Comprehensive analysis of transciptome data

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Description

This package translates microarray expression data into metadata of reduced dimension. It provides various sample-centered and group-centered visualizations, sample similarity analyses and functional enrichment analyses. The underlying SOM algorithm combines feature clustering, multidimensional scaling and dimension reduction, along with strong visualization capabilities. It enables extraction and description of functional expression modules inherent in the data. The results are given within a separate folder and can be browsed using the summary HTML file.

Details

Package: oposSOM
Type: Package
Version: 1.9.2
Date: 2016-24-03
License: GPL (>= 2)

Author(s)

Author: Henry Loeffler-Wirth <wirth@izbi.uni-leipzig.de> and Martin Kalcher <mkalcher@porkbox.net>

Maintainer: Henry Loeffler-Wirth <wirth@izbi.uni-leipzig.de>

References

Wirth, Loeffler, v.Bergen, Binder: Expression cartography of human tissues using self organizing maps. (BMC Bioinformatics 2011)

Wirth, v.Bergen, Binder: Mining SOM expression portraits: feature selection and integrating concepts of molecular function. (BioData Mining 2012)

Loeffler-Wirth, Kalcher, Binder: oposSOM: R-package for high-dimensional portraying of genome-wide expression landscapes on Bioconductor. (Bioinformatics 2015)

Examples

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# Example with artificial data
env <- opossom.new(list(dataset.name="Example",
                        dim.1stLvlSom=20))

env$indata <- matrix(runif(10000), 1000, 10)

env$group.labels <- "auto"

opossom.run(env)

# Real Example - This will take several minutes
#env <- opossom.new(list(dataset.name="Tissues",
#                        dim.1stLvlSom=30,
#                        geneset.analysis=TRUE,
#                        pairwise.comparison.list=list(
#                          list("Homeostasis"=c(1, 2), "Imune System"=c(9, 10)),
#                          list("Homeostasis"=c(1, 2), "Muscle"=c(8))
#                        )))
#
#data(opossom.tissues)
#env$indata <- opossom.tissues
#
#env$group.labels <- c(rep("Homeostasis", 2),
#                      "Endocrine",
#                      "Digestion",
#                      "Exocrine",
#                      "Epithelium",
#                      "Reproduction",
#                      "Muscle",
#                      rep("Imune System", 2),
#                      rep("Nervous System", 2))
#
#opossom.run(env)