Combining maSigPro and ASCA procedures to enhance analysis in Time-Course Microarray experiments
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Autores: Johan A. Westerhuis, Ana Conesa Cegarra
, Huub C.J. Hoefsloot, Alberto José Ferrer Riquelme , María José Nueda Roldán
- Localización: XXX Congreso Nacional de Estadística e Investigación Operativa y de las IV Jornadas de Estadística Pública: actas, 2007, ISBN 978-84-690-7249-3
- Idioma: inglés
- Texto completo no disponible (Saber más ...)
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Resumen
Gene expression profiling has imposed as a frequent approach to study the molecular basis of a wide variety of biological phenomena. In multiple series of time course experiments (MTC) a large number of genes are measured on different conditions, which are combinations of time and different experimental groups. Identifying genes with different trends over time for the experimental groups and the levels of the factors that produce the differences are the main objectives in these studies. We propose a statistical method for the analysis of MTC data that applies the multivariate ASCA strategy as pre-processing for generating noise-filtered data on the main components of variability. Secondly, the regression based maSigPro method is applied on these data to obtain a selection of genes with differential expression along time and between experimental conditions. We present our results on the application of this method to simulated and real microarray data. We will show how the approach is effective in removing structural and unwanted noise in the data set and increasing the statistical power of the inference method applied to detect differentially expressed genes.
