[# Statistical Analysis of High-Throughput Genomic and Transcriptomic Data 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 | Mark | RNA-seq quantification | RSEM | ||
| 22.10.2018 | Hubert | limma + friends | linear model simulation + design matrices | 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) | |
| 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) | |
| 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) | |
| 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 | Predicting cell types in single cell mass cytometry data (CM, SS) | |
| 10.12.2018 | Mark | loose ends: HMM, EM, robustness | segmentation, peak finding | Differential expression analysis for sequence count data (AA, PS) | |
| 17.12.2018 | Mark | hands-on session #3: single-cell RNA-seq | full scRNA-seq pipeline | tba (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'.