ArticleJournal of biomedical informatics2017
kMEn: Analyzing noisy and bidirectional transcriptional pathway responses in single subjects.
Article in Journal of biomedical informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Enhancing validation of case-control omics signatures through "minimalist" single-subject analysis (N-of-1 trials): proof of concept in sepsis.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Paired-Sample and Pathway-Anchored MLOps Framework for Robust Transcriptomic Machine Learning in Small Cohorts: Model Classification Study.JMIR bioinformatics and biotechnology · 2025Article
- 'Single-subject studies'-derived analyses unveil altered biomechanisms between very small cohorts: implications for rare diseases.Bioinformatics (Oxford, England) · 2021Article
- Evaluating single-subject study methods for personal transcriptomic interpretations to advance precision medicine.BMC medical genomics · 2019Article
- Developing a 'personalome' for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes.Briefings in bioinformatics · 2019Review
- Interpretation of 'Omics dynamics in a single subject using local estimates of dispersion between two transcriptomes.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
- A Single-Subject Method to Detect Pathways Enriched With Alternatively Spliced Genes.Frontiers in genetics · 2019Article
- The Emerging Potential for Network Analysis to Inform Precision Cancer Medicine.Journal of molecular biology · 2018Review
- Emergence of pathway-level composite biomarkers from converging gene set signals of heterogeneous transcriptomic responses.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2018Article
- N-of-1-pathways MixEnrich: advancing precision medicine via single-subject analysis in discovering dynamic changes of transcriptomes.BMC medical genomics · 2017Article
- Accounting for extra-binomial variability with differentially expressed genetic pathway data: a collaborative bioinformatic study.Stat (International Statistical Institute)Article
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Authors and funding
9 authors.
Funding
Abstract
motivationUnderstanding dynamic, patient-level transcriptomic response to therapy is an important step forward for precision medicine. However, conventional transcriptome analysis aims to discover cohort-level change, lacking the capacity to unveil patient-specific response to therapy. To address this gap, we previously developed two N-of-1-pathways methods, Wilcoxon and Mahalanobis distance, to detect unidirectionally responsive transcripts within a pathway using a pair of samples from a single subject. Yet, these methods cannot recognize bidirectionally (up and down) responsive pathways. Further, our previous approaches have not been assessed in presence of background noise and are not designed to identify differentially expressed mRNAs between two samples of a patient taken in different contexts (e.g. cancer vs non cancer), which we termed responsive transcripts (RTs).
methodsWe propose a new N-of-1-pathways method, k-Means Enrichment (kMEn), that detects bidirectionally responsive pathways, despite background noise, using a pair of transcriptomes from a single patient. kMEn identifies transcripts responsive to the stimulus through k-means clustering and then tests for an over-representation of the responsive genes within each pathway. The pathways identified by kMEn are mechanistically interpretable pathways significantly responding to a stimulus.
resultsIn ∼9000 simulations varying six parameters, superior performance of kMEn over previous single-subject methods is evident by: (i) improved precision-recall at various levels of bidirectional response and (ii) lower rates of false positives (1-specificity) when more than 10% of genes in the genome are differentially expressed (background noise). In a clinical proof-of-concept, personal treatment-specific pathways identified by kMEn correlate with therapeutic response (p-value<0.01).
conclusionThrough improved single-subject transcriptome dynamics of bidirectionally-regulated signals, kMEn provides a novel approach to identify mechanism-level biomarkers.
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