qiime gneiss gradient-clustering (version 2019.4)

--i-table: ARTIFACT FeatureTable[Frequency | RelativeFrequency | Composition] The feature table containing the samples in which the columns will be clustered. [required]:

--m-gradient-column: COLUMN MetadataColumn[Numeric] Contains gradient values to sort the features and samples. [required]:

--p-no-weighted : Specifies if abundance or presence/absence information should be used to perform the clustering. [default: False]:

--m-gradient-files

--m-gradient-file 0

Hierarchical clustering using gradient information.

Build a bifurcating tree that represents a hierarchical clustering of features. The hiearchical clustering uses Ward hierarchical clustering based on the mean difference of gradients that each feature is observed in. This method is primarily used to sort the table to reveal the underlying block-like structures.

Parameters

table : FeatureTable[Frequency | RelativeFrequency | Composition]: The feature table containing the samples in which the columns will be clustered.
gradient : MetadataColumn[Numeric]: Contains gradient values to sort the features and samples.
weighted : Bool, optional: Specifies if abundance or presence/absence information should be used to perform the clustering.

Returns

clustering : Hierarchy: A hierarchy of feature identifiers where each tip corresponds to the feature identifiers in the table. This tree can contain tip ids that are not present in the table, but all feature ids in the table must be present in this tree.