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Related Concept Videos

Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

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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Frequency-dependent Selection

When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.Positive Frequency-Dependent SelectionIn positive...
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Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
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Commonly used reporter...
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What is Gene Expression?

Overview
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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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Published on: August 16, 2017

Appearance frequency modulated gene set enrichment testing.

Jun Ma1, Maureen A Sartor, H V Jagadish

  • 1Department of EECS, University of Michigan, Ann Arbor, USA. majun@umich.edu

BMC Bioinformatics
|March 23, 2011
PubMed
Summary
This summary is machine-generated.

Incorporating gene appearance frequency into gene set enrichment analysis (GSEA) and LRpath improves reproducibility and biological relevance. This approach enhances the interpretation of microarray data for systems biology insights.

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Genomics

Background:

  • Gene set enrichment testing is crucial for systems biology interpretation of microarray data.
  • Current methods often treat all genes equally, overlooking their varying roles and frequencies in biological pathways.
  • Genes can be specialized or play ubiquitous roles (e.g., housekeeping functions) across multiple pathways.

Purpose of the Study:

  • To develop and present an approach that incorporates gene appearance frequency (from KEGG pathways) into existing gene set enrichment methods.
  • To enhance the reproducibility and biological meaningfulness of microarray data analysis.
  • To improve upon Gene Set Enrichment Analysis (GSEA) and the LRpath framework.

Main Methods:

  • Developed a novel approach integrating gene appearance frequency into GSEA and LRpath.
  • Applied the modified methods (GSEA-AF and LRpath-AF) to breast cancer microarray datasets comparing histological grades 1 and 3.
  • Validated the approach using lung cancer datasets.

Main Results:

  • GSEA with appearance frequency (GSEA-AF) demonstrated higher correlation between experiments and more overlapping top gene sets compared to original GSEA.
  • Several cancer-related gene sets showed higher Normalized Enrichment Scores (NES) with GSEA-AF.
  • Similar improvements in performance were observed when applying the appearance frequency integration to the LRpath framework (LRpath-AF) and in lung cancer datasets.

Conclusions:

  • Introduced a novel method for integrating KEGG PATHWAY information into gene set enrichment testing.
  • The appearance frequency of genes significantly enhances the performance of GSEA and LRpath.
  • Integrating gene appearance frequency from KEGG PATHWAY generally improves gene set analysis methods both statistically and biologically.