Publication Date


Series Number

Preprint #04-26


Microarray data are subject to multiple sources of measurement error. One source of potentially significant error is the settings of the instruments (laser and sensor) that are used to obtain the measurements of gene expression. Because ‘optimal’ settings may vary from slide to slide, operators typically scan each slide multiple times and then choose the reading with the fewest over-exposed and under-exposed spots. We propose a hierarchical modeling approach to estimating gene expression that combines all available readings on each spot. The basic premise is that all readings contribute some information about gene expression and that after appropriate re-scaling, it would be possible to combine all readings into a single estimate. We illustrate the use of the model using expression data from a maize embryogenesis experiment and assess the statistical properties of the proposed expression estimates using a simulation experiment. As expected, combining all available scans using a reasonable approach to do so results in expression estimates with noticeably lower bias and root mean squared error relative to other approaches that have been proposed.


This preprint was published as Tanzy Love & Alicia Carriquiry, "Repeated Measurements on Distinct Scales With Censoring-A Bayesian Approach Applied to Microarrary Analysis of Maize", Journal of the American Statistical Association (2009): 524-540, doi: 10.1198/jasa.2009.0019.