def count_overlaps(self, other, **kwargs):
kwargs = fill_kwargs(kwargs)
from pyranges.methods.coverage import _number_overlapping
counts = pyrange_apply(_number_overlapping, self, other, **kwargs)
return pr.PyRanges(counts)
def overlap(self, other, **kwargs):
kwargs["sparse"] = {"self": False, "other": True}
kwargs = fill_kwargs(kwargs)
dfs = pyrange_apply(_overlap, self, other, **kwargs)
return PyRanges(dfs)
def intersect(self, other, **kwargs):
kwargs = fill_kwargs(kwargs)
kwargs["sparse"] = {"self": False, "other": True}
dfs = pyrange_apply(_intersection, self, other, **kwargs)
return PyRanges(dfs)
def join(self, other, **kwargs):
from pyranges.methods.join import _write_both
kwargs = fill_kwargs(kwargs)
dfs = pyrange_apply(_write_both, self, other, **kwargs)
return PyRanges(dfs)
def insert(self, other, col, **kwargs):
from pyranges.methods.insert import _insert
kwargs["columns"] = col
kwargs = fill_kwargs(kwargs)
dfs = pyrange_apply(_insert, self, other, **kwargs)
return PyRanges(dfs)
def nearest(self, other, **kwargs):
from pyranges.methods.nearest import _nearest
kwargs = fill_kwargs(kwargs)
dfs = pyrange_apply(_nearest, self, other, **kwargs)
return PyRanges(dfs)
def apply_pair(self, other, f, kwargs, strand=False, as_pyranges=True):
f = ray.remote(f)
result = pyrange_apply(f, self, other, **kwargs)
if not as_pyranges:
return result
else:
return PyRanges(result)
def nearest(self, other, **kwargs):
from pyranges.methods.nearest import _nearest
kwargs = fill_kwargs(kwargs)
if kwargs.get("how") in "upstream downstream".split():
assert other.stranded, "If doing upstream or downstream nearest, other pyranges must be stranded"
dfs = pyrange_apply(_nearest, self, other, **kwargs)
return PyRanges(dfs)
def set_intersect(self, other, **kwargs):
kwargs = fill_kwargs(kwargs)
strandedness = kwargs["strandedness"]
strand = True if strandedness else False
self_clusters = self.merge(strand=strand, **kwargs)
other_clusters = other.merge(strand=strand, **kwargs)
dfs = pyrange_apply(_intersection, self_clusters, other_clusters,
**kwargs)
return PyRanges(dfs)
def overlap(self, other, **kwargs):
kwargs["sparse"] = {"self": False, "other": True}
kwargs["how"] = "first"
kwargs = fill_kwargs(kwargs)
dfs = pyrange_apply(_overlap, self, other, **kwargs)
# if kwargs.get("return_indexes"):
# return dfs
# else:
return pr.PyRanges(dfs)
def join(self, other, **kwargs):
from pyranges.methods.join import _write_both
slack = kwargs.get("slack")
if slack:
self.Start__slack = self.Start
self.End__slack = self.End
self = self.slack(slack)
if "suffix" in kwargs:
suffixes = "", kwargs["suffix"]
kwargs["suffixes"] = suffixes
kwargs = fill_kwargs(kwargs)
if "new_pos" in kwargs:
if kwargs["new_pos"] in "intersection union".split():
suffixes = kwargs.get("suffixes")
assert suffixes is not None, "Must give two non-empty suffixes when using new_pos with intersection or union."
assert suffixes[
0], "Must have nonempty first suffix when using new_pos with intersection or union."
assert suffixes[
1], "Must have nonempty second suffix when using new_pos with intersection or union."
# def get_items_dtypes(s):
# columns = s.columns
# dtypes = (s.dfs.values())
how = kwargs.get("how")
if how in ["left", "outer"]:
kwargs["example_header_other"] = other.head(1).df
if how in ["right", "outer"]:
kwargs["example_header_self"] = self.head(1).df
dfs = pyrange_apply(_write_both, self, other, **kwargs)
gr = PyRanges(dfs)
if slack:
gr.Start = gr.Start__slack
gr.End = gr.End__slack
gr = gr.drop(like="(Start|End).*__slack")
new_position = kwargs.get("new_pos")
if new_position:
gr = gr.new_position(new_pos=new_position,
suffixes=kwargs["suffixes"])
return gr
def subtract(self, other, **kwargs):
from pyranges.methods.subtraction import _subtraction
kwargs["sparse"] = {"self": False, "other": True}
kwargs = fill_kwargs(kwargs)
strandedness = kwargs["strandedness"]
strand = True if strandedness else False
other_clusters = other.merge(strand=strand, **kwargs)
result = pyrange_apply(_subtraction, self, other_clusters, **kwargs)
return PyRanges(result)
def apply_pair(self, other, f, kwargs=None, strand=False,
as_pyranges=True):
if kwargs is None:
kwargs = {}
kwargs = fill_kwargs(kwargs)
f = ray.remote(f)
result = pyrange_apply(f, self, other, **kwargs)
if not as_pyranges:
return result
else:
return PyRanges(result)
def apply_pair(self,
other,
f,
strandedness=False,
as_pyranges=True,
**kwargs):
kwargs.update({"strandedness": strandedness})
kwargs = fill_kwargs(kwargs)
result = pyrange_apply(f, self, other, **kwargs)
if not as_pyranges:
return result
else:
return PyRanges(result)
def coverage(self, other, **kwargs):
kwargs = fill_kwargs(kwargs)
counts = self.count_overlaps(other, keep_nonoverlapping=True, **kwargs)
strand = True if kwargs["strandedness"] else False
other = other.merge(count=True, strand=strand)
from pyranges.methods.coverage import _coverage
# print(counts)
counts = pr.PyRanges(pyrange_apply(_coverage, counts, other, **kwargs))
# print("counts" * 100)
# print(counts)
return counts
def join(self, other, **kwargs):
from pyranges.methods.join import _write_both
slack = kwargs.get("slack")
if slack:
self.Start__slack = self.Start
self.End__slack = self.End
self = self.slack(slack)
if "suffix" in kwargs:
suffixes = "", kwargs["suffix"]
kwargs["suffixes"] = suffixes
kwargs = fill_kwargs(kwargs)
if "new_pos" in kwargs:
if kwargs["new_pos"] in "intersection union".split():
suffixes = kwargs.get("suffixes")
assert suffixes is not None, "Must give two non-empty suffixes when using new_pos with intersection or union."
assert suffixes[
0], "Must have nonempty first suffix when using new_pos with intersection or union."
assert suffixes[
1], "Must have nonempty second suffix when using new_pos with intersection or union."
dfs = pyrange_apply(_write_both, self, other, **kwargs)
gr = PyRanges(dfs)
if slack:
gr.Start = gr.Start__slack
gr.End = gr.End__slack
gr = gr.drop(like="(Start|End).*__slack")
new_position = kwargs.get("new_pos")
if new_position:
gr = gr.new_position(new_pos=new_position,
suffixes=kwargs["suffixes"])
return gr
def relative_distance(self, other, **kwargs):
self = self.pr
kwargs["sparse"] = {"self": True, "other": True}
kwargs = pr.pyranges.fill_kwargs(kwargs)
result = pyrange_apply(_relative_distance, self, other, **kwargs) # pylint: disable=E1132
result = pd.Series(np.concatenate(list(result.values())))
not_nan = ~np.isnan(result)
result.loc[not_nan] = np.floor(result[not_nan] * 100) / 100
vc = result.value_counts(dropna=False).to_frame().reset_index()
vc.columns = "reldist count".split()
vc.insert(vc.shape[1], "total", len(result))
vc.insert(vc.shape[1], "fraction", vc["count"] / len(result))
vc = vc.sort_values("reldist", ascending=True)
vc = vc.reset_index(drop=True)
return vc
def introns(self, by="gene"):
kwargs = {"by": by}
kwargs = pr.pyranges.fill_kwargs(kwargs)
assert by in ["gene", "transcript"]
id_column = by_to_id[by]
gr = self.pr.sort(id_column)
if not len(gr):
return pr.PyRanges()
exons = gr.subset(lambda df: df.Feature == "exon")
exons = exons.merge(by=id_column)
by_gr = gr.subset(lambda df: df.Feature == by)
result = pyrange_apply(_introns2, by_gr, exons, **kwargs)
return pr.PyRanges(result)
def relative_distance(self, other):
"""Compute spatial correllation between two sets.
Metric which describes relative distance between each interval in one
set and two closest intervals in another.
Parameters
----------
other : PyRanges
Intervals to compare with.
chromsizes : int, dict, DataFrame or PyRanges
Integer representing genome length or mapping from chromosomes
to its length.
strandedness : {None, "same", "opposite", False}, default None, i.e. "auto"
Whether to compute without regards to strand or on same or opposite.
Returns
-------
pandas.DataFrame
DataFrame containing the frequency of each relative distance.
See Also
--------
pyranges.statistics.jaccard : compute the jaccard coefficient
pyranges.statistics.forbes : compute the forbes coefficient
Examples
--------
>>> gr, gr2 = pr.data.chipseq(), pr.data.chipseq_background()
>>> chromsizes = pr.data.chromsizes()
>>> gr.stats.relative_distance(gr2)
reldist count total fraction
0 0.00 264 9956 0.026517
1 0.01 226 9956 0.022700
2 0.02 206 9956 0.020691
3 0.03 235 9956 0.023604
4 0.04 194 9956 0.019486
5 0.05 241 9956 0.024207
6 0.06 201 9956 0.020189
7 0.07 191 9956 0.019184
8 0.08 192 9956 0.019285
9 0.09 191 9956 0.019184
10 0.10 186 9956 0.018682
11 0.11 203 9956 0.020390
12 0.12 218 9956 0.021896
13 0.13 209 9956 0.020992
14 0.14 201 9956 0.020189
15 0.15 178 9956 0.017879
16 0.16 202 9956 0.020289
17 0.17 197 9956 0.019787
18 0.18 208 9956 0.020892
19 0.19 202 9956 0.020289
20 0.20 191 9956 0.019184
21 0.21 188 9956 0.018883
22 0.22 213 9956 0.021394
23 0.23 192 9956 0.019285
24 0.24 199 9956 0.019988
25 0.25 181 9956 0.018180
26 0.26 172 9956 0.017276
27 0.27 191 9956 0.019184
28 0.28 190 9956 0.019084
29 0.29 192 9956 0.019285
30 0.30 201 9956 0.020189
31 0.31 212 9956 0.021294
32 0.32 213 9956 0.021394
33 0.33 177 9956 0.017778
34 0.34 197 9956 0.019787
35 0.35 163 9956 0.016372
36 0.36 191 9956 0.019184
37 0.37 198 9956 0.019888
38 0.38 160 9956 0.016071
39 0.39 188 9956 0.018883
40 0.40 200 9956 0.020088
41 0.41 188 9956 0.018883
42 0.42 230 9956 0.023102
43 0.43 197 9956 0.019787
44 0.44 224 9956 0.022499
45 0.45 184 9956 0.018481
46 0.46 198 9956 0.019888
47 0.47 187 9956 0.018783
48 0.48 200 9956 0.020088
49 0.49 194 9956 0.019486
"""
self = self.pr
kwargs = {}
kwargs["sparse"] = {"self": True, "other": True}
kwargs = pr.pyranges.fill_kwargs(kwargs)
result = pyrange_apply(_relative_distance, self, other, **kwargs) # pylint: disable=E1132
result = pd.Series(np.concatenate(list(result.values())))
not_nan = ~np.isnan(result)
result.loc[not_nan] = np.floor(result[not_nan] * 100) / 100
vc = result.value_counts(dropna=False).to_frame().reset_index()
vc.columns = "reldist count".split()
vc.insert(vc.shape[1], "total", len(result))
vc.insert(vc.shape[1], "fraction", vc["count"] / len(result))
vc = vc.sort_values("reldist", ascending=True)
vc = vc.reset_index(drop=True)
return vc
def introns(self, by="gene", nb_cpu=1):
"""Return the introns.
Parameters
----------
by : str, {"gene", "transcript"}, default "gene"
Whether to find introns per gene or transcript.
nb_cpu: int, default 1
How many cpus to use. Can at most use 1 per chromosome or chromosome/strand tuple.
Will only lead to speedups on large datasets.
See Also
--------
pyranges.genomicfeatures.GenomicFeaturesMethods.tss : return the transcription start sites
Examples
--------
>>> gr = pr.data.ensembl_gtf()
>>> gr
+--------------+------------+--------------+-----------+-----------+------------+--------------+------------+------------------------------------+-------+
| Chromosome | Source | Feature | Start | End | Score | Strand | Frame | gene_biotype | +19 |
| (category) | (object) | (category) | (int32) | (int32) | (object) | (category) | (object) | (object) | ... |
|--------------+------------+--------------+-----------+-----------+------------+--------------+------------+------------------------------------+-------|
| 1 | havana | gene | 11868 | 14409 | . | + | . | transcribed_unprocessed_pseudogene | ... |
| 1 | havana | transcript | 11868 | 14409 | . | + | . | transcribed_unprocessed_pseudogene | ... |
| 1 | havana | exon | 11868 | 12227 | . | + | . | transcribed_unprocessed_pseudogene | ... |
| 1 | havana | exon | 12612 | 12721 | . | + | . | transcribed_unprocessed_pseudogene | ... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1 | havana | gene | 1173055 | 1179555 | . | - | . | lncRNA | ... |
| 1 | havana | transcript | 1173055 | 1179555 | . | - | . | lncRNA | ... |
| 1 | havana | exon | 1179364 | 1179555 | . | - | . | lncRNA | ... |
| 1 | havana | exon | 1173055 | 1176396 | . | - | . | lncRNA | ... |
+--------------+------------+--------------+-----------+-----------+------------+--------------+------------+------------------------------------+-------+
Stranded PyRanges object has 2,446 rows and 28 columns from 1 chromosomes.
For printing, the PyRanges was sorted on Chromosome and Strand.
19 hidden columns: gene_id, gene_name, gene_source, gene_version, tag, transcript_biotype, transcript_id, transcript_name, transcript_source, transcript_support_level, ... (+ 9 more.)
>>> gr.features.introns(by="gene")
+--------------+----------------+------------+-----------+-----------+------------+--------------+------------+-------+
| Chromosome | Source | Feature | Start | End | Score | Strand | Frame | +20 |
| (object) | (object) | (object) | (int32) | (int32) | (object) | (category) | (object) | ... |
|--------------+----------------+------------+-----------+-----------+------------+--------------+------------+-------|
| 1 | ensembl_havana | intron | 1173926 | 1174265 | . | + | . | ... |
| 1 | ensembl_havana | intron | 1174321 | 1174423 | . | + | . | ... |
| 1 | ensembl_havana | intron | 1174489 | 1174520 | . | + | . | ... |
| 1 | ensembl_havana | intron | 1175034 | 1179188 | . | + | . | ... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1 | havana | intron | 874591 | 875046 | . | - | . | ... |
| 1 | havana | intron | 875155 | 875525 | . | - | . | ... |
| 1 | havana | intron | 875625 | 876526 | . | - | . | ... |
| 1 | havana | intron | 876611 | 876754 | . | - | . | ... |
+--------------+----------------+------------+-----------+-----------+------------+--------------+------------+-------+
Stranded PyRanges object has 311 rows and 28 columns from 1 chromosomes.
For printing, the PyRanges was sorted on Chromosome and Strand.
20 hidden columns: gene_biotype, gene_id, gene_name, gene_source, gene_version, tag, transcript_biotype, transcript_id, transcript_name, ... (+ 11 more.)
>>> gr.features.introns(by="transcript")
+--------------+----------------+------------+-----------+-----------+------------+--------------+------------+----------------------------------+-------+
| Chromosome | Source | Feature | Start | End | Score | Strand | Frame | gene_biotype | +19 |
| (object) | (object) | (object) | (int32) | (int32) | (object) | (category) | (object) | (object) | ... |
|--------------+----------------+------------+-----------+-----------+------------+--------------+------------+----------------------------------+-------|
| 1 | havana | intron | 818202 | 818722 | . | + | . | lncRNA | ... |
| 1 | ensembl_havana | intron | 960800 | 961292 | . | + | . | protein_coding | ... |
| 1 | ensembl_havana | intron | 961552 | 961628 | . | + | . | protein_coding | ... |
| 1 | ensembl_havana | intron | 961750 | 961825 | . | + | . | protein_coding | ... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1 | havana | intron | 732207 | 732980 | . | - | . | transcribed_processed_pseudogene | ... |
| 1 | havana_tagene | intron | 168165 | 169048 | . | - | . | lncRNA | ... |
| 1 | havana_tagene | intron | 165942 | 167958 | . | - | . | lncRNA | ... |
| 1 | havana_tagene | intron | 168165 | 169048 | . | - | . | lncRNA | ... |
+--------------+----------------+------------+-----------+-----------+------------+--------------+------------+----------------------------------+-------+
Stranded PyRanges object has 1,043 rows and 28 columns from 1 chromosomes.
For printing, the PyRanges was sorted on Chromosome and Strand.
19 hidden columns: gene_id, gene_name, gene_source, gene_version, tag, transcript_biotype, transcript_id, transcript_name, transcript_source, transcript_support_level, ... (+ 9 more.)
"""
kwargs = {"by": by, "nb_cpu": nb_cpu}
kwargs = pr.pyranges.fill_kwargs(kwargs)
assert by in ["gene", "transcript"]
id_column = by_to_id[by]
gr = self.pr.sort(id_column)
if not len(gr):
return pr.PyRanges()
exons = gr.subset(lambda df: df.Feature == "exon")
exons = exons.merge(by=id_column)
by_gr = gr.subset(lambda df: df.Feature == by)
result = pyrange_apply(_introns2, by_gr, exons, **kwargs)
return pr.PyRanges(result)
def k_nearest(self, other, k=1, **kwargs):
from pyranges.methods.k_nearest import _nearest
from sorted_nearest import get_all_ties, get_different_ties
kwargs = fill_kwargs(kwargs)
kwargs["stranded"] = self.stranded and other.stranded
overlap = kwargs.get("overlap", True)
ties = kwargs.get("ties", False)
self = pr.PyRanges({k: v.copy() for k, v in self.dfs.items()})
try: # if k is an array
k = k.values
except:
pass
self.__k__ = k
self.__IX__ = np.arange(len(self))
# from time import time
# start = time()
dfs = pyrange_apply(_nearest, self, other, **kwargs)
# end = time()
# print("nearest", end - start)
nearest = PyRanges(dfs)
# nearest.msp()
# raise
# print("nearest len", len(nearest))
if not overlap:
# self = self.drop(like="__k__|__IX__")
result = nearest#.drop(like="__k__|__IX__")
else:
from collections import defaultdict
overlap_kwargs = {k: v for k, v in kwargs.items()}
# print("kwargs ties:", kwargs.get("ties"))
overlap_kwargs["how"] = defaultdict(lambda: None, {"first": "first", "last": "last"})[kwargs.get("ties")]
# start = time()
overlaps = self.join(other, **overlap_kwargs)
# end = time()
# print("overlaps", end - start)
overlaps.Distance = 0
# print("overlaps len", len(overlaps))
result = pr.concat([overlaps, nearest])
if not len(result):
return pr.PyRanges()
# print(result)
# print(overlaps.drop(like="__").df)
# raise
# start = time()
new_result = {}
if ties in ["first", "last"]:
# method = "tail" if ties == "last" else "head"
# keep = "last" if ties == "last" else "first"
for c, df in result:
# start = time()
# print(c)
# print(df)
df = df.sort_values(["__IX__", "Distance"])
grpby = df.groupby("__k__", sort=False)
dfs = []
for k, kdf in grpby:
# print("k", k)
# print(kdf)
# dist_bool = ~kdf.Distance.duplicated(keep=keep)
# print(dist_bool)
# kdf = kdf[dist_bool]
grpby2 = kdf.groupby("__IX__", sort=False)
# f = getattr(grpby2, method)
_df = grpby2.head(k)
# print(_df)
dfs.append(_df)
# raise
if dfs:
new_result[c] = pd.concat(dfs)
# print(new_result[c])
elif ties == "different" or not ties:
for c, df in result:
# print(df)
if df.empty:
continue
dfs = []
df = df.sort_values(["__IX__", "Distance"])
grpby = df.groupby("__k__", sort=False)
# for each index
# want to keep until we have k
# then keep all with same distance
for k, kdf in grpby:
# print("kdf " * 10)
# print("k " * 5, k)
# print(kdf["__IX__ Distance".split()])
# print(kdf.dtypes)
# print(kdf.index.dtypes)
# if ties:
if ties:
lx = get_different_ties(kdf.index.values, kdf.__IX__.values, kdf.Distance.astype(np.int64).values, k)
else:
lx = get_all_ties(kdf.index.values, kdf.__IX__.values, kdf.Distance.astype(np.int64).values, k)
# print(lx)
# else:
# lx = get_all_ties(kdf.index.values, kdf.__IX__.values, kdf.Distance.astype(np.int64).values, k)
_df = kdf.reindex(lx)
# print("_df", _df)
dfs.append(_df)
if dfs:
new_result[c] = pd.concat(dfs)
result = pr.PyRanges(new_result)
if not result.__IX__.is_monotonic:
result = result.sort("__IX__")
result = result.drop(like="__IX__|__k__")
self = self.drop(like="__k__|__IX__")
def prev_to_neg(df, kwargs):
strand = df.Strand.iloc[0] if "Strand" in df else "+"
suffix = kwargs["suffix"]
bools = df["End" + suffix] < df.Start
if not strand == "+":
bools = ~bools
df.loc[bools, "Distance"] = -df.loc[bools, "Distance"]
return df
# print(result)
result = result.apply(prev_to_neg, suffix=kwargs["suffix"])
# print(result)
# end = time()
# print("final stuff", end - start)
return result