Fall/Herbst-semester 2019
Mondays 9.00-9.45 (Y27-H-46), 10.00-10.45 (Y27-H-46)
Monday 11.00-11.45 (Y01-F-50)
Dr. Hubert Rehrauer, Group Leader of Genome Informatics at FGCZ
Prof. Dr. Mark Robinson, Associate Professor of Statistical Genomics, IMLS, UZH
Katharina Hembach, PhD Student, Robinson Lab, IMLS, UZH
Pierre-Luc Germain, Postdoctoral Fellow, Robinson Lab, IMLS, UZH and Molecular and Behavioral Neuroscience Lab, D-HEST, ETH Zurich
Helena Crowell, PhD Student, Robinson Lab, IMLS, UZH
| Date | Lecturer | Topic | Exercise | JC1 | JC2 |
|---|---|---|---|---|---|
| 16.09.2019 | Mark + Hubert | admin; mol. bio. basics | R markdown; git(hub) | ||
| 23.09.2019 | Mark | interactive technology/statistics session | group exercise: technology pull request | ||
| 30.09.2019 | Hubert | NGS intro; exploratory data analysis | EDA in R | ||
| 07.10.2019 | Hubert | mapping | Rsubread | ||
| 14.10.2019 | Mark | limma + friends | linear model simulation + design matrices | Redefining CpG islands using hidden Markov models (AM,LW,MM) | |
| 21.10.2019 | Hubert | RNA-seq quantification | RSEM | ||
| 28.10.2019 | Kathi | hands-on session #1: RNA-seq | FASTQC/Salmon/etc. | X | X |
| 04.11.2019 | Mark | edgeR+friends 1 | basic edgeR/voom | ||
| 11.11.2019 | Mark | edgeR+friends 2 | GLM/DEXSeq | ||
| 18.11.2019 | Hubert | single-cell 1: preprocessing, dim. reduction, clustering | scRNA exercise 1 | Integrating single-cell transcriptomic data across different conditions, technologies, and species (AA, HG) | Normalization of RNA-seq data using factor analysis of control genes or samples (J.M, F.H) |
| 25.11.2019 | Helena | hands-on session #2: cytometry | cytof null comparison | X | X |
| 02.12.2019 | Mark | single-cell 2: cell type definition, differential state | scRNA exercise 2 | ||
| 09.12.2019 | Pierre-Luc | hands-on session #3: single-cell RNA-seq | full scRNA-seq pipeline | X | X |
| 16.12.2019 | Mark | loose ends: HMM, EM, robustness | segmentation, peak finding | Shrinkage estimation of dispersion in Negative Binomial models for RNA-seq experiments with small sample size (AS, CP, IP) |
Simply Statistics blog
Getting Genetics Done blog
Omics Omics blog
Awesome single cell
Assuming you have git installed locally, you can check out the entire set of course materials with the following command (from command line):
git clone https://github.com/sta426hs2019/material.git
Alternatively, for a ZIP file of the repository, you can click on the (green) 'Clone or download' (top right) and then click 'Download ZIP'.