News: Course: Applied Statistics and Bioinformatics with R and Bioconductor. (Berlin, 22-26 January 2018)
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Applied Statistics and Bioinformatics with R and Bioconductor

22-26 January 2018, Berlin (Germany)


Dr. Levi Waldron

Dr. Ludwig Geistlinger

Waldron lab for computational biostatistics CUNY School of Public Health in New York City (


This course will provide biologists and bioinformaticians with practical statistical and data analysis skills to perform rigorous analysis of high-throughput biological data. The course assumes some familiarity with genomics and with R programming, but does not assume prior statistical training. It covers the statistical concepts necessary to design experiments and analyze high-dimensional data generated by high-throughput sequencing, including: exploratory data analysis, principal components analysis, unsupervised clustering, batch effects, linear modeling for differential expression, gene set analysis.


Each day will include a hands-on lab session, that students should attempt and hand in before the following day by committing to the course Github repository. A selection of labs will be reviewed the following day.


Monday 22nd – Classes from 09:30 to 17:30

Session 1 – Introduction

Lecture 1: Data distributions

random variables

Lecture 2: Statistical inference and sampling

populations and samples

Central Limit Theorem


Lab 1: Introduction to R and Bioconductor

Lab 2: Creating graphics

Tuesday 23nd – Classes from 09:30 to 17:30

Session 2– Hypothesis testing

Lecture 1: hypothesis testing concepts

type I and II error and power

confidence intervals

multiple hypothesis testing: false discovery rate, familywise error rate

Lecture 2: hypothesis testing in practice

hypothesis tests for categorical variables (chi-square, Fisher's exact)

Monte Carlo simulation

permutation tests

bootstrap simulation

exploratory data analysis

Lab: bootstrap simulation and permutation tests

Wednesday 24th – Classes from 09:30 to 17:30

Session 3 - Linear modeling

Lecture 1: linear modeling

linear regression and multiple regression

model matrix and model formulae

Lecture 2: generalized linear models for count data

intro to generalized linear models

logistic regression and log-linear models

Poisson and Negative Binomial error models

Zero-inflated models

Lab: RNA-seq differential expression workflow

Thursday 25th – Classes from 09:30 to 17:30

Session 4 - Unsupervised methods

Lecture 1: distances and PCA

distance in high dimensions

singular value decomposition

principal components analysis and multidimensional scaling

Lecture 2: unsupervised clustering

unsupervised clustering

batch effects

Lab 1: applications of unsupervised methods to shotgun metagenomics microbiome data analysis

Lab 2: option to work on students’ own data.

Friday 26th – Classes from 09:30 to 17:30

Session 5 - Gene set and multi-omics data analysis

Lecture 1 - gene set enrichment analysis

background on gene set testing

types and interpretations of gene set tests

advantages and pitfalls of gene set testing

Lab 1 - gene set analysis with applications to gene expression and multi-omics experiments

Lab 2 - multi-omics data analysis

Lab 3 - option to work on students’ own data.

Packages available

1) Course-only: includes course material and refreshments (530 euros; VAT incl.)

2) All-inclusive: includes course material, refreshments, meals (breakfast, lunch and dinner), accommodation (795 euros; VAT incl.)

Registration deadline: December 20th , 2017.

Full list of our courses and Workshops

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