Introduction

Although gene-expression signature-based biomarkers are often developed for clinical diagnosis, many promising signatures fail to replicate during validation. One major challenge is that biological samples used to generate and validate the signature are often from heterogeneous biological contexts-controlled or in vitro samples may be used to generate the signature, but patient samples may be used for validation. In addition, systematic technical biases from multiple genome-profiling platforms often mask true biological variation. Addressing such challenges will enable us to better elucidate disease mechanisms and provide improved guidance for personalized therapeutics.Here, we present a pathway profiling toolkit, Adaptive Signature Selection and InteGratioN (ASSIGN), which enables robust and context-specific pathway analyses by efficiently capturing pathway activity in heterogeneous sets of samples and across profiling technologies. The ASSIGN framework is based on a flexible Bayesian factor analysis approach that allows for simultaneous profiling of multiple correlated pathways and for the adaptation of pathway signatures into specific disease. We demonstrate the robustness and versatility of ASSIGN in estimating pathway activity in simulated data, cell lines perturbed pathways and in primary tissues samples including The Cancer Genome Atlas breast carcinoma samples and liver samples exposed to genotoxic carcinogens.Software for our approach is available for download at: http://www.bioconductor.org/packages/release/bioc/html/ASSIGN.html and https://github.com/wevanjohnson/ASSIGN.

Publications

  1. ASSIGN: context-specific genomic profiling of multiple heterogeneous biological pathways.
    Cite this
    Shen Y, Rahman M, Piccolo SR, Gusenleitner D, El-Chaar NN, Cheng L, Monti S, Bild AH, Johnson WE, 2015-06-01 - Bioinformatics (Oxford, England)

Credits

  1. Ying Shen
    Developer

    Division of Computational Biomedicine, Boston University School of Medicine, United States of America

  2. Mumtahena Rahman
    Developer

    Division of Computational Biomedicine, Boston University School of Medicine, United States of America

  3. Stephen R Piccolo
    Developer

    Division of Computational Biomedicine, Boston University School of Medicine, United States of America

  4. Daniel Gusenleitner
    Developer

    Division of Computational Biomedicine, Boston University School of Medicine, United States of America

  5. Nader N El-Chaar
    Developer

    Division of Computational Biomedicine, Boston University School of Medicine, United States of America

  6. Luis Cheng
    Developer

    Division of Computational Biomedicine, Boston University School of Medicine, United States of America

  7. Stefano Monti
    Developer

    Division of Computational Biomedicine, Boston University School of Medicine, United States of America

  8. Andrea H Bild
    Developer

    Division of Computational Biomedicine, Boston University School of Medicine, United States of America

  9. W Evan Johnson
    Investigator

    Division of Computational Biomedicine, Boston University School of Medicine, United States of America

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Summary
AccessionBT006462
Tool TypeApplication
Category
PlatformsLinux/Unix
Technologies
User InterfaceTerminal Command Line
Download Count0
Country/RegionUnited States of America
Submitted ByW Evan Johnson