Introduction

Cellular phenotypes result from the combined effect of multiple genes, and high-throughput techniques such as DNA microarrays and deep sequencing allow monitoring this genomic complexity. The large scale of the resulting data, however, creates challenges for interpreting results, as primary analysis often yields hundreds of genes. Gene Ontology (GO), a controlled vocabulary for gene products, enables semantic analysis of such gene sets. GO can be used to define semantic similarity between genes, which enables semantic clustering to reduce the complexity of a result set. Here, we describe how to compute semantic similarities and perform GO-based gene clustering using csbl.go, an R package for GO semantic similarity. We demonstrate the approach with expression profiles from breast cancer.

Publications

  1. Using Semantic Similarities and csbl.go for Analyzing Microarray Data.
    Cite this
    Ovaska K, 2016-01-01 - Methods in molecular biology (Clifton, N.J.)
  2. Fast gene ontology based clustering for microarray experiments.
    Cite this
    Ovaska K, Laakso M, Hautaniemi S, 2008-01-01 - BioData mining

Credits

  1. Kristian Ovaska
    Investigator

    University of Helsinki, Biomedicum Helsinki (B524a)

Community Ratings

UsabilityEfficiencyReliabilityRated By
0 user
Sign in to rate
Summary
AccessionBT001906
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesR
User InterfaceTerminal Command Line
Download Count0
Submitted ByKristian Ovaska