Microarray
Microarray Procedure
- Insert description from CSB747
Microarray Data Mining
The Gene Ontology (GO) project is a collaborative effort to address the need for consistent descriptions of gene products in different databases. The GO project has developed three structured controlled vocabularies (ontologies) that describe gene products in a species-independent manner. The Gene Ontology project provides an ontology of defined terms representing gene product properties. The ontology covers three domains:
For example, the gene product cytochrome c can be described by the molecular function term oxidoreductase activity, the biological process terms oxidative phosphorylation and induction of cell death, and the cellular component terms mitochondrial matrix and mitochondrial inner membrane.
- GO Intro - see (http://www.geneontology.org/GO.doc.shtml)
, the annotation terms are clustered based on the share of common genes by using Kappa statistics and Fuzzy heuristic clustering algorithms.
The p-values associated with each terms inside the clusters is the Fisher Exact p-values as the same as ones in regular chart report, which represent the "degree of enrichment" of the annotation term with your input gene list.
The enrichement score of each cluster is kind of new in DAVID. It comes with the geometric mean of each member's p-values in that cluster. However, it is in minus log scale, i.e. geometric mean of p-values equals 1E-10, then it will 10 in -log scale.
http://david.abcc.ncifcrf.gov/forum/cgi-bin/ikonboard.cgi?act=ST;f=3;t=1 - Enrichment score explanation
There are three separate aspects to this effort: first, the development and maintenance of the ontologies themselves; second, the annotation of gene products, which entails making associations between the ontologies and the genes and gene products in the collaborating databases; and third, development of tools that facilitate the creation, maintenance and use of ontologies.
The use of GO terms by collaborating databases facilitates uniform queries across them. The controlled vocabularies are structured so that they can be queried at different levels: for example, you can use GO to find all the gene products in the mouse genome that are involved in signal transduction, or you can zoom in on all the receptor tyrosine kinases. This structure also allows annotators to assign properties to genes or gene products at different levels, depending on the depth of knowledge about that entity.
- Insert description from CSB747
Microarray Data Mining
The Gene Ontology (GO) project is a collaborative effort to address the need for consistent descriptions of gene products in different databases. The GO project has developed three structured controlled vocabularies (ontologies) that describe gene products in a species-independent manner. The Gene Ontology project provides an ontology of defined terms representing gene product properties. The ontology covers three domains:
- cellular component - the parts of a cell or its extracellular environment
- molecular function - the elemental activities of a gene product at the molecular level, such as binding or catalysis
- biological process - operations/sets of molecular events, pertinent to functioning of integrated living units: cells, tissues, organs, and organisms.
For example, the gene product cytochrome c can be described by the molecular function term oxidoreductase activity, the biological process terms oxidative phosphorylation and induction of cell death, and the cellular component terms mitochondrial matrix and mitochondrial inner membrane.
- GO Intro - see (http://www.geneontology.org/GO.doc.shtml)
, the annotation terms are clustered based on the share of common genes by using Kappa statistics and Fuzzy heuristic clustering algorithms.
The p-values associated with each terms inside the clusters is the Fisher Exact p-values as the same as ones in regular chart report, which represent the "degree of enrichment" of the annotation term with your input gene list.
The enrichement score of each cluster is kind of new in DAVID. It comes with the geometric mean of each member's p-values in that cluster. However, it is in minus log scale, i.e. geometric mean of p-values equals 1E-10, then it will 10 in -log scale.
http://david.abcc.ncifcrf.gov/forum/cgi-bin/ikonboard.cgi?act=ST;f=3;t=1 - Enrichment score explanation
There are three separate aspects to this effort: first, the development and maintenance of the ontologies themselves; second, the annotation of gene products, which entails making associations between the ontologies and the genes and gene products in the collaborating databases; and third, development of tools that facilitate the creation, maintenance and use of ontologies.
The use of GO terms by collaborating databases facilitates uniform queries across them. The controlled vocabularies are structured so that they can be queried at different levels: for example, you can use GO to find all the gene products in the mouse genome that are involved in signal transduction, or you can zoom in on all the receptor tyrosine kinases. This structure also allows annotators to assign properties to genes or gene products at different levels, depending on the depth of knowledge about that entity.