Onassis is a container class for annotating samples metadata with concepts from dictionaries/ontologies, creating semantic sets of unique annotations, computing the distances between different semantic sets and eventually comparing the different identified conditions.
The following methods can be applied to Onassis
One or more input dictionaries to annotate samples metadata
a data frame containing the result of the annotation of the input with ontology terms
A matrix of the similarities between the entries in the entities slot
An optional score matrix containing genomic units on the rows (genes, regions) and on the columns the elements on the rows of the entities slot
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