Statistics – Methodology
Scientific paper
2011-12-08
Statistica 2-3 (2009) 153-177
Statistics
Methodology
Scientific paper
In this paper we have demonstrated a complete framework for the analysis of microarray time series data. The unique characteristics of microarry data lend themselves well to a functional data analysis approach and we have shown how this naturally extends to the inclusion of covariates such as age and sex. Our model presented here is a specialisation of the more general functional mixed-effects model and, to the best of our knowledge, we are the first to show how to derive the maximum-likelihood estimators, EM-algorithm, confidence intervals and smoother matrix with more than one fixed-effects function. We were motivated by a real data set characterising healthy human gene expression levels over time and we have aimed to improve upon the existing results with a more flexible model. By taking a roughness penalty approach, this is achieved while avoiding overfitting, allowing for a departure from the original linear mixed-effects model when the data permits it. A deeper biological interpretation is required to fully assess our success here, but the results we have highlighted in this paper suggest that we can easily attach meaning to our findings.
Berk Maurice
Montana Giovanni
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