phenobo version 2.1.6
GeneticNetworkScore is part of the phenotype and metabotype analysis implemented in PheNoBo.
This node is the successor of the PhenoToGeno node and the MetaboToGeno node.
It is the predecessor of the NetworkScore node.
The aim of GeneticNetworkScore is to refine the gene scores of PhenoToGeno and MetaboToGeno. The node calculates a new score for each gene based on a genetic network. The procedure increases the scores of genes that interact with known causal genes for the patient's condition. Therefore, the GeneticNetworkScore node enables the detection of new disease genes.
GeneticNetworkScore requires 2 tables with input data: the initial gene scores and a genetic network. For detailed information about the format of the tables have a look at the Input Port section and at the example files provided at https://github.com/marie-sophie/mapra.
The node implements a random walk with restart on a genetic network. The random walk with restart is an iterative procedure based on the function st+1 = (1-r)Mst + rs0. The function describes a random score transfer along the edges of the network. st is a vector and denotes the scores of all genes after t iterations. The vector s0 contains the initial scores calculated by PhenoToGeno or MetaboToGeno. M is a (sparse) transition matrix representing the edges of the genetic network. The entries mi,j of M give the probability of transferring scores from gene j to gene i. The parameter r gives the fraction of the original scores s0 that is not distributed within the network.
Finally, the gene scores of the random walk with restart are translated into enrichment scores. The enrichment score of a gene with gene score g is determined as log10(gn) where n denotes the total number of genes. If the enrichment score is greater than 0, the gene score is higher than expected for a random prediction (where all genes get a score of n-1). If the enrichment score is lower than 0, the gene score is lower than expected for a random prediction.
To use this node in KNIME, download the below referenced file, save it to your KNIME's plugin folder and restart KNIME.
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