Current Search: Radulovic, Dragan (x)
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Title
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The Use Of Overlapping Vs. Non-Overlapping Moving Block Bootstrapping To Estimate The Variance Of A Statistic Of Dependent Data.
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Creator
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Davis, Benjamin F., Radulovic, Dragan, Florida Atlantic University
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Abstract/Description
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Determining the variance of a statistic (such as the sample median) can be difficult. Various methods of Bootstrapping (re-sampling with replacement) were used to estimate variance of one or more statistics based on a single sample. This estimator was compared to the empirical estimators based on repeated simulations of various sample sizes from a given distribution. Of particular interest was which of the methods of Bootstrapping were most effective with a dependent data set. Different...
Show moreDetermining the variance of a statistic (such as the sample median) can be difficult. Various methods of Bootstrapping (re-sampling with replacement) were used to estimate variance of one or more statistics based on a single sample. This estimator was compared to the empirical estimators based on repeated simulations of various sample sizes from a given distribution. Of particular interest was which of the methods of Bootstrapping were most effective with a dependent data set. Different degrees of dependency were used for the simulations with dependent data.
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Date Issued
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2008
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PURL
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http://purl.flvc.org/fau/fd/FA00000738
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Subject Headings
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Bootstrap (Statistics), Social sciences--Statistical methods, Mathematical statistics, Sampling (Statistics), Estimation theory, Nonparametric statistics
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Format
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Document (PDF)
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Title
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Genetic Variation Shapes Protein Networks Mainly through Non-transcriptional Mechanisms.
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Creator
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Foss, Eric J., Radulovic, Dragan, Shaffer, Scott A., Goodlett, David R., Kruglyak, Leonid, Bedalov, Antonio, Eisen, Michael B.
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Abstract/Description
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Networks of co-regulated transcripts in genetically diverse populations have been studied extensively, but little is known about the degree to which these networks cause similar co-variation at the protein level. We quantified 354 proteins in a genetically diverse population of yeast segregants, which allowed for the first time construction of a coherent protein covariation matrix. We identified tightly co-regulated groups of 36 and 93 proteins that were made up predominantly of genes...
Show moreNetworks of co-regulated transcripts in genetically diverse populations have been studied extensively, but little is known about the degree to which these networks cause similar co-variation at the protein level. We quantified 354 proteins in a genetically diverse population of yeast segregants, which allowed for the first time construction of a coherent protein covariation matrix. We identified tightly co-regulated groups of 36 and 93 proteins that were made up predominantly of genes involved in ribosome biogenesis and amino acid metabolism, respectively. Even though the ribosomal genes were tightly co-regulated at both the protein and transcript levels, genetic regulation of proteins was entirely distinct from that of transcripts, and almost no genes in this network showed a significant correlation between protein and transcript levels. This result calls into question the widely held belief that in yeast, as opposed to higher eukaryotes, ribosomal protein levels are regulated primarily by regulating transcript levels. Furthermore, although genetic regulation of the amino acid network was more similar for proteins and transcripts, regression analysis demonstrated that even here, proteins vary predominantly as a result of non-transcriptional variation. We also found that cis regulation, which is common in the transcriptome, is rare at the level of the proteome. We conclude that most inter-individual variation in levels of these particular high abundance proteins in this genetically diverse population is not caused by variation of their underlying transcripts.
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Date Issued
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2011-09-06
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PURL
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http://purl.flvc.org/fau/fd/FAUIR000047
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Format
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Citation
