTE: All samples in the counts_table must also be in the
samplesheet, but the latter can have additional samples. If
that is the case, the samplesheet is sliced down to the
samples present in the counts_table.
'''
if ((dataset is not None) and
((counts_table is not None) or (samplesheet is not None) or
(featuresheet is not None))):
raise ValueError('Set a dataset or a counts_table/samplesheet/featuresheet, but not both')
if (dataset is None) and (samplesheet is None) and (counts_table is None):
raise ValueError('A dataset, samplesheet or counts_table must be present')
if dataset is not None:
self._from_dataset(dataset)
else:
self._from_datastructures(
counts_table=counts_table,
samplesheet=samplesheet,
featuresheet=featuresheet,
)
# Inject yourself into counts_table
self.counts.dataset = self
# Plugins
self._set_plugins(plugins=plugins)
def __str__(self):
return '{:} with {:} samples and {:} features'.format(
self.__class__.__name__,
self.n_samples,
self.n_features)
def __repr__(self):
return '<{:}: {:} samples, {:} features>'.format(
self.__class__.__name__,
self.n_samples,
self.n_features)
def __eq__(self, other):
if type(other) is not type(self):
return False
# FIXME: fillna(0) is sloppy but not so bad
ss = (self._samplesheet.fillna(0) == other._samplesheet.fillna(0)).values.all()
fs = (self._featuresheet.fillna(0) == other._featuresheet.fillna(0)).values.all()
ct = (self._counts == other._counts).values.all()
return ss and fs and ct
def __ne__(self, other):
return not self == other
def __add__(self, other):
'''Merge two Datasets.
For samples with the same names, counts will be added and metadata of
one of the Datasets used. For new samples, the new counts and
metadata will be used.
NOTE: metadata and gene names must be aligned for this operation to
succeed. If one of the two Datasets has more metadata or
features than the other, they cannot be added.
'''
selfcopy = self.copy()
selfcopy += other
return selfcopy
def __iadd__(self, other):
'''Merge two Datasets.
For samples with the same names, counts will be added and metadata of
one of the Datasets used. For new samples, the new counts and
metadata will be used.
NOTE: metadata and gene names must be aligned for this operation to
succeed. If one of the two Datasets has more metadata or
features than the other, they cannot be added.
'''
if set(self.samplemetadatanames) != set(other.samplemetadatanames):
raise IndexError('The Datasets have different sample metadata')
if set(self.featurenames) != set(other.featurenames):
raise IndexError('The Datasets have different features')
if set(self.featuremetadatanames) != set(other.featuremetadatanames):
raise IndexError('The Datasets have different feature metadata')
snames = self.samplenames
for samplename, meta in other.samplesheet.iterrows():
if samplename not in snames:
self.samplesheet.loc[samplename] = meta
self.counts.loc[:, samplename] = other.counts.loc[:, samplename]
else:
counts_col_other = other.counts.loc[:, samplename]
if isinstance(other._counts, CountsTableSparse):
counts_col_other = counts_col_other.to_dense()
if isinstance(self._counts, CountsTableSparse):
self.counts.loc[:, samplename] = (self.counts.loc[:, samplename].to_dense() + counts_col_other).to_sparse()
else:
self.counts.loc[:, samplename] += counts_col_other
return self
def _set_plugins(self, plugins=None):
'''Set plugins according to user's request'''
from .correlations import Correlation
from .plot import Plot
from .dimensionality import DimensionalityReduction
from .cluster import Cluster
from .fit import Fit
from .feature_selection import FeatureSelection
from .graph import Graph
self.correlation = Correlation(self)
self.plot = Plot(self)
self.dimensionality = DimensionalityReduction(self)
self.cluster = Cluster(self)
self.fit = Fit(self)
self.feature_selection = FeatureSelection(self)
self.graph = Graph(self)
if (plugins is not None) and len(plugins):
self._plugins = dict(plugins)
for key, val in plugins.items():
setattr(self, key, val(self))
else:
self._plugins = {}
def _from_datastructures(
self,
counts_table=None,
samplesheet=None,
featuresheet=None):
'''Set main data structures'''
from ..config import config
if counts_table is None:
if (isinstance(samplesheet, SampleSheet) or
isinstance(samplesheet, pd.DataFrame)):
self._counts = CountsTable(
data=[],
index=[],
columns=samplesheet.index)
elif isinstance(counts_table, CountsTable):
self._counts = counts_table
elif isinstance(counts_table, CountsTableSparse):
self._counts = counts_table
elif isinstance(counts_table, pd.DataFrame):
self._counts = CountsTable(counts_table)
else:
config_table = config['io']['count_tables'][counts_table]
if config_table.get('sparse', False):
self._counts = CountsTableSparse.from_tablename(counts_table)
else:
self._counts = CountsTable.from_tablename(counts_table)
if samplesheet is None:
self._samplesheet = SampleSheet(
data=[],
index=self._counts.columns)
self._samplesheet.sheetname = None
elif isinstance(samplesheet, SampleSheet):
self._samplesheet = samplesheet
elif isinstance(samplesheet, pd.DataFrame):
self._samplesheet = SampleSheet(samplesheet)
else:
self._samplesheet = SampleSheet.from_sheetname(samplesheet)
# This is the catchall for counts
if not hasattr(self, '_counts'):
self._counts = CountsTable(
data=[],
index=[],
columns=self._samplesheet.index)
if featuresheet is None:
self._featuresheet = FeatureSheet(data=[], index=self._counts.index)
self._featuresheet.sheetname = None
elif isinstance(featuresheet, FeatureSheet):
self._featuresheet = featuresheet
elif isinstance(featuresheet, pd.DataFrame):
self._featuresheet = FeatureSheet(featuresheet)
else:
self._featuresheet = FeatureSheet.from_sheetname(featuresheet)
# Uniform axes across data and metadata
self._samplesheet = self._samplesheet.loc[self._counts.columns]
#self._featuresheet = self._featuresheet.loc[self._counts.index]
def _from_dataset(self, dataset):
'''Load from config file using a dataset name or config
Args:
dataset (str or dict): if a string, a dataset with this name is
searched for in the config file. If a dict, it is interpreted as
the dataset config itself.
'''
from ..config import config, _normalize_dataset
from ..io import parse_dataset, integrated_dataset_formats
if isinstance(dataset, str):
datasetname = dataset
dataset = config['io']['datasets'][datasetname]
else:
datasetname = ''
dataset = _normalize_dataset(dataset)
if ('format' in dataset) and (dataset['format'] in integrated_dataset_formats):
d = parse_dataset({'datasetname': datasetname})
self._counts = d['counts']
self._samplesheet = d['samplesheet']
self._featuresheet = d['featuresheet']
else:
if ('samplesheet' not in dataset) and ('counts_table' not in dataset):
raise ValueError('Your dataset config must include a counts_table or a samplesheet')
if 'samplesheet' in dataset:
self._samplesheet = SampleSheet.from_datasetname(datasetname)
if 'counts_table' in dataset:
config_table = dataset['counts_table']
if config_table.get('sparse', False):
counts_table = CountsTableSparse.from_datasetname(datasetname)
else:
counts_table = CountsTable.from_datasetname(datasetname)
self._counts = counts_table
if not hasattr(self, '_samplesheet'):
self._samplesheet = SampleSheet(
data=[],
index=self._counts.columns)
self._samplesheet.sheetname = None
elif not hasattr(self, '_counts'):
self._counts = CountsTable(
data=[],
index=[],
columns=self._samplesheet.index)
if 'featuresheet' in dataset:
self._featuresheet = FeatureSheet.from_datasetname(datasetname)
else:
self._featuresheet = FeatureSheet(data=[], index=self._counts.index)
[docs] def to_dataset_file(self, filename, fmt=None, **kwargs):
'''Store dataset into an integrated dataset file
Args:
filename (str): path of the file to write to.
fmt (str or None): file format. If None, infer from the file
extension.
**kwargs (keyword arguments): depend on the format.
The additional keyword argument for the supported formats are:
- loom:
- axis_samples: `rows` or `columns` (default)
'''
if fmt is None:
fmt = filename.split('.')[-1]
if fmt == 'loom':
import loompy
matrix = self.counts.values
row_attrs = {col: self.featuresheet[col].values for col in self.featuresheet}
col_attrs = {col: self.samplesheet[col].values for col in self.samplesheet}
# Add attributes for the indices no matter what
if self.featuresheet.index.name is not None:
row_attrs[self.featuresheet.index.name] = self.featuresheet.index.values
else:
row_attrs['_index'] = self.featuresheet.index.values
if self.samplesheet.index.name is not None:
col_attrs[self.samplesheet.index.name] = self.samplesheet.index.values
else:
col_attrs['_index'] = self.samplesheet.index.values
if kwargs.get('axis_samples', 'columns') != 'columns':
matrix = matrix.T
row_attrs, col_attrs = col_attrs, row_attrs
loompy.create(filename, matrix, row_attrs, col_attrs)
else:
raise ValueError('File format not supported')
[docs] def split(self, phenotypes, copy=True):
'''Split Dataset based on one or more categorical phenotypes
Args:
phenotypes (string or list of strings): one or more phenotypes to
use for the split. Unique values of combinations of these
determine the split Datasets.
Returns:
dict of Datasets: the keys are either unique values of the
phenotype chosen or, if more than one, tuples of unique
combinations.
'''
from itertools import product
if isinstance(phenotypes, str):
phenotypes = [phenotypes]
phenos_uniques = [tuple(set(self.samplesheet.loc[:, p])) for p in phenotypes]
dss = {}
for comb in product(*phenos_uniques):
ind = np.ones(self.n_samples, bool)
for p, val in zip(phenotypes, comb):
ind &= self.samplesheet.loc[:, p] == val
if ind.sum():
samplesheet = self.samplesheet.loc[ind]
counts = self.counts.loc[:, ind]
if copy:
samplesheet = samplesheet.copy()
counts = counts.copy()
if len(phenotypes) == 1:
label = comb[0]
else:
label = comb
dss[label] = self.__class__(
samplesheet=samplesheet,
counts_table=counts,
featuresheet=self.featuresheet,
)
return dss
@property
def n_samples(self):
'''Number of samples'''
if self._samplesheet is not None:
return self._samplesheet.shape[0]
elif self._counts is not None:
return self._counts.shape[1]
else:
return 0
@property
def n_features(self):
'''Number of features'''
if self._counts is not None:
return self._counts.shape[0]
else:
return 0
@property
def samplenames(self):
'''pandas.Index of sample names'''
return self._samplesheet.index.copy()
@property
def featurenames(self):
'''pandas.Index of feature names'''
return self._counts.index.copy()
@property
def samplemetadatanames(self):
'''pandas.Index of sample metadata column names'''
return self._samplesheet.columns.copy()
@property
def featuremetadatanames(self):
'''pandas.Index of feature metadata column names'''
return self._featuresheet.columns.copy()
@property
def samplesheet(self):
'''Matrix of sample metadata.
Rows are samples, columns are metadata (e.g. phenotypes).
'''
return self._samplesheet
@samplesheet.setter
def samplesheet(self, value):
self._counts = self._counts.loc[:, value.index]
self._samplesheet = value
@property
def counts(self):
'''Matrix of gene expression counts.
Rows are features, columns are samples.
Notice: If you reset this matrix with features that are not in the
featuresheet or samples that are not in the samplesheet,
those tables will be reset to empty.
'''
return self._counts
@counts.setter
def counts(self, value):
try:
self._samplesheet = self._samplesheet.loc[value.columns]
except KeyError:
self._samplesheet = SampleSheet(data=[], index=value.columns)
try:
self._featuresheet = self._featuresheet.loc[value.index]
except KeyError:
self._featuresheet = FeatureSheet(data=[], index=value.index)
self._counts = value
self._counts.dataset = self
@property
def featuresheet(self):
'''Matrix of feature metadata.
Rows are features, columns are metadata (e.g. Gene Ontologies).
'''
return self._featuresheet
@featuresheet.setter
def featuresheet(self, value):
self._counts = self._counts.loc[value.index, :]
self._featuresheet = value
[docs] def copy(self):
'''Copy of the Dataset'''
return self.__class__(
counts_table=self._counts.copy(),
samplesheet=self._samplesheet.copy(),
featuresheet=self.featuresheet.copy(),
plugins=self._plugins)
[docs] def query_samples_by_name(
self,
samplenames,
inplace=False,
ignore_missing=False,
):
'''Select samples by name.
Args:
samplenames: names of the samples to keep.
inplace (bool): Whether to change the Dataset in place or return a
new one.
ignore_missing (bool): Whether to silently skip missing samples.
'''
if ignore_missing:
snall = self.samplenames
samplenames = [fn for fn in samplenames if fn in snall]
if inplace:
self._samplesheet = self._samplesheet.loc[samplenames]
self._counts = self._counts.loc[:, samplenames]
else:
samplesheet = self._samplesheet.loc[samplenames].copy()
counts_table = self._counts.loc[:, samplenames].copy()
featuresheet = self._featuresheet.copy()
return self.__class__(
samplesheet=samplesheet,
counts_table=counts_table,
featuresheet=featuresheet)
[docs] def query_features_by_name(
self,
featurenames,
inplace=False,
ignore_missing=False,
):
'''Select features by name.
Args:
featurenames: names of the features to keep.
inplace (bool): Whether to change the Dataset in place or return a
new one.
ignore_missing (bool): Whether to silently skip missing features.
'''
if ignore_missing:
fnall = self.featurenames
featurenames = [fn for fn in featurenames if fn in fnall]
if inplace:
self._featuresheet = self._featuresheet.loc[featurenames]
self._counts = self._counts.loc[featurenames]
else:
featuresheet = self._featuresheet.loc[featurenames].copy()
counts_table = self._counts.loc[featurenames].copy()
samplesheet = self._samplesheet.copy()
return self.__class__(
samplesheet=samplesheet,
counts_table=counts_table,
featuresheet=featuresheet)
[docs] def query_samples_by_counts(
self, expression, inplace=False,
local_dict=None):
'''Select samples based on gene expression.
Args:
expression (string): An expression compatible with
pandas.DataFrame.query.
inplace (bool): Whether to change the Dataset in place or return a
new one.
local_dict (dict): A dictionary of local variables, useful if you
are using @var assignments in your expression. By far the most
common usage of this argument is to set local_dict=locals().
Returns:
If `inplace` is True, None. Else, a Dataset.
'''
counts = self._counts.copy()
drop = []
if ('total' in expression) and ('total' not in counts.index):
counts.loc['total'] = counts.sum(axis=0)
drop.append('total')
if ('mapped' in expression) and ('mapped' not in counts.index):
counts.loc['mapped'] = counts.exclude_features(spikeins=True, other=True).sum(axis=0)
drop.append('mapped')
counts_table = counts.T.query(
expression, inplace=False,
local_dict=local_dict).T
if drop:
counts_table.drop(drop, axis=0, inplace=True)
if inplace:
self.counts = counts_table
else:
samplesheet = self._samplesheet.loc[counts_table.columns].copy()
return self.__class__(
samplesheet=samplesheet,
counts_table=counts_table,
featuresheet=self.featuresheet.copy())
[docs] def query_features_by_counts(
self, expression, inplace=False,
local_dict=None):
'''Select features based on their expression.
Args:
expression (string): An expression compatible with
pandas.DataFrame.query.
inplace (bool): Whether to change the Dataset in place or return a
new one.
local_dict (dict): A dictionary of local variables, useful if you
are using @var assignments in your expression. By far the
most common usage of this argument is to set
local_dict=locals().
Returns:
If `inplace` is True, None. Else, a Dataset.
'''
if inplace:
self._counts.query(
expression, inplace=True,
local_dict=local_dict)
self._featuresheet = self._featuresheet.loc[self._counts.index]
else:
counts_table = self._counts.query(
expression, inplace=False,
local_dict=local_dict)
samplesheet = self._samplesheet.copy()
featuresheet = self._featuresheet.loc[counts_table.index].copy()
return self.__class__(
samplesheet=samplesheet,
counts_table=counts_table,
featuresheet=featuresheet)
[docs] def rename(
self,
axis,
column,
inplace=False):
'''Rename samples or features
Args:
axis (string): Must be 'samples' or 'features'.
column (string): Must be a column of the samplesheet (for
axis='samples') or of the featuresheet (for axis='features')
with unique names of samples or features.
inplace (bool): Whether to change the Dataset in place or return a
new one.
DEPRECATED: use `reindex` instead.
'''
return self.reindex(axis, column, inplace=inplace)
[docs] def reindex(
self,
axis,
column,
drop=False,
inplace=False):
'''Reindex samples or features from a metadata column
Args:
axis (string): Must be 'samples' or 'features'.
column (string): Must be a column of the samplesheet (for
axis='samples') or of the featuresheet (for axis='features')
with unique names of samples or features.
drop (bool): Whether to drop the column from the metadata table.
inplace (bool): Whether to change the Dataset in place or return a
new one.
'''
if axis not in ('samples', 'features'):
raise ValueError('axis must be "samples" or "features"')
if inplace:
if axis == 'samples':
self._samplesheet.index = self._samplesheet.loc[:, column]
self._counts.columns = self._samplesheet.loc[:, column]
if drop:
del self._samplesheet[column]
else:
self._featuresheet.index = self._featuresheet.loc[:, column]
self._counts.index = self._featuresheet.loc[:, column]
if drop:
del self._featuresheet[column]
else:
other = self.copy()
other.reindex(
axis=axis,
column=column,
drop=drop,
inplace=True)
return other
[docs] def compare(
self,
other,
features='mapped',
phenotypes=(),
method='kolmogorov-smirnov'):
'''Statistically compare with another Dataset.
Args:
other (Dataset): The Dataset to compare with.
features (list, string, or None): Features to compare. The string
'total' means all features including spikeins and other,
'mapped' means all features excluding spikeins and other,
'spikeins' means only spikeins, and 'other' means only
'other' features. If empty list or None, do not compare
features (useful for phenotypic comparison).
phenotypes (list of strings): Phenotypes to compare.
method (string or function): Statistical test to use for the
comparison. If a string it must be one of
'kolmogorov-smirnov' or 'mann-whitney'. If a function, it
must accept two arrays as arguments (one for each
dataset, running over the samples) and return a P-value
for the comparison.
Return:
A pandas.DataFrame containing the P-values of the comparisons for
all features and phenotypes.
'''
pvalues = []
if features:
counts = self.counts
counts_other = other.counts
if features == 'total':
pass
elif features == 'mapped':
counts = counts.exclude_features(
spikeins=True, other=True, errors='ignore')
counts_other = counts_other.exclude_features(
spikeins=True, other=True, errors='ignore')
elif features == 'spikeins':
counts = counts.get_spikeins()
counts_other = counts_other.get_spikeins()
elif features == 'other':
counts = counts.get_other_features()
counts_other = counts_other.get_other_features()
else:
counts = counts.loc[features]
counts_other = counts_other.loc[features]
if method == 'kolmogorov-smirnov':
from scipy.stats import ks_2samp
for (fea, co1), (_, co2) in zip(
counts.iterrows(),
counts_other.iterrows()):
pvalues.append([fea, ks_2samp(co1.values, co2.values)[1]])
elif method == 'mann-whitney':
from scipy.stats import mannwhitneyu
for (fea, co1), (_, co2) in zip(
counts.iterrows(),
counts_other.iterrows()):
# Mann-Whitney U has issues with ties, so we handle a few
# corner cases separately
is_degenerate = False
# 1. no samples
if (len(co1.values) == 0) or (len(co2.values) == 0):
is_degenerate = True
# 2. if there is only one value over the board
tmp1 = np.unique(co1.values)
tmp2 = np.unique(co2.values)
if ((len(tmp1) == 1) and (len(tmp2) == 1) and
(tmp1[0] == tmp2[0])):
is_degenerate = True
# 3. if the arrays are the exact same
elif ((len(co1) == len(co2)) and
(np.sort(co1.values) == np.sort(co2.values)).all()):
is_degenerate = True
if is_degenerate:
pvalues.append([fea, 1])
continue
pvalues.append([fea, mannwhitneyu(
co1.values, co2.values,
alternative='two-sided')[1]])
else:
for (fea, co1) in counts.iterrows():
co2 = counts_other.loc[fea]
pvalues.append([fea, method(co1.values, co2.values)])
if phenotypes:
pheno = self.samplesheet.loc[:, phenotypes].T
pheno_other = other.samplesheet.loc[:, phenotypes].T
if method == 'kolmogorov-smirnov':
from scipy.stats import ks_2samp
for phe, val1 in pheno.iterrows():
val2 = pheno_other.loc[phe]
pvalues.append([phe, ks_2samp(val1.values, val2.values)[1]])
elif method == 'mann-whitney':
from scipy.stats import mannwhitneyu
for phe, val1 in pheno.iterrows():
val2 = pheno_other.loc[phe]
# Mann-Whitney U has issues with ties
is_degenerate = False
if ((len(np.unique(val1.values)) == 1) or
(len(np.unique(val2.values)) == 1)):
is_degenerate = True
elif ((len(val1) == len(val2)) and
(np.sort(val1.values) == np.sort(val2.values)).all()):
is_degenerate = True
if is_degenerate:
pvalues.append([phe, 1])
continue
pvalues.append([phe, mannwhitneyu(
val1.values, val2.values,
alternative='two-sided')[1]])
else:
for phe, val1 in pheno.iterrows():
val2 = pheno_other.loc[phe]
pvalues.append([phe, method(val1.values, val2.values)])
df = pd.DataFrame(pvalues, columns=['name', 'P-value'])
df.set_index('name', drop=True, inplace=True)
return df
[docs] def bootstrap(self, groupby=None):
'''Resample with replacement, aka bootstrap dataset
Args:
groupby (str or list of str or None): If None, bootstrap random
samples disregarding sample metadata. If a string or a list of
strings, boostrap over groups of samples with consistent
entries for that/those columns.
Returns:
A Dataset with the resampled samples.
'''
n = self.n_samples
if groupby is None:
ind = np.random.randint(n, size=n)
else:
meta = self.samplesheet.loc[:, groupby]
meta_unique = meta.drop_duplicates().values
n_groups = meta_unique.shape[0]
ind_groups = np.random.randint(n_groups, size=n_groups)
ind = []
for i in ind_groups:
indi = (meta == meta_unique[i]).values
if indi.ndim > 1:
indi = indi.all(axis=1)
indi = indi.nonzero()[0]
ind.extend(list(indi))
ind = np.array(ind)
snames = self.samplenames
from collections import Counter
tmp = Counter()
index = []
for i in ind:
tmp[i] += 1
index.append(snames[i]+'--sampling_'+str(tmp[i]))
index = pd.Index(index, name=self.samplenames.name)
ss = self.samplesheet.__class__(
self.samplesheet.values[ind],
index=index,
columns=self.samplesheet.columns,
)
ct = self.counts.__class__(
self.counts.values[:, ind],
index=self.counts.index,
columns=index,
)
fs = self.featuresheet.copy()
plugins = {key: val.__class__ for key, val in self._plugins}
return self.__class__(
counts_table=ct,
samplesheet=ss,
featuresheet=fs,
plugins=plugins,
)
[docs] def average(self, axis, column):
'''Average samples or features based on metadata
Args:
axis (string): Must be 'samples' or 'features'.
column (string): Must be a column of the samplesheet (for
axis='samples') or of the featuresheet (for axis='features').
Samples or features with a common value in this column are
averaged over.
Returns:
A Dataset with the averaged counts.
Note: if you average over samples, you get an empty samplesheet.
Simlarly, if you average over features, you get an empty featuresheet.
'''
if axis not in ('samples', 'features'):
raise ValueError('axis must be "samples" or "features"')
if axis == 'samples':
if column not in self.samplesheet.columns:
raise ValueError(
'{:} is not a column of the SampleSheet'.format(column))
vals = pd.Index(np.unique(self.samplesheet[column]), name=column)
n_conditions = len(vals)
counts = np.zeros(
(self.n_features, n_conditions),
dtype=self.counts.values.dtype)
for i, val in enumerate(vals):
ind = self.samplesheet[column] == val
counts[:, i] = self.counts.loc[:, ind].values.mean(axis=1)
counts = self.counts.__class__(
pd.DataFrame(
counts,
index=self.counts.index,
columns=vals))
featuresheet = self._featuresheet.copy()
return Dataset(
counts_table=counts,
featuresheet=featuresheet)
elif axis == 'features':
if column not in self.featuresheet.columns:
raise ValueError(
'{:} is not a column of the FeatureSheet'.format(column))
vals = pd.Index(np.unique(self.featuresheet[column]), name=column)
n_conditions = len(vals)
counts = np.zeros(
(n_conditions, self.n_samples),
dtype=self.counts.values.dtype)
for i, val in enumerate(vals):
ind = self.featuresheet[column] == val
counts[i] = self.counts.loc[ind].values.mean(axis=0)
counts = self.counts.__class__(
pd.DataFrame(
counts,
index=vals,
columns=self.counts.columns))
samplesheet = self._samplesheet.copy()
return Dataset(
counts_table=counts,
samplesheet=samplesheet)
