Integrated data analysis for genome-wide research.

@article{citeulike:2194375, abstract = {Integrated data analysis is introduced as the intermediate level of a systems biology approach to analyse different 'omics' datasets, i.e., genome-wide measurements of transcripts, protein levels or protein-protein interactions, and metabolite levels aiming at generating a coherent understanding of biological function. In this chapter we focus on different methods of correlation analyses ranging from simple pairwise correlation to kernel canonical correlation which were recently applied in molecular biology. Several examples are presented to illustrate their application. The input data for this analysis frequently originate from different experimental platforms. Therefore, preprocessing steps such as data normalisation and missing value estimation are inherent to this approach. The corresponding procedures, potential pitfalls and biases, and available software solutions are reviewed. The multiplicity of observations obtained in omics-profiling experiments necessitates the application of multiple testing correction techniques.}, address = {Institute for Biology and Biochemistry, University Potsdam, c/o MPI-MP Am M\"{u}hlenberg 1, D-14476 Potsdam-Golm, Germany. steinfath@mpimp-golm.mpg.de}, author = {Steinfath, M. and Repsilber, D. and Scholz, M. and Walther, D. and Selbig, J. }, citeulike-article-id = {2194375}, issn = {1023-294X}, journal = {EXS}, keywords = {comparative-genomics, genomics, statistics}, pages = {309--329}, posted-at = {2008-05-14 18:15:46}, priority = {5}, title = {Integrated data analysis for genome-wide research.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/17432273}, volume = {97}, year = {2007} }

TY - JOUR ID - citeulike:2194375 L3 - citeulike-article-id:2194375 TI - Integrated data analysis for genome-wide research. JF - EXS VL - 97 SP - 309 EP - 329 AD - Institute for Biology and Biochemistry, University Potsdam, c/o MPI-MP Am Mühlenberg 1, D-14476 Potsdam-Golm, Germany. steinfath@mpimp-golm.mpg.de SN - 1023-294X N2 - Integrated data analysis is introduced as the intermediate level of a systems biology approach to analyse different 'omics' datasets, i.e., genome-wide measurements of transcripts, protein levels or protein-protein interactions, and metabolite levels aiming at generating a coherent understanding of biological function. In this chapter we focus on different methods of correlation analyses ranging from simple pairwise correlation to kernel canonical correlation which were recently applied in molecular biology. Several examples are presented to illustrate their application. The input data for this analysis frequently originate from different experimental platforms. Therefore, preprocessing steps such as data normalisation and missing value estimation are inherent to this approach. The corresponding procedures, potential pitfalls and biases, and available software solutions are reviewed. The multiplicity of observations obtained in omics-profiling experiments necessitates the application of multiple testing correction techniques. KW - comparative-genomics KW - genomics KW - statistics AU - Steinfath, M AU - Repsilber, D AU - Scholz, M AU - Walther, D AU - Selbig, J PY - 2007/// UR - http://view.ncbi.nlm.nih.gov/pubmed/17432273 ER -