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Related Concept Videos

Introduction to R01:11

Introduction to R

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 functionality,...

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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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DepthTools: an R package for a robust analysis of gene expression data.

Aurora Torrente1, Sara López-Pintado, Juan Romo

  • 1Functional Genomics Team, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK. aurora@ebi.ac.uk

BMC Bioinformatics
|July 27, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces depthTools, an R package offering robust statistical analysis for high-dimensional gene expression data. It aids in understanding tumor variations and developing personalized treatments.

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Area of Science:

  • Bioinformatics
  • Statistical Genetics

Background:

  • High-density DNA microarrays and oligonucleotide chips generate complex, high-dimensional data crucial for gene expression analysis and disease diagnosis.
  • Existing statistical methods may not be sufficiently robust for analyzing this complex genomic data.

Purpose of the Study:

  • To develop and present a novel R package, depthTools, for robust statistical analysis of gene expression data.
  • To provide tools for visualization and inference applicable to high-dimensional genomic datasets.

Main Methods:

  • Utilizes an efficient implementation of Modified Band Depth for robust statistical analysis.
  • The depthTools package is implemented in R, a widely used statistical programming language.
  • Includes a user-friendly interface via an R-commander plugin.

Main Results:

  • depthTools provides robust statistical analysis for gene expression data.
  • The package offers effective visualization and inference tools for high-dimensional data.
  • Successfully applied to analyze complex genomic datasets.

Conclusions:

  • The depthTools package demonstrates utility in analyzing genome-level variation between tumors.
  • Facilitates a better understanding of tumor heterogeneity.
  • Supports the development of personalized treatment strategies.