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Statistical Analysis of High-Throughput Genomic and Transcriptomic Data

Fall/Herbst-semester 2018

Lectures

Mondays 9.00-9.45 (Y27-H-46), 10.00-10.45 (Y27-H-46)

Exercises

Monday 11.00-11.45 (Y01-F-50)

Lecturers

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

Schedule

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

Useful Links

Simply Statistics blog
Getting Genetics Done blog
Omics Omics blog

Course material

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'.

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