MAY 13, 2015 10:30 AM PDT
Statistical methods for bulk and single-cell RNA-seq experiments
Presented at the Genetics and Genomics Virtual Event
4 56 2163

Speakers:
  • Professor, Biostatistics & Medical Informatics, University of Wisconsin - Madison
    Biography
      I am a Professor in the Department of Biostatistics and Medical Informatics at UW-Madison, and an Affiliate Faculty Member in the Department of Statistics. My research is motivated by questions arising in studies of genetics and genomics, specifically as they pertain to complex diseases and personalized genomic medicine. In particular, my research group has developed statistical methods to address several questions in the design and analysis of static and time course microarray experiments, expression quantitative trait loci (eQTL) mapping, network reconstruction, next-generation sequencing experiments, and most recently personalized genomic medicine. I am fortunate to serve on the editorial boards of the Annals of Applied Statistics, Bayesian Analysis, Genetics, and the Genomics, Bioinformatics, and Systems Biology Section of Biology Direct and to be a permanent member of the NIH Genomics, Computational Biology, and Technology (GCAT) study section. I am also the head of Statistical Genetics and Genomics at UW-Madison's Institute for Clinical and Translational Research (ICTR). In addition to being PI on an NIGMS R01 to develop statistical methods for data analysis and integration in genomic based studies of disease, I am co-PI on an NSF grant for statistical modeling and inference of vast matrices for complex problems, and co-I on 6 other NIH grants.

    Abstract:
    I will discuss recent statistical methods for identifying differentially expressed genes in static and time course bulk RNA-seq experiments. I will also provide an overview of the opportunities and challenges provided by single cell RNA-seq and will discuss a statistical method we have developed for characterizing gene expression dynamics and sample heterogeneity in single cell RNA-seq experiments. Learning objectives: 1. Participants will learn the basics of RNA-seq analysis with a focus on normalization and identification of differentially expressed genes.

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