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Deep phenotyping
Calculate the clinical similarity / distance based on the boolean vectors (doesn't require the structure of the HPO DAG). Example metrics:
Hamming
Dice
Jaccard
Calculate clinical similarity while taking semantic similarity into account (requires the HPO DAG). Example metrics:
HRSS
Phenotypes were ranked on phenotypic specificity. (Inspired by Hanneke et al)
The annotated HPO file (HPOA) was used to infer the genes associated with the phenotypes. These genes were supplied to PHRANK. This tool outperforms Phevor, PhenIX and Phenomizer at ranking the causative genes & diseases.
Input: Pherank requires a list of ensemble ID's The python module mygene was used to convert the entrez gene IDs to ensemble IDs.
We first explored the potential of clustering for deepphenotyping on the ACTG1 ACTB case study collection.
The Deep Phenotyping clustering algorithm is validated on the COG-CDG case study collection of Hanneke et al. Where we hope to acquire the same pathophysiological insights.