Functional grouping of genes using spectral clustering and gene ontology

Abstract

Abstract-With the invention of high throughput methods, researchers are capable of producing large amounts of biological data. During the analysis of such data the need for a functional grouping of genes arises. In this paper, we propose a new method based on spectral clustering for the partitioning of genes according to their biological function. The functional information is based on Gene Ontology annotation, a mechanism to capture functional knowledge in a shareable and computer processable form. Our functional cluster method promises to automatize, speed up and therefore improve biological data analysis.

Proteomic systems evaluation of the molecular validity of preclinical psychosis models compared to schizophrenia brain pathology

“…This was done by modifying an approach from genetic research (Fröhlich et al, 2006;Speer et al, 2005) to obtain a numerical quantification for the closeness of the models to the disease by computing a similarity score between rodent and human domains. These scores were obtained through evaluating the average of the best matching GO term similarity between the domain vectors, where the pairwise similarity scores between GO terms were obtained using the LLE kernel described in Section 2.3.3.…”

Comparison of the Data-based and Gene Ontology-Based Approaches to Cluster Validation Methods for Gene Microarrays

“…It is possible to represent relationships between gene products and annotation terms encoded in these hierarchies. Previous research has applied GO information to detect over-represented functional annotations in clusters of genes obtained from expression analyses [15,16,17].…”

Spectral Clustering Gene Ontology Terms to Group Genes by Function

“…Many methods are scoring techniques describing a list of genes annotated with GO terms [2,6,7,11,17,23]. But to our knowledge and apart from our earlier publications [20,19], there exists no automatic functional GO-based clustering method. One method is related to clustering and can be used to indicate which clusters are present in the data [3].…”

“…We decided to cluster GO terms, not genes, because of two reasons: first, we do not face the problem of combining different similarities per gene like in earlier publications [19,20] and second, after mapping the genes back to the GO, they can be present in more than one functional cluster which is biologically plausible, since they can also fulfill more than one biological function.…”