Autumn Semester 2020 takes place in a mixed form of online and classroom teaching.
Please read the published information on the individual courses carefully.

Mark Robinson: Catalogue data in Autumn Semester 2018

Name Prof. Dr. Mark Robinson
(Professor Universität Zürich (UZH))
Address
Universität Zürich
Winterthurerstrasse 190
Inst. Molecular Life Sciences
8057 Zürich
SWITZERLAND
Telephone044 635 48 48
E-mailmark.robinson@math.ethz.ch
DepartmentMathematics
RelationshipLecturer

NumberTitleECTSHoursLecturers
401-5640-00LZüKoSt: Seminar on Applied Statistics Information 0 credits1KM. Kalisch, R. Furrer, L. Held, T. Hothorn, M. H. Maathuis, M. Mächler, L. Meier, N. Meinshausen, M. Robinson, C. Strobl, S. van de Geer
AbstractAbout 5 talks on applied statistics.
ObjectiveSee how statistical methods are applied in practice.
ContentThere will be about 5 talks on how statistical methods are applied in practice.
Prerequisites / NoticeThis is no lecture. There is no exam and no credit points will be awarded. The current program can be found on the web:
http://stat.ethz.ch/events/zukost
Course language is English or German and may depend on the speaker.
401-6282-00LStatistical Analysis of High-Throughput Genomic and Transcriptomic Data (University of Zurich)
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: STA426

Mind the enrolment deadlines at UZH:
https://www.uzh.ch/cmsssl/en/studies/application/mobilitaet.html
5 credits3GH. Rehrauer, M. Robinson
AbstractA range of topics will be covered, including basic molecular biology, genomics technologies and in particular, a wide range of statistical and computational methods that have been used in the analysis of DNA microarray and high throughput sequencing experiments.
Objective-Understand the fundamental "scientific process" in the field of Statistical Bioinformatics
-Be equipped with the skills/tools to preprocess genomic data (Unix, Bioconductor, mapping, etc.) and ensure reproducible research (Sweave)
-Have a general knowledge of the types of data and biological applications encountered with microarray and sequencing data
-Have the general knowledge of the range of statistical methods that get used with microarray and sequencing data
-Gain the ability to apply statistical methods/knowledge/software to a collaborative biological project
-Gain the ability to critical assess the statistical bioinformatics literature
-Write a coherent summary of a bioinformatics problem and its solution in statistical terms
ContentLectures will include: microarray preprocessing; normalization; exploratory data analysis techniques such as clustering, PCA and multidimensional scaling; Controlling error rates of statistical tests (FPR versus FDR versus FWER); limma (linear models for microarray analysis); mapping algorithms (for RNA/ChIP-seq); RNA-seq quantification; statistical analyses for differential count data; isoform switching; epigenomics data including DNA methylation; gene set analyses; classification
Lecture notesLecture notes, published manuscripts
Prerequisites / NoticePrerequisites: Basic knowlegde of the programming language R, sufficient knowledge in statistics

Former course title: Statistical Methods for the Analysis of Microarray and Short-Read Sequencing Data