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Related Experiment Video

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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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MIDAS: Mining differentially activated subpaths of KEGG pathways from multi-class RNA-seq data.

Sangseon Lee1, Youngjune Park2, Sun Kim3

  • 1Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea.

Methods (San Diego, Calif.)
|June 6, 2017
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Summary

MIDAS algorithm identifies condition-specific subpaths using gene expression data for multiple phenotypes. This method enhances biological mechanism discovery and cancer subtype classification.

Keywords:
KEGG pathwayMulti-classNetwork centralityRNA-seqSubpath

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Pathway-based analysis of high-throughput transcriptome data is crucial for understanding biological mechanisms.
  • Existing methods often fail to fully utilize RNA-seq gene expression quantity information and handle multiple phenotypes effectively.
  • Subpath activity calculation typically averages statistical scores, neglecting quantitative gene expression data.

Purpose of the Study:

  • To develop an algorithm, MIDAS, for identifying condition-specific subpaths with varying activities across multiple phenotypes.
  • To fully leverage gene expression quantity and network centrality information in subpath analysis.
  • To address limitations of existing methods in handling RNA-seq data and multiple phenotypes.

Main Methods:

  • Developed the MIDAS algorithm to determine condition-specific subpaths using comprehensive gene expression quantity and network centrality information.
  • Applied MIDAS to TCGA breast cancer RNA-seq data across five molecular subtypes.
  • Evaluated subpath discriminant power for cancer subtype classification and prognostic value in survival analysis.

Main Results:

  • Identified 36 differentially active subpaths in TCGA breast cancer data, all supported by existing literature.
  • Demonstrated that these subpaths possess significant discriminant power for classifying cancer subtypes.
  • Showcased the prognostic power of identified subpaths in survival analysis.
  • Outperformed the PATHOME method in identifying literature-supported subpaths and genes.

Conclusions:

  • MIDAS effectively identifies condition-specific subpaths by utilizing gene expression quantity and network centrality.
  • The identified subpaths are biologically relevant, discriminative for cancer subtypes, and prognostic.
  • MIDAS offers an improved approach for pathway-based analysis, particularly for multi-phenotype studies.