Expression profiling – clusters of possibilities

Mikael Kubista1,2
1TATAA Biocenter; 2Institute of Biotechnology, Czech Academy of Sciences


Advances in qPCR technology allow studies of increasingly large systems comprising many genes and samples. The increasing data sizes allow expression profiling both in the gene and the samples dimension, while also putting higher demands on sound statistical analysis and expertise to handle and interpret results. In this talk I present experimental design strategies for large studies, including proper design of multiplate experiments. I also present adequate pre-processing of primary qPCR data to prepare the measurement results for profiling analysis. Then I exemplify how powerful unsupervised expression profiling methods, including hierarchical clustering, heatmap, principal component analysis, and self-organizing maps, can be used to classify the expression of genes during the development of the frog Xenopus laevis and how the developmental stages from egg to tadpole can be clustered based on the genes’ expressions. Finally, I exemplify how powerful supervised methods, including potential curves, artificial neural networks, and support vector machines can be used to classify disease samples by comparing measured profiles of test samples with profiles measured on a reference set of samples.

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