Abstract
RT-PCR is a quantitative technique of molecular biology used to amplify DNA sequences starting from a sample of mRNA, typically used to explore gene expression variation across groups of treatment.
Because of the non-normal distribution of data, non-parametric methods based on the MANOVA approach and the use of permutations to obtain global F-ratio tests have been proposed to deal with this problem. The issue of analyzing univariate contrasts is addressed via Steel-type tests.
Results of a study involving 30 mice assigned to 5 differents treatment regimens are presented. MANOVA methods detect an effect of treatment on gene expression, with good agreement between methods. These results are potentially useful to draw out new biological hypothesis to be verified in following designed studies.
Future research will focus on comparison of such methods with classical strategies for analysing RT-PCR data; moreover, work will also concentrate on extending such methods to doubly multivariate design.
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Bassani, N., Ambrogi, F., Bosotti, R., Bertolotti, M., Isacchi, A., Biganzoli, E. (2010). Non-parametric MANOVA Methods for Detecting Differentially Expressed Genes in Real-Time RT-PCR Experiments. In: Masulli, F., Peterson, L.E., Tagliaferri, R. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2009. Lecture Notes in Computer Science(), vol 6160. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14571-1_5
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DOI: https://doi.org/10.1007/978-3-642-14571-1_5
Publisher Name: Springer, Berlin, Heidelberg
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