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Non-negative tensor factorization workflow for time series biomedical data.

Koki Tsuyuzaki1, Naoki Yoshida2, Tetsuo Ishikawa3

  • 1Laboratory for Bioinformatics Research, RIKEN Center for Biosystems Dynamics Research, Wako, Saitama 351-0198, Japan; Japan Science and Technology Agency, PRESTO, 7 Gobancho, Chiyoda-ku, Tokyo 102-0076, Japan.

STAR Protocols
|July 8, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces TensorLyCV, a streamlined protocol for non-negative tensor factorization (NTF) in biomedical data analysis. It simplifies complex NTF methods, making latent component extraction more accessible and reproducible.

Keywords:
BioinformaticsComputer sciencesHealth Sciences

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Area of Science:

  • Biomedical Data Analysis
  • Computational Biology
  • Bioinformatics

Background:

  • Non-negative tensor factorization (NTF) is valuable for extracting latent components from high-dimensional biomedical data.
  • The complexity and numerous steps involved in traditional NTF present a significant implementation barrier.

Purpose of the Study:

  • To present TensorLyCV, an accessible and reproducible protocol for NTF analysis.
  • To simplify the implementation of NTF for biomedical researchers.

Main Methods:

  • Utilized Snakemake workflow management system and Docker containerization for reproducibility.
  • Developed a pipeline encompassing data processing, tensor decomposition, optimal rank estimation, and visualization.
  • Applied the protocol to vaccine adverse reaction data for demonstration.

Main Results:

  • Successfully demonstrated a simplified and reproducible NTF analysis pipeline.
  • Facilitated the extraction and visualization of latent components from complex biomedical data.
  • Validated the protocol's utility using real-world vaccine adverse reaction data.

Conclusions:

  • TensorLyCV significantly lowers the barrier to implementing NTF in biomedical research.
  • The protocol enhances the accessibility and reproducibility of latent component extraction from high-dimensional data.
  • This work provides a practical tool for researchers analyzing complex biomedical datasets.