William Astle
BATMAN-an R package for the automated quantification of metabolites from NMR spectra using a Bayesian Model.
Fri, 12/30/2011 - 15:01 — SomNambulAuthors: Maria De Iorio, William Astle, Timothy Ebbels, Jie Hao
Subjects: Applications
AbstractMotivation: NMR spectra are widely used in metabolomics to obtain metabolite
profiles in complex biological mixtures. Common methods used to assign and
estimate concentrations of metabolite involve either an expert manual peak
fitting or extra pre-processing steps, such as peak alignment and binning. Peak
fitting is very time consuming and is subject to human error. Conversely,
alignment and binning can introduce artifacts and limit immediate biological
interpretation of models.A Bayesian Model of NMR Spectra for the Deconvolution and Quantification of Metabolites in Complex Biological Mixtures.
Thu, 05/12/2011 - 15:01 — SomNambulAuthors: Sylvia Richardson, Maria De Iorio, William Astle, David Stephens, Timothy Ebbels
Subjects: Methodology
AbstractNuclear Magnetic Resonance (NMR) spectra are widely used in metabolomics to
obtain profiles of metabolites dissolved in biofluids such as cell
supernatants. Methods for estimating metabolite concentrations from these
spectra are presently confined to manual peak fitting and to binning procedures
for integrating resonance peaks. Extensive information on the patterns of
spectral resonance generated by human metabolites is now available in online
databases.Population Structure and Cryptic Relatedness in Genetic Association Studies.
Mon, 10/25/2010 - 15:01 — SomNambulAuthors: William Astle, David J. Balding
Subjects: Methodology
AbstractWe review the problem of confounding in genetic association studies, which
arises principally because of population structure and cryptic relatedness.
Many treatments of the problem consider only a simple ``island'' model of
population structure. We take a broader approach, which views population
structure and cryptic relatedness as different aspects of a single confounder:
the unobserved pedigree defining the (often distant) relationships among the
study subjects.
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