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    Guidelines for using sigQC for systematic evaluation of gene signatures

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    Authors
    Dhawan, A
    Barberis, A
    Cheng, WC
    Domingo, E
    West, Catharine ML
    Maughan, T
    Scott, JG
    Harris, AL
    Buffa, FM
    Affiliation
    Computational Biology and Integrative Genomics Lab, MRC/CRUK Oxford Institute and Department of Oncology, University of Oxford, Oxford, UK
    Issue Date
    2019
    
    Metadata
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    Abstract
    With the increased use of next-generation sequencing generating large amounts of genomic data, gene expression signatures are becoming critically important tools for the interpretation of these data, and are poised to have a substantial effect on diagnosis, management, and prognosis for a number of diseases. It is becoming crucial to establish whether the expression patterns and statistical properties of sets of genes, or gene signatures, are conserved across independent datasets. Conversely, it is necessary to compare established signatures on the same dataset to better understand how they capture different clinical or biological characteristics. Here we describe how to use sigQC, a tool that enables a streamlined, systematic approach for the evaluation of previously obtained gene signatures across multiple gene expression datasets. We implemented sigQC in an R package, making it accessible to users who have knowledge of file input/output and matrix manipulation in R and a moderate grasp of core statistical principles. SigQC has been adopted in basic biology and translational studies, including, but not limited to, the evaluation of multiple gene signatures for potential clinical use as cancer biomarkers. This protocol uses a previously obtained signature for breast cancer metastasis as an example to illustrate the critical quality control steps involved in evaluating its expression, variability, and structure in breast tumor RNA-sequencing data, a different dataset from that in which the signature was originally derived. We demonstrate how the outputs created from sigQC can be used for the evaluation of gene signatures on large-scale gene expression datasets.
    Citation
    Dhawan A, Barberis A, Cheng WC, Domingo E, West C, Maughan T, et al. Guidelines for using sigQC for systematic evaluation of gene signatures. Nat Protoc. 2019 May;14(5):1377-400.
    Journal
    Nature Protocols
    URI
    http://hdl.handle.net/10541/621857
    DOI
    10.1038/s41596-019-0136-8
    PubMed ID
    30971781
    Additional Links
    https://dx.doi.org/10.1038/s41596-019-0136-8
    Type
    Article
    Language
    en
    ae974a485f413a2113503eed53cd6c53
    10.1038/s41596-019-0136-8
    Scopus Count
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