Research Description

My general interests include Bayesian and computational statistics, particularly when applied to problems in population genetics.  Specific interests include:

• estimating haplotypes from population genotype data (for which I distribute a software package PHASE).
• developing statistical models for patterns of linkage disequilibrium across multiple loci, and using these patterns to identify recombination hotspots.
• spatial modelling of allele frequency variation.

Selected Publications

2021

H Shim, Z Xing, E Pantaleo, F Luca, R Pique-Regi and M Stephens. Multi-scale Poisson process approaches for differential expression analysis of high-throughput sequencing data. arXiv:2106.13634. R package | source code implementing the analyses

P Carbonetto, A Sarkar, Z Wang and M Stephens. Non-negative matrix factorization algorithms greatly improve topic model fits. arXiv:2105.13440. R package | companion source code repository

Barbeira et al. Exploiting the GTEx resources to decipher the mechanisms at GWAS loci. Genome Biology 22: 49.

de Goede et al. Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease. Cell 184(10): 2633-2648.e19.

A Sarkar and M Stephens. Separating measurement and expression models clarifies confusion in single cell RNA-seq analysis. Nature Genetics 53: 770-777. bioRxiv preprint | Python package | companion website | companion source code repository

W Wang and M Stephens. Empirical Bayes matrix factorization. Journal of Machine Learning Research 22(120): 1-40. arXiv preprint | R package | code used to produce results in paper

Z Xing, P Carbonetto and M Stephens. Flexible signal denoising via flexible empirical Bayes shrinkage. Journal of Machine Learning Research 22(93): 1-28. arXiv preprint | R package | accompanying code and data

A E White, K K Dey, M Stephens and T D Price. Dispersal syndromes drive the formation of biogeographical regions, illustrated by the case of Wallace’s Line. Global Ecology and Biogeography 30: 685–696. accompanying code and data

M C Ward, N E Banovich, A Sarkar, M Stephens and Yoav Gilad. Dynamic effects of genetic variation on gene expression revealed following hypoxic stress in cardiomyocytes. eLife 10: e57345.

2020

Z Zhang, K Luo, Z Zou, M Qiu, J Tian, L Sieh, H Shi, Y Zou, G Wang, J Morrison, A C Zhu, M Qiao, Z Li, M Stephens, X He and C He. Genetic analyses support the contribution of mRNA N6-methyladenosine (m6A) modification to human disease heritability. Nature Genetics 52: 939–949.

S Kim-Hellmuth et al. Cell type–specific genetic regulation of gene expression across human tissues. Science 369(6509): eaaz8528.

L Jiang et al. A quantitative proteome map of the human body. Cell 183(1): 269-283.e19.

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. Nature Genetics 52: 740–747. bioRxiv preprint | code

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. Genome Biology 30: 611-621. bioRxiv preprint | R package

G Wang, A 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 82: 1273-1300. 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

2019

J D Blischak, P Carbonetto and M Stephens. Creating and sharing reproducible research code the workflowr way. F1000Research 8: 1749 [version 1; peer review: 3 approved]. R package on CRAN | R package on GitHub

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

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 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