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Introduction to R01:11

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R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...
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Related Experiment Video

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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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The R implementation of the CRAN package PATHChange, a tool to study genetic pathway alterations in transcriptomic

Carla A R S Fontoura1, Gastone Castellani2, José C M Mombach1

  • 1Department of Physics, Universidade Federal de Santa Maria, Avenida Roraima, 97105-900 Santa Maria, Brazil.

Computers in Biology and Medicine
|September 26, 2016
PubMed
Summary

PATHChange is a new R package for analyzing gene expression data. It helps identify altered biological pathways in comparative studies using Gene Expression Omnibus data.

Keywords:
CRAN packageMicroarrayPathway expressionRRNA-seq

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Interpreting large-scale transcriptomic data is crucial for biological discovery.
  • Identifying differential pathway expression aids in understanding complex biological systems.
  • Existing tools may not be optimized for specific comparative analyses with control groups.

Purpose of the Study:

  • To introduce PATHChange, a novel statistical R package.
  • To facilitate the analysis of differential pathway expression from transcriptomic data.
  • To provide a user-friendly tool for researchers working with Gene Expression Omnibus (GEO) datasets.

Main Methods:

  • PATHChange is a statistical package implemented in R.
  • It processes data downloaded from the Gene Expression Omnibus (GEO) database.
  • The package is designed for comparative studies, including those with control samples.

Main Results:

  • PATHChange enables the determination of differential pathway expression.
  • The package integrates seamlessly with GEO data.
  • Demonstrates utility through a practical example of its implementation.

Conclusions:

  • PATHChange offers a valuable tool for transcriptomic data analysis.
  • It enhances the interpretation of biological studies by focusing on pathway alterations.
  • The package supports robust comparative analyses in systems biology research.