lib5c.core.interactions module¶

class lib5c.core.interactions.InteractionMatrix(matrix, locusmap=None)[source]¶

Bases: lib5c.core.mixins.Picklable, lib5c.core.mixins.Annotatable, lib5c.core.mixins.Loggable

Class representing pairwise architectural contact frequencies between genomic loci. At its heart, this is a square, symmetric matrix whose \(ij\) th entry corresponds to the interaction frequency between the \(i\) th genomic locus and the \(j\) th genomic locus. Optionally, some metadata for the genomic loci may be included.

matrix¶

The matrix of interaction frequencies.

Type: square, symmetric numpy matrix

locusmap¶

Metadata for the genomic loci in the form of a LocusMap object. The size of the LocusMap passed should be equal to the size of matrix.

Type: LocusMap, optional

Notes

Several accessor shortcuts are provided via this class’s implementation of __getitem__(). Consult that function’s docstring for more details.

Some elements of the matrix of an InteractionMatrix may be set to np.nan in cases where data is not available or where interactions are impossible. Impossible interactions are those involving fragments with the same directionality. If a LocusMap whose constituent Locus objects have strand keys in their data attribute is provided when an InteractionMatrix is created, the impossible interactions will be set to np.nan automatically.

delete(index, inplace=True)[source]¶

Delete a Locus and all its interaction information from this InteractionMatrix.

Parameters

index (int) – The index of the Locus to delete.
inplace (bool, optional) – If True, the deletion is performed inplace, preserving the reference to the original InteractionMatrix, though the underlying matrix and locusmap attributes will be present as new objects. If False, the original InteractionMatrix object will be unaffected.

Returns

The resulting InteractionMatrix. If the operation was performed in-place, this is just a reference to this.

Return type

InteractionMatrix

Examples

>>> import numpy as np
>>> from lib5c.core.interactions import InteractionMatrix
>>> from lib5c.core.loci import Locus, LocusMap
>>> X = np.arange(16, dtype=float).reshape((4, 4))
>>> counts = X + X.T
>>> im = InteractionMatrix(counts,
...                        locusmap=LocusMap([
...     Locus('chr3', 34109023, 34113109, name='5C_329_Sox2_FOR_2',
...           strand='+', region='Sox2'),
...     Locus('chr3', 34113147, 34116141, name='5C_329_Sox2_REV_4',
...           strand='-', region='Sox2'),
...     Locus('chr3', 87282063, 87285636, name='5C_326_Nestin_REV_9',
...           strand='-', region='Nestin'),
...     Locus('chr3', 87285637, 87295935, name='5C_326_Nestin_FOR_10',
...           strand='+', region='Nestin')]))
...
>>> print(im)
InteractionMatrix of size 4
[[nan  5. 10. nan]
 [ 5. nan nan 20.]
 [10. nan nan 25.]
 [nan 20. 25. nan]]
Associated LocusMap:
LocusMap comprising 4 loci
    Range: chr3:34109023-34113109 to chr3:87285637-87295935
    Regions: ['Sox2', 'Nestin']
>>> deleted_im = im.delete(2, inplace=False)  # non-in-place delete
>>> deleted_im.print_log()
InteractionMatrix created
deleted locus at index 2 with name 5C_326_Nestin_REV_9
>>> print(deleted_im)
InteractionMatrix of size 3
[[nan  5. nan]
 [ 5. nan 20.]
 [nan 20. nan]]
Associated LocusMap:
LocusMap comprising 3 loci
    Range: chr3:34109023-34113109 to chr3:87285637-87295935
    Regions: ['Sox2', 'Nestin']
>>> im.size()  # original object unaffected
4
>>> deleted_im['5C_329_Sox2_REV_4', '5C_326_Nestin_FOR_10']
20.0
>>> deleted_im = im.delete(2)  # in-place delete
>>> deleted_im.print_log()
InteractionMatrix created
deleted locus at index 2 with name 5C_326_Nestin_REV_9
>>> print(im)  # original object affected
InteractionMatrix of size 3
[[nan  5. nan]
 [ 5. nan 20.]
 [nan 20. nan]]
Associated LocusMap:
LocusMap comprising 3 loci
    Range: chr3:34109023-34113109 to chr3:87285637-87295935
    Regions: ['Sox2', 'Nestin']

extract_region(region)[source]¶

Extract a submatrix of this Interaction Matrix corresponding to a specified region.

Parameters: region (str) – The name of the region to extract.
Returns: The submatrix of this InteractionMatrix corresponding to the specified region. This is returned as a new, independent InteractionMatrix object (see Examples below).
Return type: InteractionMatrix

Examples

>>> import numpy as np
>>> from lib5c.core.interactions import InteractionMatrix
>>> from lib5c.core.loci import Locus, LocusMap
>>> X = np.arange(16, dtype=float).reshape((4, 4))
>>> counts = X + X.T
>>> im = InteractionMatrix(counts,
...                        locusmap=LocusMap([
...     Locus('chr3', 34109023, 34113109, name='5C_329_Sox2_FOR_2',
...           strand='+', region='Sox2'),
...     Locus('chr3', 34113147, 34116141, name='5C_329_Sox2_REV_4',
...           strand='-', region='Sox2'),
...     Locus('chr3', 87282063, 87285636, name='5C_326_Nestin_REV_9',
...           strand='-', region='Nestin'),
...     Locus('chr3', 87285637, 87295935, name='5C_326_Nestin_FOR_10',
...           strand='+', region='Nestin')]))
...
>>> im.matrix
matrix([[nan,  5., 10., nan],
        [ 5., nan, nan, 20.],
        [10., nan, nan, 25.],
        [nan, 20., 25., nan]])
>>> im.get_regions()
['Sox2', 'Nestin']
>>> sox2_im = im.extract_region('Sox2')
>>> sox2_im.print_log()
InteractionMatrix created
extracted region Sox2
>>> sox2_im.matrix
matrix([[nan,  5.],
        [ 5., nan]])
>>> nestin_im = im.extract_region('Nestin')
>>> nestin_im.matrix
matrix([[nan, 25.],
        [25., nan]])

>>> from lib5c.core.interactions import InteractionMatrix
>>> im = InteractionMatrix.from_primerfile('test/primers.bed')
>>> im.get_regions()
['Sox2', 'Nestin', 'Klf4', 'gene-desert', 'Nanog-V2', 'Olig1-Olig2',
'Oct4']
>>> im.size()
1551
>>> im['5C_329_Sox2_FOR_2', '5C_329_Sox2_REV_4'] = 1.0
>>> im['5C_329_Sox2_FOR_2', '5C_329_Sox2_REV_4']
1.0
>>> sox2_im = im.extract_region('Sox2')
>>> sox2_im.get_regions()
['Sox2']
>>> sox2_im.size()
265
>>> sox2_im['5C_329_Sox2_FOR_2', '5C_329_Sox2_REV_4']
1.0
>>> sox2_im['5C_329_Sox2_FOR_2', '5C_329_Sox2_REV_4'] = 2.0
>>> im['5C_329_Sox2_FOR_2', '5C_329_Sox2_REV_4']
1.0

extract_slice(desired_slice)[source]¶

Gets a new InteractionMatrix object representing the interactions of a subset of the loci described in this InteractionMatrix as specified by a slice object.

Parameters: desired_slice (slice) – The slice to use to subset this InteractionMatrix.
Returns: The new InteractionMatrix.
Return type: InteractionMatrix

Notes

Since LocusMap objects are sorted, slices with negative steps will be reversed before being applied to the matrix attribute.

Examples

>>> import numpy as np
>>> from lib5c.core.interactions import InteractionMatrix
>>> X = np.arange(16, dtype=float).reshape((4, 4))
>>> im = InteractionMatrix(X + X.T)
>>> im.matrix
matrix([[ 0.,  5., 10., 15.],
        [ 5., 10., 15., 20.],
        [10., 15., 20., 25.],
        [15., 20., 25., 30.]])
>>> sliced_im = im[1:3]
>>> sliced_im.print_log()
InteractionMatrix created
sliced out slice(1, 3, None)
>>> sliced_im.matrix
matrix([[10., 15.],
        [15., 20.]])
>>> reverse_sliced_im = im[3:1:-1]
>>> reverse_sliced_im.matrix
matrix([[10., 15.],
        [15., 20.]])

>>> from lib5c.core.interactions import InteractionMatrix
>>> im = InteractionMatrix.from_primerfile('test/primers.bed')
>>> i = im['5C_329_Sox2_FOR_33']
>>> j = im['5C_329_Sox2_REV_89']
>>> im[i, i+1] = 1.0
>>> im[j, j-1] = 2.0
>>> im[i, j] = 3.0
>>> sliced_im = im[i:j+1]
>>> sliced_im[0, 1]
1.0
>>> sliced_im[-1, -2]
2.0
>>> sliced_im[0, -1]
3.0
>>> sliced_im.size() == 1 + j - i
True
>>> sliced_im.get_regions()
['Sox2']
>>> sliced_im.locusmap[0].get_name()
'5C_329_Sox2_FOR_33'
>>> sliced_im.locusmap[-1].get_name()
'5C_329_Sox2_REV_89'

flatten(discard_nan=True)[source]¶

Flattens the interaction values in this InteractionMatrix into a flat, non-redundant array.

Parameters: discard_nan (bool, optional) – If True, nan’s will not be filtered out of the returned array.
Returns: A flat, nonredundant array of the interaction values. The \((i, j)\) th element of this InteractionMatrix’s matrix attribute (for \(i >= j\) ) ends up at the \((i\times(i+1)/2 + j)\) th index of the flattened array. If discard_nan was True, these indices will not necessarily match up and it will not be possible to unflatten the array.
Return type: 1d numpy array

Notes

A more intuitive way to think about the ordering is to read down the columns of matrix from left to right, going to the next column whenever you reach the diagonal.

Examples

>>> import numpy as np
>>> from lib5c.core.interactions import InteractionMatrix
>>> im = InteractionMatrix(np.matrix([[   3.0, np.nan,    5.0],
...                                   [np.nan,    6.0, np.nan],
...                                   [   5.0, np.nan,    8.0]]))
...
>>> im.flatten()
array([3., 6., 5., 8.])
>>> im.flatten(discard_nan=False)
array([  3.,  nan,   6.,   5.,  nan,   8.])

flatten_cis(discard_nan=True)[source]¶

Flattens only the cis interaction values in this InteractionMatrix into a flat, non-redundant array.

Parameters: discard_nan (bool, optional) – If True, nan’s will not be filtered out of the returned array.
Returns: The result of flatten()-ing each region separately and concatenating the results.
Return type: 1d numpy array

Examples

>>> import numpy as np
>>> from lib5c.core.interactions import InteractionMatrix
>>> from lib5c.core.loci import Locus, LocusMap
>>> X = np.arange(16, dtype=float).reshape((4, 4))
>>> counts = X + X.T
>>> im = InteractionMatrix(counts,
...                        locusmap=LocusMap([
...     Locus('chr3', 34109023, 34113109, name='5C_329_Sox2_FOR_2',
...           region='Sox2'),
...     Locus('chr3', 34113147, 34116141, name='5C_329_Sox2_REV_4',
...           region='Sox2'),
...     Locus('chr3', 87282063, 87285636, name='5C_326_Nestin_REV_9',
...           region='Nestin'),
...     Locus('chr3', 87285637, 87295935, name='5C_326_Nestin_FOR_10',
...           region='Nestin')]))
...
>>> im[0, 1] = np.nan
>>> im[3, 3] = np.nan
>>> print(im)
InteractionMatrix of size 4
[[ 0. nan 10. 15.]
 [nan 10. 15. 20.]
 [10. 15. 20. 25.]
 [15. 20. 25. nan]]
Associated LocusMap:
LocusMap comprising 4 loci
    Range: chr3:34109023-34113109 to chr3:87285637-87295935
    Regions: ['Sox2', 'Nestin']
>>> im.flatten_cis()
array([  0.,  10.,  20.,  25.])
>>> im.flatten_cis(discard_nan=False)
array([  0.,  nan,  10.,  20.,  25.,  nan])

classmethod from_binfile(binfile)[source]¶

Factory method that creates an InteractionMatrix object whose matrix attribute is initialized with all zeros and whose genomic loci are described by data parsed from a BED file containing bin information.

Parameters: binfile (str) – String reference to a BED file containing bin information to be parsed and used as metadata for the genomic loci.
Returns: InteractionMatrix object.
Return type: InteractionMatrix

Examples

>>> from lib5c.core.interactions import InteractionMatrix
>>> im = InteractionMatrix.from_binfile('test/bins_new.bed')
>>> im.print_log()
InteractionMatrix created
source binfile: test/bins_new.bed
>>> im.locusmap.size()
1807
>>> im.size()
1807
>>> im['gene-desert_BIN_047', 'Nestin_BIN_000']
0.0

classmethod from_countsfile(countsfile, locusmap=None, primerfile=None, binfile=None)[source]¶

Factory method that creates an InteractionMatrix object from a .counts file.

Parameters

countsfile (str) – String reference to the .counts file to parse.
locusmap (LocusMap, optional) – Metadata for the genomic loci in the form of a LocusMap object.
primerfile (str, optional) – String reference to a BED file containing primer information to be parsed and used as metadata for the genomic loci.
binfile (str, optional) – String reference to a BED file containing bin information to be parsed and used as metadata for the genomic loci.

Returns

InteractionMatrix object parsed from the .counts file.

Return type

InteractionMatrix

Notes

At least one of locusmap, primerfile, or binfile must be passed. The matrix attribute of the returned InteractionMatrix will have the same size as whichever of these was passed.

Examples

>>> from lib5c.core.interactions import InteractionMatrix
>>> im = InteractionMatrix.from_countsfile(
...     'test/test_raw.counts', primerfile='test/primers.bed')
...
>>> im.print_log()
InteractionMatrix created
source countsfile: test/test_raw.counts
source primerfile: test/primers.bed
>>> im.locusmap.size()
1551
>>> im.size()
1551
>>> im['5C_325_Olig1-Olig2_FOR_38', '5C_331_gene-desert_REV_62']
1.0
>>> im['5C_325_Olig1-Olig2_REV_237', '5C_325_Olig1-Olig2_REV_230']
nan

classmethod from_list(list_of_interaction_matrices)[source]¶

Factory method that creates an InteractionMatrix object via iterative addition of a list of InteractionMatrix objects.

Parameters: list_of_interaction_matrices (list of InteractionMatrix) – The InteractionMatrix objects to concatenate.
Returns: The resulting InteractionMatrix.
Return type: InteractionMatrix

Notes

This function operates via naive iterate addition and so the following are basically equivalent:

summed_im = InteractionMatrix.from_list(list_of_im)
summed_im = sum(list_of_im, InteractionMatrix([]))

Neither implementation is particularly efficient. See the Examples section for details.

Examples

>>> import numpy as np
>>> from lib5c.core.interactions import InteractionMatrix
>>> im1 = InteractionMatrix(np.matrix([[1.0, 2.0], [2.0, 1.0]]))
>>> im2 = InteractionMatrix(np.matrix([[3.0, 4.0], [4.0, 3.0]]))
>>> summed_im = InteractionMatrix.from_list([im1, im2])
>>> summed_im.print_log()
InteractionMatrix created
created from addition
created from list
>>> summed_im.matrix
matrix([[1., 2., 0., 0.],
        [2., 1., 0., 0.],
        [0., 0., 3., 4.],
        [0., 0., 4., 3.]])

>>> import numpy as np
>>> from lib5c.core.interactions import InteractionMatrix
>>> from lib5c.core.loci import Locus, LocusMap
>>> im1 = InteractionMatrix(np.matrix([[np.nan, 1.0], [1.0, np.nan]]),
...                         locusmap=LocusMap([
...     Locus('chr3', 34109023, 34113109, name='5C_329_Sox2_FOR_2',
...           strand='+'),
...     Locus('chr3', 34113147, 34116141, name='5C_329_Sox2_REV_4',
...           strand='-')]))
...
>>> im2 = InteractionMatrix(np.matrix([[np.nan, 2.0], [2.0, np.nan]]),
...                         locusmap=LocusMap([
...     Locus('chr3', 87282063, 87285636, name='5C_326_Nestin_REV_9',
...           strand='-'),
...     Locus('chr3', 87285637, 87295935, name='5C_326_Nestin_FOR_10',
...           strand='+')]))
...
>>> summed_im = InteractionMatrix.from_list([im1, im2])
>>> summed_im.matrix
matrix([[nan,  1.,  0., nan],
        [ 1., nan, nan,  0.],
        [ 0., nan, nan,  2.],
        [nan,  0.,  2., nan]])

classmethod from_locusmap(locusmap)[source]¶

Factory method that creates an InteractionMatrix object whose matrix attribute is initialized with all zeros and whose genomic loci are described by a LocusMap object.

Parameters: locusmap (LocusMap) – Metadata for the genomic loci in the form of a LocusMap object.
Returns: InteractionMatrix object.
Return type: InteractionMatrix

Examples

>>> from lib5c.core.interactions import InteractionMatrix
>>> from lib5c.core.loci import LocusMap, Locus
>>> locus_list = [Locus('chr3', 34109023, 34113109),
...               Locus('chr3', 34113147, 34116141)]
...
>>> locus_map = LocusMap(locus_list)
>>> im = InteractionMatrix.from_locusmap(locus_map)
>>> im.print_log()
InteractionMatrix created
created from locusmap
>>> im.matrix
matrix([[0., 0.],
        [0., 0.]])

classmethod from_primerfile(primerfile)[source]¶

Factory method that creates an InteractionMatrix object whose matrix attribute is initialized with all zeros and whose genomic loci are described by data parsed from a BED file containing primer information.

Parameters: primerfile (str) – String reference to a BED file containing primer information to be parsed and used as metadata for the genomic loci.
Returns: InteractionMatrix object.
Return type: InteractionMatrix

Examples

>>> from lib5c.core.interactions import InteractionMatrix
>>> im = InteractionMatrix.from_primerfile('test/primers.bed')
>>> im.print_log()
InteractionMatrix created
source primerfile: test/primers.bed
>>> im.locusmap.size()
1551
>>> im.size()
1551
>>> im['5C_325_Olig1-Olig2_FOR_38', '5C_331_gene-desert_REV_62']
0.0
>>> im['5C_325_Olig1-Olig2_REV_237', '5C_325_Olig1-Olig2_REV_230']
nan

classmethod from_size(size)[source]¶

Factory method that creates an InteractionMatrix object whose matrix attribute is initialized with all zeros and whose size is specified.

Parameters: size (int) – Size of the matrix attribute of the desired InteractionMatrix object.
Returns: InteractionMatrix object.
Return type: InteractionMatrix

Examples

>>> from lib5c.core.interactions import InteractionMatrix
>>> im = InteractionMatrix.from_size(2)
>>> im.print_log()
InteractionMatrix created
created from size 2
>>> im.matrix
matrix([[0., 0.],
        [0., 0.]])

get_regions()[source]¶

Get the regions covered by this InteractionMatrix, if this can be deduced from its metadata. For this to work, the metadata in this InteractionMatrix’s locusmap attribute must consist of Locus objects with 'name' keys in their data attributes.

Returns: The ordered list of region names as strings.
Return type: list of str

Examples

>>> from lib5c.core.interactions import InteractionMatrix
>>> im = InteractionMatrix.from_primerfile('test/primers.bed')
>>> im.get_regions()
['Sox2', 'Nestin', 'Klf4', 'gene-desert', 'Nanog-V2', 'Olig1-Olig2',
'Oct4']

size()[source]¶

Get the size of the InteractionMatrix. This value is equal to either dimension of the matrix attribute as well as the number of Locus objects in the associated LocusMap, if present.

Returns: Size of this InteractionMatrix.
Return type: long

Examples

>>> from lib5c.core.interactions import InteractionMatrix
>>> im = InteractionMatrix.from_size(2)
>>> im.matrix
matrix([[0., 0.],
        [0., 0.]])
>>> im.size()
2

to_countsfile(filename, omit_zeros=True)[source]¶

Write the interaction values in this InteractionMatrix to a countsfile.

Parameters

filename (str) – String reference to file to write to.
omit_zeros (bool, optional) – If True, lines will not be written to the outfile if the counts for that line are zero.

Examples

>>> from lib5c.core.interactions import InteractionMatrix
>>> from lib5c.core.loci import Locus, LocusMap
>>> from lib5c.operators.standardization import Standardizer
>>> s = Standardizer()
>>> lm = LocusMap([
...     Locus('chr3', 34109023, 34113109, name='5C_329_Sox2_FOR_2',
...           region='Sox2'),
...     Locus('chr3', 34113147, 34116141, name='5C_329_Sox2_REV_4',
...           region='Sox2'),
...     Locus('chr3', 87282063, 87285636, name='5C_326_Nestin_REV_9',
...           region='Nestin'),
...     Locus('chr3', 87285637, 87295935, name='5C_326_Nestin_FOR_10',
...           region='Nestin')
... ])
...
>>> im1 = InteractionMatrix([[  0.,   5.,  10.,  15.],
...                          [  5.,  10.,  15.,  20.],
...                          [ 10.,  15.,  20.,  25.],
...                          [ 15.,  20.,  25.,  30.]], locusmap=lm)
...
>>> print(im1)
InteractionMatrix of size 4
[[ 0.  5. 10. 15.]
 [ 5. 10. 15. 20.]
 [10. 15. 20. 25.]
 [15. 20. 25. 30.]]
Associated LocusMap:
LocusMap comprising 4 loci
    Range: chr3:34109023-34113109 to chr3:87285637-87295935
    Regions: ['Sox2', 'Nestin']
>>> im1.to_countsfile('test/core_test.counts')
>>> im2 = InteractionMatrix.from_countsfile('test/core_test.counts',
...                                         locusmap=lm)
...
>>> print(im2)
InteractionMatrix of size 4
[[ 0.  5. 10. 15.]
 [ 5. 10. 15. 20.]
 [10. 15. 20. 25.]
 [15. 20. 25. 30.]]
Associated LocusMap:
LocusMap comprising 4 loci
    Range: chr3:34109023-34113109 to chr3:87285637-87295935
    Regions: ['Sox2', 'Nestin']

unflatten(values)[source]¶

Overwrite the matrix attribite of this InteractionMatrix object with values from a flat list, such as that created by InteractionMatrix.flatten().

Parameters: values (1d iterable of float) – A flat, nonredundant list of the interaction values. The \((i, j)\) th element of this InteractionMatrix’s matrix attribute (for \(i >= j\) ) will be set to the \((i\times(i+1)/2 + j)\) th value of the flattened list.

Examples

>>> import numpy as np
>>> from lib5c.core.interactions import InteractionMatrix
>>> im = InteractionMatrix(np.matrix([[   3.0, np.nan,    5.0],
...                                   [np.nan,    6.0, np.nan],
...                                   [   5.0, np.nan,    8.0]]))
...
>>> values = im.flatten(discard_nan=False)
>>> values
array([  3.,  nan,   6.,   5.,  nan,   8.])
>>> values += 1
>>> values
array([  4.,  nan,   7.,   6.,  nan,   9.])
>>> im.unflatten(values)
>>> print(im)
InteractionMatrix of size 3
[[ 4. nan  6.]
 [nan  7. nan]
 [ 6. nan  9.]]

unflatten_cis(values)[source]¶

Overwrite only the cis interaction values of the matrix attribite of this InteractionMatrix object with values from a flat list, such as that created by InteractionMatrix.flatten_cis().

Parameters: values (1d list of float) – A flat, nonredundant list of the cis interaction values. The order should match what would be expected from flatten()-ing each region of the InteractionMatrix seperately and concatenating the results.

Examples

>>> import numpy as np
>>> from lib5c.core.interactions import InteractionMatrix
>>> from lib5c.core.loci import Locus, LocusMap
>>> X = np.reshape(range(16), (4, 4)).astype(float)
>>> counts = X + X.T
>>> im = InteractionMatrix(counts,
...                        locusmap=LocusMap([
...     Locus('chr3', 34109023, 34113109, name='5C_329_Sox2_FOR_2',
...           strand='+', region='Sox2'),
...     Locus('chr3', 34113147, 34116141, name='5C_329_Sox2_REV_4',
...           strand='-', region='Sox2'),
...     Locus('chr3', 87282063, 87285636, name='5C_326_Nestin_REV_9',
...           strand='-', region='Nestin'),
...     Locus('chr3', 87285637, 87295935, name='5C_326_Nestin_FOR_10',
...           strand='+', region='Nestin')]))
...
>>> print(im)
InteractionMatrix of size 4
[[nan  5. 10. nan]
 [ 5. nan nan 20.]
 [10. nan nan 25.]
 [nan 20. 25. nan]]
Associated LocusMap:
LocusMap comprising 4 loci
    Range: chr3:34109023-34113109 to chr3:87285637-87295935
    Regions: ['Sox2', 'Nestin']
>>> values = im.flatten_cis(discard_nan=False)
>>> values
array([nan,  5., nan, nan, 25., nan])
>>> values += 1
>>> values
array([nan,  6., nan, nan, 26., nan])
>>> im.unflatten_cis(values)
>>> print(im)
InteractionMatrix of size 4
[[nan  6. 10. nan]
 [ 6. nan nan 20.]
 [10. nan nan 26.]
 [nan 20. 26. nan]]
Associated LocusMap:
LocusMap comprising 4 loci
    Range: chr3:34109023-34113109 to chr3:87285637-87295935
    Regions: ['Sox2', 'Nestin']