seidels/material

Links to materials for STA 426 Fall Semester 2017

Statistical Analysis of High-Throughput Genomic and Transcriptomic Data

Fall/Herbst-semester 2017

Lectures

Mondays 9.00-9.45 (Y27-H-46), 10.00-10.45 (Y11-J-05)

Exercises

Monday 11.00-11.45 (Y11-J-05)

Lecturers

Ms. Gosia Nowicka, finished 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	JC1	JC2
18.09.2017	Mark	admin, mol. biology basics, R markdown
25.09.2017	Hubert	NGS intro; exploratory data analysis
02.10.2017	Mark + Hubert	interactive technology session
09.10.2017	Hubert	mapping
16.10.2017	Mark	limma 1
23.10.2017	Mark	limma 2
30.10.2017	Hubert	RNA-seq quantification	Assessment of batch-correction methods for scRNA-seq data with a new test metric (EC)	Assessing intratumor heterogeneity and tracking longitudinal and spatial clonal evolutionary history by next-generation sequencing
(SS)
06.11.2017	Mark	edgeR+friends 1	Why Most Published Research Findings Are False; Is most published research really false? (PM, SS)	Gene-level differential analysis at transcript-level resolution (CL)
13.11.2017	Charlotte	hands-on session #1: RNA-seq	X	X
20.11.2017	Mark	edgeR+friends 2	High Dimensional Classification with combined Adaptive Sparse PLS and Logistic Regression-link (TF, YY)	BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions (SO)
27.11.2017	Hubert	classification	Bayesian approach to single-cell differential expression analysis (UJ)	Guidance for RNA-seq co-expression network construction and analysis: safety in numbers (CS)
04.12.2017	Mark	single-cell	Removal of batch effects using distribution-matching residual networks (MH, SG)	DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning (DR)
11.12.2017	Gosia	hands-on session #2: mass cytometry	X	X
18.12.2017	Mark	epigenomics, DNA methylation, ChIP data, gene set analysis	Linear models enable powerful differential activity analysis in massively parallel reporter assays (DP, ZY)

Useful Links

Simply Statistics blog
Getting Genetics Done blog
Omics Omics blog
Staying Current in Bioinformatics & Genomics: 2017 Edition

Course material

Assuming you have git installed, you can check out the entire set of course materials with the following command (from command line):

git clone https://github.com/sta426hs2017/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'.