Stephens, Matthew - University of Chicago

研究领域

My lab works on a wide variety of problems at the interface of Statistics and Genetics. We often tackle problems where novel statistical methods are required, or can learn something new compared with existing approaches. Thus, much of our research involves developing new statistical methodology, many of which have a non-trivial computational component. People in my lab tend to come from a quantitative background (e.g., Math, Statistics, Computer Science), with varying levels of formal or informal Biology training.

近期论文

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A K Sarkar and M Stephens. Separating measurement and expression models clarifies confusion in single cell RNA-seq analysis. bioRxiv doi:10.1101/2020.04.07.030007. Python package | companion source code repository G Wang, A K Sarkar, P Carbonetto and M Stephens. A simple new approach to variable selection in regression, with application to genetic fine mapping. Journal of the Royal Statistical Society, Series B forthcoming. R package | accompanying code and data resources Y Kim, P Carbonetto, M Stephens and M Anitescu. A fast algorithm for maximum likelihood estimation of mixture proportions using sequential quadratic programming. Journal of Computational and Graphical Statistics 29: 261-273. arXiv preprint | accompanying code resources | R package J D Blischak, P Carbonetto and M Stephens. Creating and sharing reproducible research code the workflowr way. F1000Research 8: 1749 [version 1; peer review: awaiting peer review]. R package on CRAN | R package on GitHub C J Hsiao, P Tung, J D Blischak, J Burnett, K Barr, K K Dey, M Stephens and Y Gilad. Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis. bioRxiv doi:10.1101/526848. R package J Morrison, N Knoblauch, J Marcus, M Stephens and X He. Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics. bioRxiv doi:10.1101/682237. M Lu and M Stephens. Empirical Bayes estimation of normal means, accounting for uncertainty in estimated standard errors. arXiv:1901.10679. accompanying code and data Z Xing, P Carbonetto and M Stephens. Flexible signal denoising via flexible empirical Bayes shrinkage. arXiv:1605.07787. R package | accompanying code and data D Gerard and M Stephens. Unifying and generalizing methods for removing unwanted variation based on negative controls. Statistica Sinica forthcoming. arXiv preprint | R package | code used to produce results in paper M C Turchin and M Stephens. Bayesian multivariate reanalysis of large genetic studies identifies many new associations. PLoS Genetics 15(10): e1008431. bioRxiv preprint | R package S Zhao, J Liu, P Nanga, Y Liu, A E Cicek, N Knoblauch, C He, M Stephens and X He. Detailed modeling of positive selection improves detection of cancer driver genes. Nature Communications 10: 3399. bioRxiv preprint | accompanying code and data resources A K Sarkar, P-Y Tung, J D Blischak, J E Burnett, Y I Li, M Stephens and Y Gilad. Discovery and characterization of variance QTLs in human induced pluripotent stem cells. PLoS Genetics 15(4): e1008045. accompanying code and data H Al-Asadi, K K Dey, J Novembre and M Stephens. Inference and visualization of DNA damage patterns using a grade of membership model. Bioinformatics 35(8): 1292-1298. R package A E White, K K Dey, D Mohan, M Stephens and T D Price. Regional influences on community structure across the tropical-temperate divide. Nature Communications 10: 2646. R package H Al-Asadi, D Petkova, M Stephens and J Novembre. Estimating recent migration and population size surfaces. PLoS Genetics 15(1): e1007908. software S M Urbut, G Wang, P Carbonetto and M Stephens. Flexible statistical methods for estimating and testing effects in genomic studies with multiple conditions. Nature Genetics 51(1): 187-195. bioRxiv preprint | R package | accompanying code and data resources W Wang and M Stephens. Empirical Bayes matrix factorization. arXiv:1802.06931. R package | code used to produce results in paper K K Dey and M Stephens. CorShrink: Empirical Bayes shrinkage estimation of correlations, with applications. bioRxiv doi:10.1101/368316. R package | accompanying code and data resources L Sun and M Stephens. Solving the empirical Bayes normal means problem with correlated noise. arXiv:1812.07488. R package | accompanying code and data resources L F V Ferrão, R G Ferrão, M A G Ferrão, A Fonseca, P Carbonetto, M Stephens and A A F Garcia. Accurate genomic prediction of Coffea canephora in multiple environments using whole-genome statistical models. Heredity 122, 261-275. data