Mark Robinson: Katalogdaten im Herbstsemester 2023 |
| Name | Herr Prof. Dr. Mark Robinson (Professor Universität Zürich (UZH)) |
| Adresse | Universität Zürich Winterthurerstrasse 190 Inst. Molecular Life Sciences 8057 Zürich SWITZERLAND |
| Telefon | 044 635 48 48 |
| mark.robinson@math.ethz.ch | |
| Departement | Mathematik |
| Beziehung | Dozent |
| Nummer | Titel | ECTS | Umfang | Dozierende | ||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 401-5640-00L | ZüKoSt: Seminar on Applied Statistics  | 0 KP | 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 | ||||||||||||||||||||
| Kurzbeschreibung | Etwa 3 Vorträge zur angewandten Statistik. | |||||||||||||||||||||||
| Lernziel | Kennenlernen von statistischen Methoden in ihrer Anwendung in verschiedenen Anwendungsgebieten. | |||||||||||||||||||||||
| Inhalt | In etwa 3 Einzelvorträgen pro Semester werden Methoden der Statistik einzeln oder überblicksartig vorgestellt, oder es werden Probleme und Problemtypen aus einzelnen Anwendungsgebieten besprochen. | |||||||||||||||||||||||
| Voraussetzungen / Besonderes | Dies ist keine Vorlesung. Es wird keine Prüfung durchgeführt, und es werden keine Kreditpunkte vergeben. Nach besonderem Programm: http://stat.ethz.ch/events/zukost Lehrsprache ist Englisch oder Deutsch je nach ReferentIn. | |||||||||||||||||||||||
| Kompetenzen |
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| 401-6282-00L | Statistical Analysis of High-Throughput Genomic and Transcriptomic Data (University of Zurich) Der Kurs muss direkt an der UZH als incoming student belegt werden. UZH Modulkürzel: STA426 Beachten Sie die Einschreibungstermine an der UZH: https://www.uzh.ch/cmsssl/de/studies/application/deadlines.html | 5 KP | 3G | H. Rehrauer, M. Robinson | ||||||||||||||||||||
| Kurzbeschreibung | 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. | |||||||||||||||||||||||
| Lernziel | -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 | |||||||||||||||||||||||
| Inhalt | 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 | |||||||||||||||||||||||
| Skript | Lecture notes, published manuscripts | |||||||||||||||||||||||
| Voraussetzungen / Besonderes | 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 | |||||||||||||||||||||||