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Related Experiment Video

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Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
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Published on: October 11, 2019

Biasogram: visualization of confounding technical bias in gene expression data.

Marcin Krzystanek1, Zoltan Szallasi, Aron C Eklund

  • 1Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Lyngby, Denmark.

Plos One
|April 25, 2013
PubMed
Summary

This study introduces a visualization method to identify genes correlated with clinical outcomes while accounting for technical bias in gene expression data. It helps detect potential false positives in microarray datasets.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Biostatistics

Background:

  • Gene expression profiles are crucial for identifying genes linked to clinical variables like patient outcomes or drug responses.
  • Expression measurements can be affected by technical biases (e.g., RNA quality, hybridization) that may confound clinical associations.

Purpose of the Study:

  • To develop and demonstrate a visualization method for assessing gene expression data quality.
  • To identify genes potentially misattributed to clinical variables due to technical biases.

Main Methods:

  • A novel visualization technique projecting gene expression data onto a plane defined by a clinical variable and a technical variable.
  • Application of the method to three clinical trial microarray datasets.

Main Results:

  • The visualization method effectively highlights correlations between genes, clinical variables, and technical variables.
  • Identified a dataset where potential gene discoveries were confounded by technical bias, illustrating the method's utility.

Conclusions:

  • This projection method serves as a critical quality control step in analyzing gene expression data.
  • It aids in distinguishing true biological signals from technical artifacts, thereby reducing false positive findings in clinical research.