# HG changeset patch # User q2d2 # Date 1661804688 0 # Node ID c41392b0d87eb7fccbafd5db54c453b158acd659 planemo upload for repository https://github.com/qiime2/galaxy-tools/tree/main/tools/suite_qiime2__sample_classifier commit 9023cfd83495a517fbcbb6f91d5b01a6f1afcda1 diff -r 000000000000 -r c41392b0d87e qiime2__sample_classifier__metatable.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/qiime2__sample_classifier__metatable.xml Mon Aug 29 20:24:48 2022 +0000 @@ -0,0 +1,96 @@ + + + + + Convert (and merge) positive numeric metadata (in)to feature table. + + quay.io/qiime2/core:2022.8 + + q2galaxy version sample_classifier + q2galaxy run sample_classifier metatable '$inputs' + + + + + + + + + + + + + + + + + + +
+ + + + + hasattr(value.metadata, "semantic_type") and value.metadata.semantic_type in ['FeatureTable[Frequency]'] + + + + + + + + + + + + +
+
+ + + + + +QIIME 2: sample-classifier metatable +==================================== +Convert (and merge) positive numeric metadata (in)to feature table. + + +Outputs: +-------- +:converted_table.qza: Converted feature table + +| + +Description: +------------ +Convert numeric sample metadata from TSV file into a feature table. Optionally merge with an existing feature table. Only numeric metadata will be converted; categorical columns will be silently dropped. By default, if a table is used as input only samples found in both the table and metadata (intersection) are merged, and others are silently dropped. Set missing_samples="error" to raise an error if samples found in the table are missing from the metadata file. The metadata file can always contain a superset of samples. Note that columns will be dropped if they are non-numeric, contain no unique values (zero variance), contain only empty cells, or contain negative values. This method currently only converts postive numeric metadata into feature data. Tip: convert categorical columns to dummy variables to include them in the output feature table. + + +| + + + + 10.21105/joss.00934 + @article{cite2, + author = {Pedregosa, Fabian and Varoquaux, Gaël and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and Vanderplas, Jake and Passos, Alexandre and Cournapeau, David and Brucher, Matthieu and Perrot, Matthieu and Duchesnay, Édouard}, + journal = {Journal of machine learning research}, + number = {Oct}, + pages = {2825--2830}, + title = {Scikit-learn: Machine learning in Python}, + volume = {12}, + year = {2011} +} + + 10.1038/s41587-019-0209-9 + +
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