The “ELIXIR Software Deployment Group” is one of the six working groups of the ELIXIR tools platform. It has been recently formed to support bioinformatics software deployment efforts including packages and containers. At the moment this group is sustained by ELIXIR Nodes sharing a common interest to work together on a harmonised strategy.
This course provides a practical guide to producing figures for use in reports and publications.
It is a wide ranging course which looks at how to design figures to clearly and fairly represent your data, the practical aspects of graph creation, the allowable manipulation of bitmap images and compositing and editing of final figures.
The course will use a number of different open source software packages and is illustrated with a number of example figures adapted from common analysis tools.
Within the framework of ELIXIR Belgium, this Python for data processing course is organised together with Geert Jan Bex, KU Leuven/UHasselt. Date: 5-6 October 2017 Goal: Learning how to use Python for data processing Solve real-world problems in the area…
The post Python for Data Processing appeared first on Dutch Techcentre for Life Sciences.
This course is aimed at life scientists who are working in the field of metagenomics, in the early stages of their data analysis, and who already have some prior experience in using bioinformatics in their research.
This hands-on course introduces the participants to single-cell RNA-seq data analysis concepts and tools. It consists of two alternative days, please select based on your computational background EITHER 1) 28.9 Single cell RNA-seq data analysis with R (previous experience with R required), OR 2) 29.9 Single cell RNA-seq data analysis with Chipster (suitable for everybody)
Machine learning gives computers the ability to learn without being explicitly programmed. It encompasses a broad range of approaches to data analysis with applicability across the biological sciences. Lectures will introduce commonly used algorithms and provide insight into their theoretical underpinnings. In the practicals students will apply these algorithms to real biological data-sets using the R language and environment.