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Related Concept Videos

DNA Microarrays02:34

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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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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
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NONPARAMETRIC ESTIMATION OF GENEWISE VARIANCE FOR MICROARRAY DATA.

Jianqing Fan1, Yang Feng, Yue S Niu

  • 1Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ 08544.

Annals of Statistics
|November 16, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel nonparametric model to estimate gene-wise variance in high-dimensional data, improving the detection of differentially expressed genes and aiding microarray data analysis.

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Area of Science:

  • Biostatistics
  • Genomics
  • Statistical modeling

Background:

  • Accurate estimation of genewise variance is crucial for identifying differentially expressed genes and validating microarray data normalization.
  • High-dimensional data presents challenges due to numerous nuisance parameters and correlated measurements.
  • Existing methods struggle with estimating variance functions in complex, high-dimensional settings.

Purpose of the Study:

  • To develop novel nonparametric estimators for genewise variance and measurement correlation in high-dimensional data.
  • To extend the Neyman-Scott model for broader applicability beyond microarray analysis.
  • To improve the statistical power for detecting differentially expressed genes.

Main Methods:

  • Introduction of a two-way nonparametric model, extending the Neyman-Scott framework.
  • Development of two novel nonparametric estimators for the genewise variance function.
  • Proposal of semiparametric estimators for measurement correlation by solving nonlinear equations.
  • Establishment of asymptotic normality for the proposed estimators.

Main Results:

  • The proposed estimators demonstrate robust finite sample properties through simulation studies.
  • The methodology effectively addresses challenges of high-dimensional data with correlated measurements.
  • The developed estimators enhance the power of tests for identifying statistically significant gene expression differences.
  • Successful illustration of the methodology using data from the MicroArray Quality Control (MAQC) project.

Conclusions:

  • The novel nonparametric approach provides reliable genewise variance estimation in complex datasets.
  • The method offers a significant advancement for differential gene expression analysis and data normalization validation.
  • This work has broad implications for statistical analysis in genomics and other high-dimensional fields.