Docstring:
Usage: qiime diversity-lib alpha-passthrough [OPTIONS]
Computes a vector of values (one value for each samples in a feature table)
using the scikit-bio implementation of the selected alpha diversity metric.
Inputs:
--i-table ARTIFACT FeatureTable[Frequency]
The feature table containing the samples for which a
selected metric should be computed. [required]
Parameters:
--p-metric TEXT Choices('ace', 'berger_parker_d', 'brillouin_d', 'chao1',
'chao1_ci', 'dominance', 'doubles', 'enspie', 'esty_ci', 'fisher_alpha',
'gini_index', 'goods_coverage', 'heip_e', 'kempton_taylor_q',
'lladser_pe', 'margalef', 'mcintosh_d', 'mcintosh_e', 'menhinick',
'michaelis_menten_fit', 'osd', 'robbins', 'simpson', 'simpson_e',
'singles', 'strong') The alpha diversity metric to be computed. [required]
Outputs:
--o-vector ARTIFACT SampleData[AlphaDiversity]
Vector containing per-sample values for the chosen
metric. [required]
Miscellaneous:
--output-dir PATH Output unspecified results to a directory
--verbose / --quiet Display verbose output to stdout and/or stderr
during execution of this action. Or silence output if
execution is successful (silence is golden).
--example-data PATH Write example data and exit.
--citations Show citations and exit.
--use-cache DIRECTORY Specify the cache to be used for the intermediate
work of this action. If not provided, the default
cache under $TMP/qiime2/ will be used.
IMPORTANT FOR HPC USERS: If you are on an HPC system
and are using parallel execution it is important to
set this to a location that is globally accessible to
all nodes in the cluster.
--help Show this message and exit.
Examples:
# ### example: basic
qiime diversity-lib alpha-passthrough \
--i-table feature-table.qza \
--p-metric simpson \
--o-vector simpson-vector.qza
Import:
from qiime2.plugins.diversity_lib.methods import alpha_passthrough
Docstring:
Alpha Passthrough (non-phylogenetic)
Computes a vector of values (one value for each samples in a feature table)
using the scikit-bio implementation of the selected alpha diversity metric.
Parameters
----------
table : FeatureTable[Frequency]
The feature table containing the samples for which a selected metric
should be computed.
metric : Str % Choices('ace', 'berger_parker_d', 'brillouin_d', 'chao1', 'chao1_ci', 'dominance', 'doubles', 'enspie', 'esty_ci', 'fisher_alpha', 'gini_index', 'goods_coverage', 'heip_e', 'kempton_taylor_q', 'lladser_pe', 'margalef', 'mcintosh_d', 'mcintosh_e', 'menhinick', 'michaelis_menten_fit', 'osd', 'robbins', 'simpson', 'simpson_e', 'singles', 'strong')
The alpha diversity metric to be computed.
Returns
-------
vector : SampleData[AlphaDiversity]
Vector containing per-sample values for the chosen metric.
