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'N-of-1-pathways' unveils personal deregulated mechanisms from a single pair of RNA-Seq samples: towards precision medicine.

Gardeux V, Achour I, Li J, Maienschein-Cline M, Li H, Pesce L, Parinandi G, Bahroos N, Winn R, Foster I, Garcia JG, Lussier YA - J Am Med Inform Assoc (2014)

Bottom Line: Cross-patient N-of-1-pathways obtains comparable results with conventional genesets enrichment analysis (GSEA) and differentially expressed gene (DEG) enrichment, validated in three external evaluations.Patients were ranked based on the similarity of their deregulated mechanisms to those of an independent gold standard, generating unsupervised clusters of diametric extreme survival phenotypes (p=0.03).The N-of-1-pathways framework provides a robust statistical and relevant biological interpretation of individual disease-free survival that is often overlooked in conventional cross-patient studies.

View Article: PubMed Central - PubMed

Affiliation: Department of Medicine, Bio5 Institute, UA Cancer Center, University of Arizona, Tucson, Arizona, USA Department of Medicine, University of Illinois at Chicago, Chicago, Illinois, USA Department of Informatics, School of Engineering, EISTI (École Internationale des Sciences du Traitement de l'Information), Cergy-Pontoise, France Institute for Translational Health Informatics, University of Illinois at Chicago, Chicago, Illinois, USA.

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Concordant deregulated pathways (genesets) uncovered by N-of-1-pathways, ssGSEA, differentially expressed gene (DEG) enrichment, and genesets enrichment analysis (GSEA) methods within the exploration dataset (internal validation). To evaluate the Gene Ontology annotations of Biological Process (GO-BP) associated terms yielded by the N-of-1-pathways method, we compared these pathways to those found by a single sample method: ssGSEA, and two well-established cohort-based methods: differentially expressed gene (DEG) enrichment and GSEA. We then generated precision-recall curves based on the perfect GO overlap (A), and GO semantic similarity overlap (B; GO-Information Theoretic Similarity (GO-ITS) ≥0.7; see the ‘Information Theory Similarity’ section in the Methods). When GSEA is chosen as the proxy gold standard (see the ‘Proxy gold standard for the internal and external validations’ section in the Methods), the N-of-1-pathways method uncovered deregulated pathways comparable to, or better than, those of DEG enrichment analysis, with or without GO-ITS analysis, respectively. When DEG enrichment is chosen as the proxy gold standard, N-of-1-pathways performed marginally better than GSEA (see online supplementary figure S2). Bonf, Bonferroni; FDR, false discovery rate; PPV, positive predictive value.
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AMIAJNL2013002519F2: Concordant deregulated pathways (genesets) uncovered by N-of-1-pathways, ssGSEA, differentially expressed gene (DEG) enrichment, and genesets enrichment analysis (GSEA) methods within the exploration dataset (internal validation). To evaluate the Gene Ontology annotations of Biological Process (GO-BP) associated terms yielded by the N-of-1-pathways method, we compared these pathways to those found by a single sample method: ssGSEA, and two well-established cohort-based methods: differentially expressed gene (DEG) enrichment and GSEA. We then generated precision-recall curves based on the perfect GO overlap (A), and GO semantic similarity overlap (B; GO-Information Theoretic Similarity (GO-ITS) ≥0.7; see the ‘Information Theory Similarity’ section in the Methods). When GSEA is chosen as the proxy gold standard (see the ‘Proxy gold standard for the internal and external validations’ section in the Methods), the N-of-1-pathways method uncovered deregulated pathways comparable to, or better than, those of DEG enrichment analysis, with or without GO-ITS analysis, respectively. When DEG enrichment is chosen as the proxy gold standard, N-of-1-pathways performed marginally better than GSEA (see online supplementary figure S2). Bonf, Bonferroni; FDR, false discovery rate; PPV, positive predictive value.

Mentions: Since a gold standard (GS) for lung adenocarcinoma does not exist, we generated proxy GSs151718 in order to objectively assess the accuracy of the significantly deregulated mechanisms identified by N-of-1-pathways (see online supplementary methods and figures 2–5).


'N-of-1-pathways' unveils personal deregulated mechanisms from a single pair of RNA-Seq samples: towards precision medicine.

Gardeux V, Achour I, Li J, Maienschein-Cline M, Li H, Pesce L, Parinandi G, Bahroos N, Winn R, Foster I, Garcia JG, Lussier YA - J Am Med Inform Assoc (2014)

Concordant deregulated pathways (genesets) uncovered by N-of-1-pathways, ssGSEA, differentially expressed gene (DEG) enrichment, and genesets enrichment analysis (GSEA) methods within the exploration dataset (internal validation). To evaluate the Gene Ontology annotations of Biological Process (GO-BP) associated terms yielded by the N-of-1-pathways method, we compared these pathways to those found by a single sample method: ssGSEA, and two well-established cohort-based methods: differentially expressed gene (DEG) enrichment and GSEA. We then generated precision-recall curves based on the perfect GO overlap (A), and GO semantic similarity overlap (B; GO-Information Theoretic Similarity (GO-ITS) ≥0.7; see the ‘Information Theory Similarity’ section in the Methods). When GSEA is chosen as the proxy gold standard (see the ‘Proxy gold standard for the internal and external validations’ section in the Methods), the N-of-1-pathways method uncovered deregulated pathways comparable to, or better than, those of DEG enrichment analysis, with or without GO-ITS analysis, respectively. When DEG enrichment is chosen as the proxy gold standard, N-of-1-pathways performed marginally better than GSEA (see online supplementary figure S2). Bonf, Bonferroni; FDR, false discovery rate; PPV, positive predictive value.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4215042&req=5

AMIAJNL2013002519F2: Concordant deregulated pathways (genesets) uncovered by N-of-1-pathways, ssGSEA, differentially expressed gene (DEG) enrichment, and genesets enrichment analysis (GSEA) methods within the exploration dataset (internal validation). To evaluate the Gene Ontology annotations of Biological Process (GO-BP) associated terms yielded by the N-of-1-pathways method, we compared these pathways to those found by a single sample method: ssGSEA, and two well-established cohort-based methods: differentially expressed gene (DEG) enrichment and GSEA. We then generated precision-recall curves based on the perfect GO overlap (A), and GO semantic similarity overlap (B; GO-Information Theoretic Similarity (GO-ITS) ≥0.7; see the ‘Information Theory Similarity’ section in the Methods). When GSEA is chosen as the proxy gold standard (see the ‘Proxy gold standard for the internal and external validations’ section in the Methods), the N-of-1-pathways method uncovered deregulated pathways comparable to, or better than, those of DEG enrichment analysis, with or without GO-ITS analysis, respectively. When DEG enrichment is chosen as the proxy gold standard, N-of-1-pathways performed marginally better than GSEA (see online supplementary figure S2). Bonf, Bonferroni; FDR, false discovery rate; PPV, positive predictive value.
Mentions: Since a gold standard (GS) for lung adenocarcinoma does not exist, we generated proxy GSs151718 in order to objectively assess the accuracy of the significantly deregulated mechanisms identified by N-of-1-pathways (see online supplementary methods and figures 2–5).

Bottom Line: Cross-patient N-of-1-pathways obtains comparable results with conventional genesets enrichment analysis (GSEA) and differentially expressed gene (DEG) enrichment, validated in three external evaluations.Patients were ranked based on the similarity of their deregulated mechanisms to those of an independent gold standard, generating unsupervised clusters of diametric extreme survival phenotypes (p=0.03).The N-of-1-pathways framework provides a robust statistical and relevant biological interpretation of individual disease-free survival that is often overlooked in conventional cross-patient studies.

View Article: PubMed Central - PubMed

Affiliation: Department of Medicine, Bio5 Institute, UA Cancer Center, University of Arizona, Tucson, Arizona, USA Department of Medicine, University of Illinois at Chicago, Chicago, Illinois, USA Department of Informatics, School of Engineering, EISTI (École Internationale des Sciences du Traitement de l'Information), Cergy-Pontoise, France Institute for Translational Health Informatics, University of Illinois at Chicago, Chicago, Illinois, USA.

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Related in: MedlinePlus