Comparisons module¶

class fanc.architecture.comparisons.ComparisonMatrix(*args, **kwargs)¶

Bases: fanc.matrix.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()`

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.

compare(weight1, weight2)¶

Compare two edge weights.

Parameters:	weight1 – float weight2 – float
Returns:	float

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.

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

classmethod from_matrices(matrix1, matrix2, file_name=None, tmpdir=None, mode='w', log_cmp=False, ignore_infinite=True, ignore_zeros=False, scale=True, **kwargs)¶

Create a comparison matrix from two compatible matrix objects.

The resulting object can be treated like any other matrix in FAN-C, offering the same convenience functions for regions and edges.

Parameters:

matrix1 – First matrix object, such as a Hi-C matrix
matrix2 – Second matrix object, such as a Hi-C matrix
file_name – Path to the comparison output file
tmpdir – Optional. If True, will work in temporary directory until file is closed
mode – Write mode of the output file. Only change this if you know what you are doing - setting this to ‘a’ could lead to unexpected consequences!
log_cmp – If True, log2-transform the comparison matrix value after the comparison has been performed. Useful, for example, for fold-change matrices
ignore_infinite – If True, will remove infinite values from the final comparison matrix
ignore_zeros – If True, will only compare edge weights when both are non-zero.
scale – Scale matrices to the same sequencing depth (sum of all edge weights) before the comparison. You can set this to False if you know the type of normalisation you performed already takes care of this.
kwargs – Keyword arguments passed to edges()

Returns:

ComparisonMatrix

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.architecture.comparisons.ComparisonRegions(*args, **kwargs)¶

Bases: fanc.regions.RegionsTable

class ChromosomeDescription¶

Bases: tables.description.IsDescription

Description of the chromosomes in this object.

class RegionDescription¶

Bases: tables.description.IsDescription

Description of a genomic region for PyTables Table

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.

compare(score1, score2)¶

Compare two edge weights.

Parameters:	score1 – float score2 – float
Returns:	float

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

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()¶: Write buffered data to file.

classmethod from_regions(region_based1, region_based2, attribute='score', file_name=None, tmpdir=None, log=False, score_field=None, **kwargs)¶

Compare genomic tracks with region-associated scores.

All scores are assumed to be floats.

Parameters:

region_based1 – First RegionBased object
region_based2 – Second RegionBased object
attribute – Name of the attribute to be compared. Typically “score”
file_name – Optional path to an output file
tmpdir – Optional. If True, will work in temporary directory until file is closed
log – If True, will log2-transform values after comparison
score_field – Name of the attribute comparison scores will be saved to. Will use attribute if not provided.
kwargs – Keyword arguments passed on to regions()

Returns:

ComparisonRegions

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

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_dict¶

Return a dictionary with region index as keys and regions as values.

Returns:	dict {region.ix: region, …}

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.architecture.comparisons.ComparisonScores(*args, **kwargs)¶

Bases: fanc.architecture.domains.RegionScoreParameterTable

class ChromosomeDescription¶

Bases: tables.description.IsDescription

Description of the chromosomes in this object.

class RegionDescription¶

Bases: tables.description.IsDescription

Description of a genomic region for PyTables Table

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.

compare(score1, score2)¶

Compare two edge weights.

Parameters:	score1 – float score2 – float
Returns:	float

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

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()¶: Write buffered data to file.

classmethod from_scores(scores1, scores2, attributes=None, file_name=None, tmpdir=None, log=False, field_prefix='cmp_', **kwargs)¶

Compare parameter-based scores in a RegionScoreParameterTable.

Parameters:

scores1 – First RegionScoreParameterTable
scores2 – Second RegionScoreParameterTable
attributes – If None, will do all possible comparisons. Provide a list of region attributes (e.g. [“insulation_1000000”, “insulation_2000000”]) for specific comparisons.
file_name – Optional path to an output file
tmpdir – Optional. If True, will work in temporary directory until file is closed
log – log2-transform values after comparison
field_prefix – Prefix of the output field
kwargs – Keyword arguments passed on to regions()

Returns:

ComparisonScores

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

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_dict¶

Return a dictionary with region index as keys and regions as values.

Returns:	dict {region.ix: region, …}

score_regions(parameter, **kwargs)¶: Construct a new object with regions that have a score attribute which corresponds to scores calculated with this parameter. :param parameter: Use scores calculated with this parameter (e.g. window size) :param kwargs: Keyword arguments passed to RegionsTable :return: RegionScoreTable

scores(parameter, scores=None)¶

Return scores for a specific parameter size as list.

Parameters:	parameter – Parameter scores were calculated for, such as window size scores – If provided, set scores for this parameter to the ones in this list.
Returns:	list of scores

to_bed(file_name, parameter, subset=None)¶

Write scores to BED file.

Parameters:	file_name – Path to output file parameter – Parameter the scores were calculated for, such as window size subset – A `GenomicRegion` or region string specifying a region range to be written to file, e.g. “chr19:1-1mb”

to_bigwig(file_name, parameter, subset=None)¶

Write scores to BigWig file.

Parameters:	file_name – Path to output file parameter – Parameter the scores were calculated for, such as window size subset – A `GenomicRegion` or region string specifying a region range to be written to file, e.g. “chr19:1-1mb”

to_gff(file_name, parameter, subset=None)¶

Write scores to GFF file.

Parameters:	file_name – Path to output file parameter – Parameter the scores were calculated for, such as window size subset – A `GenomicRegion` or region string specifying a region range to be written to file, e.g. “chr19:1-1mb”

class fanc.architecture.comparisons.DifferenceMatrix(*args, **kwargs)¶

Bases: fanc.architecture.comparisons.ComparisonMatrix

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.

compare(weight1, weight2)¶

Compare two edge weights.

Parameters:	weight1 – float weight2 – float
Returns:	float

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.

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

classmethod from_matrices(matrix1, matrix2, file_name=None, tmpdir=None, mode='w', log_cmp=False, ignore_infinite=True, ignore_zeros=False, scale=True, **kwargs)¶

Create a comparison matrix from two compatible matrix objects.

The resulting object can be treated like any other matrix in FAN-C, offering the same convenience functions for regions and edges.

Parameters:

matrix1 – First matrix object, such as a Hi-C matrix
matrix2 – Second matrix object, such as a Hi-C matrix
file_name – Path to the comparison output file
tmpdir – Optional. If True, will work in temporary directory until file is closed
mode – Write mode of the output file. Only change this if you know what you are doing - setting this to ‘a’ could lead to unexpected consequences!
log_cmp – If True, log2-transform the comparison matrix value after the comparison has been performed. Useful, for example, for fold-change matrices
ignore_infinite – If True, will remove infinite values from the final comparison matrix
ignore_zeros – If True, will only compare edge weights when both are non-zero.
scale – Scale matrices to the same sequencing depth (sum of all edge weights) before the comparison. You can set this to False if you know the type of normalisation you performed already takes care of this.
kwargs – Keyword arguments passed to edges()

Returns:

ComparisonMatrix

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.architecture.comparisons.DifferenceRegions(*args, **kwargs)¶

Bases: fanc.architecture.comparisons.ComparisonRegions

class ChromosomeDescription¶

Bases: tables.description.IsDescription

Description of the chromosomes in this object.

class RegionDescription¶

Bases: tables.description.IsDescription

Description of a genomic region for PyTables Table

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.

compare(score1, score2)¶

Compare two edge weights.

Parameters:	score1 – float score2 – float
Returns:	float

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

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()¶: Write buffered data to file.

classmethod from_regions(region_based1, region_based2, attribute='score', file_name=None, tmpdir=None, log=False, score_field=None, **kwargs)¶

Compare genomic tracks with region-associated scores.

All scores are assumed to be floats.

Parameters:

region_based1 – First RegionBased object
region_based2 – Second RegionBased object
attribute – Name of the attribute to be compared. Typically “score”
file_name – Optional path to an output file
tmpdir – Optional. If True, will work in temporary directory until file is closed
log – If True, will log2-transform values after comparison
score_field – Name of the attribute comparison scores will be saved to. Will use attribute if not provided.
kwargs – Keyword arguments passed on to regions()

Returns:

ComparisonRegions

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

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_dict¶

Return a dictionary with region index as keys and regions as values.

Returns:	dict {region.ix: region, …}

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.architecture.comparisons.DifferenceScores(*args, **kwargs)¶

Bases: fanc.architecture.comparisons.ComparisonScores

class ChromosomeDescription¶

Bases: tables.description.IsDescription

Description of the chromosomes in this object.

class RegionDescription¶

Bases: tables.description.IsDescription

Description of a genomic region for PyTables Table

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.

compare(score1, score2)¶

Compare two edge weights.

Parameters:	score1 – float score2 – float
Returns:	float

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

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()¶: Write buffered data to file.

classmethod from_scores(scores1, scores2, attributes=None, file_name=None, tmpdir=None, log=False, field_prefix='cmp_', **kwargs)¶

Compare parameter-based scores in a RegionScoreParameterTable.

Parameters:

scores1 – First RegionScoreParameterTable
scores2 – Second RegionScoreParameterTable
attributes – If None, will do all possible comparisons. Provide a list of region attributes (e.g. [“insulation_1000000”, “insulation_2000000”]) for specific comparisons.
file_name – Optional path to an output file
tmpdir – Optional. If True, will work in temporary directory until file is closed
log – log2-transform values after comparison
field_prefix – Prefix of the output field
kwargs – Keyword arguments passed on to regions()

Returns:

ComparisonScores

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

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_dict¶

Return a dictionary with region index as keys and regions as values.

Returns:	dict {region.ix: region, …}

score_regions(parameter, **kwargs)¶: Construct a new object with regions that have a score attribute which corresponds to scores calculated with this parameter. :param parameter: Use scores calculated with this parameter (e.g. window size) :param kwargs: Keyword arguments passed to RegionsTable :return: RegionScoreTable

scores(parameter, scores=None)¶

Return scores for a specific parameter size as list.

Parameters:	parameter – Parameter scores were calculated for, such as window size scores – If provided, set scores for this parameter to the ones in this list.
Returns:	list of scores

to_bed(file_name, parameter, subset=None)¶

Write scores to BED file.

Parameters:	file_name – Path to output file parameter – Parameter the scores were calculated for, such as window size subset – A `GenomicRegion` or region string specifying a region range to be written to file, e.g. “chr19:1-1mb”

to_bigwig(file_name, parameter, subset=None)¶

Write scores to BigWig file.

Parameters:	file_name – Path to output file parameter – Parameter the scores were calculated for, such as window size subset – A `GenomicRegion` or region string specifying a region range to be written to file, e.g. “chr19:1-1mb”

to_gff(file_name, parameter, subset=None)¶

Write scores to GFF file.

Parameters:	file_name – Path to output file parameter – Parameter the scores were calculated for, such as window size subset – A `GenomicRegion` or region string specifying a region range to be written to file, e.g. “chr19:1-1mb”

class fanc.architecture.comparisons.FoldChangeMatrix(*args, **kwargs)¶

Bases: fanc.architecture.comparisons.ComparisonMatrix

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.

compare(weight1, weight2)¶

Compare two edge weights.

Parameters:	weight1 – float weight2 – float
Returns:	float

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.

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

classmethod from_matrices(matrix1, matrix2, file_name=None, tmpdir=None, mode='w', log_cmp=False, ignore_infinite=True, ignore_zeros=False, scale=True, **kwargs)¶

Create a comparison matrix from two compatible matrix objects.

The resulting object can be treated like any other matrix in FAN-C, offering the same convenience functions for regions and edges.

Parameters:

matrix1 – First matrix object, such as a Hi-C matrix
matrix2 – Second matrix object, such as a Hi-C matrix
file_name – Path to the comparison output file
tmpdir – Optional. If True, will work in temporary directory until file is closed
mode – Write mode of the output file. Only change this if you know what you are doing - setting this to ‘a’ could lead to unexpected consequences!
log_cmp – If True, log2-transform the comparison matrix value after the comparison has been performed. Useful, for example, for fold-change matrices
ignore_infinite – If True, will remove infinite values from the final comparison matrix
ignore_zeros – If True, will only compare edge weights when both are non-zero.
scale – Scale matrices to the same sequencing depth (sum of all edge weights) before the comparison. You can set this to False if you know the type of normalisation you performed already takes care of this.
kwargs – Keyword arguments passed to edges()

Returns:

ComparisonMatrix

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.architecture.comparisons.FoldChangeRegions(*args, **kwargs)¶

Bases: fanc.architecture.comparisons.ComparisonRegions

class ChromosomeDescription¶

Bases: tables.description.IsDescription

Description of the chromosomes in this object.

class RegionDescription¶

Bases: tables.description.IsDescription

Description of a genomic region for PyTables Table

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.

compare(score1, score2)¶

Compare two edge weights.

Parameters:	score1 – float score2 – float
Returns:	float

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

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()¶: Write buffered data to file.

classmethod from_regions(region_based1, region_based2, attribute='score', file_name=None, tmpdir=None, log=False, score_field=None, **kwargs)¶

Compare genomic tracks with region-associated scores.

All scores are assumed to be floats.

Parameters:

region_based1 – First RegionBased object
region_based2 – Second RegionBased object
attribute – Name of the attribute to be compared. Typically “score”
file_name – Optional path to an output file
tmpdir – Optional. If True, will work in temporary directory until file is closed
log – If True, will log2-transform values after comparison
score_field – Name of the attribute comparison scores will be saved to. Will use attribute if not provided.
kwargs – Keyword arguments passed on to regions()

Returns:

ComparisonRegions

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

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_dict¶

Return a dictionary with region index as keys and regions as values.

Returns:	dict {region.ix: region, …}

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.architecture.comparisons.FoldChangeScores(*args, **kwargs)¶

Bases: fanc.architecture.comparisons.ComparisonScores

class ChromosomeDescription¶

Bases: tables.description.IsDescription

Description of the chromosomes in this object.

class RegionDescription¶

Bases: tables.description.IsDescription

Description of a genomic region for PyTables Table

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.

compare(score1, score2)¶

Compare two edge weights.

Parameters:	score1 – float score2 – float
Returns:	float

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

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()¶: Write buffered data to file.

classmethod from_scores(scores1, scores2, attributes=None, file_name=None, tmpdir=None, log=False, field_prefix='cmp_', **kwargs)¶

Compare parameter-based scores in a RegionScoreParameterTable.

Parameters:

scores1 – First RegionScoreParameterTable
scores2 – Second RegionScoreParameterTable
attributes – If None, will do all possible comparisons. Provide a list of region attributes (e.g. [“insulation_1000000”, “insulation_2000000”]) for specific comparisons.
file_name – Optional path to an output file
tmpdir – Optional. If True, will work in temporary directory until file is closed
log – log2-transform values after comparison
field_prefix – Prefix of the output field
kwargs – Keyword arguments passed on to regions()

Returns:

ComparisonScores

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

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_dict¶

Return a dictionary with region index as keys and regions as values.

Returns:	dict {region.ix: region, …}

score_regions(parameter, **kwargs)¶: Construct a new object with regions that have a score attribute which corresponds to scores calculated with this parameter. :param parameter: Use scores calculated with this parameter (e.g. window size) :param kwargs: Keyword arguments passed to RegionsTable :return: RegionScoreTable

scores(parameter, scores=None)¶

Return scores for a specific parameter size as list.

Parameters:	parameter – Parameter scores were calculated for, such as window size scores – If provided, set scores for this parameter to the ones in this list.
Returns:	list of scores

to_bed(file_name, parameter, subset=None)¶

Write scores to BED file.

Parameters:	file_name – Path to output file parameter – Parameter the scores were calculated for, such as window size subset – A `GenomicRegion` or region string specifying a region range to be written to file, e.g. “chr19:1-1mb”

to_bigwig(file_name, parameter, subset=None)¶

Write scores to BigWig file.

Parameters:	file_name – Path to output file parameter – Parameter the scores were calculated for, such as window size subset – A `GenomicRegion` or region string specifying a region range to be written to file, e.g. “chr19:1-1mb”

to_gff(file_name, parameter, subset=None)¶

Write scores to GFF file.

Parameters:	file_name – Path to output file parameter – Parameter the scores were calculated for, such as window size subset – A `GenomicRegion` or region string specifying a region range to be written to file, e.g. “chr19:1-1mb”

fanc.architecture.comparisons.hic_pca(*hics, sample_size=None, region=None, strategy='variance', scale=True, log=False, ignore_zeros=False, oe_enrichment=None, min_distance=None, max_distance=None, background_ligation=False, min_libraries_above_background=1, **kwargs)¶

Run a PCA analysis on a set of Hi-C matrices.

Note: this is not a compartment analysis. Use ABCompartmentMatrix for that purpose.

Parameters:

hics – Two or more Hi-C objects
sample_size – Optional. Set an upper limit on the number of edges sampled for this analysis. If not specified, will use all applicable edges. Used in conjunction with strategy to determine which edges to prioritise
region – Optionally specify a region string to limit the PCA to that region.
strategy – Sort order of edges. Used in conjunction with sample_size. One of “variance” (default), “fold-change”, or “passthrough”. variance: edges sorted by variance of contact strength across samples; fold-change: edges sorted by size of fold-change of contact strength across samples; passthrough: unsorted, edges appear in order they are stored in the object
scale – If True (default), the matrix values are scaled to their sequencing depth before running PCA. If you are using the default normalisation, matrix entries correspond to contact probabilities and the margins are equal to 1 and there is not Need for scaling, so you can set this to False in order to save computational time.
log – Log-transform contact strength prior to PCA
ignore_zeros – Only use contacts that are non-zero in all samples
oe_enrichment – Used for “fold-change” strategy, at least on edge must have an O/E of this value or larger.
min_distance – regions must be at least this far apart (in base pairs) to be used for PCA
max_distance – regions must be at least this close together (in base pairs) to be used for PCA
background_ligation – Use the average inter-chromosomal contact strength as background ligation signal and only use pixels where at least min_libraries_above_background samples have an O/E signal above background
min_libraries_above_background – Minimum number of libraries/samples that must have an O/E above background ligation signal for each pixel.
kwargs – Keyword arguments passed to edges()

Returns:

sklearn PCA object, PCA result