# HG changeset patch # User q2d2 # Date 1661803825 0 # Node ID 4b93d690c2500967e50ec5803c1f21c42aa45243 planemo upload for repository https://github.com/qiime2/galaxy-tools/tree/main/tools/suite_qiime2__longitudinal commit 9023cfd83495a517fbcbb6f91d5b01a6f1afcda1 diff -r 000000000000 -r 4b93d690c250 qiime2__longitudinal__maturity_index.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/qiime2__longitudinal__maturity_index.xml Mon Aug 29 20:10:25 2022 +0000 @@ -0,0 +1,151 @@ + + + + + Microbial maturity index prediction. + + quay.io/qiime2/core:2022.8 + + q2galaxy version longitudinal + q2galaxy run longitudinal maturity_index '$inputs' + + + + + + + + + hasattr(value.metadata, "semantic_type") and value.metadata.semantic_type in ['FeatureTable[Frequency]'] + + + + + + + + + + + + + + + + + + + + value is not None and len(value) > 0 + + + + + + value is not None and len(value) > 0 + + + + + + value is not None and len(value) > 0 + +
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+ + + + + + + + + + + + +QIIME 2: longitudinal maturity-index +==================================== +Microbial maturity index prediction. + + +Outputs: +-------- +:sample_estimator.qza: Trained sample estimator. +:feature_importance.qza: Importance of each input feature to model accuracy. +:predictions.qza: Predicted target values for each input sample. +:model_summary.qzv: Summarized parameter and (if enabled) feature selection information for the trained estimator. +:accuracy_results.qzv: Accuracy results visualization. +:maz_scores.qza: Microbiota-for-age z-score predictions. +:clustermap.qzv: Heatmap of important feature abundance at each time point in each group. +:volatility_plots.qzv: Interactive volatility plots of MAZ and maturity scores, target (column) predictions, and the sample metadata. + +| + +Description: +------------ +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. + + +| + + + + 10.1038/nature13421 + 10.21105/joss.00934 + 10.1128/mSystems.00219-18 + 10.1038/s41587-019-0209-9 + +
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