Peaks module

class fanc.peaks.DistancePeakFilter(cutoff=1, mask=None)

Bases: fanc.peaks.PeakFilter

Filter for peaks where regions are closer than this cutoff in bins.

valid(row)

Map valid_peak to MaskFilter.valid(self, row).

Parameters:row – A pytables Table row.
Returns:The boolean value returned by valid_edge.
valid_peak(peak)

Evaluate whether a peak passes FDR cutoffs set in __init__ :param peak: An Edge object :return: True if peak passes internal FDR cutoffs, False otherwise

class fanc.peaks.EnrichmentPeakFilter(enrichment_cutoff=None, enrichment_ll_cutoff=None, enrichment_h_cutoff=None, enrichment_v_cutoff=None, enrichment_d_cutoff=None, mask=None)

Bases: fanc.peaks.PeakFilter

Filter peaks that do not have a sufficiently strong observed/expected ratio.

valid(row)

Map valid_peak to MaskFilter.valid(self, row).

Parameters:row – A pytables Table row.
Returns:The boolean value returned by valid_edge.
valid_peak(peak)

Determine if a RaoPeak object is valid or should be filtered.

When implementing custom PeakFilter this method must be overridden. It should return False for RaoPeak objects that are to be fitered and True otherwise.

Internally, the RaoPeakInfo object will iterate over all RaoPeak instances to determine their validity on an individual basis.

Parameters:peak – A RaoPeak object
Returns:True if PeakFilter is valid, False otherwise
class fanc.peaks.FdrPeakFilter(mask=None, fdr_cutoff=None, fdr_ll_cutoff=None, fdr_v_cutoff=None, fdr_h_cutoff=None, fdr_d_cutoff=None)

Bases: fanc.peaks.PeakFilter

Filter for peaks that do not pass a certain FDR cutoff.

valid(row)

Map valid_peak to MaskFilter.valid(self, row).

Parameters:row – A pytables Table row.
Returns:The boolean value returned by valid_edge.
valid_peak(peak)

Evaluate whether a peak passes FDR cutoffs set in __init__ :param peak: An Edge object :return: True if peak passes interal FDR cutoffs, False otherwise

class fanc.peaks.FdrSumFilter(cutoff=1.0, mask=None)

Bases: fanc.peaks.PeakFilter

Remove peaks that have a q-value sum > cutoff.

valid(row)

Map valid_peak to MaskFilter.valid(self, row).

Parameters:row – A pytables Table row.
Returns:The boolean value returned by valid_edge.
valid_peak(peak)

Determine if a RaoPeak object is valid or should be filtered.

When implementing custom PeakFilter this method must be overridden. It should return False for RaoPeak objects that are to be fitered and True otherwise.

Internally, the RaoPeakInfo object will iterate over all RaoPeak instances to determine their validity on an individual basis.

Parameters:peak – A RaoPeak object
Returns:True if PeakFilter is valid, False otherwise
class fanc.peaks.LazyPeak(row, nodes_table, bin_size=1)

Bases: fanc.matrix.LazyEdge

Container for a Peak/enriched contact in a Hi-C matrix.

This class implements LazyPeak, which provides lazy loading of attributes from a PyTables table row.

class fanc.peaks.MappabilityPeakFilter(mask=None, mappability_cutoff=None, mappability_ll_cutoff=None, mappability_v_cutoff=None, mappability_h_cutoff=None, mappability_d_cutoff=None)

Bases: fanc.peaks.PeakFilter

Filter for peaks that do not pass a certain FDR cutoff.

valid(row)

Map valid_peak to MaskFilter.valid(self, row).

Parameters:row – A pytables Table row.
Returns:The boolean value returned by valid_edge.
valid_peak(peak)

Evaluate whether a peak passes FDR cutoffs set in __init__ :param peak: An Edge object :return: True if peak passes interal FDR cutoffs, False otherwise

class fanc.peaks.ObservedPeakFilter(cutoff=1, mask=None)

Bases: fanc.peaks.PeakFilter

Filter for peaks that do not pass a certain FDR cutoff.

valid(row)

Map valid_peak to MaskFilter.valid(self, row).

Parameters:row – A pytables Table row.
Returns:The boolean value returned by valid_edge.
valid_peak(peak)

Evaluate whether a peak passes FDR cutoffs set in __init__ :param peak: An Edge object :return: True if peak passes interal FDR cutoffs, False otherwise

class fanc.peaks.Peak(source, sink, *args, **kwargs)

Bases: fanc.matrix.Edge

Container for a Peak/enriched contact in a Hi-C matrix.

class fanc.peaks.PeakFilter(mask=None)

Bases: fanc.general.MaskFilter

Abstract class that provides filtering functionality for the peaks in a RaoPeakInfo object.

Extends MaskFilter and overrides valid(self, row) to make RaoPeakInfo filtering more “natural”.

To create custom filters for the RapPeakInfo object, extend this class and override the valid_peak(self, peak) method. valid_peak should return False for a specific Edge object if the object is supposed to be filtered/masked and True otherwise. See DiagonalFilter for an example.

Pass a custom filter to the filter() method in Hic to apply it.

valid(row)

Map valid_peak to MaskFilter.valid(self, row).

Parameters:row – A pytables Table row.
Returns:The boolean value returned by valid_edge.
valid_peak(peak)

Determine if a RaoPeak object is valid or should be filtered.

When implementing custom PeakFilter this method must be overridden. It should return False for RaoPeak objects that are to be fitered and True otherwise.

Internally, the RaoPeakInfo object will iterate over all RaoPeak instances to determine their validity on an individual basis.

Parameters:peak – A RaoPeak object
Returns:True if PeakFilter is valid, False otherwise
class fanc.peaks.PeakInfo(file_name=None, mode='a', tmpdir=None, _table_name_regions='regions', _table_name_peaks='edges')

Bases: fanc.matrix.RegionMatrixTable

General-purpose class for recording peaks in Hic (and similar) data.

A peak has the following information: source, sink: coordinates of the highest peak pixel in the Hi-C matrix observed: observed value of the peak in the Hi-C matrix, generally uncorrected expected: expected value of the peak at this position in the Hi-C matrix p_value: a P-value that reflects how likely it is to observe a peak with these properties at random x, y: coordinates of the peak centroid, if it is larger than one pixel radius: radius of the peak, expressed in bins (can be converted to base pairs)

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()
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()
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)

Bulk-add edges from a list.

List items can be any of the supported edge types, list, tuple, dict, or Edge. Repeatedly calls add_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.
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 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()

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 read-only 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 Hi-C 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 down-sampled 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 of RegionPairsContainer. Here, we will use the Hic implementation for demonstration purposes, but the usage is exactly the same for all compatible objects implementing RegionPairsContainer, including JuicerHic and CoolerHic.

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 the RegionPairsContainer:

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 or GenomicRegion, 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-C RegionPairsContainer 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 the LazyEdge attributes. Therefore do not use lazy iterators if you need to store edge objects for later access. For example, the following code works as expected list(hic.edges()), with all Edge objects stored in the list, while this code list(hic.edges(lazy=True)) will result in a list of identical LazyEdge 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, the bias 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 or inter_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, 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 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
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.
filter_rao(queue=False)

Convenience function that applies a RaoMergedPeakFilter.

Parameters: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.

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 Hi-C matrix. Unmappable regoins will be masked in the returned vector unless the masked parameter is set to False.

By default, corrected matrix entries are summed up. To get uncorrected matrix marginals use norm=False. Generally, all parameters accepted by edges() 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:

RegionMatrix
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 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_data(key, value=None)

Retrieve or add vector-data 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 region-based 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 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
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
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 of RegionBased objects
Returns: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
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 fanc.peaks.RaoMergedPeakFilter(cutoff=0.02, mask=None)

Bases: fanc.peaks.PeakFilter

Filter merged peaks exactly the same way that Rao et al. (2014) do.

It removes peaks that are singlets and have a q-value sum >.02.

valid(row)

Map valid_peak to MaskFilter.valid(self, row).

Parameters:row – A pytables Table row.
Returns:The boolean value returned by valid_edge.
valid_peak(peak)

Determine if a RaoPeak object is valid or should be filtered.

When implementing custom PeakFilter this method must be overridden. It should return False for RaoPeak objects that are to be fitered and True otherwise.

Internally, the RaoPeakInfo object will iterate over all RaoPeak instances to determine their validity on an individual basis.

Parameters:peak – A RaoPeak object
Returns:True if PeakFilter is valid, False otherwise
class fanc.peaks.RaoPeakCaller(p=None, w_init=None, min_locus_dist=None, max_w=20, min_ll_reads=16, process_inter=False, correct_inter='fdr', n_processes=4, slice_size=2000, min_mappable_fraction=0.7, cluster=False)

Bases: object

Class that calls peaks the same way Rao et al. (2014) propose.

Every pixel in a Hi-C matrix is evaluated based on its local “neighborhood”, i.e. the pixel’s observed value is compared to the expected value calculated from all pixels in its close surroundings.

If a pixel is significantly enriched with respect to all investigated neighborhoods, it is assumed to be a “peak”.

Four neighborhood types are calculated:

  • “donut”: Pixels surrounding the investigated pixel in a certain distance range
  • “lower-left” Pixels to the “lower-left” of a given pixel
  • “horizontal”: Pixels left and right of a given pixel
  • “vertical”: Pixels above and below a given pixel

While the first neighborhood type most generally calculates enrichment of local background, the other types of neighborhoods serve mostly to exclude false-positive results from non-peak structures, such as TAD boundaries.

The RaoPeakCaller is initialized with the peak calling parameters and run using call_peaks().

FDRs for intra-chromosomal peaks are automatically corrected for multiple testing using the “lamda-chunking” methodology introduced in Rao et al. 2014. FDRs for inter-chromosomal peaks are corrected by default using the Benjamini Hochberg false-discovery rate correction (but ‘bonferroni’ is also an option)

call_peaks(hic, chromosome_pairs=None, file_name=None, intra_expected=None, inter_expected=None)

Call peaks in Hi-C matrix.

This method will determine each pixel’s likelihood to be a “true” peak. By default, only pixels with non-zero count and an observed/expected ratio > 1.0 for each neighborhood will be reported, because these can by definition not be true peaks.

The peak calling behavior can be influenced by modifying the object attributes set when initializing RaoPeakCaller.

Parameters:
  • hic – A Hic object
  • chromosome_pairs – If None, all chromosome pairs will be investigated for peaks. Otherwise specify a list of chromosome name tuples (e.g. [(‘chr1’, ‘chr1’), (‘chr1’, ‘chr3’), …])
  • file_name – An optional filename that backs the returned RaoPeakInfo object
  • intra_expected – A dict of the form <chromosome>:<list of expected values> to override expected value calculation
  • inter_expected – A float describing the expected value for inter-chromosomal contact matrix entries
Returns:

RaoPeakInfo object
static e_d(m, i, j, e, w=1, p=0)

Compute the average value of pixels in the “donut” neighborhood of a pixel.

static e_h(m, i, j, e, w=1, p=0)

Compute the average value of pixels in the horizontal neighborhood of a pixel.

static e_ll(m, i, j, e, w=1, p=0)

Compute the average value of pixels in the lower-left neighborhood of a pixel.

static e_v(m, i, j, e, w=1, p=0)

Compute the average value of pixels in the vertical neighborhood of a pixel.

static find_chunk(value, chunk_func=<function RaoPeakCaller.<lambda>>)

Use bisection to find a matching lambda chunk for a given expected value.

static ll_sum(m, i, j, w=1, p=0)

Compute the sum of pixels in the lower-left neighborhood of a pixel.

class fanc.peaks.RaoPeakFilter(mask=None)

Bases: fanc.peaks.PeakFilter

Filter peaks exactly the same way that Rao et al. (2014) do.

It only retains peaks that

  1. are at least 2-fold enriched over either the donut or lower-left neighborhood
  2. are at least 1.5-fold enriched over the horizontal and vertical neighborhoods
  3. are at least 1.75-fold enriched over both the donut and lower-left neighborhood
  4. have an FDR <= 0.1 in every neighborhood
valid(row)

Map valid_peak to MaskFilter.valid(self, row).

Parameters:row – A pytables Table row.
Returns:The boolean value returned by valid_edge.
valid_peak(peak)

Determine if a RaoPeak object is valid or should be filtered.

When implementing custom PeakFilter this method must be overridden. It should return False for RaoPeak objects that are to be fitered and True otherwise.

Internally, the RaoPeakInfo object will iterate over all RaoPeak instances to determine their validity on an individual basis.

Parameters:peak – A RaoPeak object
Returns:True if PeakFilter is valid, False otherwise
class fanc.peaks.RaoPeakInfo(file_name=None, mode='a', tmpdir=None, _table_name_regions='regions', _table_name_peaks='edges')

Bases: fanc.matrix.RegionMatrixTable

Information about peaks called by RaoPeakCaller.

A peak has the following information:

source, sink: coordinates of the highest peak pixel in the Hi-C matrix observed: observed value of the peak in the Hi-C matrix, generally uncorrected e_ll: expected value of the peak given its lower-left neighborhood e_h: expected value of the peak given its horizontal neighborhood e_v: expected value of the peak given its vertical neighborhood e_d: expected value of the peak given its surrounding (donut) neighborhood e_ll_chunk: “lambda-chunk” this peak falls into given its ‘ll’ neighborhood e_h_chunk: “lambda-chunk” this peak falls into given its ‘h’ neighborhood e_v_chunk: “lambda-chunk” this peak falls into given its ‘v’ neighborhood e_d_chunk: “lambda-chunk” this peak falls into given its ‘d’ neighborhood fdr_ll: FDR of the peak given its lower-left neighborhood fdr_h: FDR of the peak given its horizontal neighborhood fdr_v: FDR of the peak given its vertical neighborhood fdr_d: FDR of the peak given its surrounding (donut) neighborhood

For more information about neighborhoods and peak information, see RaoPeakCaller.

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()
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()
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)

Bulk-add edges from a list.

List items can be any of the supported edge types, list, tuple, dict, or Edge. Repeatedly calls add_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.
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 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()

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 read-only 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 Hi-C 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 down-sampled 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 of RegionPairsContainer. Here, we will use the Hic implementation for demonstration purposes, but the usage is exactly the same for all compatible objects implementing RegionPairsContainer, including JuicerHic and CoolerHic.

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 the RegionPairsContainer:

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 or GenomicRegion, 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-C RegionPairsContainer 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 the LazyEdge attributes. Therefore do not use lazy iterators if you need to store edge objects for later access. For example, the following code works as expected list(hic.edges()), with all Edge objects stored in the list, while this code list(hic.edges(lazy=True)) will result in a list of identical LazyEdge 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, the bias 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 or inter_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, 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 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
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.
filter_enrichment(enrichment_ll_cutoff=1.0, enrichment_h_cutoff=1.0, enrichment_v_cutoff=1.0, enrichment_d_cutoff=1.0, queue=False)

Convenience function that applies a ObservedExpectedRatioPeakFilter. The actual algorithm and rationale used for filtering will depend on the internal _mapper attribute.

Parameters:queue – If True, filter will be queued and can be executed along with other queued filters using run_queued_filters
filter_fdr(fdr_cutoff, queue=False)

Convenience function that applies a FdrPeakFilter. The actual algorithm and rationale used for filtering will depend on the internal _mapper attribute.

Parameters:
  • fdr_cutoff – The false-discovery rate of every neighborhood enrichment must be lower or equal to this threshold
  • queue – If True, filter will be queued and can be executed along with other queued filters using run_queued_filters
filter_mappability(cutoff, queue=False)

Convenience function that applies a MappabilityPeakFilter. The actual algorithm and rationale used for filtering will depend on the internal _mapper attribute.

Parameters:
  • cutoff – Minimum mappability (fraction of 1)
  • queue – If True, filter will be queued and can be executed along with other queued filters using run_queued_filters
filter_observed(cutoff, queue=False)

Convenience function that applies a ObservedPeakFilter. The actual algorithm and rationale used for filtering will depend on the internal _mapper attribute.

Parameters:
  • cutoff – Minimum observed value
  • queue – If True, filter will be queued and can be executed along with other queued filters using run_queued_filters
filter_rao(queue=False)

Convenience function that applies all filters Rao et al. (2014) do.

Parameters: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.

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 Hi-C matrix. Unmappable regoins will be masked in the returned vector unless the masked parameter is set to False.

By default, corrected matrix entries are summed up. To get uncorrected matrix marginals use norm=False. Generally, all parameters accepted by edges() 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:

RegionMatrix
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
merged_peaks(file_name=None, euclidian_distance=20000)

Merge spatially proximal peaks.

Parameters:
  • file_name – Optional file to save merged peak info to.
  • euclidian_distance – Maximal distance in base pairs to still consider two peaks to be the same
Returns:

PeakInfo
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_data(key, value=None)

Retrieve or add vector-data 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 region-based 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 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
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
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 of RegionBased objects
Returns: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
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()
fanc.peaks.overlap_peaks(peaks, max_distance=6000)

Calculate overlap between different peak calls.

Useful for comparing peak calls across different samples or conditions.

Parameters:
  • peaks (dict) – Peaks to overlap. Dictionary of fanc.data.network.PeakInfo, keys are dataset names.
  • max_distance (int) – Maximum distance between peaks for overlap
Returns:

DataFrame of overlap statistics and dictionary containing overlapping peaks. Keys are sets of dataset names.
Return type:

(pandas.DataFrame, fanc.data.network.PeakInfo)