[Statlist] Two talks by Prof. Terry Speed on 7/3/2014

[Schaffner Portillo Maroussia]

Dear Mr. Stahel,



Here is the correct announcement. Thank you!

Best,

Maroussia



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Dear all,

We would like to inform you that, on March 7th, Professor Terry Speed (Walter and Eliza Hall Institute & UC Berkeley) will be giving:

1) a Genomics seminar at UNIL (11h00 sharp, Génopode Auditorium B, UNIL).

2) a Statistics Seminar at EPFL (15h00 sharp, CE2, EPFL).

Please scroll down for the titles and abstracts.

Center for Integrative Genomics Seminar
Friday, 7 March 2014 – 11h00 (sharp)
Génopode Auditorium B (UNIL Dorigny)

Normalization of RNA-Seq Data: Are the ERCC Spike-In Controls Reliable?
Professor Terry Speed
Walter and Eliza Hall Institute & UC Berkeley

(Host: Mauro Delorenzi)

Abstract:

The External RNA Control Consortium (ERCC) developed a set of 92 synthetic polyadenylated RNA standards that mimic natural eukaryotic mRNA (Jiang et al., 2011). The standards are designed to have a wide range of lengths (250-2,000 nucleotides) and GC-contents (5-51%).  The ERCC standards can be spiked into RNA at various concentrations prior to the library preparation step and serve as negative and positive controls in RNA-Seq. Ambion commercializes spike-in control mixes, ERCC ExFold RNA Spike-in Control Mix 1 and 2, each containing the same set of 92 standards, but at different concentrations. We investigate the use of the ERCC spike-in controls for two main purposes: (a) Quality assessment/quality control (QA/QC) of RNA-Seq data and benchmarking of normalization and differential expression (DE) methods, and (b) Direct inclusion in between-sample normalization procedures. We have two RNA-seq data sets which make use of the ERCC controls: a local one concerning treated and untreated zebrafish tissue, and some of the SEQC samples. A variety of normalization methods will be compared, both using and not using the ERCC controls. Joint work with Sandrine Dudoit, Davide Risso and John Ngai, all from UC Berkeley.

EPFL Statistics Seminar
Friday, 7 March 2014 – 15h00 (sharp)
CE2 (EPFL)

Removing unwanted variation: from principal components to random effects.
Professor Terry Speed
Walter and Eliza Hall Institute & UC Berkeley

(Hosts: Anthony Davison, Darlene Goldstein, Victor Panaretos)

Abstract:

Ordinary least-squares is a venerable tool for the analysis of scientific data originating in the work of A-M. Legendre and C. F. Gauss around 1800. Gauss used the method extensively in astronomy and geodesy. Generalized least squares is more recent, originating with A. C. Aitken in 1934, though weighted least squares was widely used long before that. At around the same time (1933) H. Hotelling introduced principal components analysis to psychology. Its modern form is the singular value decomposition. In 1907, motivated by social science, G. U. Yule presented a new notation and derived some identities for linear regression and correlation. Random effects models date back to astronomical work in the mid-19th century, but it was through the work of C. R. Henderson and others in animal science in the 1950s that their connexion with generalized least squares was firmly made. These are the diverse origins of our story, which concerns the removal of unwanted variation in high-dimensional genomic and other “omic” data using negative controls. We start with a linear model that Gauss would recognize, with ordinary least squares in mind, but we add unobserved terms to deal with unwanted variation. A singular value decomposition, one of Yule's identities, and negative control measurements (here genes) permit the identification of our model. In a surprising twist, our initial solution turns out to be equivalent to a form of generalized least squares. This is the starting point for much of our recent work. In this talk I will try to explain how a rather eclectic mix of familiar statistical ideas can combine with equally familiar notions from biology (negative and positive controls) to give a useful new set of tools for omic data analysis. Other statisticians have come close to the same endpoint from a different perspectives, including Bayesian, sparse linear and random effects models. Joint work with Johann Gagnon-Bartsch and Laurent Jacob.

Best regards.

Maroussia Schaffner
SB-MATHAA-SMAT

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