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bioRxiv · 10.1101/662338

A Bayesian Framework for Detecting Gene Expression Outliers in Individual Samples

Vivian, J.·Eizenga, J.·Beale, H. C.·Vaske, O. M.·Paten, B.
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Abstract

ObjectiveMany 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 inter-sample variance. Moreover, some cancer samples have misidentified tissues or origin, or even composite-tissue phenotypes. Even if an appropriate comparison set can be identified, most differential expression tools are not designed to accommodate comparing to a single patient sample.\n\nMaterials and 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 under-expression.\n\nResultsWe 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 tissues samples. Further, our results show that the method can identify appropriate comparison sets from samples of mixed lineage and rediscover numerous known gene-cancer expression patterns.\n\nConclusionsThis exploratory method is suitable for identifying expression outliers from comparative 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 their pediatric cohort.

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Vivian, J., Eizenga, J., Beale, H. C., Vaske, O. M., Paten, B.. 2019-06-06. A Bayesian Framework for Detecting Gene Expression Outliers in Individual Samples. https://doi.org/10.1101/662338

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