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    Evaluating single-subject study methods for personal transcriptomic interpretations to advance precision medicine

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    Author
    Rachid Zaim, Samir
    Kenost, Colleen
    Berghout, Joanne
    Vitali, Francesca
    Zhang, Helen Hao
    Lussier, Yves A
    Affiliation
    Univ Arizona Hlth Sci, Ctr Biomed Informat & Biostat
    Univ Arizona, Grad Interdisciplinary Program Stat
    Univ Arizona, Dept Math, Coll Sci
    Univ Arizona, Ctr Canc
    Issue Date
    2019-07-11
    Keywords
    Genomic medicine
    Medical genomics
    N-of-1
    N-of-1 studies
    Precision medicine
    Single-subject studies
    Transcriptome
    
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    Show full item record
    Publisher
    BMC
    Citation
    Zaim, S. R., Kenost, C., Berghout, J., Vitali, F., Zhang, H. H., & Lussier, Y. A. (2019). Evaluating single-subject study methods for personal transcriptomic interpretations to advance precision medicine. BMC medical genomics, 12(5), 96.
    Journal
    BMC MEDICAL GENOMICS
    Rights
    Copyright © The Author(s). 2019 Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    Background Gene expression profiling has benefited medicine by providing clinically relevant insights at the molecular candidate and systems levels. However, to adopt a more precision' approach that integrates individual variability including omics data into risk assessments, diagnoses, and therapeutic decision making, whole transcriptome expression needs to be interpreted meaningfully for single subjects. We propose an all-against-one framework that uses biological replicates in isogenic conditions for testing differentially expressed genes (DEGs) in a single subject (ss) in the absence of an appropriate external reference standard or replicates. To evaluate our proposed all-against-one framework, we construct reference standards (RSs) with five conventional replicate-anchored analyses (NOISeq, DEGseq, edgeR, DESeq, DESeq2) and the remainder were treated separately as single-subject sample pairs for ss analyses (without replicates).ResultsEight ss methods (NOISeq, DEGseq, edgeR, mixture model, DESeq, DESeq2, iDEG, and ensemble) for identifying genes with differential expression were compared in Yeast (parental line versus snf2 deletion mutant; n=42/condition) and a MCF7 breast-cancer cell line (baseline versus stimulated with estradiol; n=7/condition). Receiver-operator characteristic (ROC) and precision-recall plots were determined for eight ss methods against each of the five RSs in both datasets. Consistent with prior analyses of these data, similar to 50% and similar to 15% DEGs were obtained in Yeast and MCF7 datasets respectively, regardless of the RSs method. NOISeq, edgeR, and DESeq were the most concordant for creating a RS. Single-subject versions of NOISeq, DEGseq, and an ensemble learner achieved the best median ROC-area-under-the-curve to compare two transcriptomes without replicates regardless of the RS method and dataset (>90% in Yeast, >0.75 in MCF7). Further, distinct specific single-subject methods perform better according to different proportions of DEGs.ConclusionsThe all-against-one framework provides a honest evaluation framework for single-subject DEG studies since these methods are evaluated, by design, against reference standards produced by unrelated DEG methods. The ss-ensemble method was the only one to reliably produce higher accuracies in all conditions tested in this conservative evaluation framework. However, single-subject methods for identifying DEGs from paired samples need improvement, as no method performed with precision>90% and obtained moderate levels of recall.
    Note
    Open access journal
    ISSN
    1755-8794
    PubMed ID
    31296218
    DOI
    10.1186/s12920-019-0513-8
    Version
    Final published version
    Sponsors
    University of Arizona Health Sciences Center for Biomedical Informatics and Biostatistics; BIO5 Institute; NIH [U01AI122275, HL132532, NCI P30CA023074, 1UG3OD023171, 1S10RR029030]
    ae974a485f413a2113503eed53cd6c53
    10.1186/s12920-019-0513-8
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