Abstract
Differences of molecular processes are reflected, among others, by differences in gene expression levels of the involved cells. High-throughput methods such as microarrays and deep sequencing approaches are increasingly used to obtain these expression profiles. Often differences of gene expression across different conditions such as tumor vs inflammation are investigated. Top scoring differential genes are considered as candidates for further analysis. Measured differences may not be related to a biological process as they can also be caused by variation in measurement or by other sources of noise. A method for reducing the influence of noise is to combine the available samples. Here, we analyze different types of combination methods, early and late aggregation and compare these statistical and positional rank aggregation methods in a simulation study and by experiments on real microarray data.
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Acknowledgements
This work was funded in part by the German federal ministry of education and research (BMBF) within the framework of the program of medical genome research (PaCa-Net; Project ID PKB-01GS08) and the framework GERONTOSYS 2 (Forschungskern SyStaR, Project ID 0315894A), and by the German Science Foundation (SFB 1074, Project Z1) and the International Graduate School in Molecular Medicine at Ulm University (GSC270). The responsibility for the content lies exclusively with the authors.
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Burkovski, A., Lausser, L., Kraus, J.M., Kestler, H.A. (2014). Rank Aggregation for Candidate Gene Identification. In: Spiliopoulou, M., Schmidt-Thieme, L., Janning, R. (eds) Data Analysis, Machine Learning and Knowledge Discovery. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-01595-8_31
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DOI: https://doi.org/10.1007/978-3-319-01595-8_31
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