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ROMA: Representation and Quantification of Module Activity from Target Expression Data.

Loredana Martignetti1, Laurence Calzone1, Eric Bonnet1

  • 1Computational and Systems Biology of Cancer, Institut CurieParis, France; PSL Research UniversityParis, France; Institut National de la Santé et de la Recherche Médicale U900Paris, France; Mines ParisTechParis, France.

Frontiers in Genetics
|March 1, 2016
PubMed
Summary
This summary is machine-generated.

ROMA software quantifies gene set activity from high-throughput data. It estimates transcription factor activity and identifies coordinated gene modules using principal component analysis for systems biology applications.

Keywords:
coordinated pathwaygene expressiongene setmodule activityoverdispersed pathwayproteomicstranscription factors

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Genomics

Background:

  • Quantifying gene set activity is crucial for analyzing high-throughput data in systems biology.
  • Estimating unmeasurable factors like transcription factor activity from target gene expression is a common challenge.

Purpose of the Study:

  • To introduce ROMA (Representation and quantification Of Module Activities), a Java software for fast and robust computation of gene set activities.
  • To provide a tool for analyzing coordinated gene expression modules and estimating regulatory factor activity.

Main Methods:

  • ROMA utilizes a linear model approximating gene set expression data with its first principal component.
  • The software offers weighted, robust, and centered principal component computation methods.
  • It distinguishes overdispersed and coordinated modules and computes their statistical significance.

Main Results:

  • ROMA provides a robust method for quantifying module activities based on principal component analysis.
  • The software can identify overdispersed and coordinated gene modules with statistical significance.
  • Demonstrated applicability in estimating transcription factor activity and finding pathways in single-cell transcriptomics.

Conclusions:

  • ROMA offers a novel and efficient approach for gene set activity quantification in systems biology.
  • The software facilitates the analysis of gene regulation and pathway identification.
  • ROMA is a valuable tool for researchers working with high-throughput gene expression data.