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Related Concept Videos

Statistical Significance01:37

Statistical Significance

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Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
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A complete procedure for testing a claim about a population proportion is provided here.
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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...
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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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Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
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A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
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Gene set bagging for estimating the probability a statistically significant result will replicate.

Andrew E Jaffe, John D Storey, Hongkai Ji

  • 1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore MD 21205, USA. jleek@jhsph.edu.

BMC Bioinformatics
|December 17, 2013
PubMed
Summary
This summary is machine-generated.

Gene set bagging enhances the stability of gene set enrichment analysis. This method estimates replication probability, improving biological inference beyond traditional p-values for high-throughput data.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Significance analysis is crucial for identifying disease-associated genomic features.
  • High-throughput data analysis requires robust methods for feature ranking and validation.
  • Gene set enrichment analysis (GSEA) is widely used but can yield unstable results.

Purpose of the Study:

  • To introduce gene set bagging as a novel approach for assessing gene set stability.
  • To measure the probability of gene set replication in future studies.
  • To improve the reliability of biological inference from high-throughput genomic data.

Main Methods:

  • Gene set bagging involves resampling high-throughput data.
  • Performing gene-set analysis on resampled data to assess category replication.
  • Estimating replication probability (R) for gene sets.

Main Results:

  • Gene set enrichment analysis categories can be unstable under resampling.
  • Gene set bagging accurately estimates replication probability (R).
  • The proposed method demonstrates superior replication estimation compared to p-values in simulations.

Conclusions:

  • Gene lists derived solely from p-values may lack stability.
  • Gene set bagging offers a more reliable approach for biological inference.
  • This method enhances the robustness of findings from high-throughput studies.