Sandrine Dudoit, PhD
Professor, Division of Biostatistics, School
of Public Health, and Department of Statistics
Chair and Head Graduate Advisor, Graduate Group in Biostatistics
University of California,
101 Haviland Hall, #7358
Berkeley, CA 94720-7358
Tel: (510) 643-1108
Fax: (510) 643-5163
Prospective graduate students. Please take the time to read carefully the information on the program website before contacting us. You will find answers to most of your questions regarding application procedures, admission criteria, degree requirements, and financial support on this website. If you still have questions, please contact Ms. Sharon Norris regarding administrative matters or me for academic matters.
PB HLTH 295 -- Statistics and Genomics Seminar
Spring 2017 schedule [HTML]
PB HLTH C240F/STAT C245F -- Statistical Genomics II
C. A. Vallejos, D. Risso, A. Scialdone, S. Dudoit, and J. C. Marioni. Normalizing single-cell RNA sequencing data: challenges and opportunities. Nature Methods, 14(6):1-7, 2017.
R. B. Fletcher, D. Das, L. Gadye, K. N. Street, A. Baudhuin, A. Wagner, M. B. Cole, Q. Flores, Y. G. Choi, N. Yosef, E. Purdom, S. Dudoit, D. Risso, and J. Ngai.
Deconstructing Olfactory Stem Cell Trajectories at Single-Cell Resolution
Cell Stem Cell, 2017.
Press release [PDF].
D. Risso, F. Perraudeau, S. Gribkova, S. Dudoit, and J.-P. Vert.
ZINB-WaVE: A general and flexible method for signal extraction from single-cell RNA-seq data, 2017.
K. Street, D. Risso, R. B Fletcher, D. Das, J. Ngai, N. Yosef, E. Purdom, and S. Dudoit.
Slingshot: Cell lineage and pseudotime inference for single-cell transcriptomics, 2017.
Keynote Presentation, Intelligent Systems for Molecular Biology (ISMB), Orlando, FL, July 11, 2016.
Using Single-Cell Transcriptome Sequencing to Infer Olfactory Stem Cell Fate Trajectories.
Workshop, BioC 2016: Where Software and Biology Connect, Stanford, CA, June 25, 2016.
Analysis of Single-Cell RNA-Seq Data with R and Bioconductor.
R package bioc2016singlecell: [GitHub]
Remove Unwanted Variation from RNA-Seq Data.
D. Risso and S. Dudoit.
Bioconductor R package: [Current release version]
Single Cell Overview of Normalized Expression data.
M. Cole and D. Rissso.
R package version 0.99.0, works with R version 3.3 and Bioconductor version 3.4: [GitHub]
slingshot: Tools for ordering single-cell sequencing.
R package version 0.0.3-4: [GitHub]
Zero-Inflated Negative Binomial-based Wanted Variation Extraction (ZINB-WaVE).
D. Risso, S. Gribkova, and J.-P. Vert.
R package version 0.1.2: [GitHub]
Research and Teaching Activities
My research and teaching activities concern the development and
application of statistical methods and software for the analysis
of biomedical and genomic data.
Statistical methodology. My methodological research
interests regard high-dimensional inference and include
exploratory data analysis (EDA), visualization, loss-based
estimation with cross-validation (e.g., density estimation,
regression, model selection), and multiple hypothesis testing.
Applications to biomedical and genomic research. Much of my
methodological work is motivated by statistical inference
questions arising in biological research and, in particular, the
design and analysis of high-throughput microarray and sequencing
gene expression experiments, e.g., RNA-Seq for transcriptome
analysis and genome annotation and ChIP-Seq for DNA-protein
interaction profiling (e.g., transcription factor binding). My
contributions include: exploratory data analysis, normalization
and expression quantitation, differential expression analysis,
class discovery, prediction, integration of biological annotation
metadata (e.g., Gene Ontology (GO) annotation).
Statistical computing. I am also interested in statistical
computing and, in particular, reproducible research. I am a
founding core developer of the Bioconductor Project (http://www.bioconductor.org), an open-source and open-development software project for the analysis of biomedical and genomic data.
Curriculum Vitae and Biography
Curriculum Vitae: [PDF]
Berkeley Institute for Data Science, Senior Fellow
Berkeley Stem Cell
California Institute for
Quantitative Biosciences (QB3)
Center for Computational
Biology (CCB), UC Berkeley
Graduate Group in
Biostatistics, UC Berkeley
Group in Computational and Data Science and Engineering (CDSE), UC
Groupin Computational and Genomic Biology, UC Berkeley
Bioinformatics and Molecular Biostatistics (CBMB), UC San Francisco