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ctsGE-clustering subgroups of expression data.

Michal Sharabi-Schwager1, Etti Or1, Ron Ophir1

  • 1Department of Fruit Tree Sciences, Institute of Plant Sciences, Agricultural Research Organization, Volcani Center, Rishon Lezion, Israel.

Bioinformatics (Oxford, England)
|March 24, 2017
PubMed
Summary
This summary is machine-generated.

The ctsGE R-package offers a novel two-step clustering approach for gene-expression data, organizing and exploring patterns without data loss. This method enhances the analysis of noisy biological datasets.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene-expression data analysis often requires a filtering step to handle noise.
  • Existing methods may eliminate valuable information during data preprocessing.

Purpose of the Study:

  • To introduce the ctsGE R-package as an alternative to traditional filtering methods for gene-expression data.
  • To present a novel two-step clustering approach for organizing and exploring gene-expression patterns.

Main Methods:

  • The ctsGE package implements a sorting step, dividing data into groups based on time-series median relationships.
  • Clustering is performed in two steps: defining an expression index to group genes, followed by k-means clustering within groups.
  • An interactive visualization tool is provided for exploring gene-expression patterns and subclusters.

Main Results:

  • The proposed method effectively organizes and explores gene-expression data.
  • It avoids the information loss associated with traditional filtering techniques.
  • The approach facilitates the discovery of gene expression patterns and subclusters.

Conclusions:

  • The ctsGE R-package provides an effective alternative to filtering for clustering noisy gene-expression data.
  • This two-step clustering strategy preserves information and enhances data exploration.
  • The package enables comprehensive analysis and visualization of gene expression patterns.