Knowledge-guided analysis of "omics" data using the KnowEnG cloud platform
Fig 4
Knowledge-guided gene prioritization.
(A) In standard mode (top), each gene’s expression is tested for association with phenotypic labels, e.g., with a t test. In the (bottom) knowledge-guided mode (ProGENI algorithm), each gene’s expression is first transformed by taking into account expression levels of its network neighbors, and these “network-smoothed” expression values are tested for association with phenotype. The resulting ranking of genes is subjected to second phase of network-based smoothing to obtain the final ranking. (B) Visualization of results from the Gene Prioritization pipeline, used here to identify top genes associated with each tumor type (based on expression data). Users may choose to analyze and visualize results for multiple phenotypes together and configure how many top genes per phenotype the report should include. (C) Known driver genes for each tumor type that are highly prioritized by standard and/or knowledge-guided modes of Gene Prioritization. (D) Comparison between tumor type–related genes identified using the Gene Prioritization pipeline in standard mode (“GP_noNet”) or knowledge-guided mode using HumanNet (“GP_hnInt”), based on their enrichment for GO terms. The axes represent the negative logarithm (base 10) of p-value of enrichment between the set of highly prioritized genes (from either method) for a tumor type and the most enriched GO category for that set. GO, gene ontology.