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CrossICC: iterative consensus clustering of cross-platform gene expression data without adjusting batch effect.

Qi Zhao1, Yu Sun1,2, Zekun Liu1

  • 1State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.

Briefings in Bioinformatics
|September 26, 2020
PubMed
Summary

CrossICC is a new R package for cancer subtyping using gene expression data. It enables robust subtype discovery across multiple datasets without batch effect adjustment, aiding personalized cancer care.

Keywords:
batch effectcancer subtypingcross-platformgene expression

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Unsupervised clustering of gene expression data is crucial for cancer subtyping.
  • Subtypes derived from single datasets often lack generalizability across platforms due to batch effects.
  • Integrating multi-platform data is essential for robust cancer subtype identification.

Purpose of the Study:

  • To introduce CrossICC, an R package for unsupervised clustering of multi-dataset gene expression data.
  • To enable the discovery of accurate and applicable cancer subtypes without batch effect correction.
  • To facilitate the identification of robust cancer subtypes with translational implications for personalized medicine.

Main Methods:

  • CrossICC employs an iterative strategy to derive optimal gene signatures and cluster numbers.
  • It utilizes a consensus similarity matrix generated through consensus clustering.
  • The package offers visualization and performance evaluation tools for identified subtypes.

Main Results:

  • CrossICC facilitates unsupervised clustering of gene expression data from multiple datasets/platforms.
  • It overcomes the challenge of batch effects in multi-dataset integration for cancer subtyping.
  • The package aids in discovering robust cancer subtypes applicable across different data sources.

Conclusions:

  • CrossICC provides a robust solution for cancer subtyping using multi-platform gene expression data.
  • The package can lead to the discovery of clinically relevant cancer subtypes.
  • CrossICC has the potential to advance personalized cancer care through improved subtyping accuracy.