401-6282-00L Statistical Analysis of High-Throughput Genomic and Transcriptomic Data (University of Zurich)
Semester | Herbstsemester 2023 |
Dozierende | H. Rehrauer, M. Robinson |
Periodizität | jährlich wiederkehrende Veranstaltung |
Lehrsprache | Englisch |
Kommentar | 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 |
Lehrveranstaltungen
Nummer | Titel | Umfang | Dozierende | ||||
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401-6282-00 G | Statistical Analysis of High-Throughput Genomic and Transcriptomic Data (University of Zurich) **Course at University of Zurich** | 3 Std. |
| H. Rehrauer, M. Robinson |
Katalogdaten
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 |
Leistungskontrolle
Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird) | |
Leistungskontrolle als Semesterkurs | |
ECTS Kreditpunkte | 5 KP |
Prüfende | H. Rehrauer, M. Robinson |
Form | benotete Semesterleistung |
Prüfungssprache | Englisch |
Repetition | Repetition nur nach erneuter Belegung der Lerneinheit möglich. |
Zusatzinformation zum Prüfungsmodus | Registration modalities, date and venue of this performance assessment are specified solely by the UZH. |
Lernmaterialien
Keine öffentlichen Lernmaterialien verfügbar. | |
Es werden nur die öffentlichen Lernmaterialien aufgeführt. |
Gruppen
Keine Informationen zu Gruppen vorhanden. |
Einschränkungen
Keine zusätzlichen Belegungseinschränkungen vorhanden. |
Angeboten in
Studiengang | Bereich | Typ | |
---|---|---|---|
Computational Biology and Bioinformatics Master | Bioinformatics | W | |
Statistik Master | Fachbezogene Wahlfächer | W |