HPC

UNIX command line This course introduces the UNIX command line on a Linux or Mac. It serves as a foundation of our Bash scripting and CBE HPC cluster introduction courses. Instructor: András Aszódi. Topics UNIX overview: general principles. Working with files and directories. Users, groups, permissions, environment variables. Command pipelines, job control. Out of scope […]

Bash scripting This course teaches you how to write Bash shell scripts. It is aimed at colleagues who plan to work with UNIX-like operating systems including (but not limited to) HPC clusters. Instructor: András Aszódi. Topics Basics: variables, arrays, shell arithmetics. Flow control: logical decisions, iteration loops. Modularity: Bash functions. File path manipulations. Command-line argument […]

CBE HPC cluster introduction This course provides an introduction to the CBE ("CLIP Batch Environment") high-performance computing cluster which is part of the Vienna Biocenter Cloud Infrastructure Project. Instructor: András Aszódi. Topics Introduction to the cluster: architecture, storage layout, access. Environment modules. Batch mode: SLURM queuing system, job scripts. Job arrays, task arrays, MPI jobs. […]

Regular expressions This course introduces regular expressions that can be used to find patterns in textual data, including nucleic acid and protein sequences. It is aimed at colleagues who wish to write their own data analysis scripts and are familiar with Python and/or R and/or the UNIX command line. Instructor: András Aszódi. Topics Introduction to […]

R as a programming language This course is aimed at colleagues who would like to learn how to use the statistical programming language "R". We will learn about the data structures and functions in R, and how to write R scripts. Instructor: András Aszódi. Topics Introduction to R: general principles. Data structures in R: vectors, […]

Think Statistics with R The main aim of this course is to teach you to how to approach data analysis problems with classical statistics. We focus on the intuition behind statistical methodologies rather than on "how to run a t-test with R" (which we will also learn, by the way). First we review the foundations […]

Advanced plotting with ggplot in R The aim of this course is to teach you how to create informative scientific plots using the ggplot package in R. Instructor: András Aszódi. Topics Fundamentals of scientific visualisation. Introduction to ggplot. Data preparation, plot creation workflow. ggplot details: bars, boxes, densities, function plots, faceting. Prerequisites Basic familiarity with […]

Linear regression with R The aim of this course is to teach you how to analyse your data using simple linear models. We focus on linear regression in order to improve your generic statistics knowledge. Please note that we cannot go into the specific data analysis problems of your particular project. Instructor: András Aszódi. Topics […]

ANOVA with R Picture source: Fig.2 from Harp et al, eClinical Medicine Volume 39, September 2021, 101045 DOI: https://doi.org/10.1016/j.eclinm.2021.101045. The aim of this short course is to explain why we have to analyse variances if we want to compare group means using ANOVA techniques. This lecture is best taken with the Linear regression with R […]

Generalised linear models In our experiments we often count how many times something happened: how many mice died, how many people got infected by a pathogen etc. You need "counting statistics" techniques to analyse these data. This course teaches you one such technique, "generalised linear models" (GLMs). Please note that we cannot go into the […]

Nonlinear regression with R The aim of this course is to teach you how to analyse your data if there are complicated relationships between the variables. Please note that we cannot go into the specific data analysis problems of your particular project. Instructor: András Aszódi. Topics How to fit a PCR curve? Simple nonlinear regression. […]

Bayesian Statistics with R The Bayesian framework represents an alternative approach to statistical modelling. Bayesian analysis describes how to update our initially incomplete "prior" knowledge with experimental observations (the "evidence") to obtain a better understanding (the "posterior") of the phenomenon studied. This concept fits very well the way how scientific research works. In addition, Bayesian […]