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Updated: Jun 26, 2026

X-ray Dose Reduction through Adaptive Exposure in Fluoroscopic Imaging
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Published on: September 11, 2011

BatchFLEX: feature-level equalization of X-batch.

Joshua T Davis1, Alyssa N Obermayer1, Alex C Soupir1

  • 1Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, United States.

Bioinformatics (Oxford, England)
|October 3, 2024
PubMed
Summary
This summary is machine-generated.

BatchFLEX is a new Shiny app that helps researchers visualize and correct batch effects in gene expression data. This tool simplifies the process, improving the accuracy of downstream biological analyses.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Integrative analysis of heterogeneous expression data is challenging due to technical variations.
  • Selecting batch effect correction methods is time-consuming for biologists.

Purpose of the Study:

  • To present BatchFLEX, a Shiny application for visualizing and correcting batch effects.
  • To facilitate the selection and assessment of batch correction methods for gene expression data.

Main Methods:

  • Developed BatchFLEX, a user-friendly Shiny app.
  • Integrated several established batch effect correction methods.
  • Enabled visualization of variance contribution before and after correction.

Main Results:

  • BatchFLEX successfully visualized and corrected batch effects in ImmGen microarray data.
  • Enhanced expression signals distinguishing immune cell types.
  • Demonstrated the impact of batch correction on gene expression ranks and pathway scores.

Conclusions:

  • BatchFLEX simplifies batch effect correction for gene expression data analysis.
  • Real-time assessment of batch correction is crucial for reliable downstream analysis.
  • The tool enhances biological signal interpretation in heterogeneous datasets.