q2-composition

This QIIME 2 plugin supports methods for compositional data analysis.

version: 2024.10.0

website: https://github.com/qiime2/q2-composition

user support:

Please post to the QIIME 2 forum for help with this plugin: https://forum.qiime2.org

Actions¶

Artifact Classes¶

Formats¶

composition add-pseudocount¶

Increment all counts in table by pseudocount.

Inputs¶

table: FeatureTable[Frequency]

The feature table to which pseudocounts should be added.[required]

Parameters¶

pseudocount: Int

The value to add to all counts in the feature table.[default: 1]

Outputs¶

composition_table: FeatureTable[Composition]

The resulting feature table.[required]

composition ancombc¶

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

Citations¶

Lin & Peddada, 2020

Inputs¶

table: FeatureTable[Frequency]

The feature table to be used for ANCOM-BC computation.[required]

Parameters¶

metadata: Metadata

The sample metadata.[required]

formula: Str

How the microbial absolute abundances for each taxon depend on the variables within the metadata.[required]

p_adj_method: Str % Choices('holm', 'hochberg', 'hommel', 'bonferroni', 'BH', 'BY', 'fdr', 'none')

Method to adjust p-values.[default: 'holm']

prv_cut: Float

A numerical fraction between 0-1. Taxa with prevalences less than this value will be excluded from the analysis.[default: 0.1]

lib_cut: Int

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]

reference_levels: 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]

tol: Float

The iteration convergence tolerance for the E-M algorithm.[default: 1e-05]

max_iter: Int

The maximum number of iterations for the E-M algorithm.[default: 100]

conserve: Bool

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]

alpha: Float

Level of significance.[default: 0.05]

Outputs¶

differentials: FeatureData[DifferentialAbundance]

The calculated per-feature differentials.[required]

Examples¶

ancombc_single_formula¶

wget -O 'table.qza' \
  'https://amplicon-docs.qiime2.org/en/latest/data/examples/composition/ancombc/1/table.qza'

wget -O 'metadata.tsv' \
  'https://amplicon-docs.qiime2.org/en/latest/data/examples/composition/ancombc/1/metadata.tsv'

qiime composition ancombc \
  --i-table table.qza \
  --m-metadata-file metadata.tsv \
  --p-formula bodysite \
  --o-differentials dataloaf.qza

from qiime2 import Artifact
from qiime2 import Metadata
from urllib import request
import qiime2.plugins.composition.actions as composition_actions

url = 'https://amplicon-docs.qiime2.org/en/latest/data/examples/composition/ancombc/1/table.qza'
fn = 'table.qza'
request.urlretrieve(url, fn)
table = Artifact.load(fn)

url = 'https://amplicon-docs.qiime2.org/en/latest/data/examples/composition/ancombc/1/metadata.tsv'
fn = 'metadata.tsv'
request.urlretrieve(url, fn)
metadata_md = Metadata.load(fn)

dataloaf, = composition_actions.ancombc(
    table=table,
    metadata=metadata_md,
    formula='bodysite',
)

library(reticulate)

Artifact <- import("qiime2")$Artifact
Metadata <- import("qiime2")$Metadata
composition_actions <- import("qiime2.plugins.composition.actions")
request <- import("urllib")$request

url <- 'https://amplicon-docs.qiime2.org/en/latest/data/examples/composition/ancombc/1/table.qza'
fn <- 'table.qza'
request$urlretrieve(url, fn)
table <- Artifact$load(fn)

url <- 'https://amplicon-docs.qiime2.org/en/latest/data/examples/composition/ancombc/1/metadata.tsv'
fn <- 'metadata.tsv'
request$urlretrieve(url, fn)
metadata_md <- Metadata$load(fn)

action_results <- composition_actions$ancombc(
    table=table,
    metadata=metadata_md,
    formula='bodysite',
)
dataloaf <- action_results$differentials

from q2_composition._examples import ancombc_single_formula

ancombc_single_formula(use)

ancombc_multi_formula_with_reference_levels¶

wget -O 'table.qza' \
  'https://amplicon-docs.qiime2.org/en/latest/data/examples/composition/ancombc/2/table.qza'

wget -O 'metadata.tsv' \
  'https://amplicon-docs.qiime2.org/en/latest/data/examples/composition/ancombc/2/metadata.tsv'

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

from qiime2 import Artifact
from qiime2 import Metadata
from urllib import request
import qiime2.plugins.composition.actions as composition_actions

url = 'https://amplicon-docs.qiime2.org/en/latest/data/examples/composition/ancombc/2/table.qza'
fn = 'table.qza'
request.urlretrieve(url, fn)
table = Artifact.load(fn)

url = 'https://amplicon-docs.qiime2.org/en/latest/data/examples/composition/ancombc/2/metadata.tsv'
fn = 'metadata.tsv'
request.urlretrieve(url, fn)
metadata_md = Metadata.load(fn)

dataloaf, = composition_actions.ancombc(
    table=table,
    metadata=metadata_md,
    formula='bodysite + animal',
    reference_levels=['bodysite::tongue', 'animal::dog'],
)

library(reticulate)

Artifact <- import("qiime2")$Artifact
Metadata <- import("qiime2")$Metadata
composition_actions <- import("qiime2.plugins.composition.actions")
request <- import("urllib")$request

url <- 'https://amplicon-docs.qiime2.org/en/latest/data/examples/composition/ancombc/2/table.qza'
fn <- 'table.qza'
request$urlretrieve(url, fn)
table <- Artifact$load(fn)

url <- 'https://amplicon-docs.qiime2.org/en/latest/data/examples/composition/ancombc/2/metadata.tsv'
fn <- 'metadata.tsv'
request$urlretrieve(url, fn)
metadata_md <- Metadata$load(fn)

action_results <- composition_actions$ancombc(
    table=table,
    metadata=metadata_md,
    formula='bodysite + animal',
    reference_levels=list('bodysite::tongue', 'animal::dog'),
)
dataloaf <- action_results$differentials

from q2_composition._examples import ancombc_multi_formula_with_reference_levels

ancombc_multi_formula_with_reference_levels(use)

composition ancom¶

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

Citations¶

Mandal et al., 2015

Inputs¶

table: FeatureTable[Composition]

The feature table to be used for ANCOM computation.[required]

Parameters¶

metadata: MetadataColumn[Categorical]

The categorical sample metadata column to test for differential abundance across.[required]

transform_function: Str % Choices('sqrt', 'log', 'clr')

The method applied to transform feature values before generating volcano plots.[default: 'clr']

difference_function: Str % Choices('mean_difference', 'f_statistic')

The method applied to visualize fold difference in feature abundances across groups for volcano plots.[optional]

filter_missing: Bool

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¶

visualization: Visualization

<no description>[required]

composition tabulate¶

Generate tabular view of ANCOM-BC output, which includes per-page views for the log-fold change (lfc), standard error (se), P values, Q values, and W scores.

Inputs¶

data: FeatureData[DifferentialAbundance]

The ANCOM-BC output to be tabulated.[required]

Outputs¶

visualization: Visualization

<no description>[required]

composition da-barplot¶

Generate bar plot views of ANCOM-BC output. One plot will be present per column in the ANCOM-BC output. The significance_threshold, effect_size_threshold and feature_ids filter results are intersected, such that only features that remain after all three filters have been applied will be present in the output.

Inputs¶

data: FeatureData[DifferentialAbundance]

The ANCOM-BC output to be plotted.[required]

Parameters¶

effect_size_label: Str

Label for effect sizes in data.[default: 'lfc']

feature_id_label: Str

Label for feature ids in data.[default: 'id']

error_label: Str

Label for effect size errors in data.[default: 'se']

significance_label: Str

Label for statistical significance level in data.[default: 'q_val']

significance_threshold: Float % Range(0.0, 1.0, inclusive_end=True)

Exclude features with statistical significance level greater (i.e., less significant) than this threshold.[default: 1.0]

effect_size_threshold: Float % Range(0.0, None)

Exclude features with an absolute value of effect size less than this threshold.[default: 0.0]

feature_ids: Metadata

Exclude features if their ids are not included in this index.[optional]

level_delimiter: Str

If feature ids encode hierarchical information, split the levels when generating feature labels in the visualization using this delimiter.[optional]

label_limit: Int

Set the maximum length that will be viewable for axis labels. You can set this parameter if your axis labels are being cut off.[optional]

Outputs¶

visualization: Visualization

<no description>[required]