lib5c.operators.qnorm module¶

class lib5c.operators.qnorm.QuantileNormalizer(tie='lowest')[source]¶

Bases: lib5c.operators.base.MultiInteractionMatrixOperator

Operator for quantile normalizing InteractionMatrix objects.

tie¶

How this QuantileNormalizer will resolve ties. If 'lowest', it will set all tied entries to the value of the lowest rank. If 'average', it will set all tied entries to the average value across the tied ranks.

Type:	{‘lowest’, ‘average’}

Notes

This operator will first standardize the target InteractionMatrix objects, including propagation of nan’s, if they have locusmap attributes defined. Otherwise, the target InteractionMatrix objects must be the same size.

apply_inplace(targets, **kwargs)[source]¶

Quantile normalizes the target InteractionMatrix objects.

Parameters:	targets (list of InteractionMatrix) – The InteractionMatrix objects to quantile normalize. These must either have `locusmap` attributes or be the same size. kwargs (other keyword arguments) – To be utilized by subclasses.
Returns:	The standardized InteractionMatrix objects.
Return type:	list of InteractionMatrix

Examples

>>> import numpy as np
>>> from lib5c.core.interactions import InteractionMatrix
>>> from lib5c.operators.qnorm import QuantileNormalizer
>>> q = QuantileNormalizer()
>>> im1 = InteractionMatrix([[    5., np.nan,     3.],
...                          [np.nan,     2., np.nan],
...                          [    3., np.nan,     4.]])
...
>>> im2 = InteractionMatrix([[    4., np.nan,     4.],
...                          [np.nan,     1., np.nan],
...                          [    4., np.nan,     2.]])
...
>>> im3 = InteractionMatrix([[    3., np.nan,     6.],
...                          [np.nan,     4., np.nan],
...                          [    6., np.nan,     8.]])
...
>>> results = q.apply([im1, im2, im3])
>>> print results[0]
InteractionMatrix of size 3
[[5.66666667        nan 3.        ]
 [       nan 2.                nan]
 [3.                nan 4.66666667]]
>>> print results[1]
InteractionMatrix of size 3
[[4.66666667        nan 4.66666667]
 [       nan 2.                nan]
 [4.66666667        nan 3.        ]]
>>> print results[2]
InteractionMatrix of size 3
[[2.                nan 4.66666667]
 [       nan 3.                nan]
 [4.66666667        nan 5.66666667]]

>>> import numpy as np
>>> from lib5c.core.interactions import InteractionMatrix
>>> from lib5c.core.loci import Locus, LocusMap
>>> from lib5c.operators.qnorm import QuantileNormalizer
>>> q = QuantileNormalizer()
>>> lm = LocusMap([
...     Locus('chr3', 34109023, 34113109),
...     Locus('chr3', 34113147, 34116141),
...     Locus('chr3', 87282063, 87285636),
...     Locus('chr3', 87285637, 87295935)
... ])
...
>>> im1 = InteractionMatrix([[  0.,   5.,  10.,  15.],
...                          [  5.,  10.,  15.,  20.],
...                          [ 10.,  15.,  20.,  25.],
...                          [ 15.,  20.,  25.,  30.]], locusmap=lm)
...
>>> im2 = InteractionMatrix([[    1., np.nan,    11.],
...                          [np.nan,    11.,    21.],
...                          [   11.,    21.,    16.]], locusmap=lm[:3])
...
>>> results = q.apply([im1, im2])
>>> print results[0]
InteractionMatrix of size 3
[[ 0.5  nan 10.5]
 [ nan 10.5 15.5]
 [10.5 15.5 20.5]]
Associated LocusMap:
LocusMap comprising 3 loci
    Range: chr3:34109023-34113109 to chr3:87282063-87285636
>>> print results[1]
InteractionMatrix of size 3
[[ 0.5  nan 10.5]
 [ nan 10.5 20.5]
 [10.5 20.5 15.5]]
Associated LocusMap:
LocusMap comprising 3 loci
    Range: chr3:34109023-34113109 to chr3:87282063-87285636
>>> results[0].print_log()
InteractionMatrix created
standardized with propagate_nan=True
deleted locus at index 3
qnormed with tie=lowest

>>> import numpy as np
>>> from lib5c.core.interactions import InteractionMatrix
>>> from lib5c.operators.qnorm import QuantileNormalizer
>>> from lib5c.core.loci import Locus, LocusMap
>>> locus_list = [Locus('chr3', 34109023, 34113109, region='Sox2'),
...               Locus('chr3', 34113147, 34116141, region='Sox2'),
...               Locus('chr3', 87282063, 87285636, region='Nestin'),
...               Locus('chr3', 87285637, 87295935, region='Nestin')]
...
>>> locus_map = LocusMap(locus_list)
>>> X = np.reshape(range(16), (4, 4)).astype(float)
>>> im1 = InteractionMatrix(X + X.T, locusmap=locus_map)
>>> 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']
>>> im2 = InteractionMatrix((X + X.T) + 1, locusmap=locus_map)
>>> print im2
InteractionMatrix of size 4
[[ 1.  6. 11. 16.]
 [ 6. 11. 16. 21.]
 [11. 16. 21. 26.]
 [16. 21. 26. 31.]]
Associated LocusMap:
LocusMap comprising 4 loci
    Range: chr3:34109023-34113109 to chr3:87285637-87295935
    Regions: ['Sox2', 'Nestin']
>>> q = QuantileNormalizer()
>>> results = q.apply_by_region([im1, im2])
>>> print results[0]
InteractionMatrix of size 4
[[  0.5   5.5   0.    0. ]
 [  5.5  10.5   0.    0. ]
 [  0.    0.   20.5  25.5]
 [  0.    0.   25.5  30.5]]
Associated LocusMap:
LocusMap comprising 4 loci
    Range: chr3:34109023-34113109 to chr3:87285637-87295935
    Regions: ['Sox2', 'Nestin']
>>> print results[1]
InteractionMatrix of size 4
[[  0.5   5.5   0.    0. ]
 [  5.5  10.5   0.    0. ]
 [  0.    0.   20.5  25.5]
 [  0.    0.   25.5  30.5]]
Associated LocusMap:
LocusMap comprising 4 loci
    Range: chr3:34109023-34113109 to chr3:87285637-87295935
    Regions: ['Sox2', 'Nestin']
>>> results[0].print_log()
InteractionMatrix created
applying by region
extracted region Sox2
standardized with propagate_nan=True
qnormed with tie=lowest
done applying by region