def test_transformed(self):
"""correctly return transform counts"""
input = dict(counts=[[0, 1], [2, 3], [4, 5], [6, 7], [8, 9]])
coll = RegionCollection(**input)
c, r, stderr = coll.transformed()
self.assertEqual(c, [4, 5])
freqs = coll.asfreqs()
c, r, stderr = freqs.transformed(counts_func=column_sum)
self.assertFloatEqual(
c, numpy.array([20. / 45., 25. / 45], dtype=c.dtype))
class RegionStudy(object):
""" Specifies the RegionCollection associated with an expression
data set. Used to collate data for plot_counts.py.
Members: collection (a RegionCollection), window_start, window_end,
collection_label
Methods: filterByGenes, filterByCutoff, normaliseByBases,
asPlotLines
"""
def __init__(self, collection_fn, counts_func, *args, **kwargs):
super(RegionStudy, self).__init__(*args, **kwargs)
rr = RunRecord('Study')
# Keep the source file name for labelling purposes
self.collection_path = collection_fn
fn = collection_fn.split('/')[-1].rstrip('.gz')
self.collection_label = ' '.join(fn.replace('_', ' ').split('.')[:-1])
try:
self.data_collection = RegionCollection(filename=collection_fn)
except IOError:
rr.dieOnCritical('Collection will not load', collection_fn)
# Frequency normalized counts need to be converted
if counts_func is column_sum:
self.data_collection = self.data_collection.asfreqs()
self.counts_func = counts_func
# Get feature window start and end
try:
self.window_upstream =\
self.data_collection.info['args']['window_upstream']
except KeyError:
rr.dieOnCritical('Collection value not defined', 'window_upstream')
try:
self.window_downstream =\
self.data_collection.info['args']['window_downstream']
except KeyError:
rr.dieOnCritical('Collection value not defined',
'window_downstream')
try:
self.feature_type =\
self.data_collection.info['args']['feature_type']
except KeyError:
self.feature_type = 'Unknown'
def filterByGenes(self,
db_path,
chrom=None,
include_samples=None,
exclude_samples=None):
""" keep only results that match selected genes """
rr = RunRecord('filterByGenes')
if not include_samples and not exclude_samples and not chrom:
return
rr.addInfo('Starting no. of genes', self.data_collection.N)
session = make_session(db_path)
if include_samples:
for sample in include_samples:
rr.addInfo('Restricting plot by include sample', sample)
if exclude_samples:
for sample in exclude_samples:
rr.addInfo('Restricting plot by exclude sample', sample)
if not chrom is None:
rr.addInfo('Restricting plot to chromosome', chrom)
filter_gene_ids = get_gene_ids(session,
chrom=chrom,
include_targets=include_samples,
exclude_targets=exclude_samples)
self.data_collection =\
self.data_collection.filteredByLabel(filter_gene_ids)
rr.addInfo('Remaining genes', self.data_collection.N)
if self.data_collection is None or\
len(self.data_collection.ranks) == 0:
rr.dieOnCritical('Genes remaining after filtering', '0')
def filterByCutoff(self, cutoff=None):
""" keep only results that pass Chebyshev cutoff """
rr = RunRecord('filterByCutoff')
rr.addInfo('Starting no. of genes', self.data_collection.N)
# exclude outlier genes using one-sided Chebyshev
if cutoff is not None and cutoff != 0.0:
try:
cutoff = float(cutoff)
if cutoff < 0.0 or cutoff >= 1.0:
rr.addError('Cutoff out of range', cutoff)
rr.addInfo('Cutoff set to default', 0.05)
cutoff = 0.05
except ValueError:
rr.addError('Cutoff not given as float', cutoff)
rr.addInfo('Cutoff set to default', 0.05)
cutoff = 0.05
# Do Chebyshev filtering
self.data_collection =\
self.data_collection.filteredChebyshevUpper(p=cutoff)
rr.addInfo('Used Chebyshev filter cutoff', cutoff)
rr.addInfo('No. genes after normalisation filter',
self.data_collection.N)
else:
rr.addInfo('Outlier cutoff filtering', 'Off')
if self.data_collection is None or\
self.data_collection.ranks.max() == 0:
rr.dieOnCritical('No data after filtering', 'Failure')
def normaliseByRPM(self):
""" This requires 'mapped tags', 'tag count' or 'base count' to be present
in the collection and gives counts per mapped million tags/bases.
Mapped tags is the total experimental mapped tags.
Tag count and base count are region specific.
"""
rr = RunRecord('normaliseByRPM')
try:
norm_RPM = self.data_collection.info['args']['mapped tags']
rr.addInfo("'mapped tags' value", norm_RPM)
except KeyError:
rr.addError('Info field not found', 'mapped tags')
return
norm_factor = 1000000.0 / norm_RPM
rr.addInfo('normalising by RPMs', norm_factor)
normalised_counts = []
for c in self.data_collection.counts:
c2 = c * norm_factor
normalised_counts.append(c2)
self.data_collection.counts = numpy.array(normalised_counts)
def _groupAllGeneCounts(self):
""" Group counts for all genes and return as a single PlotLine.
Called by asPlotLines or _groupNGeneCounts().
Returns a list.
"""
rr = RunRecord('_groupAllGeneCounts')
counts, ranks, se = self.data_collection.transformed(\
counts_func=self.counts_func)
if not len(counts):
rr.dieOnCritical('No counts data in', 'Study._groupAllGeneCounts')
ranks = 0 # rank is irrelevant for 'all' genes
# Always name single lines by their collection name
label = self.collection_label
plot_lines = [PlotLine(counts, ranks, label, study=label, stderr=se)]
return plot_lines
def _groupNoGeneCounts(self):
""" Don't group counts. Simply return a PlotLine for each set of
counts.
Called by asPlotLines()
"""
rr = RunRecord('_groupNoGeneCounts')
counts = self.data_collection.counts
ranks = self.data_collection.ranks
labels = self.data_collection.labels
plot_lines = []
for c, r, l in zip(counts, ranks, labels):
if self.counts_func == stdev:
stdev_ = c.std()
if stdev_ > 0:
c = (c - c.mean()) / stdev_
plot_lines.append(
PlotLine(c, r, l, study=self.collection_label))
else:
plot_lines.append(
PlotLine(c, r, l, study=self.collection_label))
# If no data was returned default to groupAllCollectionCounts
if not len(plot_lines):
rr.dieOnCritical('No data in collection', 'Failure')
# If a single line is created label it with the collection name
if len(plot_lines) == 1:
plot_lines[0].label = [self.collection_label]
return plot_lines
def _groupNGeneCounts(self, group_size, p=0.0):
""" Group counts for N genes and return as PlotLines. Defaults to
_groupAllGeneCounts() if group size is too large.
Called by asPlotLines()
"""
rr = RunRecord('_groupNGeneCounts')
plot_lines = []
for index, (c,r,l,se) in enumerate(self.data_collection.\
iterTransformedGroups(group_size=group_size,
counts_func=self.counts_func, p=p)):
plot_lines.append(
PlotLine(c,
rank=r,
label=l,
study=self.collection_label,
stderr=se))
# If no data was returned default to groupAllCollectionCounts
if not len(plot_lines):
rr.addWarning('Defaulting to ALL features. Not enough '+\
'features for group of size', group_size)
plotLines = self._groupAllGeneCounts()
return plotLines
return plot_lines
def asPlotLines(self, group_size, group_location, p=0.0):
"""
Returns a list of PlotLine objects from this study.
'p' is the Chebyshev cut-off if not None
"""
rr = RunRecord('asPlotLines')
if p > 0.0:
rr.addInfo('Applying per-line Chebyshev filtering', p)
if type(group_size) is str and group_size.lower() == 'all':
plot_lines = self._groupAllGeneCounts()
elif type(group_size) is int:
if group_size == 1:
plot_lines = self._groupNoGeneCounts()
else:
plot_lines = self._groupNGeneCounts(group_size, p=p)
else:
rr.dieOnCritical('group_size, wrong type or value',
[type(group_size), group_size])
if group_location.lower() != 'all':
rr.addInfo('grouping genes from location', group_location)
plot_lines.sort(key=lambda x: x.rank)
if group_location.lower() == 'top':
plot_lines = [plot_lines[0]]
elif group_location.lower() == 'middle':
plot_lines = [plot_lines[int(len(plot_lines) / 2)]]
elif group_location.lower() == 'bottom':
plot_lines = [plot_lines[-1]]
rr.addInfo('Plottable lines from study', len(plot_lines))
return plot_lines