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
  • distributions

Lecture 2: Statistical inference and sampling

  • populations and samples
  • Central Limit Theorem
  • t-distribution

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.

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