Evaluating single-subject study methods for personal transcriptomic interpretations to advance precision medicine
AuthorRachid Zaim, Samir
Zhang, Helen Hao
Lussier, Yves A
AffiliationUniv Arizona Hlth Sci, Ctr Biomed Informat & Biostat
Univ Arizona, Grad Interdisciplinary Program Stat
Univ Arizona, Dept Math, Coll Sci
Univ Arizona, Ctr Canc
MetadataShow full item record
CitationZaim, 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.
JournalBMC MEDICAL GENOMICS
RightsCopyright © 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.
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AbstractBackground 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.
NoteOpen access journal
VersionFinal published version
SponsorsUniversity of Arizona Health Sciences Center for Biomedical Informatics and Biostatistics; BIO5 Institute; NIH [U01AI122275, HL132532, NCI P30CA023074, 1UG3OD023171, 1S10RR029030]
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