Peter D. Wentzell

Learn More
PETER D. WENTZELL, DARREN T. ANDREWS, DAVID C. HAMILTON, KLAAS FABER AND BRUCE R. KOWALSKI 1 Trace Analysis Research Centre, Department of Chemistry, Dalhousie University, Halifax, Nova Scotia B3H 4J3, Canada 2 Department of Mathematics, Statistics and Computing Science, Dalhousie University, Halifax, Nova Scotia B3H 3J5, Canada 3 Center for Process(More)
Most cells on earth exist in a quiescent state. In yeast, quiescence is induced by carbon starvation, and exit occurs when a carbon source becomes available. To understand how cells survive in, and exit from this state, mRNA abundance was examined using oligonucleotide-based microarrays and quantitative reverse transcription-polymerase chain reaction. Cells(More)
Two new approaches to multivariate calibration are described that, for the first time, allow information on measurement uncertainties to be included in the calibration process in a statistically meaningful way. The new methods, referred to as maximum likelihood principal components regression (MLPCR) and maximum likelihood latent root regression (MLLRR),(More)
As a powerful method for exploratory data analysis, projection pursuit (PP) often outperforms principal component analysis (PCA) to discover important data structure. PP was proposed in 1970s but has not been widely used in chemistry largely because of the difficulty in the optimization of projection indices. In this work, new algorithms, referred as(More)
Here we describe an automated, pressure-driven, sampling device for harvesting 10 to 30 ml samples, in replicate, with intervals as short as 10 s. Correlation between biological replicate time courses measured by microarrays was extremely high. The sampler enables sampling at intervals within the range of many important biological processes.
Modeling of gene expression data from time course experiments often involves the use of linear models such as those obtained from principal component analysis (PCA), independent component analysis (ICA), or other methods. Such methods do not generally yield factors with a clear biological interpretation. Moreover, implicit assumptions about the measurement(More)
The increased need for multiple statistical comparisons under conditions of non-independence in bioinformatics applications, such as DNA microarray data analysis, has led to the development of alternatives to the conventional Bonferroni correction for adjusting P-values. The use of the false discovery rate (FDR), in particular, has grown considerably.(More)
A method is described for the characterization of measurement errors with non-uniform variance (heteroscedastic noise) in contiguous signal vectors (e.g., spectra, chromatograms) that does not require the use of replicated measurements. High-pass digital filters based on inverted Blackman windowed sinc smoothing coefficients are employed to provide point(More)
Procedures to compensate for correlated measurement errors in multivariate data analysis are described. These procedures Ž . are based on the method of maximum likelihood principal component analysis MLPCA , previously described in the literature. MLPCA is a decomposition method similar to conventional PCA, but it takes into account measurement uncertainty(More)