Fork me on GitHub

ancombc: Analysis of Composition of Microbiomes with Bias Correction

Citations

Docstring:

Usage: qiime composition ancombc [OPTIONS]

  Apply Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-
  BC) to identify features that are differentially abundant across groups.

Inputs:
  --i-table ARTIFACT FeatureTable[Frequency]
                         The feature table to be used for ANCOM-BC
                         computation.                               [required]
Parameters:
  --m-metadata-file METADATA...
    (multiple            The sample metadata.
     arguments will be
     merged)                                                        [required]
  --p-formula TEXT       How the microbial absolute abundances for each taxon
                         depend on the variables within the `metadata`.
                                                                    [required]
  --p-p-adj-method TEXT Choices('holm', 'hochberg', 'hommel', 'bonferroni',
    'BH', 'BY', 'fdr', 'none')
                         Method to adjust p-values.          [default: 'holm']
  --p-prv-cut NUMBER     A numerical fraction between 0-1. Taxa with
                         prevalences less than this value will be excluded
                         from the analysis.                     [default: 0.1]
  --p-lib-cut INTEGER    A numerical threshold for filtering samples based on
                         library sizes. Samples with library sizes less than
                         this value will be excluded from the analysis.
                                                                  [default: 0]
  --p-reference-levels TEXT...
    List[Str]            Define the reference level(s) to be used for
                         categorical columns found in the `formula`. These
                         categorical factors are dummy coded relative to the
                         reference(s) provided. The syntax is as follows:
                         "column_name::column_value"                [optional]
  --p-tol NUMBER         The iteration convergence tolerance for the E-M
                         algorithm.                           [default: 1e-05]
  --p-max-iter INTEGER   The maximum number of iterations for the E-M
                         algorithm.                             [default: 100]
  --p-conserve / --p-no-conserve
                         Whether to use a conservative variance estimator for
                         the test statistic. It is recommended if the sample
                         size is small and/or the number of differentially
                         abundant taxa is believed to be large.
                                                              [default: False]
  --p-alpha NUMBER       Level of significance.                [default: 0.05]
Outputs:
  --o-differentials ARTIFACT FeatureData[DifferentialAbundance]
                         The calculated per-feature differentials.  [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: ancombc single formula
  qiime composition ancombc \
    --i-table table.qza \
    --m-metadata-file metadata.tsv \
    --p-formula bodysite \
    --o-differentials dataloaf.qza

  # ### example: ancombc multi formula with reference levels
  qiime composition ancombc \
    --i-table table.qza \
    --m-metadata-file metadata.tsv \
    --p-formula 'bodysite + animal' \
    --p-reference-levels bodysite::tongue animal::dog \
    --o-differentials dataloaf.qza

Import:

from qiime2.plugins.composition.methods import ancombc

Docstring:

Analysis of Composition of Microbiomes with Bias Correction

Apply Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-
BC) to identify features that are differentially abundant across groups.

Parameters
----------
table : FeatureTable[Frequency]
    The feature table to be used for ANCOM-BC computation.
metadata : Metadata
    The sample metadata.
formula : Str
    How the microbial absolute abundances for each taxon depend on the
    variables within the `metadata`.
p_adj_method : Str % Choices('holm', 'hochberg', 'hommel', 'bonferroni', 'BH', 'BY', 'fdr', 'none'), optional
    Method to adjust p-values.
prv_cut : Float, optional
    A numerical fraction between 0-1. Taxa with prevalences less than this
    value will be excluded from the analysis.
lib_cut : Int, optional
    A numerical threshold for filtering samples based on library sizes.
    Samples with library sizes less than this value will be excluded from
    the analysis.
reference_levels : List[Str], optional
    Define the reference level(s) to be used for categorical columns found
    in the `formula`. These categorical factors are dummy coded relative to
    the reference(s) provided. The syntax is as follows:
    "column_name::column_value"
tol : Float, optional
    The iteration convergence tolerance for the E-M algorithm.
max_iter : Int, optional
    The maximum number of iterations for the E-M algorithm.
conserve : Bool, optional
    Whether to use a conservative variance estimator for the test
    statistic. It is recommended if the sample size is small and/or the
    number of differentially abundant taxa is believed to be large.
alpha : Float, optional
    Level of significance.

Returns
-------
differentials : FeatureData[DifferentialAbundance]
    The calculated per-feature differentials.