Stephanie Hicks
Applied statistics for genomics & biomedical data
Associate Professor
Department of Biostatistics
Bloomberg School of Public Health
Research Overview
Dr. Stephanie Hicks’ research interests focus around developing statistical methodology, and open-source software for biomedical data analysis, which often contains noisy or missing data and systematic biases. Specifically, her research addresses statistical challenges in epigenomics, single-cell genomics, and spatial transcriptomics leading to an improved quantification and understanding of biological variability. Her work in genomics has led to developing fast, accurate and widely used statistical methods and software for the analysis of single-cell RNA-sequencing data, and most recently spatial transcriptomics data from the 10X Genomics Visium platform, which she makes available via open source software. Applications of these methods from single-cell profiling data include investigating high-grade serous ovarian cancer, high-grade glioma childhood cancer, and chronic myeloid leukemia cancer. She actively contributes software packages to the Bioconductor project, and is involved in teaching courses for data science and the analysis of genomics data.
Selected Publications
- Heil BJ, Hoffman MM, Markowetz F, Lee SI, Greene CS, Hicks SC. Reproducibility standards for machine learning in the life sciences. Nature Methods, 2021.
- Maynard KR, Collado-Torres L, Weber LM, Uytingco C, Barry BK, Williams SR, Catallini JL 2nd, Tran MN, Besich Z, Tippani M, Chew J, Yin Y, Kleinman JE, Hyde TM, Rao N, Hicks SC, Martinowich K, Jaffe AE. Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex. Nature Neuroscience, 2021. Erratum in: Nature Neuroscience, 2021.
- Lähnemann D, Köster J, Szczurek E, McCarthy DJ, Hicks SC, Robinson MD, Vallejos CA, Campbell KR, Beerenwinkel N, Mahfouz A, Pinello L, Skums P, Stamatakis A, Attolini CS, Aparicio S, Baaijens J, Balvert M, Barbanson B, Cappuccio A, Corleone G, Dutilh BE, Florescu M, Guryev V, Holmer R, Jahn K, Lobo TJ, Keizer EM, Khatri I, Kielbasa SM, Korbel JO, Kozlov AM, Kuo TH, Lelieveldt BPF, Mandoiu II, Marioni JC, Marschall T, Mölder F, Niknejad A, Raczkowski L, Reinders M, Ridder J, Saliba AE, Somarakis A, Stegle O, Theis FJ, Yang H, Zelikovsky A, McHardy AC, Raphael BJ, Shah SP, Schönhuth A. Eleven grand challenges in single-cell data science. Genome Biology, 2020.
- Amezquita RA, Lun ATL, Becht E, Carey VJ, Carpp LN, Geistlinger L, Marini F, Rue-Albrecht K, Risso D, Soneson C, Waldron L, Pagès H, Smith ML, Huber W, Morgan M, Gottardo R, Hicks SC. Orchestrating single-cell analysis with Bioconductor. Nature Methods, 2020.
- Townes FW, Hicks SC, Aryee MJ, Irizarry RA. Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model. Genome Biology, 2019.