Sandrine Dudoit, PhD
Professor and Chair, Department of Statistics
Professor, Division of Epidemiology and Biostatistics, School of Public Health
University of California, Berkeley
367 Evans Hall, #3860
Berkeley, CA 94720-3860
Fax: (510) 642-7892
N.B. Prospective graduate students. Please take the time to read carefully the information on the program websites (Statistics, Biostatistics) before contacting us. You will find answers to most of your questions regarding application procedures, admission criteria, degree requirements, and financial support on these websites. If you still have questions regarding administrative matters, please contact La Shana Porlaris (Statistics) or Janene Martinez (Biostatistics).
NIH Brain Initiative award:
A Comprehensive Whole-Brain Atlas of Cell Types in the Mouse
NIH press releases:
NIH BRAIN Initiative builds on early advances,
NIH BRAIN Initiative launches cell census
UC Berkeley press release: $65.5 million from NIH to create brain atlas
STAT 278B -- Statistics and Genomics Seminar
Data 100/COMPSCI C100/STAT C100: Principles and Techniques of Data Science
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.
Normalization (software scone):
M. B. Cole, D. Risso, A. Wagner, D. DeTomaso, J. Ngai, E. Purdom, S. Dudoit, and N. Yosef.
Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq.
Expression quantitation, normalization, dimensionality reduction (software zinbwave):
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.
Nature Communications, 2018.
K. Van den Berge, F. Perraudeau, C. Soneson, M. I. Love, D. Risso, J.-P. Vert, M. D. Robinson, S. Dudoit, and L. Clement.
Observation weights to unlock bulk RNA-seq tools for zero inflation and single-cell applications.
Cell lineage inference (software slingshot):
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.
Workflow (software scone, zinbwave, clusterExperiment, slingshot):
F. Perraudeau, D. Risso, K. Street, E. Purdom, and S. Dudoit.
Bioconductor workflow for single-cell RNA sequencing: Normalization, dimensionality reduction, clustering, and lineage inference.
F1000Research, 6:1158, July 21, 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, 20(6): 817-830, 2017.
Press release [PDF].
L. Gadye, D. Das, M. A. Sanchez, K. Street, A. Baudhuin, A. Wagner, M. B. Cole, Y. G. Choi, N. Yosef, E. Purdom, S. Dudoit, D. Risso, J. Ngai, and R. B. Fletcher.
Injury activates transient olfactory stem cell states with diverse lineage capacities.
Cell Stem Cell, 21(6): 775-790, 2017.
F. Perraudeau, S. Dudoit, and J. H. Bullard.
Accurate Determination of Bacterial Abundances in Human Metagenomes Using Full-length 16S Sequencing Reads.
Statistical Methods and Software for Investigating Stem Cell Differentiation Using Single-Cell Transcriptome Sequencing, Department of Statistics and Actuarial Science, Simon Fraser University, March 23, 2018.
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]
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., single-cell transcriptome sequencing (RNA-Seq) for discovering novel cell types and for the study of stem cell differentiation. My contributions include: exploratory data analysis, normalization
and expression quantitation, differential expression analysis,
class discovery, prediction, cell lineage inference, 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]