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Related Concept Videos

Interpreting R Charts01:22

Interpreting R Charts

R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum values—of a sample...
Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
Gene Families01:57

Gene Families

Gene families consist of groups of genes proposed to have originated from a common ancestor. Typically these arise through events in which a gene or genes are mistakenly duplicated during cell division. Unlike their parent genes (which are subject to selection pressure to maintain function), these gene copies do not need to preserve their sequences and may evolve at a relatively faster rate.
Occasionally these regions can be adapted to take on new roles within the organism, becoming novel genes...
Gene Families01:57

Gene Families

Gene families consist of groups of genes proposed to have originated from a common ancestor. Typically these arise through events in which a gene or genes are mistakenly duplicated during cell division. Unlike their parent genes (which are subject to selection pressure to maintain function), these gene copies do not need to preserve their sequences and may evolve at a relatively faster rate.
Occasionally these regions can be adapted to take on new roles within the organism, becoming novel genes...
The R Chart01:02

The R Chart

In statistical process control, control charts, particularly R charts, are instrumental in monitoring process variations and identifying non-random patterns that run charts might miss. R charts track the variability within process subgroups, which is crucial when standard deviation use is impractical or unknown process variations exist.
R charts are pivotal for pinpointing shifts in process variability. Stability is indicated when all data points remain within the defined upper and lower...
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Related Experiment Video

Updated: Jun 20, 2026

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

GOATEA: gene set enrichment analysis in R with shiny interactive visualizations.

Maurits A W Unkel1, Jeff A Beeler2,3, Steven A Kushner4,5

  • 1Department of Psychiatry, Erasmus MC University Medical Center, Rotterdam, The Netherlands.

BMC Bioinformatics
|June 19, 2026
PubMed
Summary
This summary is machine-generated.

Geneset Ordinal Association Test Enrichment Analysis (GOATEA) is a new R/Shiny application that enhances pathway analysis. It offers interactive visualization and multi-omics integration for hypothesis generation, accessible to all researchers.

Keywords:
BioinformaticsGOATGOATEAGSEAGenomicsInteractiveProteomicsRShinyVisualization

More Related Videos

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

Related Experiment Videos

Last Updated: Jun 20, 2026

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics and Proteomics

Background:

  • High-throughput technologies generate complex biological data requiring sophisticated analysis.
  • Existing enrichment tools often lack user-friendliness, advanced features like multi-contrast comparison, and integrated gene/network context.
  • The Geneset Ordinal Association Test (GOAT) is a statistical method for evaluating differential expression in gene or protein sets.

Purpose of the Study:

  • To develop an accessible and comprehensive tool for pathway enrichment analysis.
  • To extend the GOAT algorithm with interactive visualization and multi-contrast capabilities.
  • To integrate gene-level and network-based context for bottom-up pathway analysis and hypothesis generation.

Main Methods:

  • Development of GOATEA, an R/Shiny application implementing and extending the GOAT algorithm.
  • Integration of interactive visualization, multi-contrast comparison, and gene/network context.
  • Application to the Colameo dataset with paired transcriptomic and proteomic data for multi-omics analysis.

Main Results:

  • GOATEA enables independent and integrated analysis of transcriptomic and proteomic data.
  • Demonstrated capability for multi-omics analysis and simultaneous comparison of multiple contrasts.
  • Identified shared genes and emphasized protein-protein interactions for hypothesis generation through integrated network context.

Conclusions:

  • GOATEA streamlines enrichment analysis with a user-friendly interface and interactive visualizations.
  • Facilitates exploratory analysis and hypothesis generation for researchers of all technical backgrounds.
  • Available as an open-source tool with comprehensive documentation and usage vignettes.