Florian Schmid1, Matthias Schmid2, Christoph Müssel1, J Eric Sträng1, Christian Buske3, Lars Bullinger4, Johann M Kraus1, Hans A Kestler5. 1. Institute of Medical Systems Biology, Ulm University, Ulm 89069, Germany. 2. Institut für Medizinische Biometrie, Informatik und Epidemiologie, Universität Bonn, Bonn 53127, Germany. 3. Institute of Experimental Cancer Research. 4. Department of Internal Medicine III, Ulm University, Ulm 89069, Germany and. 5. Institute of Medical Systems Biology, Ulm University, Ulm 89069, Germany, Leibniz Institute on Ageing - Fritz Lipmann Institute and FSU Jena, Jena 07745, Germany.
Abstract
UNLABELLED: Over the past years growing knowledge about biological processes and pathways revealed complex interaction networks involving many genes. In order to understand these networks, analysis of differential expression has continuously moved from single genes towards the study of gene sets. Various approaches for the assessment of gene sets have been developed in the context of gene set analysis (GSA). These approaches are bridging the gap between raw measurements and semantically meaningful terms.We present a novel approach for assessing uncertainty in the definition of gene sets. This is an essential step when new gene sets are constructed from domain knowledge or given gene sets are suspected to be affected by uncertainty. Quantification of uncertainty is implemented in the R-package GiANT. We also included widely used GSA methods, embedded in a generic framework that can readily be extended by custom methods. The package provides an easy to use front end and allows for fast parallelization. AVAILABILITY AND IMPLEMENTATION: The package GiANT is available on CRAN. CONTACTS: hans.kestler@leibniz-fli.de or hans.kestler@uni-ulm.de.
UNLABELLED: Over the past years growing knowledge about biological processes and pathways revealed complex interaction networks involving many genes. In order to understand these networks, analysis of differential expression has continuously moved from single genes towards the study of gene sets. Various approaches for the assessment of gene sets have been developed in the context of gene set analysis (GSA). These approaches are bridging the gap between raw measurements and semantically meaningful terms.We present a novel approach for assessing uncertainty in the definition of gene sets. This is an essential step when new gene sets are constructed from domain knowledge or given gene sets are suspected to be affected by uncertainty. Quantification of uncertainty is implemented in the R-package GiANT. We also included widely used GSA methods, embedded in a generic framework that can readily be extended by custom methods. The package provides an easy to use front end and allows for fast parallelization. AVAILABILITY AND IMPLEMENTATION: The package GiANT is available on CRAN. CONTACTS: hans.kestler@leibniz-fli.de or hans.kestler@uni-ulm.de.
Authors: Umesh Tharehalli; Michael Svinarenko; Johann M Kraus; Silke D Kühlwein; Robin Szekely; Ute Kiesle; Annika Scheffold; Thomas F E Barth; Alexander Kleger; Reinhold Schirmbeck; Hans A Kestler; Thomas Seufferlein; Franz Oswald; Sarah-Fee Katz; André Lechel Journal: Int J Mol Sci Date: 2018-11-29 Impact factor: 5.923