Juicer module¶
-
class
fanc.compatibility.juicer.
JuicerHic
(hic_file, resolution=None, mode='r', tmpdir=None, norm=None)¶ Bases:
fanc.matrix.RegionMatrixContainer
-
add_contact
(contact, *args, **kwargs)¶ Alias for
add_edge()
Parameters: - contact –
Edge
- args – Positional arguments passed to
_add_edge()
- kwargs – Keyword arguments passed to
_add_edge()
- contact –
-
add_contacts
(contacts, *args, **kwargs)¶ Alias for
add_edges()
-
add_edge
(edge, check_nodes_exist=True, *args, **kwargs)¶ Add an edge / contact between two regions to this object.
Parameters: - edge –
Edge
, dict with at least the attributes source and sink, optionally weight, or a list of length 2 (source, sink) or 3 (source, sink, weight). - check_nodes_exist – Make sure that there are nodes that match source and sink indexes
- args – Positional arguments passed to
_add_edge()
- kwargs – Keyword arguments passed to
_add_edge()
- edge –
-
add_edge_from_dict
(edge, *args, **kwargs)¶ Direct method to add an edge from dict input.
Parameters: edge – dict with at least the keys “source” and “sink”. Additional keys will be loaded as edge attributes
-
add_edge_from_edge
(edge, *args, **kwargs)¶ Direct method to add an edge from
Edge
input.Parameters: edge – Edge
-
add_edge_from_list
(edge, *args, **kwargs)¶ Direct method to add an edge from list or tuple input.
Parameters: edge – List or tuple. Should be of length 2 (source, sink) or 3 (source, sink, weight)
-
add_edge_simple
(source, sink, weight=None, *args, **kwargs)¶ Direct method to add an edge from
Edge
input.Parameters: - source – Source region index
- sink – Sink region index
- weight – Weight of the edge
-
add_edges
(edges, *args, **kwargs)¶ Bulk-add edges from a list.
List items can be any of the supported edge types, list, tuple, dict, or
Edge
. Repeatedly callsadd_edge()
, so may be inefficient for large amounts of data.Parameters: edges – List (or iterator) of edges. See add_edge()
for details
-
add_region
(region, *args, **kwargs)¶ Add a genomic region to this object.
This method offers some flexibility in the types of objects that can be loaded. See parameters for details.
Parameters: region – Can be a GenomicRegion
, a str in the form ‘<chromosome>:<start>-<end>[:<strand>], a dict with at least the fields ‘chromosome’, ‘start’, and ‘end’, optionally ‘ix’, or a list of length 3 (chromosome, start, end) or 4 (ix, chromosome, start, end).
-
static
bin_intervals
(intervals, bins, interval_range=None, smoothing_window=None, nan_replacement=None, zero_to_nan=False)¶ Bin a given set of intervals into a fixed number of bins.
Parameters: - intervals – iterator of tuples (start, end, score)
- bins – Number of bins to divide the region into
- interval_range – Optional. Tuple (start, end) in base pairs of range of interval to be binned. Useful if intervals argument does not cover to exact genomic range to be binned.
- smoothing_window – Size of window (in bins) to smooth scores over
- nan_replacement – NaN values in the scores will be replaced with this value
- zero_to_nan – If True, will convert bins with score 0 to NaN
Returns: iterator of tuples: (start, end, score)
-
static
bin_intervals_equidistant
(intervals, bin_size, interval_range=None, smoothing_window=None, nan_replacement=None, zero_to_nan=False)¶ Bin a given set of intervals into bins with a fixed size.
Parameters: - intervals – iterator of tuples (start, end, score)
- bin_size – Size of each bin in base pairs
- interval_range – Optional. Tuple (start, end) in base pairs of range of interval to be binned. Useful if intervals argument does not cover to exact genomic range to be binned.
- smoothing_window – Size of window (in bins) to smooth scores over
- nan_replacement – NaN values in the scores will be replaced with this value
- zero_to_nan – If True, will convert bins with score 0 to NaN
Returns: iterator of tuples: (start, end, score)
-
bin_size
¶ Return the length of the first region in the dataset.
Assumes all bins have equal size.
Returns: int
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binned_regions
(region=None, bins=None, bin_size=None, smoothing_window=None, nan_replacement=None, zero_to_nan=False, *args, **kwargs)¶ Same as region_intervals, but returns
GenomicRegion
objects instead of tuples.Parameters: - region – String or class:~GenomicRegion object denoting the region to be binned
- bins – Number of bins to divide the region into
- bin_size – Size of each bin (alternative to bins argument)
- smoothing_window – Size of window (in bins) to smooth scores over
- nan_replacement – NaN values in the scores will be replaced with this value
- zero_to_nan – If True, will convert bins with score 0 to NaN
- args – Arguments passed to _region_intervals
- kwargs – Keyword arguments passed to _region_intervals
Returns: iterator of
GenomicRegion
objects
-
bins_to_distance
(bins)¶ Convert fraction of bins to base pairs
Parameters: bins – float, fraction of bins Returns: int, base pairs
-
chromosome_bins
¶ Returns a dictionary of chromosomes and the start and end index of the bins they cover.
Returned list is range-compatible, i.e. chromosome bins [0,5] cover chromosomes 1, 2, 3, and 4, not 5.
-
chromosome_lengths
¶ Returns a dictionary of chromosomes and their length in bp.
-
chromosomes
()¶ Get a list of chromosome names.
-
close
(remove_tmp=True)¶ Close this Juicer file and run exit operations.
If file was opened with tmpdir in read-only mode: close file and delete temporary copy.
Parameters: remove_tmp – If False, does not delete temporary copy of file.
-
distance_to_bins
(distance)¶ Convert base pairs to fraction of bins.
Parameters: distance – distance in base pairs Returns: float, distance as fraction of bin size
-
edge_data
(attribute, *args, **kwargs)¶ Iterate over specific edge attribute.
Parameters: - attribute – Name of the attribute, e.g. “weight”
- args – Positional arguments passed to
edges()
- kwargs – Keyword arguments passed to
edges()
Returns: iterator over edge attribute
-
edge_subset
(key=None, *args, **kwargs)¶ Get a subset of edges.
This is an alias for
edges()
.Returns: generator ( Edge
)
-
edges
¶ Iterate over contacts / edges.
edges()
is the central function ofRegionPairsContainer
. Here, we will use theHic
implementation for demonstration purposes, but the usage is exactly the same for all compatible objects implementingRegionPairsContainer
, includingJuicerHic
andCoolerHic
.import fanc # file from FAN-C examples hic = fanc.load("output/hic/binned/fanc_example_1mb.hic")
We can easily find the number of edges in the sample
Hic
object:len(hic.edges) # 8695
When used in an iterator context,
edges()
iterates over all edges in theRegionPairsContainer
:for edge in hic.edges: # do something with edge print(edge) # 42--42; bias: 5.797788472650082e-05; sink_node: chr18:42000001-43000000; source_node: chr18:42000001-43000000; weight: 0.12291311562018173 # 24--28; bias: 6.496381719803623e-05; sink_node: chr18:28000001-29000000; source_node: chr18:24000001-25000000; weight: 0.025205961072838057 # 5--76; bias: 0.00010230955745211447; sink_node: chr18:76000001-77000000; source_node: chr18:5000001-6000000; weight: 0.00961709840049876 # 66--68; bias: 8.248432587969082e-05; sink_node: chr18:68000001-69000000; source_node: chr18:66000001-67000000; weight: 0.03876763316345468 # ...
Calling
edges()
as a method has the same effect:# note the '()' for edge in hic.edges(): # do something with edge print(edge) # 42--42; bias: 5.797788472650082e-05; sink_node: chr18:42000001-43000000; source_node: chr18:42000001-43000000; weight: 0.12291311562018173 # 24--28; bias: 6.496381719803623e-05; sink_node: chr18:28000001-29000000; source_node: chr18:24000001-25000000; weight: 0.025205961072838057 # 5--76; bias: 0.00010230955745211447; sink_node: chr18:76000001-77000000; source_node: chr18:5000001-6000000; weight: 0.00961709840049876 # 66--68; bias: 8.248432587969082e-05; sink_node: chr18:68000001-69000000; source_node: chr18:66000001-67000000; weight: 0.03876763316345468 # ...
Rather than iterate over all edges in the object, we can select only a subset. If the key is a string or a
GenomicRegion
, all non-zero edges connecting the region described by the key to any other region are returned. If the key is a tuple of strings orGenomicRegion
, only edges between the two regions are returned.# select all edges between chromosome 19 # and any other region: for edge in hic.edges("chr19"): print(edge) # 49--106; bias: 0.00026372303696871666; sink_node: chr19:27000001-28000000; source_node: chr18:49000001-50000000; weight: 0.003692122517562033 # 6--82; bias: 0.00021923129703834945; sink_node: chr19:3000001-4000000; source_node: chr18:6000001-7000000; weight: 0.0008769251881533978 # 47--107; bias: 0.00012820949175399097; sink_node: chr19:28000001-29000000; source_node: chr18:47000001-48000000; weight: 0.0015385139010478917 # 38--112; bias: 0.0001493344481069762; sink_node: chr19:33000001-34000000; source_node: chr18:38000001-39000000; weight: 0.0005973377924279048 # ... # select all edges that are only on # chromosome 19 for edge in hic.edges(('chr19', 'chr19')): print(edge) # 90--116; bias: 0.00021173151730025176; sink_node: chr19:37000001-38000000; source_node: chr19:11000001-12000000; weight: 0.009104455243910825 # 135--135; bias: 0.00018003890596887822; sink_node: chr19:56000001-57000000; source_node: chr19:56000001-57000000; weight: 0.10028167062466517 # 123--123; bias: 0.00011063368998965993; sink_node: chr19:44000001-45000000; source_node: chr19:44000001-45000000; weight: 0.1386240135570439 # 92--93; bias: 0.00040851066434864896; sink_node: chr19:14000001-15000000; source_node: chr19:13000001-14000000; weight: 0.10090213409411629 # ... # select inter-chromosomal edges # between chromosomes 18 and 19 for edge in hic.edges(('chr18', 'chr19')): print(edge) # 49--106; bias: 0.00026372303696871666; sink_node: chr19:27000001-28000000; source_node: chr18:49000001-50000000; weight: 0.003692122517562033 # 6--82; bias: 0.00021923129703834945; sink_node: chr19:3000001-4000000; source_node: chr18:6000001-7000000; weight: 0.0008769251881533978 # 47--107; bias: 0.00012820949175399097; sink_node: chr19:28000001-29000000; source_node: chr18:47000001-48000000; weight: 0.0015385139010478917 # 38--112; bias: 0.0001493344481069762; sink_node: chr19:33000001-34000000; source_node: chr18:38000001-39000000; weight: 0.0005973377924279048 # ...
By default,
edges()
will retrieve all edge attributes, which can be slow when iterating over a lot of edges. This is why all file-based FAN-CRegionPairsContainer
objects support lazy loading, where attributes are only read on demand.for edge in hic.edges('chr18', lazy=True): print(edge.source, edge.sink, edge.weight, edge) # 42 42 0.12291311562018173 <fanc.matrix.LazyEdge for row /edges/chrpair_0_0.row (Row), pointing to row #0> # 24 28 0.025205961072838057 <fanc.matrix.LazyEdge for row /edges/chrpair_0_0.row (Row), pointing to row #1> # 5 76 0.00961709840049876 <fanc.matrix.LazyEdge for row /edges/chrpair_0_0.row (Row), pointing to row #2> # 66 68 0.03876763316345468 <fanc.matrix.LazyEdge for row /edges/chrpair_0_0.row (Row), pointing to row #3> # ...
Warning
The lazy iterator reuses the
LazyEdge
object in every iteration, and overwrites theLazyEdge
attributes. Therefore do not use lazy iterators if you need to store edge objects for later access. For example, the following code works as expectedlist(hic.edges())
, with allEdge
objects stored in the list, while this codelist(hic.edges(lazy=True))
will result in a list of identicalLazyEdge
objects. Always ensure you do all edge processing in the loop when working with lazy iterators!When working with normalised contact frequencies, such as obtained through matrix balancing in the example above,
edges()
automatically returns normalised edge weights. In addition, thebias
attribute will (typically) have a value different from 1.When you are interested in the raw contact frequency, use the
norm=False
parameter:for edge in hic.edges('chr18', lazy=True, norm=False): print(edge.source, edge.sink, edge.weight) # 42 42 2120.0 # 24 28 388.0 # 5 76 94.0 # 66 68 470.0 # ...
You can also choose to omit all intra- or inter-chromosomal edges using
intra_chromosomal=False
orinter_chromosomal=False
, respectively.Returns: Iterator over Edge
or equivalent.
-
edges_dict
(*args, **kwargs)¶ Edges iterator with access by bracket notation.
This iterator always returns unnormalised edges.
Returns: dict or dict-like iterator
-
expected_values
(selected_chromosome=None, norm=True, *args, **kwargs)¶ Calculate the expected values for genomic contacts at all distances.
This calculates the expected values between genomic regions separated by a specific distance. Expected values are calculated as the average weight of edges between region pairs with the same genomic separation, taking into account unmappable regions.
It will return a tuple with three values: a list of genome-wide intra-chromosomal expected values (list index corresponds to number of separating bins), a dict with chromosome names as keys and intra-chromosomal expected values specific to each chromosome, and a float for inter-chromosomal expected value.
Parameters: - selected_chromosome – (optional) Chromosome name. If provided, will only return expected values for this chromosome.
- norm – If False, will calculate the expected values on the unnormalised matrix.
- args – Not used in this context
- kwargs – Not used in this context
Returns: list of intra-chromosomal expected values, dict of intra-chromosomal expected values by chromosome, inter-chromosomal expected value
-
expected_values_and_marginals
(selected_chromosome=None, norm=True, *args, **kwargs)¶ Calculate the expected values for genomic contacts at all distances and the whole matrix marginals.
This calculates the expected values between genomic regions separated by a specific distance. Expected values are calculated as the average weight of edges between region pairs with the same genomic separation, taking into account unmappable regions.
It will return a tuple with three values: a list of genome-wide intra-chromosomal expected values (list index corresponds to number of separating bins), a dict with chromosome names as keys and intra-chromosomal expected values specific to each chromosome, and a float for inter-chromosomal expected value.
Parameters: - selected_chromosome – (optional) Chromosome name. If provided, will only return expected values for this chromosome.
- norm – If False, will calculate the expected values on the unnormalised matrix.
- args – Not used in this context
- kwargs – Not used in this context
Returns: list of intra-chromosomal expected values, dict of intra-chromosomal expected values by chromosome, inter-chromosomal expected value
-
find_region
(query_regions, _regions_dict=None, _region_ends=None, _chromosomes=None)¶ Find the region that is at the center of a region.
Parameters: query_regions – Region selector string, :class:~GenomicRegion, or list of the former Returns: index (or list of indexes) of the region at the center of the query region
-
intervals
(*args, **kwargs)¶ Alias for region_intervals.
-
mappable
(region=None)¶ Get the mappability vector of this matrix.
-
marginals
(masked=True, *args, **kwargs)¶ Get the marginals vector of this Hic matrix.
Sums up all contacts for each bin of the Hi-C matrix. Unmappable regoins will be masked in the returned vector unless the
masked
parameter is set toFalse
.By default, corrected matrix entries are summed up. To get uncorrected matrix marginals use
norm=False
. Generally, all parameters accepted byedges()
are supported.Parameters: - masked – Use a numpy masked array to mask entries corresponding to unmappable regions
- kwargs – Keyword arguments passed to
edges()
-
matrix
(key=None, log=False, default_value=None, mask=True, log_base=2, *args, **kwargs)¶ Assemble a
RegionMatrix
from region pairs.Parameters: - key – Matrix selector. See
edges()
for all supported key types - log – If True, log-transform the matrix entries. Also see log_base
- log_base – Base of the log transformation. Default: 2; only used when log=True
- default_value – (optional) set the default value of matrix entries that have no associated edge/contact
- mask – If False, do not mask unmappable regions
- args – Positional arguments passed to
regions_and_matrix_entries()
- kwargs – Keyword arguments passed to
regions_and_matrix_entries()
Returns: - key – Matrix selector. See
-
classmethod
merge
(pairs, *args, **kwargs)¶ Merge two or more
RegionPairsContainer
objects.Parameters: - pairs –
list
ofRegionPairsContainer
- args – Positional arguments passed to constructor of this class
- kwargs – Keyword arguments passed to constructor of this class
- pairs –
-
possible_contacts
()¶ Calculate the possible number of contacts in the genome.
This calculates the number of potential region pairs in a genome for any possible separation distance, taking into account the existence of unmappable regions.
It will calculate one number for inter-chromosomal pairs, return a list with the number of possible pairs where the list index corresponds to the number of bins separating two regions, and a dictionary of lists for each chromosome.
Returns: possible intra-chromosomal pairs, possible intra-chromosomal pairs by chromosome, possible inter-chromosomal pairs
-
region_bins
(*args, **kwargs)¶ Return slice of start and end indices spanned by a region.
Parameters: args – provide a GenomicRegion
here to get the slice of start and end bins of onlythis region. To get the slice over all regions leave this blank.Returns:
-
region_intervals
(region, bins=None, bin_size=None, smoothing_window=None, nan_replacement=None, zero_to_nan=False, score_field='score', *args, **kwargs)¶ Return equally-sized genomic intervals and associated scores.
Use either bins or bin_size argument to control binning.
Parameters: - region – String or class:~GenomicRegion object denoting the region to be binned
- bins – Number of bins to divide the region into
- bin_size – Size of each bin (alternative to bins argument)
- smoothing_window – Size of window (in bins) to smooth scores over
- nan_replacement – NaN values in the scores will be replaced with this value
- zero_to_nan – If True, will convert bins with score 0 to NaN
- args – Arguments passed to _region_intervals
- kwargs – Keyword arguments passed to _region_intervals
Returns: iterator of tuples: (start, end, score)
-
region_subset
(region, *args, **kwargs)¶ Takes a class:~GenomicRegion and returns all regions that overlap with the supplied region.
Parameters: region – String or class:~GenomicRegion object for which covered bins will be returned.
-
regions
¶ Iterate over genomic regions in this object.
Will return a
GenomicRegion
object in every iteration. Can also be used to get the number of regions by calling len() on the object returned by this method.Returns: RegionIter
-
regions_and_edges
(key, *args, **kwargs)¶ Convenient access to regions and edges selected by key.
Parameters: - key – Edge selector, see
edges()
- args – Positional arguments passed to
edges()
- kwargs – Keyword arguments passed to
edges()
Returns: list of row regions, list of col regions, iterator over edges
- key – Edge selector, see
-
regions_and_matrix_entries
(key=None, score_field=None, *args, **kwargs)¶ Convenient access to non-zero matrix entries and associated regions.
Parameters: - key – Edge key, see
edges()
- oe – If True, will divide observed values by their expected value at the given distance. False by default
- oe_per_chromosome – If True (default), will do a per-chromosome O/E calculation rather than using the whole matrix to obtain expected values
- score_field – (optional) any edge attribute that returns a number
can be specified here for filling the matrix. Usually
this is defined by the
_default_score_field
attribute of the matrix class. - args – Positional arguments passed to
edges()
- kwargs – Keyword arguments passed to
edges()
Returns: list of row regions, list of col regions, iterator over (i, j, weight) tuples
- key – Edge key, see
-
regions_dict
¶ Return a dictionary with region index as keys and regions as values.
Returns: dict {region.ix: region, …}
-
static
regions_identical
(pairs)¶ Check if the regions in all objects in the list are identical.
Parameters: pairs – list
ofRegionBased
objectsReturns: True if chromosome, start, and end are identical between all regions in the same list positions.
-
scaling_factor
(matrix, weight_column=None)¶ Compute the scaling factor to another matrix.
Calculates the ratio between the number of contacts in this Hic object to the number of contacts in another Hic object.
Parameters: - matrix – A
Hic
object - weight_column – Name of the column to calculate the scaling factor on
Returns: float
- matrix – A
-
to_bed
(file_name, subset=None, **kwargs)¶ Export regions as BED file
Parameters: - file_name – Path of file to write regions to
- subset – optional
GenomicRegion
or str to write only regions overlapping this region - kwargs – Passed to
write_bed()
-
to_bigwig
(file_name, subset=None, **kwargs)¶ Export regions as BigWig file.
Parameters: - file_name – Path of file to write regions to
- subset – optional
GenomicRegion
or str to write only regions overlapping this region - kwargs – Passed to
write_bigwig()
-
to_gff
(file_name, subset=None, **kwargs)¶ Export regions as GFF file
Parameters: - file_name – Path of file to write regions to
- subset – optional
GenomicRegion
or str to write only regions overlapping this region - kwargs – Passed to
write_gff()
-