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Related Concept Videos

DNA Microarrays02:34

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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Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
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Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

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Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer
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Evaluating surrogate variables for improving microarray multiple testing inference.

Jared K Lunceford1, Guang Chen, Peter H Hu

  • 1Merck Sharp & Dohme Corporation, Biostatistics, Whitehouse Station, NJ, USA. jared_lunceford@merck.com

Pharmaceutical Statistics
|October 14, 2010
PubMed
Summary
This summary is machine-generated.

Surrogate variable analysis helps address complex dependencies in high-dimensional data, improving multiple testing and statistical power in genomics research.

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Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • High-dimensional data, common in genomics, presents complex feature dependencies.
  • These dependencies can negatively impact simultaneous statistical inference and multiple testing.
  • Latent variables can drive these complex correlation structures.

Purpose of the Study:

  • To illustrate the effects of latent variables on dependence testing and multiple inference.
  • To review and assess the Surrogate Variable Analysis (SVA) method.
  • To evaluate SVA's utility in complex, real-world microarray data.

Main Methods:

  • Review of the Surrogate Variable Analysis (SVA) method.
  • Simulations designed to mimic complex feature dependence in microarray data.
  • Application of SVA to a Merck microarray dataset.

Main Results:

  • Surrogate Variable Analysis (SVA) effectively addresses complex correlation structures.
  • SVA improves the variability of false positive rates in multiple testing.
  • SVA leads to increased statistical power in high-dimensional analyses.

Conclusions:

  • Surrogate Variable Analysis (SVA) is a viable strategy for the multiple testing dependence problem.
  • SVA is particularly useful when dealing with complex feature correlation structures.
  • The method demonstrates practical utility in genomic data analysis.