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Related Concept Videos

Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
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DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.In the early 20th century,...
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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Expected Frequencies in Goodness-of-Fit Tests01:19

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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Published on: October 11, 2018

On the probability of correct selection for large k populations, with application to microarray data.

Xinping Cui1, Jason Wilson

  • 1Department of Statistics, University of California, Riverside, CA 92521, USA. xinping.cui@ucr.edu

Biometrical Journal. Biometrische Zeitschrift
|October 22, 2008
PubMed
Summary

This study adapts Ranking and Selection Methodology (RSM) for large datasets, developing methods to estimate the probability of correct selection (PCS) for identifying top genes or features. This enhances gene selection and filtering quality assessment in complex data analysis.

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Area of Science:

  • Statistical Research
  • Computational Biology
  • Bioinformatics

Background:

  • Modern statistical research faces challenges with high-dimensional data (k > 1000), common in fields like microarray and neuroimaging.
  • The focus in such large-scale data analysis often shifts from significance testing to data selection, filtering, and screening.
  • Classical Ranking and Selection Methodology (RSM) traditionally assesses the probability of correct selection (PCS) for identifying the single best population.

Purpose of the Study:

  • To extend and adapt existing RSM selection goals (d-best and G-best) for applicability to problems with extremely large numbers of populations (k >> 1000).
  • To develop methods for estimating the probability of correct selection (PCS) when selecting multiple (t > 1) populations.
  • To provide a reliable measure for assessing the quality of gene selection or filtering steps in high-dimensional data analysis.

Main Methods:

  • Adaptation of two RSM selection goals, d-best and G-best, to suit large-scale problems.
  • Implementation of PCS estimation techniques for selecting multiple populations (t > 1) using the adapted selection goals.
  • Validation through a simulation study and application to a benchmark microarray dataset.

Main Results:

  • The proposed methods effectively extend RSM for large k scenarios, enabling the assessment of multiple population selections.
  • PCS estimation provides a quantifiable measure of the quality for specific gene selection or filtering procedures.
  • The approach demonstrated versatility and effectiveness on a real-world microarray dataset.

Conclusions:

  • The adapted d-best and G-best selection methods, coupled with PCS estimation, offer a powerful tool for evaluating selections in large-scale statistical problems.
  • This methodology is particularly valuable for assessing the performance of gene selection and filtering in high-dimensional biological data.
  • The proposed method is generalizable and applicable to any domain dealing with extremely large population sizes.