Fall/Herbst-semester 2018
Mondays 9.00-9.45 (Y27-H-46), 10.00-10.45 (Y27-H-46)
Monday 11.00-11.45 (Y01-F-50)
Mr. Lukas Weber, final year PhD student, IMLS, UZH
Dr. Hubert Rehrauer, Group Leader of Genome Informatics at FGCZ
Prof. Dr. Mark Robinson, Associate Professor of Statistical Genomics, IMLS, UZH
Dr. Charlotte Soneson, Postdoctoral Associate, IMLS, UZH
| Date | Lecturer | Topic | Exercise | JC1 | JC2 |
|---|---|---|---|---|---|
| 17.09.2018 | Mark + Hubert | admin; mol. bio. basics | R markdown; git(hub) | ||
| 24.09.2018 | Hubert | NGS intro; exploratory data analysis | EDA in R | ||
| 01.10.2018 | Mark + Hubert | interactive technology/statistics session | group exercise: technology pull request | ||
| 08.10.2018 | Hubert | mapping | Rsubread | ||
| 15.10.2018 | Hubert | RNA-seq quantification | RSEM | ||
| 22.10.2018 | Mark | limma + friends | linear model simulation + design matrices | Averaged gene expressions for regression (AS, LB, MK) | Detection and accurate false discovery rate control of differentially methylated regions from whole genome bisulfite sequencing (DT, HP) |
| 29.10.2018 | Charlotte | hands-on session #1: RNA-seq | FASTQC/Salmon/etc. | Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis (MS, CR) | X |
| 05.11.2018 | Mark | edgeR+friends 1 | basic edgeR/voom | Overcoming systematic errors caused by log-transformation of normalized single-cell RNA sequencing data (RB, RG) | A Universal Algorithm to Detect Rare or Novel Cell Types in High-Throughput Single-Cell Gene Expression Data (JC, TF, MS) |
| 12.11.2018 | Mark | edgeR+friends 2 | GLM/DEXSeq | A general and flexible method for signal extraction from single-cell RNA-seq data (AL, VL, JB) | Integrating single-cell transcriptomic data across different conditions, technologies, and species (PV, FN, ES) |
| 19.11.2018 | Mark | single-cell dim. reduction + clustering; FDR | conquer | Normalization of RNA-seq data using factor analysis of control genes or samples (RM, JD, CV) | Diffusion maps for high-dimensional single-cell analysis of differentiation data (SP, GK) |
| 26.11.2018 | Lukas | hands-on session #2: cytometry | cytof null comparison | Epigenome-wide association studies without the need for cell-type composition (RL, SG) | X |
| 03.12.2018 | Hubert | classification | MLInterfaces | ||
| 10.12.2018 | Mark | loose ends: HMM, EM, robustness | segmentation, peak finding | Differential expression analysis for sequence count data (AA, PS) | Visualizing Data using t-SNE (MJT, TB, MP) |
| 17.12.2018 | Mark | hands-on session #3: single-cell RNA-seq | full scRNA-seq pipeline | Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies (SB,ST) | x |
Simply Statistics blog
Getting Genetics Done blog
Omics Omics blog
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/sta426hs2018/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'.