def retained_intron(annotation, density,
exon_offset, intron_offset,
annotation_type="rmats"):
"""
Creates an r x c pandas dataframe of r events for a
Retained Intron (RI) feature.
A RI matrix will contain two distinct regions:
|_]----||----[_|
Parameters
----------
annotation : str
path of file containing the annotation
density : density.ReadDensity
object containing the positive and negative BigWig files
exon_offset : int
how far into the exon boundary to plot
intron_offset : int
how far from the exon boundary to plot
annotation_type : str
may be rmats format or any additional defined format in Feature
Returns
-------
pandas.DataFrame : dataframe of r events for an MXE feature.
"""
three_upstream = {}
five_downstream = {}
with open(annotation) as f:
for line in f:
if not line.startswith('event_name') and not line.startswith('ID') and not line.startswith('annotation'):
event = line.rstrip() # .split('\t')[0]
upstream_interval, downstream_interval = Feature.Retained_intron(
event,
annotation_type
).get_bedtools()
"""three prime upstream region"""
wiggle = intervals.three_prime_site(
density, downstream_interval, upstream_interval,
exon_offset, intron_offset
)
three_upstream[event] = wiggle
"""five prime site of downstream region"""
wiggle = intervals.five_prime_site(
density, upstream_interval, downstream_interval,
exon_offset, intron_offset
)
five_downstream[event] = wiggle
three_upstream = pd.DataFrame(three_upstream).T
five_downstream = pd.DataFrame(five_downstream).T
ra = pd.concat([three_upstream, five_downstream], axis=1)
ra.columns = range(0, ra.shape[1])
return ra
def unscaled_region(
annotation, density, annotation_type,
upstream_offset, downstream_offset
):
"""
Given an exon, return a dataframe of densities
Parameters
----------
annotation : basestring
filename of the annotation file to use
density : density.ReadDensity
object that contains positive and negative normalized density *.bw
upstream_offset : int
number of bases into the exon to return densities for. NOTE: this
nomenclature assumes we're plotting (+) exon|intron junctions, so
exons are 'upstream'. However 'upstream_offset' still refers to
exons in (-) intron|exon features.
downstream_offset : int
number of bases from the exon boundary to return. NOTE: this
nomenclature assumes we're plotting (+) exon|intron junctions, so
introns are 'downstream'. However 'downstream_offset' still refers to
introns in (-) intron|exon features.
annotation_type : basestring
name of the annotation feature described by density.Feature
Returns
-------
dataframe with (r rows and c cols) describing the density values across
a list of exons defined by annotation_file.
r = intron_offset + exon_offset + exon_offset + intron_offset
c = number of annotations in annotation_file
"""
up = {} # describes for every event the upstream region
down = {} # describes for every event the downstream region
with open(annotation) as f:
for line in f:
if not line.startswith('event_name') and not line.startswith('ID') and not line.startswith('annotation'):
event = line.rstrip() # .split('\t')[0]
interval = Feature.Feature(
event,
annotation_type
).get_bedtool()
""" calculate five prime site region """
# [ ]---|----[ | ]
wiggle = intervals.five_prime_site(
density, None, interval, upstream_offset,
downstream_offset, stop_at_midpoint=True
)
up[event] = wiggle
""" calculate the three prime site region """
wiggle = intervals.three_prime_site(
density, None, interval, upstream_offset,
downstream_offset, stop_at_midpoint=True
)
down[event] = wiggle
up = pd.DataFrame(up).T
down = pd.DataFrame(down).T
# combine both regions in order to normalize together.
ra = pd.concat([up, down], axis=1)
ra.columns = range(0, ra.shape[1])
return ra
def skipped_exon(annotation, density, exon_offset, intron_offset,
annotation_type="rmats"):
"""
Creates an r x c pandas dataframe of r events for a skipped
exon feature. An SE matrix will contain four distinct regions:
|_]----||----[__||__]----||----[_|
- [..exon_offset]--intron_offset--... 3' site of upstream exon
- ...--intron_offset--[exon_offset..] 5' site of upstream skipped exon
- [..exon_offset]--intron_offset--... 3' site of downstream skipped exon
- ..--intron_offset--[exon_offset..] 5' site of downstream exon
Parameters
----------
annotation : str
path of file containing the annotation
density : density.ReadDensity
object containing positive and negative BigWig files
exon_offset : int
how far into the exon boundary to plot
intron_offset : int
how far after the exon boundary to plot
annotation_type : str
may be rmats format or any additional defined format in Feature
Returns
-------
"""
three_upstream = {}
five_skipped = {}
three_skipped = {}
five_downstream = {}
with open(annotation) as f:
for line in f:
if not line.startswith('event_name') and not line.startswith('ID') and not line.startswith('annotation'):
event = line.rstrip()
try:
upstream_interval, interval, downstream_interval = \
Feature.Skipped_exon(
event,
annotation_type
).get_bedtools()
except Exception as e:
print("Having trouble parsing event: \
{} (assumed type: {})".format(event, annotation_type))
"""three prime upstream region"""
wiggle = intervals.three_prime_site(
density, interval, upstream_interval,
exon_offset, intron_offset
)
three_upstream[event] = wiggle
"""five prime site of skipped region"""
wiggle = intervals.five_prime_site(
density, upstream_interval, interval,
exon_offset, intron_offset
)
five_skipped[event] = wiggle
"""three prime site of skipped region"""
wiggle = intervals.three_prime_site(
density, downstream_interval, interval,
exon_offset, intron_offset
)
three_skipped[event] = wiggle
"""five prime site of downstream region"""
wiggle = intervals.five_prime_site(
density, interval, downstream_interval,
exon_offset, intron_offset
)
five_downstream[event] = wiggle
three_upstream = pd.DataFrame(three_upstream).T
five_skipped = pd.DataFrame(five_skipped).T
three_skipped = pd.DataFrame(three_skipped).T
five_downstream = pd.DataFrame(five_downstream).T
ra = pd.concat(
[three_upstream, five_skipped, three_skipped, five_downstream],
axis=1
)
ra.columns = range(0, ra.shape[1])
return ra
def alt_3p_splice_site(annotation, density, exon_offset, intron_offset,
annotation_type="rmats"):
"""
Creates an r x c pandas dataframe of r events for an
alternative 3' splice site feature. An A3SS matrix will
contain three distinct regions:
__|__]------||-----[__|____
__|__]------| |------[__|
- the [..exon_offset]--intron_offset--... 3' site of an upstream exon
- the ..--intron_offset--[exon_offset..] 5' site of the alt1 spliced exon
- the ..--intron_offset--[exon_offset..] 5' site of the alt2 spliced exon
Parameters
----------
annotation : str
path of file containing the annotation
density : density.ReadDensity
object containing positive and negative BigWig files
exon_offset : int
how far into the exon boundary to plot
intron_offset : int
how far after the exon boundary to plot
annotation_type : str
may be rmats format or any additional defined format in Feature
Returns
-------
pandas.DataFrame : a dataframe of r events for an A3SS feature.
"""
three_upstream = {}
five_alt1 = {}
five_alt2 = {}
with open(annotation) as f:
for line in f:
if not line.startswith('event_name') and not line.startswith('ID') and not line.startswith('annotation'):
event = line.rstrip()
upstream, alt1, alt2 = Feature.Alt_3p_splice_site(
event, annotation_type
).get_bedtools()
""" upstream region """
wiggle = intervals.three_prime_site(
density, alt1, upstream, exon_offset, intron_offset
)
three_upstream[event] = wiggle
""" five prime site of alt1 (longer exon) """
wiggle = intervals.five_prime_site(
density, upstream, alt1, exon_offset, intron_offset
)
five_alt1[event] = wiggle
""" five prime site of alt2 (shorter exon) """
wiggle = intervals.five_prime_site(
density, upstream, alt2, exon_offset, intron_offset
)
five_alt2[event] = wiggle
three_upstream = pd.DataFrame(three_upstream).T
five_alt1 = pd.DataFrame(five_alt1).T
five_alt2 = pd.DataFrame(five_alt2).T
ra = pd.concat([three_upstream, five_alt1, five_alt2], axis=1)
ra.columns = range(0, ra.shape[1])
return ra
def mutually_exc_exon(annotation, density, exon_offset, intron_offset,
annotation_type="rmats"):
"""
Creates an r x c pandas dataframe of r events for a mutually exclusive
exon feature. An MXE matrix will contain six distinct regions:
|_]----||----[__||__]----||----[__||__]----||----[_|
- [..exon_offset]--intron_offset--... 3' site of an upstream exon
- ...--intron_offset--[exon_offset..] 5' site of upstream skipped exon
- [..exon_offset]--intron_offset--... 3' site of upstream skipped exon
- ...--intron_offset--[exon_offset..] 5' site of downstream skipped exon
- [..exon_offset]--intron_offset--... 3' site of downstream skipped exon
- ..--intron_offset--[exon_offset..] 5' site of downstream exon
Parameters
----------
annotation : basestring
path of file containing the annotation
density : density.ReadDensity
object containing positive and negative BigWig files
exon_offset : int
how far into the exon boundary to plot
intron_offset : int
how far after the exon boundary to plot
annotation_type : basestring
Must be "rmats" or any additional defined format (see: density.Feature)
Returns
-------
pandas.DataFrame
A dataframe of r events for an MXE feature (see: description).
"""
three_upstream = {}
three_up_mxe = {}
five_up_mxe = {}
three_down_mxe = {}
five_down_mxe = {}
five_downstream = {}
with open(annotation) as f:
for line in f:
if not line.startswith('event_name') and not line.startswith('ID') and not line.startswith('annotation'):
event = line.rstrip() # .split('\t')[0]
upstream_interval, upstream_mxe_interval, \
downstream_mxe_interval, downstream_interval = \
Feature.Mutually_exclusive_exon(
event,
annotation_type
).get_bedtools()
"""three prime upstream region"""
wiggle = intervals.three_prime_site(
density, upstream_mxe_interval, upstream_interval,
exon_offset, intron_offset
)
three_upstream[event] = wiggle
"""five prime site of mxe1 (upstream mxe) region"""
wiggle = intervals.five_prime_site(
density, upstream_interval, upstream_mxe_interval,
exon_offset, intron_offset
)
five_up_mxe[event] = wiggle
"""three prime site of mxe1 (upstream mxe) region"""
wiggle = intervals.three_prime_site(
density, downstream_mxe_interval, upstream_mxe_interval,
exon_offset, intron_offset
)
three_up_mxe[event] = wiggle
"""five prime site of mxe2 (downstream mxe) region"""
wiggle = intervals.five_prime_site(
density, upstream_mxe_interval, downstream_mxe_interval,
exon_offset, intron_offset
)
five_down_mxe[event] = wiggle
"""three prime site of mxe2 (downstream mxe) region"""
wiggle = intervals.three_prime_site(
density, downstream_interval, downstream_mxe_interval,
exon_offset, intron_offset
)
three_down_mxe[event] = wiggle
"""five prime site of downstream region"""
wiggle = intervals.five_prime_site(
density, downstream_mxe_interval, downstream_interval,
exon_offset, intron_offset
)
five_downstream[event] = wiggle
# TODO make this more efficient.
three_upstream = pd.DataFrame(three_upstream).T
five_up_mxe = pd.DataFrame(five_up_mxe).T
three_up_mxe = pd.DataFrame(three_up_mxe).T
five_down_mxe = pd.DataFrame(five_down_mxe).T
three_down_mxe = pd.DataFrame(three_down_mxe).T
five_downstream = pd.DataFrame(five_downstream).T
ra = pd.concat([
three_upstream, five_up_mxe, three_up_mxe,
five_down_mxe, three_down_mxe, five_downstream
], axis=1
)
ra.columns = range(0, ra.shape[1])
return ra
def phastcon_region(annotation, density, annotation_type, exon_offset,
intron_offset, peak, mask_df):
"""
Produces a matrix corresponding to a region that contains a peak using values
that only overlap that peak
Parameters
----------
annotation
density
annotation_type
upstream_offset
downstream_offset
peak: density.Peak
Returns
-------
"""
three_upstream = {}
five_downstream = {}
with open(annotation) as f:
for line in f:
if not line.startswith('event_name') and not line.startswith(
'ID') and not line.startswith('annotation'):
event = line.rstrip() # .split('\t')[0]
upstream_interval, downstream_interval = Feature.Phastcon(
event, annotation_type).get_bedtools()
"""three prime upstream region"""
wiggle = intervals.three_prime_site(density,
downstream_interval,
upstream_interval,
exon_offset,
intron_offset,
fill_pads_with=-1)
if mask_df:
region = intervals.bedtool_from_renamed_twobed_index(
event, 'upstream')
masked_interval = peak.values(region.chrom, region.start,
region.end, region.strand)
if sum(masked_interval) > 0:
for pos in masked_interval.index:
wiggle[pos] = wiggle[pos] if masked_interval.loc[
pos] > 0 else np.nan
# if event == 'chr1\t1234724\t1234736\tENST00000354700.5\t0\t-\tchr1\t1235210\t1235285\tENST00000354700.5\t0\t-':
# print("upstream", region)
# print(wiggle)
else:
wiggle = [np.nan for pos in wiggle]
three_upstream[event] = wiggle
"""five prime site of downstream region"""
wiggle = intervals.five_prime_site(density,
upstream_interval,
downstream_interval,
exon_offset,
intron_offset,
fill_pads_with=-1)
if mask_df:
region = intervals.bedtool_from_renamed_twobed_index(
event, 'downstream')
masked_interval = peak.values(region.chrom, region.start,
region.end, region.strand)
if sum(masked_interval) > 0:
for pos in masked_interval.index:
wiggle[pos] = wiggle[pos] if (
masked_interval.loc[pos] > 0
and wiggle[pos] >= 0) else np.nan
# if event == 'chr1\t1234724\t1234736\tENST00000354700.5\t0\t-\tchr1\t1235210\t1235285\tENST00000354700.5\t0\t-':
# print("downstream", region)
else:
wiggle = [np.nan for pos in wiggle]
five_downstream[event] = wiggle
three_upstream = pd.DataFrame(three_upstream).T
five_downstream = pd.DataFrame(five_downstream).T
ra = pd.merge(three_upstream,
five_downstream,
how='outer',
left_index=True,
right_index=True)
ra = ra.replace(-1, np.nan)
# ra = pd.concat([three_upstream, five_downstream], axis=1)
ra.columns = range(0, ra.shape[1])
return ra
"""