Fork me on GitHub

biom-format.org

biom.table.Table.norm

«  biom.table.Table.nonzero_counts   ::   Contents   ::   biom.table.Table.pa  »

biom.table.Table.norm

Table.norm(axis='sample', inplace=True)

Normalize in place sample values by an observation, or vice versa.

Parameters:

axis : {‘sample’, ‘observation’}, optional

The axis to use for normalization.

inplace : bool, optional

Defaults to True. If True, performs the normalization in place. Otherwise, returns a new table with the normalization applied.

Returns:

biom.Table

The normalized table

Examples

>>> import numpy as np
>>> from biom.table import Table

Create a 2x2 table:

>>> data = np.asarray([[2, 0], [6, 1]])
>>> table = Table(data, ['O1', 'O2'], ['S1', 'S2'])

Get a version of the table normalized on the ‘sample’ axis, leaving the original table untouched:

>>> new_table = table.norm(inplace=False)
>>> print table 
# Constructed from biom file
#OTU ID S1  S2
O1  2.0 0.0
O2  6.0 1.0
>>> print new_table 
# Constructed from biom file
#OTU ID S1  S2
O1  0.25    0.0
O2  0.75    1.0

Get a version of the table normalized on the ‘observation’ axis, again leaving the original table untouched:

>>> new_table = table.norm(axis='observation', inplace=False)
>>> print table 
# Constructed from biom file
#OTU ID S1  S2
O1  2.0 0.0
O2  6.0 1.0
>>> print new_table 
# Constructed from biom file
#OTU ID S1  S2
O1  1.0 0.0
O2  0.857142857143  0.142857142857

Do the same normalization on ‘observation’, this time in-place:

>>> table.norm(axis='observation')
2 x 2 <class 'biom.table.Table'> with 3 nonzero entries (75% dense)
>>> print table 
# Constructed from biom file
#OTU ID S1  S2
O1  1.0 0.0
O2  0.857142857143  0.142857142857

«  biom.table.Table.nonzero_counts   ::   Contents   ::   biom.table.Table.pa  »