Docstring:
Usage: qiime longitudinal maturity-index [OPTIONS]
Calculates a "microbial maturity" index from a regression model trained on
feature data to predict a given continuous metadata column, e.g., to predict
age as a function of microbiota composition. The model is trained on a
subset of control group samples, then predicts the column value for all
samples. This visualization computes maturity index z-scores to compare
relative "maturity" between each group, as described in
doi:10.1038/nature13421. This method can be used to predict between-group
differences in relative trajectory across any type of continuous metadata
gradient, e.g., intestinal microbiome development by age, microbial
succession during wine fermentation, or microbial community differences
along environmental gradients, as a function of two or more different
"treatment" groups.
Inputs:
--i-table ARTIFACT FeatureTable[Frequency]
Feature table containing all features that should be
used for target prediction. [required]
Parameters:
--m-metadata-file METADATA...
(multiple
arguments will be
merged) [required]
--p-state-column TEXT Numeric metadata column containing sampling time
(state) data to use as prediction target. [required]
--p-group-by TEXT Categorical metadata column to use for plotting and
significance testing between main treatment groups.
[required]
--p-control TEXT Value of group-by to use as control group. The
regression model will be trained using only control
group data, and the maturity scores of other groups
consequently will be assessed relative to this group.
[required]
--p-individual-id-column TEXT
Optional metadata column containing IDs for
individual subjects. Adds individual subject
(spaghetti) vectors to volatility charts if a column
name is provided. [optional]
--p-estimator TEXT Choices('RandomForestRegressor',
'ExtraTreesRegressor', 'GradientBoostingRegressor', 'AdaBoostRegressor',
'ElasticNet', 'Ridge', 'Lasso', 'KNeighborsRegressor', 'LinearSVR', 'SVR')
Regression model to use for prediction.
[default: 'RandomForestRegressor']
--p-n-estimators INTEGER
Range(1, None) Number of trees to grow for estimation. More trees
will improve predictive accuracy up to a threshold
level, but will also increase time and memory
requirements. This parameter only affects ensemble
estimators, such as Random Forest, AdaBoost,
ExtraTrees, and GradientBoosting. [default: 100]
--p-test-size PROPORTION
Range(0.0, 1.0) Fraction of input samples to exclude from training
set and use for classifier testing. [default: 0.5]
--p-step PROPORTION Range(0.0, 1.0, inclusive_start=False)
If optimize-feature-selection is True, step is the
percentage of features to remove at each iteration.
[default: 0.05]
--p-cv INTEGER Number of k-fold cross-validations to perform.
Range(1, None) [default: 5]
--p-random-state INTEGER
Seed used by random number generator. [optional]
--p-n-jobs INTEGER Number of jobs to run in parallel. [default: 1]
--p-parameter-tuning / --p-no-parameter-tuning
Automatically tune hyperparameters using random grid
search. [default: False]
--p-optimize-feature-selection / --p-no-optimize-feature-selection
Automatically optimize input feature selection using
recursive feature elimination. [default: False]
--p-stratify / --p-no-stratify
Evenly stratify training and test data among
metadata categories. If True, all values in column
must match at least two samples. [default: False]
--p-missing-samples TEXT Choices('error', 'ignore')
How to handle missing samples in metadata. "error"
will fail if missing samples are detected. "ignore"
will cause the feature table and metadata to be
filtered, so that only samples found in both files
are retained. [default: 'error']
--p-feature-count INTEGER
Range(0, None) Filter feature table to include top N most important
features. Set to zero to include all features.
[default: 50]
Outputs:
--o-sample-estimator ARTIFACT SampleEstimator[Regressor]
Trained sample estimator. [required]
--o-feature-importance ARTIFACT FeatureData[Importance]
Importance of each input feature to model accuracy.
[required]
--o-predictions ARTIFACT SampleData[RegressorPredictions]
Predicted target values for each input sample.
[required]
--o-model-summary VISUALIZATION
Summarized parameter and (if enabled) feature
selection information for the trained estimator.
[required]
--o-accuracy-results VISUALIZATION
Accuracy results visualization. [required]
--o-maz-scores ARTIFACT SampleData[RegressorPredictions]
Microbiota-for-age z-score predictions. [required]
--o-clustermap VISUALIZATION
Heatmap of important feature abundance at each time
point in each group. [required]
--o-volatility-plots VISUALIZATION
Interactive volatility plots of MAZ and maturity
scores, target (column) predictions, and the sample
metadata. [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.
--help Show this message and exit.
Import:
from qiime2.plugins.longitudinal.pipelines import maturity_index
Docstring:
Microbial maturity index prediction.
Calculates a "microbial maturity" index from a regression model trained on
feature data to predict a given continuous metadata column, e.g., to
predict age as a function of microbiota composition. The model is trained
on a subset of control group samples, then predicts the column value for
all samples. This visualization computes maturity index z-scores to compare
relative "maturity" between each group, as described in
doi:10.1038/nature13421. This method can be used to predict between-group
differences in relative trajectory across any type of continuous metadata
gradient, e.g., intestinal microbiome development by age, microbial
succession during wine fermentation, or microbial community differences
along environmental gradients, as a function of two or more different
"treatment" groups.
Parameters
----------
table : FeatureTable[Frequency]
Feature table containing all features that should be used for target
prediction.
metadata : Metadata
state_column : Str
Numeric metadata column containing sampling time (state) data to use as
prediction target.
group_by : Str
Categorical metadata column to use for plotting and significance
testing between main treatment groups.
control : Str
Value of group_by to use as control group. The regression model will be
trained using only control group data, and the maturity scores of other
groups consequently will be assessed relative to this group.
individual_id_column : Str, optional
Optional metadata column containing IDs for individual subjects. Adds
individual subject (spaghetti) vectors to volatility charts if a column
name is provided.
estimator : Str % Choices('RandomForestRegressor', 'ExtraTreesRegressor', 'GradientBoostingRegressor', 'AdaBoostRegressor', 'ElasticNet', 'Ridge', 'Lasso', 'KNeighborsRegressor', 'LinearSVR', 'SVR'), optional
Regression model to use for prediction.
n_estimators : Int % Range(1, None), optional
Number of trees to grow for estimation. More trees will improve
predictive accuracy up to a threshold level, but will also increase
time and memory requirements. This parameter only affects ensemble
estimators, such as Random Forest, AdaBoost, ExtraTrees, and
GradientBoosting.
test_size : Float % Range(0.0, 1.0), optional
Fraction of input samples to exclude from training set and use for
classifier testing.
step : Float % Range(0.0, 1.0, inclusive_start=False), optional
If optimize_feature_selection is True, step is the percentage of
features to remove at each iteration.
cv : Int % Range(1, None), optional
Number of k-fold cross-validations to perform.
random_state : Int, optional
Seed used by random number generator.
n_jobs : Int, optional
Number of jobs to run in parallel.
parameter_tuning : Bool, optional
Automatically tune hyperparameters using random grid search.
optimize_feature_selection : Bool, optional
Automatically optimize input feature selection using recursive feature
elimination.
stratify : Bool, optional
Evenly stratify training and test data among metadata categories. If
True, all values in column must match at least two samples.
missing_samples : Str % Choices('error', 'ignore'), optional
How to handle missing samples in metadata. "error" will fail if missing
samples are detected. "ignore" will cause the feature table and
metadata to be filtered, so that only samples found in both files are
retained.
feature_count : Int % Range(0, None), optional
Filter feature table to include top N most important features. Set to
zero to include all features.
Returns
-------
sample_estimator : SampleEstimator[Regressor]
Trained sample estimator.
feature_importance : FeatureData[Importance]
Importance of each input feature to model accuracy.
predictions : SampleData[RegressorPredictions]
Predicted target values for each input sample.
model_summary : Visualization
Summarized parameter and (if enabled) feature selection information for
the trained estimator.
accuracy_results : Visualization
Accuracy results visualization.
maz_scores : SampleData[RegressorPredictions]
Microbiota-for-age z-score predictions.
clustermap : Visualization
Heatmap of important feature abundance at each time point in each
group.
volatility_plots : Visualization
Interactive volatility plots of MAZ and maturity scores, target
(column) predictions, and the sample metadata.
