Functional Enrichment
2 min
for knowledge based insights into biological processes and disease associations over‑represented in the generated graph (or a selection of graph nodes) you can use functional enrichment you can perform gene ontology ( 1 docid p tocfcinjhyxtcb7fnb ) biological pathway, molecular function or cellular component enrichment analysis, as well as disease associated genes (resource clingen ( 2 docid p tocfcinjhyxtcb7fnb )), rare disease associated genes (resource orphadata ( 3 docid p tocfcinjhyxtcb7fnb )), or mavatar curated list of genes docid vnez3h3bxwdbsz9436xy this test will tell whether genes within any of these category functions appear more often than expected by chance among the selected genes this can support you to gain knowledge based insights into biological context of your gene lists the enrichment p value is calculated using the hypergeometric distribution function adjusting for multiple testing with benjamini hochberg (bh) to decrease the false discovery rate (fdr) the gene background is defined as the intersection of the genes included in the network and in the go database to account for the directed acylic graph (dag) nature of go terms, an “elim” approach is implemented, as previously described by alexa et al , bioinformatics, 2006 ( 4 docid p tocfcinjhyxtcb7fnb ) enrichment results are presented in a figure where top ten enriched terms are presented in rows the gene ratio (overlap between selected genes and the term) is presented by the size of the dot, the significance is represented by the color (the deeper orange color the lower the adjusted for multiple testing p value) hoover with your cursor over the term name to get detailed information about the term itself, its size, overlap, gene ratio and adjusted p value click on the orange count number to see which genes overlap between the generated graph and the term hoovering over the dot will provide you with exact number of gene ratio by right clicking on the term name, you can select add to generate graph docid\ fnr1vl5zxsmm8jomxdyc5 and generate a new graph including all genes within that term or gene list references the gene ontology consortium, the gene ontology knowledgebase in 2023 genetics 224 (2023), doi 10 1093/genetics/iyad031 ( https //geneontology org/ https //geneontology org/ ) welcome to clingen (available at https //www clinicalgenome org/ https //www clinicalgenome org/ ) orphadata – orphanet datasets (available at https //www orphadata com/ https //www orphadata com/ ) a alexa, j rahnenführer, t lengauer, improved scoring of functional groups from gene expression data by decorrelating go graph structure bioinformatics 22, 1600–1607 (2006) ( https //doi org/10 1093/bioinformatics/btl140 https //doi org/10 1093/bioinformatics/btl140 )