News: Course: Applied Statistics and Bioinformatics with R and Bioconductor. (Berlin, 22-26 January 2018)
gravatar for carlopecoraro2
3.5 years ago by
carlopecoraro22.0k wrote:

enter image description here

Applied Statistics and Bioinformatics with R and Bioconductor


22-26 January 2018




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


Come to the first class with the following installed:

● R and Bioconductor:

● R Studio:

● Github desktop client (or any other Github client):

Additionally, please create an account at, and use it to introduce yourself at


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.

For more information about the course, please visit our WEBSITE

ADD COMMENTlink written 3.5 years ago by carlopecoraro22.0k
Please log in to add an answer.


Use of this site constitutes acceptance of our User Agreement and Privacy Policy.
Powered by Biostar version 2.3.0
Traffic: 1132 users visited in the last hour