R functions for analysis genomic distribution of genes lists
produced by transcriptomic studies
- Perform statistical tests to unveil genomic clusters -
Produces garphical interpretations of the statistical results
in pdf files - Perform a Gene Ontology analysis and produces
graphic results in pdf files

A visual, intuitive, easy to use software giving access to a new
type of information buried into your microarray data.
Perform statistical tests to unveil genomic clusters,
produces garphical interpretations of the statistical results
in pdf files, perform a Gene Ontology analysis and produces
graphic results in pdf files

c(person("Eemeli", "Leppaaho", role = c("aut","cre"),
email = "eemeli.leppaaho@aalto.fi"),
person("Seppo", "Virtanen", role = c("aut"),
email = "s.virtanen@warwick.ac.uk"),
person("Muhammad", "Ammad-ud-din", role = "ctb",
email = "muhammad.ammad-ud-din@aalto.fi"),
person("Suleiman A", "Khan", role = "ctb",
email = "suleiman.khan@helsinki.fi"),
person("Tommi", "Suvitaival", role = "ctb",
email = "tsvv@steno.dk"),
person("Inka", "Saarinen", role = "ctb",
email = "inka.saarinen@aalto.fi"),
person("Samuel", "Kaski", role = "ctb",
email = "samuel.kaski@aalto.fi"))
Eemeli Leppaaho [aut, cre],
Seppo Virtanen [aut],
Muhammad Ammad-ud-din [ctb],
Suleiman A Khan [ctb],
Tommi Suvitaival [ctb],
Inka Saarinen [ctb],
Samuel Kaski [ctb]
Factor analysis implementation for multiple data sources, i.e., for groups of variables. The whole data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The model group factor analysis (GFA) is inferred with Gibbs sampling.

Factor analysis implementation for multiple data sources, i.e., for groups of variables. The whole data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The model group factor analysis (GFA) is inferred with Gibbs sampling, and it has been presented originally by Virtanen et al. (2012), and extended in Klami et al. (2015) <DOI:10.1109/TNNLS.2014.2376974> and Bunte et al. (2016) <DOI:10.1093/bioinformatics/btw207>; for details, see the citation info.