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ancom: Apply ANCOM to identify features that differ in abundance.

Citations
  • Siddhartha Mandal, Will Van Treuren, Richard A White, Merete Eggesbø, Rob Knight, and Shyamal D Peddada. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microbial ecology in health and disease, 26(1):27663, 2015. URL: https://pubmed.ncbi.nlm.nih.gov/26028277/, doi:10.3402/mehd.v26.27663.

Docstring:

Usage: qiime composition ancom [OPTIONS]

  Apply Analysis of Composition of Microbiomes (ANCOM) to identify features
  that are differentially abundant across groups.

Inputs:
  --i-table ARTIFACT FeatureTable[Composition]
                         The feature table to be used for ANCOM computation.
                                                                    [required]
Parameters:
  --m-metadata-file METADATA
  --m-metadata-column COLUMN  MetadataColumn[Categorical]
                         The categorical sample metadata column to test for
                         differential abundance across.             [required]
  --p-transform-function TEXT Choices('sqrt', 'log', 'clr')
                         The method applied to transform feature values
                         before generating volcano plots.     [default: 'clr']
  --p-difference-function TEXT Choices('mean_difference', 'f_statistic')
                         The method applied to visualize fold difference in
                         feature abundances across groups for volcano plots.
                                                                    [optional]
  --p-filter-missing / --p-no-filter-missing
                         If True, samples with missing metadata values will
                         be filtered from the table prior to analysis. If
                         False, an error will be raised if there are any
                         missing metadata values.             [default: False]
Outputs:
  --o-visualization VISUALIZATION
                                                                    [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.

Import:

from qiime2.plugins.composition.visualizers import ancom

Docstring:

Apply ANCOM to identify features that differ in abundance.

Apply Analysis of Composition of Microbiomes (ANCOM) to identify features
that are differentially abundant across groups.

Parameters
----------
table : FeatureTable[Composition]
    The feature table to be used for ANCOM computation.
metadata : MetadataColumn[Categorical]
    The categorical sample metadata column to test for differential
    abundance across.
transform_function : Str % Choices('sqrt', 'log', 'clr'), optional
    The method applied to transform feature values before generating
    volcano plots.
difference_function : Str % Choices('mean_difference', 'f_statistic'), optional
    The method applied to visualize fold difference in feature abundances
    across groups for volcano plots.
filter_missing : Bool, optional
    If True, samples with missing metadata values will be filtered from the
    table prior to analysis. If False, an error will be raised if there are
    any missing metadata values.

Returns
-------
visualization : Visualization