ArticleJCO clinical cancer informatics2020
Bayesian Framework for Detecting Gene Expression Outliers in Individual Samples.
Article in JCO clinical cancer informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Multi-omics and high-spatial-resolution omics: deciphering complexity in neurological disorders.GigaScience · 2025Pooled it
- Integrating Machine Learning and Multi-Omics to Explore Neutrophil Heterogeneity.Biomedicines · 2025Review
- Review
- N-of-one differential gene expression without control samples using a deep generative model.Genome biology · 2023Article
- Applications of multi-omics analysis in human diseases.MedComm · 2023Review
- Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis.Cancers · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
purposeMany antineoplastics are designed to target upregulated genes, but quantifying upregulation in a single patient sample requires an appropriate set of samples for comparison. In cancer, the most natural comparison set is unaffected samples from the matching tissue, but there are often too few available unaffected samples to overcome high intersample variance. Moreover, some cancer samples have misidentified tissues of origin or even composite-tissue phenotypes. Even if an appropriate comparison set can be identified, most differential expression tools are not designed to accommodate comparisons to a single patient sample.
methodsWe propose a Bayesian statistical framework for gene expression outlier detection in single samples. Our method uses all available data to produce a consensus background distribution for each gene of interest without requiring the researcher to manually select a comparison set. The consensus distribution can then be used to quantify over- and underexpression.
resultsWe demonstrate this method on both simulated and real gene expression data. We show that it can robustly quantify overexpression, even when the set of comparison samples lacks ideally matched tissue samples. Furthermore, our results show that the method can identify appropriate comparison sets from samples of mixed lineage and rediscover numerous known gene-cancer expression patterns.
conclusionThis exploratory method is suitable for identifying expression outliers from comparative RNA sequencing (RNA-seq) analysis for individual samples, and Treehouse, a pediatric precision medicine group that leverages RNA-seq to identify potential therapeutic leads for patients, plans to explore this method for processing its pediatric cohort.
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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.