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PLIDA: cross-platform gene expression normalization using perturbed topic models.

Amit G Deshwar1, Quaid Morris

  • 1Edward S. Rogers Sr. Department of Electrical and Computer Engineering, Department of Molecular Genetics, Department of Computer Science and Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 1A1, Canada.

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This study introduces a novel cross-platform normalization method using topic models for gene expression data. The approach effectively integrates diverse datasets, outperforming current tools.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene expression data are generated across various platforms, posing challenges for data integration due to platform-specific biases.
  • Combining and comparing datasets from different platforms requires robust normalization strategies.

Purpose of the Study:

  • To develop a novel cross-platform normalization method for gene expression data.
  • To enable accurate comparison and integration of datasets generated on disparate platforms.

Main Methods:

  • Utilizes topic models to summarize gene expression patterns within each dataset.
  • Normalizes summarized expression patterns using per-gene multiplicative weights.
  • Employs a cross-platform normalization strategy based on learned topics.

Main Results:

  • Successfully normalizes gene expression data across multiple platforms, even with systematic differences between samples.
  • Enables simultaneous normalization of data from an arbitrary number of platforms.
  • Supports online normalization for data collected individually or in small batches.
  • Demonstrates superior performance compared to existing state-of-the-art platform normalization methods.

Conclusions:

  • The proposed topic model-based method offers a powerful solution for cross-platform normalization of gene expression data.
  • This approach enhances data comparability and integration in genomics research.
  • The method is adaptable for various data collection scenarios, including online normalization.