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Numerical deconvolution of cDNA microarray signal: simulation study.

Simon Rosenfeld·Thomas Wang·Young Kim·John Milner
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Abstract

A computational model for simulation of the cDNA microarray experiments has been created. The simulation allows one to foresee the statistical properties of replicated experiments without actually performing them. We introduce a new concept, the so-called bio-weight, which allows for reconciliation between conflicting meanings of biological and statistical significance in microarray experiments. It is shown that, for a small sample size, the bio-weight is a more powerful criterion of the presence of a signal in microarray data as compared to the standard approach based on t test. Joint simulation of microarray and quantitative PCR data shows that the genes recovered by using the bio-weight have better chances to be confirmed by PCR than those obtained by the t test technique. We also employ extreme value considerations to derive plausible cutoff levels for hypothesis testing.

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Cell Line, TumorComputational BiologyDNA, ComplementaryHumansMaleNeoplasm ProteinsOligonucleotide Array Sequence AnalysisPolymerase Chain ReactionProstatic NeoplasmsReproducibility of ResultsJournal Article

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Simon Rosenfeld, Thomas Wang, Young Kim, John Milner. 2004. Numerical deconvolution of cDNA microarray signal: simulation study.. https://doi.org/10.1196/annals.1310.012

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