NDLI: Gene Set Based Integrated Data Analysis Reveals Phenotypic Differences in a Brain Cancer Model

Content Provider	PubMed Central
Author	Petersen, Kjell Rajcevic, Uros Rahim, Siti Aminah Abdul Jonassen, Inge Kalland, Karl-henning Jimenez, Connie R. Rolf, Bjerkvig Niclou, Simone P.
Editor	Ying, Xu
Copyright Year	2013
Abstract	A key challenge in the data analysis of biological high-throughput experiments is to handle the often low number of samples in the experiments compared to the number of biomolecules that are simultaneously measured. Combining experimental data using independent technologies to illuminate the same biological trends, as well as complementing each other in a larger perspective, is one natural way to overcome this challenge. In this work we investigated if integrating proteomics and transcriptomics data from a brain cancer animal model using gene set based analysis methodology, could enhance the biological interpretation of the data relative to more traditional analysis of the two datasets individually. The brain cancer model used is based on serial passaging of transplanted human brain tumor material (glioblastoma - GBM) through several generations in rats. These serial transplantations lead over time to genotypic and phenotypic changes in the tumors and represent a medically relevant model with a rare access to samples and where consequent analyses of individual datasets have revealed relatively few significant findings on their own. We found that the integrated analysis both performed better in terms of significance measure of its findings compared to individual analyses, as well as providing independent verification of the individual results. Thus a better context for overall biological interpretation of the data can be achieved.
Related Links	http://dx.doi.org/10.1371/journal.pone.0068288
Starting Page	68288
File Format	PDF
ISSN	19326203
e-ISSN	19326203
Journal	PLoS ONE
Issue Number	7
Volume Number	8
Language	English
Publisher	Public Library of Science
Publisher Date	2013-07-01
Access Restriction	Open
Rights Holder	Public Library of Science
Subject Keyword	Biochemistry, Genetics and Molecular Biology(all) Agricultural and Biological Sciences(all) Medicine(all) Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Multidisciplinary

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