ArticleStatistical methods in medical research2018
Testing for differentially expressed genetic pathways with single-subject N-of-1 data in the presence of inter-gene correlation.
Article in Statistical methods in medical research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Functional Depth Biomarkers Distinguish Lung Squamous Cell Carcinoma from Lung Adenocarcinoma.Research square · 2026Article
- 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
- 'Single-subject studies'-derived analyses unveil altered biomechanisms between very small cohorts: implications for rare diseases.Bioinformatics (Oxford, England) · 2021Article
- Module Analysis Using Single-Patient Differential Expression Signatures Improves the Power of Association Studies for Alzheimer's Disease.Frontiers in genetics · 2020Article
- 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
- A Single-Subject Method to Detect Pathways Enriched With Alternatively Spliced Genes.Frontiers in genetics · 2019Article
- Emergence of pathway-level composite biomarkers from converging gene set signals of heterogeneous transcriptomic responses.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2018Article
- Accounting for extra-binomial variability with differentially expressed genetic pathway data: a collaborative bioinformatic study.Stat (International Statistical Institute)Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Modern precision medicine increasingly relies on molecular data analytics, wherein development of interpretable single-subject ("N-of-1") signals is a challenging goal. A previously developed global framework, N-of-1- pathways, employs single-subject gene expression data to identify differentially expressed gene set pathways in an individual patient. Unfortunately, the limited amount of data within the single-subject, N-of-1 setting makes construction of suitable statistical inferences for identifying differentially expressed gene set pathways difficult, especially when non-trivial inter-gene correlation is present. We propose a method that exploits external information on gene expression correlations to cluster positively co-expressed genes within pathways, then assesses differential expression across the clusters within a pathway. A simulation study illustrates that the cluster-based approach exhibits satisfactory false-positive error control and reasonable power to detect differentially expressed gene set pathways. An example with a single N-of-1 patient's triple negative breast cancer data illustrates use of the methodology.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.