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Functional Heatmap: an automated and interactive pattern recognition tool to integrate time with multi-omics assays.

Joshua R Williams1,2, Ruoting Yang1,2, John L Clifford2

  • 1Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD, 21702-5010, USA.

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Summary

Functional Heatmap is a new tool for analyzing omic time-series data. It helps researchers discover patterns and biological functions across multiple tissues and conditions, simplifying complex data analysis.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Life science research increasingly uses large-scale, multi-tissue, and time-point omics experiments.
  • Omics time-series data can reveal collective analyte patterns, synchronized analyte behavior, and temporal evolution of biological functions.
  • Current tools lack a unified interface for comprehensively answering these time-series questions.

Purpose of the Study:

  • To introduce Functional Heatmap, a novel tool for analyzing time-series multi-omics data.
  • To provide a unified interface for identifying analyte patterns and their associated biological functions over time.
  • To enable comparison of patterns across multiple experimental conditions.

Main Methods:

  • Functional Heatmap utilizes a Master Panel and Combined page for time-series data visualization.
  • The tool dissects multi-omics time-series readouts into patterned clusters with linked biological functions.
  • Interactive and exportable analyses are generated in various formats (heatmap, line-chart, text).

Main Results:

  • Identifies cascades of functional changes over time in omics data.
  • Enables inverse comparison of biological patterns across multiple experimental conditions.
  • Provides interactive, searchable, and reproducible web-based results.

Conclusions:

  • Functional Heatmap automates pattern recognition in time-series multi-omics assays.
  • Reduces manual effort in pattern discovery and comparison through visual translation of statistical models.
  • Facilitates identification of hidden trends in functional changes from omics data across tissues and conditions over time.