Stochastic Modeling Lab

This lab is focused on designing, refining and inferring model structures in tasks with large, heterogeneous data sources. The lab is also developing methodologies for error-rate control and prioritization in ranking, selection and data integration tasks.

Related CPCP Publications

SCnorm: robust normalization of single-cell RNA-seq data. Bacher R, Chu LF, Leng N, Gasch A, Thomson J, Stewart R, Newton M, Kendziorski C. Nature Methods, 2017

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A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Korthauer K, Chu LF, Newton M, Li Y, Thomson J, Stewart R, Kendziorski K. Genome Biology 17:222, 2016

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Subclonal diversity arises early even in small colorectal tumours and contributes to differential growth fates. Sievers C, Zou L, Pickhardt P, Matkowskyj K, Albrecht D, Clipson L, Bacher J, Pooler BD, Moawad F, Cash B, Reichelderfer M, Vo T, Newton M, Larget B, Halberg R. Gut, 2016

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Further support for aneuploidy tolerance in wild yeast and effects of dosage compensation on gene copy-number evolution. Gasch A, Hose J, Newton M, Sardi M, Yong M, Wang Z. eLife 5:e14409, 2016

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Making the cut: improved ranking and selection for large-scale inference. Henderson NC, Newton MA. Journal of the Royal Statistical Society Series B 78(4):781-804

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Michael Newton


Xiuyu Ma

Tien Vo

Huikun Zhang


Rolemodel software

rvalues software