BiGGEsTS is a free and open source software tool providing an integrated environment for the biclustering (Madeira and Oliveira, 2004) analysis of time series gene expression data. It offers a complete set of operations for retrieving potentially relevant information from the gene expression data, relying either on visualization or additional techniques for manipulating and processing this particular kind of data. Visualization includes colored matrices, expression evolution charts and pattern charts, as well as dendograms derived from the results obtained by applying hierarchical clustering algorithms on the gene expression data and ontology graphs highlighting the relevant biological terms annotated with the dataset genes in the Gene Ontology for specific organisms. BiGGEsTS integrates well known techniques for preprocessing data: filtering genes, filling missing values, smoothing, normalization and discretization. This software makes available to the scientific community state of the art biclustering algorithms (Madeira et al., 2010) (Madeira and Oliveira, 2009) (Zhang et al., 2005) specifically developed for time series expression data and suited to extract subsets of genes that exhibit coherent expression evolutions in specific subsets of experimental conditions, that is, biclusters. Biclusters may be analyzed with Gene Ontology annotations to find out which contain statistically relevant biological information or even filtered or sorted according to several numerical and statistical criteria.
Keywords for this software
References in zbMATH (referenced in 2 articles )
Showing results 1 to 2 of 2.
- Martella, Francesca; Alfò, Marco: A finite mixture approach to joint clustering of individuals and multivariate discrete outcomes (2017)
- Parmeet Bhatia and Serge Iovleff and Gérard Govaert: blockcluster: An R Package for Model-Based Co-Clustering (2017) not zbMATH