def find_nearest_features(peaks,
features,
distance=None,
tss_only=False,
only_differentially_expressed=False):
"""
Locate nearest features for each peak
Arguments:
features (FeatureList): list of features
peaks (PeakList): list of peaks
distance (int): optional cut-off distance to apply
tss_only (bool): only consider distances from the
feature TSS (default is to consider distances from
both the TSS and TES)
only_differentially_expressed (bool): only consider
features that are flagged as differentially expressed
pad (bool): add extra 'None' items to output
FeatureSet so that it contains max_closest results
Yields:
tuple: Peak object and a FeatureSet object with the
nearest features, for each peak in the input PeakSet
"""
# Find nearest features to each peak
for peak in peaks:
# Only consider features on same chromosome
feature_list = features.filterByChr(peak.chrom)
# Differentially-expressed features only?
if only_differentially_expressed:
feature_list = feature_list.filterByFlag(1)
if tss_only:
sort_features_by_tss_distances(peak, feature_list)
else:
sort_features_by_edge_distances(peak, feature_list)
# Apply distance cut-off
if distance is not None:
closest = FeatureSet()
for feature in feature_list:
if tss_only:
if distances.distance_tss(peak, feature) > distance:
break
else:
if distances.distance_closest_edge(peak,
feature) > distance:
break
closest.addFeature(feature)
feature_list = closest
# Return at least one (null) result
if not feature_list:
feature_list.addFeature(None)
# Return result
yield (peak, feature_list)
def find_nearest_features(peaks,features,distance=None,tss_only=False,
only_differentially_expressed=False):
"""
Locate nearest features for each peak
Arguments:
features (FeatureList): list of features
peaks (PeakList): list of peaks
distance (int): optional cut-off distance to apply
max_closest (int): optional maximum number of peaks
to find per feature
tss_only (bool): only consider distances from the
feature TSS (default is to consider distances from
both the TSS and TES)
only_differentially_expressed (bool): only consider
features that are flagged as differentially expressed
pad (bool): add extra 'None' items to output
FeatureSet so that it contains max_closest results
Yields:
tuple: Peak object and a FeatureSet object with the
nearest features, for each peak in the input PeakSet
"""
# Find nearest features to each peak
for peak in peaks:
# Only consider features on same chromosome
feature_list = features.filterByChr(peak.chrom)
# Differentially-expressed features only?
if only_differentially_expressed:
feature_list = feature_list.filterByFlag(1)
if tss_only:
sort_features_by_tss_distances(peak,feature_list)
else:
sort_features_by_edge_distances(peak,feature_list)
# Apply distance cut-off
if distance is not None:
closest = FeatureSet()
for feature in feature_list:
if tss_only:
if distances.distance_tss(peak,feature) > distance:
break
else:
if distances.distance_closest_edge(peak,feature) > distance:
break
closest.addFeature(feature)
feature_list = closest
# Return at least one (null) result
if not feature_list:
feature_list.addFeature(None)
# Return result
yield (peak,feature_list)
def _value_for(self,attr):
"""
Return the value for the specified attribute
Given the name of a field/attribute (see above for
a list and definition of each), return the value
for the current peak/feature pair (which should have
been set by the calling method in the '_context_peak'
and '_context_feature' properties).
Arguments:
attr (string): attribute name
Returns:
Value of the field for the current peak/feature
pair
Raises:
AttributeError: if valid ``attr`` cannot be derived
KeyError: if ``attr`` is not a recognised attribute
name
"""
peak = self._context_peak
feature = self._context_feature
is_features = self._is_features
if attr == 'peak.id':
return peak.id
elif attr == 'chr' or attr == 'peak.chr':
return peak.chrom
elif attr == 'peak.start' or attr == 'start':
return peak.start
elif attr == 'peak.end' or attr == 'end':
return peak.end
elif attr == 'id' or attr == 'feature.id':
return feature.id
elif attr == 'feature.chr':
return feature.chrom
elif attr == 'feature.start':
return feature.start
elif attr == 'feature.end':
return feature.end
elif attr == 'TSS':
return feature.tss
elif attr == 'TES':
return feature.tes
elif attr == 'strand' or attr == 'feature.strand':
return feature.strand
elif attr == 'differentially_expressed':
return feature.flag
elif attr == 'dist_closest':
return distances.distance_closest_edge(peak,feature)
elif attr == 'dist_TSS':
return distances.distance_tss(peak,feature)
elif attr == 'dist_TES':
return distances.distance_tes(peak,feature)
elif attr == 'overlap_feature' or attr == 'in_the_feature':
if distances.regions_overlap((peak.start,peak.end),
(feature.tss,feature.tes)):
overlap_feature = 1
else:
overlap_feature = 0
if attr == 'in_the_feature':
overlap_feature = ('YES' if overlap_feature == 1 else 'NO')
return overlap_feature
elif attr == 'overlap_promoter':
if self._promoter_region is not None:
promoter = feature.getPromoterRegion(*self._promoter_region)
if distances.regions_overlap((peak.start,peak.end),
promoter):
overlap_promoter = 1
else:
overlap_promoter = 0
else:
raise Exception("'overlap_promoter' requested but no "
"promoter region has been defined")
return overlap_promoter
elif attr == 'direction':
if self._is_features:
direction = distances.direction(feature,peak)
else:
direction = distances.direction(peak,feature)
if direction == distances.UPSTREAM:
return 'U'
elif direction == distances.DOWNSTREAM:
return 'D'
else:
return '.'
elif attr == 'features_inbetween':
raise NotImplementedError("'features_inbetween' not implemented")
else:
raise KeyError("Unrecognised report field: '%s'" % attr)
def _value_for(self,attr):
"""
Return the value for the specified attribute
Given the name of a field/attribute (see above for
a list and definition of each), return the value
for the current peak/feature pair (which should have
been set by the calling method in the '_context_peak'
and '_context_feature' properties).
Arguments:
attr (string): attribute name
Returns:
Value of the field for the current peak/feature
pair
Raises:
AttributeError: if valid ``attr`` cannot be derived
KeyError: if ``attr`` is not a recognised attribute
name
"""
peak = self._context_peak
feature = self._context_feature
extra_data = self._extra_data
is_features = self._is_features
if attr == 'peak.id':
return peak.id
elif attr == 'chr' or attr == 'peak.chr':
return peak.chrom
elif attr == 'peak.start' or attr == 'start':
return peak.start
elif attr == 'peak.end' or attr == 'end':
return peak.end
elif attr == 'peak.file':
return peak.source_file
elif attr == 'id' or attr == 'feature.id':
return feature.id
elif attr == 'feature.chr':
return feature.chrom
elif attr == 'feature.start':
return feature.start
elif attr == 'feature.end':
return feature.end
elif attr == 'feature.file':
return feature.source_file
elif attr == 'TSS':
return feature.tss
elif attr == 'TES':
return feature.tes
elif attr == 'strand' or attr == 'feature.strand':
return feature.strand
elif attr == 'differentially_expressed':
return feature.flag
elif attr == 'dist_closest':
return distances.distance_closest_edge(peak,feature)
elif attr == 'dist_TSS':
return distances.distance_tss(peak,feature)
elif attr == 'dist_TES':
return distances.distance_tes(peak,feature)
elif attr == 'overlap_feature' or attr == 'in_the_feature':
if distances.regions_overlap((peak.start,peak.end),
(feature.tss,feature.tes)):
overlap_feature = 1
else:
overlap_feature = 0
if attr == 'in_the_feature':
overlap_feature = ('YES' if overlap_feature == 1 else 'NO')
return overlap_feature
elif attr == 'overlap_promoter':
if self._promoter_region is not None:
promoter = feature.getPromoterRegion(*self._promoter_region)
if distances.regions_overlap((peak.start,peak.end),
promoter):
overlap_promoter = 1
else:
overlap_promoter = 0
else:
raise Exception("'overlap_promoter' requested but no "
"promoter region has been defined")
return overlap_promoter
elif attr == 'direction':
if self._is_features:
direction = distances.direction(feature,peak)
else:
direction = distances.direction(peak,feature)
if direction == distances.UPSTREAM:
return 'U'
elif direction == distances.DOWNSTREAM:
return 'D'
else:
return '.'
elif attr == 'features_inbetween':
raise NotImplementedError("'features_inbetween' not implemented")
else:
# Check extra data items
try:
return extra_data[attr]
except KeyError:
raise KeyError("Unrecognised report field: '%s'" % attr)