qiime longitudinal maturity-index (version 2019.4)

--i-table: ARTIFACT FeatureTable[Frequency] Feature table containing all features that should be used for target prediction. [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: :

--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, inclusive_start=False) 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-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: :

--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]:

--m-metadata-file [required]s

--m-metadata-file [required] 0

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, inclusive_start=False), 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.
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.