TY - JOUR
T1 - Gene Set Based Integrated Data Analysis Reveals Phenotypic Differences in a Brain Cancer Model
AU - Petersen, Kjell
AU - Rajcevic, Uros
AU - Abdul Rahim, Siti Aminah
AU - Jonassen, Inge
AU - Kalland, Karl Henning
AU - Jimenez, Connie R.
AU - Bjerkvig, Rolf
AU - Niclou, Simone P.
PY - 2013/7/9
Y1 - 2013/7/9
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84879958104&partnerID=8YFLogxK
UR - https://www.ncbi.nlm.nih.gov/pubmed/23874576
U2 - 10.1371/journal.pone.0068288
DO - 10.1371/journal.pone.0068288
M3 - Article
C2 - 23874576
AN - SCOPUS:84879958104
SN - 1932-6203
VL - 8
JO - PLoS ONE
JF - PLoS ONE
IS - 7
M1 - e68288
ER -