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Multiscale analysis of count data through topic alignment.

Julia Fukuyama1, Kris Sankaran2, Laura Symul3

  • 1Department of Statistics, Indiana University Bloomington, 919 E 10th Street, Bloomington, IN 47408, USA.

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Summary
This summary is machine-generated.

Topic modeling helps analyze biological data, but choosing the number of topics (K) is challenging. Our new topic alignment method reveals consistent patterns across different K values, offering deeper biological insights.

Keywords:
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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Genomics

Background:

  • Topic modeling is widely used for analyzing biological count data.
  • Selecting the optimal number of topics (K) for topic models is a significant challenge in data analysis.
  • A definitive method for choosing K is lacking, and a true optimal value may not exist.

Purpose of the Study:

  • To develop a novel method, termed topic alignment, for studying relationships between topic models with varying numbers of topics (K).
  • To introduce three new diagnostics based on topic alignment to assess topic consistency and evolution.
  • To provide a more insightful approach to biological data interpretation than selecting a single K value.

Main Methods:

  • Topic alignment: a method to compare topic models with different K values.
  • Development of three alignment-based diagnostics to identify consistent, transient, or splitting topics.
  • Visual representation of cross-model topic relationships.
  • Application to simulated and real biological count data.
  • Release of the 'alto' R package for implementing these methods.

Main Results:

  • Topic alignment effectively visualizes relationships between models with different K.
  • The diagnostics successfully identify topics that are consistently present, transient, or split as K increases.
  • The approach provides enhanced biological insights into data-generating processes.
  • Demonstrated effectiveness on both simulated and real biological datasets.

Conclusions:

  • Topic alignment offers a robust framework for exploring topic model structures across different K values.
  • The developed diagnostics enhance the interpretability of topic models in biological data analysis.
  • This strategy provides a more comprehensive understanding of biological count data compared to single-model approaches.