Hic module¶

class
fanc.hic.
DiagonalFilter
(hic, distance=0, mask=None)¶ Bases:
fanc.hic.HicEdgeFilter
Filter contacts in the diagonal of a
Hic
matrix.
set_hic_object
(hic_object)¶ Set the
Hic
instance to be filtered by this HicEdgeFilter.Used internally by
Hic
instance.Parameters: hic_object – Hic
object

valid
(row)¶ Map valid_edge to MaskFilter.valid(self, row).
Parameters: row – A pytables Table row. Returns: The boolean value returned by valid_edge.


class
fanc.hic.
Hic
(file_name=None, mode='a', tmpdir=None, partition_strategy='auto', additional_region_fields=None, additional_edge_fields=None, _table_name_regions='regions', _table_name_edges='edges', _edge_buffer_size='3G')¶ Bases:
fanc.matrix.RegionMatrixTable
Central class for working with HiC data.
This class adds functions for matrix binning and filtering to the base class
RegionMatrixTable
.
class
ChromosomeDescription
¶ Bases:
tables.description.IsDescription
Description of the chromosomes in this object.

class
MaskDescription
¶ Bases:
tables.description.IsDescription

class
RegionDescription
¶ Bases:
tables.description.IsDescription
Description of a genomic region for PyTables Table

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, flush=True, *args, **kwargs)¶ Bulkadd 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_mask_description
(name, description)¶ Add a mask description to the _mask table and return its ID.
Parameters:  name (str) – name of the mask
 description (str) – description of the mask
Returns: id of the mask
Return type: int

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).

add_regions
(regions, *args, **kwargs)¶ Bulk insert multiple genomic regions.
Parameters: regions – List (or any iterator) with objects that describe a genomic region. See add_region
for options.

bias_vector
(vector=None)¶ Get or set the vector of region biases in this object.
This internally sets the “bias” attribute of each region in the object.
Parameters: vector – a numpy array with bias values Returns: a numpy array with bias values

bin
(bin_size, threads=1, chromosomes=None, *args, **kwargs)¶ Map edges in this object to equidistant bins.
Parameters:  bin_size – Bin size in base pairs
 threads – Number of threads used for binning
Returns: Hic
object

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

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 rangecompatible, 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
()¶ List all chromosomes in this regions table. :return: list of chromosome names.

close
(copy_tmp=True, remove_tmp=True)¶ Close this HDF5 file and run exit operations.
If file was opened with tmpdir in readonly mode: close file and delete temporary copy.
If file was opened with tmpdir in write or append mode: Replace original file with copy and delete copy.
Parameters:  copy_tmp – If False, does not overwrite original with modified file.
 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

downsample
(n, file_name=None)¶ Sample edges from this object.
Sampling is always done on uncorrected HiC matrices.
Parameters:  n – Sample size or reference object. If n < 1 will be interpreted as a fraction of total reads in this object.
 file_name – Output file name for downsampled object.
Returns: RegionPairsTable

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 FANC 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) # 4242; bias: 5.797788472650082e05; sink_node: chr18:4200000143000000; source_node: chr18:4200000143000000; weight: 0.12291311562018173 # 2428; bias: 6.496381719803623e05; sink_node: chr18:2800000129000000; source_node: chr18:2400000125000000; weight: 0.025205961072838057 # 576; bias: 0.00010230955745211447; sink_node: chr18:7600000177000000; source_node: chr18:50000016000000; weight: 0.00961709840049876 # 6668; bias: 8.248432587969082e05; sink_node: chr18:6800000169000000; source_node: chr18:6600000167000000; 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) # 4242; bias: 5.797788472650082e05; sink_node: chr18:4200000143000000; source_node: chr18:4200000143000000; weight: 0.12291311562018173 # 2428; bias: 6.496381719803623e05; sink_node: chr18:2800000129000000; source_node: chr18:2400000125000000; weight: 0.025205961072838057 # 576; bias: 0.00010230955745211447; sink_node: chr18:7600000177000000; source_node: chr18:50000016000000; weight: 0.00961709840049876 # 6668; bias: 8.248432587969082e05; sink_node: chr18:6800000169000000; source_node: chr18:6600000167000000; 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 nonzero 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) # 49106; bias: 0.00026372303696871666; sink_node: chr19:2700000128000000; source_node: chr18:4900000150000000; weight: 0.003692122517562033 # 682; bias: 0.00021923129703834945; sink_node: chr19:30000014000000; source_node: chr18:60000017000000; weight: 0.0008769251881533978 # 47107; bias: 0.00012820949175399097; sink_node: chr19:2800000129000000; source_node: chr18:4700000148000000; weight: 0.0015385139010478917 # 38112; bias: 0.0001493344481069762; sink_node: chr19:3300000134000000; source_node: chr18:3800000139000000; weight: 0.0005973377924279048 # ... # select all edges that are only on # chromosome 19 for edge in hic.edges(('chr19', 'chr19')): print(edge) # 90116; bias: 0.00021173151730025176; sink_node: chr19:3700000138000000; source_node: chr19:1100000112000000; weight: 0.009104455243910825 # 135135; bias: 0.00018003890596887822; sink_node: chr19:5600000157000000; source_node: chr19:5600000157000000; weight: 0.10028167062466517 # 123123; bias: 0.00011063368998965993; sink_node: chr19:4400000145000000; source_node: chr19:4400000145000000; weight: 0.1386240135570439 # 9293; bias: 0.00040851066434864896; sink_node: chr19:1400000115000000; source_node: chr19:1300000114000000; weight: 0.10090213409411629 # ... # select interchromosomal edges # between chromosomes 18 and 19 for edge in hic.edges(('chr18', 'chr19')): print(edge) # 49106; bias: 0.00026372303696871666; sink_node: chr19:2700000128000000; source_node: chr18:4900000150000000; weight: 0.003692122517562033 # 682; bias: 0.00021923129703834945; sink_node: chr19:30000014000000; source_node: chr18:60000017000000; weight: 0.0008769251881533978 # 47107; bias: 0.00012820949175399097; sink_node: chr19:2800000129000000; source_node: chr18:4700000148000000; weight: 0.0015385139010478917 # 38112; bias: 0.0001493344481069762; sink_node: chr19:3300000134000000; source_node: chr18:3800000139000000; 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 filebased FANCRegionPairsContainer
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 interchromosomal 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 dictlike 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 genomewide intrachromosomal expected values (list index corresponds to number of separating bins), a dict with chromosome names as keys and intrachromosomal expected values specific to each chromosome, and a float for interchromosomal 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 intrachromosomal expected values, dict of intrachromosomal expected values by chromosome, interchromosomal expected value

expected_values_and_marginals
(selected_chromosome=None, norm=True, force=False, *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 genomewide intrachromosomal expected values (list index corresponds to number of separating bins), a dict with chromosome names as keys and intrachromosomal expected values specific to each chromosome, and a float for interchromosomal 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 intrachromosomal expected values, dict of intrachromosomal expected values by chromosome, interchromosomal expected value

filter
(edge_filter, queue=False, log_progress=True)¶ Filter edges in this object by using a
MaskFilter
.Parameters:  edge_filter – Class implementing
MaskFilter
.  queue – If True, filter will be queued and can be executed
along with other queued filters using
run_queued_filters()
 log_progress – If true, process iterating through all edges will be continuously reported.
 edge_filter – Class implementing

filter_diagonal
(distance=0, queue=False)¶ Convenience function that applies a
DiagonalFilter
.Parameters:  distance – Distance from the diagonal up to which matrix entries will be filtered/removed. The default, 0, filters only the diagonal itself.
 queue – If True, filter will be queued and can be executed along with other queued filters using run_queued_filters

filter_low_coverage_regions
(rel_cutoff=None, cutoff=None, queue=False)¶ Convenience function that applies a
LowCoverageFilter
.The cutoff can be provided in two ways: 1. As an absolute threshold. Regions with contact count below this absolute threshold are filtered 2. As a fraction relative to the median contact count of all regions.
If both is supplied, whichever threshold is lower will be selected.
If no parameter is supplied, rel_cutoff will be chosen as 0.1.
Parameters:  rel_cutoff – A cutoff as a fraction (01) of the median contact count of all regions.
 cutoff – A cutoff in absolute contact counts (can be float) below which regions are considered “low coverage”
 queue – If True, filter will be queued and can be executed along with other queued filters using run_queued_filters

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

flush
(silent=False, update_mappability=True)¶ Write data to file and flush buffers.
Parameters:  silent – do not print flush progress
 update_mappability – After writing data, update mappability and expected values

get_mask
(key)¶ Search _mask table for key and return Mask.
Parameters:  key (int) – search by mask name
 key – search by mask ID
Returns: Mask

get_masks
(ix)¶ Extract mask IDs encoded in parameter and return masks.
IDs are powers of 2, so a single int field in the table can hold multiple masks by simply adding up the IDs. Similar principle to UNIX chmod (although that uses base 8)
Parameters: ix (int) – integer that is the sum of powers of 2. Note that this value is not necessarily itself a power of 2. Returns: list of Masks extracted from ix Return type: list (Mask)

intervals
(*args, **kwargs)¶ Alias for region_intervals.

load_from_hic
(hic, threads=1, chromosomes=None, _edges_by_overlap_method=<function _edge_overlap_split_rao>, _regions_soft_max=50000)¶ Load data from another
Hic
object.If this object has no associated regions, the regions and contacts of the provided object will simply be copied.
If regions are already present, the contacts of the provided matrix will be binned into the regions of this object using the overlap method provided.
Parameters:  hic – Another
Hic
object  threads – Number of parallel processing threads. More threads also means higher memory usage.
 _edges_by_overlap_method – A function that maps reads from
one genomic region to others using
a supplied overlap map. By default
it uses the Rao et al. (2014) method.
See
_edge_overlap_split_rao()
 _regions_soft_max – Maximum dimension of each processed submatrix per thread. This is a soft maximum, which may be increased as required for very large chromosomes or small bin sizes
 hic – Another

mappable
(region=None)¶ Get the mappability of regions in this object.
A “mappable” region has at least one contact to another region in the genome.
Returns: array
where True means mappable and False unmappable

marginals
(masked=True, *args, **kwargs)¶ Get the marginals vector of this Hic matrix.
Sums up all contacts for each bin of the HiC 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, logtransform 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
(matrices, *args, **kwargs)¶ Merge multiple
RegionMatrixContainer
objects.Merging is done by adding the weight of edges in each object.
Parameters: matrices – list of RegionMatrixContainer
Returns: merged RegionMatrixContainer

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 interchromosomal 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 intrachromosomal pairs, possible intrachromosomal pairs by chromosome, possible interchromosomal 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_data
(key, value=None)¶ Retrieve or add vectordata to this object. If there is existing data in this object with the same name, it will be replaced
Parameters:  key – Name of the data column
 value – vector with regionbased data (one entry per region)

region_intervals
(region, bins=None, bin_size=None, smoothing_window=None, nan_replacement=None, zero_to_nan=False, score_field='score', *args, **kwargs)¶ Return equallysized 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 nonzero 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 perchromosome 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.

run_queued_filters
(log_progress=True)¶ Run queued filters.
Parameters: log_progress – If true, process iterating through all edges will be continuously reported.

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

subset
(*regions, **kwargs)¶ Subset a Hic object by specifying one or more subset regions.
Parameters:  regions – string or GenomicRegion object(s)
 kwargs – Supports
file_name: destination file name of subset Hic object;
tmpdir: if True works in tmp until object is closed
additional parameters are passed to
edges()
Returns: Hic

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()

class

class
fanc.hic.
HicEdgeFilter
(hic=None, mask=None)¶ Bases:
fanc.general.MaskFilter
Abstract class that provides filtering functionality for the edges/contacts in a
Hic
object.Extends MaskFilter and overrides valid(self, row) to make
HicEdge
filtering more “natural”.To create custom filters for the
Hic
object, extend this class and override the valid_edge(self, edge) method. valid_edge should return False for a specificHicEdge
object if the object is supposed to be filtered/masked and True otherwise. SeeDiagonalFilter
for an example.Pass a custom filter to the
filter()
method inHic
to apply it.
set_hic_object
(hic_object)¶ Set the
Hic
instance to be filtered by this HicEdgeFilter.Used internally by
Hic
instance.Parameters: hic_object – Hic
object

valid
(row)¶ Map valid_edge to MaskFilter.valid(self, row).
Parameters: row – A pytables Table row. Returns: The boolean value returned by valid_edge.

valid_edge
(edge)¶ Determine if a
HicEdge
object is valid or should be filtered.When implementing custom HicEdgeFilter this method must be overridden. It should return False for
HicEdge
objects that are to be fitered and True otherwise.Internally, the
Hic
object will iterate over all HicEdge instances to determine their validity on an individual basis.Parameters: edge – A HicEdge
objectReturns: True if HicEdge
is valid, False otherwise


class
fanc.hic.
LegacyHic
(file_name=None, mode='a', tmpdir=None, partition_strategy='chromosome', additional_region_fields=None, additional_edge_fields=None, _table_name_regions='nodes', _table_name_edges='edges', _edge_buffer_size='3G')¶ Bases:
fanc.hic.Hic

class
ChromosomeDescription
¶ Bases:
tables.description.IsDescription
Description of the chromosomes in this object.

class
MaskDescription
¶ Bases:
tables.description.IsDescription

class
RegionDescription
¶ Bases:
tables.description.IsDescription
Description of a genomic region for PyTables Table

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, flush=True, *args, **kwargs)¶ Bulkadd 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_mask_description
(name, description)¶ Add a mask description to the _mask table and return its ID.
Parameters:  name (str) – name of the mask
 description (str) – description of the mask
Returns: id of the mask
Return type: int

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).

add_regions
(regions, *args, **kwargs)¶ Bulk insert multiple genomic regions.
Parameters: regions – List (or any iterator) with objects that describe a genomic region. See add_region
for options.

bias_vector
()¶ Get or set the vector of region biases in this object.
This internally sets the “bias” attribute of each region in the object.
Parameters: vector – a numpy array with bias values Returns: a numpy array with bias values

bin
(bin_size, threads=1, chromosomes=None, *args, **kwargs)¶ Map edges in this object to equidistant bins.
Parameters:  bin_size – Bin size in base pairs
 threads – Number of threads used for binning
Returns: Hic
object

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

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 rangecompatible, 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
()¶ List all chromosomes in this regions table. :return: list of chromosome names.

close
(copy_tmp=True, remove_tmp=True)¶ Close this HDF5 file and run exit operations.
If file was opened with tmpdir in readonly mode: close file and delete temporary copy.
If file was opened with tmpdir in write or append mode: Replace original file with copy and delete copy.
Parameters:  copy_tmp – If False, does not overwrite original with modified file.
 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

downsample
(n, file_name=None)¶ Sample edges from this object.
Sampling is always done on uncorrected HiC matrices.
Parameters:  n – Sample size or reference object. If n < 1 will be interpreted as a fraction of total reads in this object.
 file_name – Output file name for downsampled object.
Returns: RegionPairsTable

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 FANC 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) # 4242; bias: 5.797788472650082e05; sink_node: chr18:4200000143000000; source_node: chr18:4200000143000000; weight: 0.12291311562018173 # 2428; bias: 6.496381719803623e05; sink_node: chr18:2800000129000000; source_node: chr18:2400000125000000; weight: 0.025205961072838057 # 576; bias: 0.00010230955745211447; sink_node: chr18:7600000177000000; source_node: chr18:50000016000000; weight: 0.00961709840049876 # 6668; bias: 8.248432587969082e05; sink_node: chr18:6800000169000000; source_node: chr18:6600000167000000; 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) # 4242; bias: 5.797788472650082e05; sink_node: chr18:4200000143000000; source_node: chr18:4200000143000000; weight: 0.12291311562018173 # 2428; bias: 6.496381719803623e05; sink_node: chr18:2800000129000000; source_node: chr18:2400000125000000; weight: 0.025205961072838057 # 576; bias: 0.00010230955745211447; sink_node: chr18:7600000177000000; source_node: chr18:50000016000000; weight: 0.00961709840049876 # 6668; bias: 8.248432587969082e05; sink_node: chr18:6800000169000000; source_node: chr18:6600000167000000; 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 nonzero 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) # 49106; bias: 0.00026372303696871666; sink_node: chr19:2700000128000000; source_node: chr18:4900000150000000; weight: 0.003692122517562033 # 682; bias: 0.00021923129703834945; sink_node: chr19:30000014000000; source_node: chr18:60000017000000; weight: 0.0008769251881533978 # 47107; bias: 0.00012820949175399097; sink_node: chr19:2800000129000000; source_node: chr18:4700000148000000; weight: 0.0015385139010478917 # 38112; bias: 0.0001493344481069762; sink_node: chr19:3300000134000000; source_node: chr18:3800000139000000; weight: 0.0005973377924279048 # ... # select all edges that are only on # chromosome 19 for edge in hic.edges(('chr19', 'chr19')): print(edge) # 90116; bias: 0.00021173151730025176; sink_node: chr19:3700000138000000; source_node: chr19:1100000112000000; weight: 0.009104455243910825 # 135135; bias: 0.00018003890596887822; sink_node: chr19:5600000157000000; source_node: chr19:5600000157000000; weight: 0.10028167062466517 # 123123; bias: 0.00011063368998965993; sink_node: chr19:4400000145000000; source_node: chr19:4400000145000000; weight: 0.1386240135570439 # 9293; bias: 0.00040851066434864896; sink_node: chr19:1400000115000000; source_node: chr19:1300000114000000; weight: 0.10090213409411629 # ... # select interchromosomal edges # between chromosomes 18 and 19 for edge in hic.edges(('chr18', 'chr19')): print(edge) # 49106; bias: 0.00026372303696871666; sink_node: chr19:2700000128000000; source_node: chr18:4900000150000000; weight: 0.003692122517562033 # 682; bias: 0.00021923129703834945; sink_node: chr19:30000014000000; source_node: chr18:60000017000000; weight: 0.0008769251881533978 # 47107; bias: 0.00012820949175399097; sink_node: chr19:2800000129000000; source_node: chr18:4700000148000000; weight: 0.0015385139010478917 # 38112; bias: 0.0001493344481069762; sink_node: chr19:3300000134000000; source_node: chr18:3800000139000000; 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 filebased FANCRegionPairsContainer
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 interchromosomal 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 dictlike iterator

expected_values
(selected_chromosome=None, norm=True, force=False, *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 genomewide intrachromosomal expected values (list index corresponds to number of separating bins), a dict with chromosome names as keys and intrachromosomal expected values specific to each chromosome, and a float for interchromosomal 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 intrachromosomal expected values, dict of intrachromosomal expected values by chromosome, interchromosomal expected value

expected_values_and_marginals
(selected_chromosome=None, norm=True, force=False, *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 genomewide intrachromosomal expected values (list index corresponds to number of separating bins), a dict with chromosome names as keys and intrachromosomal expected values specific to each chromosome, and a float for interchromosomal 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 intrachromosomal expected values, dict of intrachromosomal expected values by chromosome, interchromosomal expected value

filter
(edge_filter, queue=False, log_progress=True)¶ Filter edges in this object by using a
MaskFilter
.Parameters:  edge_filter – Class implementing
MaskFilter
.  queue – If True, filter will be queued and can be executed
along with other queued filters using
run_queued_filters()
 log_progress – If true, process iterating through all edges will be continuously reported.
 edge_filter – Class implementing

filter_diagonal
(distance=0, queue=False)¶ Convenience function that applies a
DiagonalFilter
.Parameters:  distance – Distance from the diagonal up to which matrix entries will be filtered/removed. The default, 0, filters only the diagonal itself.
 queue – If True, filter will be queued and can be executed along with other queued filters using run_queued_filters

filter_low_coverage_regions
(rel_cutoff=None, cutoff=None, queue=False)¶ Convenience function that applies a
LowCoverageFilter
.The cutoff can be provided in two ways: 1. As an absolute threshold. Regions with contact count below this absolute threshold are filtered 2. As a fraction relative to the median contact count of all regions.
If both is supplied, whichever threshold is lower will be selected.
If no parameter is supplied, rel_cutoff will be chosen as 0.1.
Parameters:  rel_cutoff – A cutoff as a fraction (01) of the median contact count of all regions.
 cutoff – A cutoff in absolute contact counts (can be float) below which regions are considered “low coverage”
 queue – If True, filter will be queued and can be executed along with other queued filters using run_queued_filters

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

flush
(silent=False, update_mappability=True)¶ Write data to file and flush buffers.
Parameters:  silent – do not print flush progress
 update_mappability – After writing data, update mappability and expected values

get_mask
(key)¶ Search _mask table for key and return Mask.
Parameters:  key (int) – search by mask name
 key – search by mask ID
Returns: Mask

get_masks
(ix)¶ Extract mask IDs encoded in parameter and return masks.
IDs are powers of 2, so a single int field in the table can hold multiple masks by simply adding up the IDs. Similar principle to UNIX chmod (although that uses base 8)
Parameters: ix (int) – integer that is the sum of powers of 2. Note that this value is not necessarily itself a power of 2. Returns: list of Masks extracted from ix Return type: list (Mask)

intervals
(*args, **kwargs)¶ Alias for region_intervals.

load_from_hic
(hic, threads=1, chromosomes=None, _edges_by_overlap_method=<function _edge_overlap_split_rao>, _regions_soft_max=50000)¶ Load data from another
Hic
object.If this object has no associated regions, the regions and contacts of the provided object will simply be copied.
If regions are already present, the contacts of the provided matrix will be binned into the regions of this object using the overlap method provided.
Parameters:  hic – Another
Hic
object  threads – Number of parallel processing threads. More threads also means higher memory usage.
 _edges_by_overlap_method – A function that maps reads from
one genomic region to others using
a supplied overlap map. By default
it uses the Rao et al. (2014) method.
See
_edge_overlap_split_rao()
 _regions_soft_max – Maximum dimension of each processed submatrix per thread. This is a soft maximum, which may be increased as required for very large chromosomes or small bin sizes
 hic – Another

mappable
()¶ Get the mappability of regions in this object.
A “mappable” region has at least one contact to another region in the genome.
Returns: array
where True means mappable and False unmappable

marginals
(masked=True, *args, **kwargs)¶ Get the marginals vector of this Hic matrix.
Sums up all contacts for each bin of the HiC 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, logtransform 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
(matrices, *args, **kwargs)¶ Merge multiple
RegionMatrixContainer
objects.Merging is done by adding the weight of edges in each object.
Parameters: matrices – list of RegionMatrixContainer
Returns: merged RegionMatrixContainer

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 interchromosomal 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 intrachromosomal pairs, possible intrachromosomal pairs by chromosome, possible interchromosomal 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_data
(key, value=None)¶ Retrieve or add vectordata to this object. If there is existing data in this object with the same name, it will be replaced
Parameters:  key – Name of the data column
 value – vector with regionbased data (one entry per region)

region_intervals
(region, bins=None, bin_size=None, smoothing_window=None, nan_replacement=None, zero_to_nan=False, score_field='score', *args, **kwargs)¶ Return equallysized 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 nonzero 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 perchromosome 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.

run_queued_filters
(log_progress=True)¶ Run queued filters.
Parameters: log_progress – If true, process iterating through all edges will be continuously reported.

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

subset
(*regions, **kwargs)¶ Subset a Hic object by specifying one or more subset regions.
Parameters:  regions – string or GenomicRegion object(s)
 kwargs – Supports
file_name: destination file name of subset Hic object;
tmpdir: if True works in tmp until object is closed
additional parameters are passed to
edges()
Returns: Hic

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()

class

class
fanc.hic.
LowCoverageFilter
(hic_object, cutoff=None, rel_cutoff=None, mask=None)¶ Bases:
fanc.hic.HicEdgeFilter
Filter a
HicEdge
if it connects a region that does not have a contact count larger than a specified cutoff.If the cutoff is not provided, it is automatically chosen at 10% of the mean contact count of all regions.

set_hic_object
(hic_object)¶ Set the
Hic
instance to be filtered by this HicEdgeFilter.Used internally by
Hic
instance.Parameters: hic_object – Hic
object

valid
(row)¶ Map valid_edge to MaskFilter.valid(self, row).
Parameters: row – A pytables Table row. Returns: The boolean value returned by valid_edge.

valid_edge
(edge)¶ Check if an edge falls into a lowcoverage region.


fanc.hic.
ice_balancing
(hic, tolerance=0.01, max_iterations=500, whole_matrix=True, inter_chromosomal=True, intra_chromosomal=True, restore_coverage=False, sqrt=True)¶ Apply ICE balancing to HiC matrices.
Iteratively calculates and divides by the matrix margins.
Parameters:  hic – HiC object
 tolerance – Error tolerance (marginal error)
 max_iterations – Maximum number of iterations to perform to achieve error tolerance
 whole_matrix – Correct the whole matrix at once. Default is to correct each chromosome individually.
 inter_chromosomal – Include interchromosomal contacts in balancing (only whole matrix)
 intra_chromosomal – Include intrachromosomal contacts in balancing (only whole matrix)
 restore_coverage – Restore the matrix to its original coverage after balancing, i.e. the sum of contacts in the matrix after balancing remains (roughly) the same
Returns: bias vector

fanc.hic.
sqrt_vanilla_coverage_norm
(*args, **kwargs)¶ Apply vanilla coverage normalisation to HiC matrices with sqrt bias vectors.
Identical to ice_balancing with max_iterations set to 1.
Parameters:  args – see ice_balancing
 kwargs – ice_balancing
Returns: bias vector (numpy)

fanc.hic.
vanilla_coverage_norm
(*args, **kwargs)¶ Apply vanilla coverage normalisation to HiC matrices.
Identical to ice_balancing with max_iterations set to 1 and sqrt to False.
Parameters:  args – see ice_balancing
 kwargs – ice_balancing
Returns: bias vector (numpy)