Mark Robinson: Catalogue data in Autumn Semester 2023 |
| Name | Prof. Dr. Mark Robinson (Professor Universität Zürich (UZH)) |
| Address | Universität Zürich Winterthurerstrasse 190 Inst. Molecular Life Sciences 8057 Zürich SWITZERLAND |
| Telephone | 044 635 48 48 |
| mark.robinson@math.ethz.ch | |
| Department | Mathematics |
| Relationship | Lecturer |
| Number | Title | ECTS | Hours | Lecturers | ||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 401-5640-00L | ZüKoSt: Seminar on Applied Statistics  | 0 credits | 1K | M. Kalisch, F. Balabdaoui, A. Bandeira, P. L. Bühlmann, R. Furrer, L. Held, T. Hothorn, M. Mächler, L. Meier, N. Meinshausen, J. Peters, M. Robinson, C. Strobl | ||||||||||||||||||||
| Abstract | About 3 talks on applied statistics. | |||||||||||||||||||||||
| Learning objective | See how statistical methods are applied in practice. | |||||||||||||||||||||||
| Content | There will be about 3 talks on how statistical methods are applied in practice. | |||||||||||||||||||||||
| Prerequisites / Notice | This 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. | |||||||||||||||||||||||
| Competencies |
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| 401-6282-00L | Statistical 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 as an incoming student. UZH Module Code: STA426 Mind the enrolment deadlines at UZH: https://www.uzh.ch/cmsssl/en/studies/application/deadlines.html | 5 credits | 3G | H. Rehrauer, M. Robinson | ||||||||||||||||||||
| Abstract | A 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. | |||||||||||||||||||||||
| Learning 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 | |||||||||||||||||||||||
| Content | Lectures 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 notes | Lecture notes, published manuscripts | |||||||||||||||||||||||
| Prerequisites / Notice | Prerequisites: 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 | |||||||||||||||||||||||