How to use Python and R with RDF Data
This training module will provide researchers with an introduction on how use R and Python in combination with SPARQL to query RDF data.

** Scientific topics: **Medical informatics, FAIR data, Data management, Computer science

** Operations: **Data retrieval, Data handling, Query and retrieval

** Keywords: **Clinical data, SPARQL, Query data, RDF, Knowledge graph, Python, R, GraphDB

** Resource type: **Video, Training materials, Mock data, E-learning

How to use Python and R with RDF Data
https://sphn.ch/training/pythonandr/
http://tess.elixir-uk.org/materials/how-to-use-python-and-r-with-rdf-data
This training module will provide researchers with an introduction on how use R and Python in combination with SPARQL to query RDF data.
Personalized Health Informatics Group
Petar Horki
Sabine Österle
Vasundra Touré
Medical informatics
FAIR data
Data management
Computer science
Clinical data, SPARQL, Query data, RDF, Knowledge graph, Python, R, GraphDB
Research Scientists
Data Managers
Biomedical Researchers
Bioinformaticians
Data Scientists

Statistics with R
The aim of this course is to teach you how to perform basic statistical analysis using R. First we review the foundations (sampling theory, discrete and continuous distributions), then we focus on classical hypothesis testing. This course will improve your generic statistics knowledge....

** Scientific topics: **Statistics and probability

** Keywords: **Biostatistics, R

Statistics with R
https://www.mygoblet.org/training-portal/materials/statistics-r
http://tess.elixir-uk.org/materials/statistics-with-r
The aim of this course is to teach you how to perform basic statistical analysis using R. First we review the foundations (sampling theory, discrete and continuous distributions), then we focus on classical hypothesis testing. This course will improve your generic statistics knowledge.
Topics:
Sampling theory: obtaining information about a population via sampling.
Sample characteristics (location, dispersion, skewness), estimation of the mean, standard error of the mean.
Discrete and continuous probability distributions. Central limit theorem.
Hypothesis testing. Basic principles, one- and two-sided testing, types of errors, power calculations.
"Cookbook of tests": location testing, normality, variance comparisons,
counting statistics, contingency tables, regression tests.
András Aszódi
Statistics and probability
Biostatistics, R
2016-04-21
2017-10-09

Working with Affymetrix CEL files in R
This tutorial shows how to download some public Affymetrix microarray data, load the data into R, calculate expression values and do some very simple plotting

** Keywords: **Affymetrix, Microarrays, R

Working with Affymetrix CEL files in R
https://www.mygoblet.org/training-portal/materials/working-affymetrix-cel-files-r
http://tess.elixir-uk.org/materials/working-with-affymetrix-cel-files-in-r
This tutorial shows how to download some public Affymetrix microarray data, load the data into R, calculate expression values and do some very simple plotting
Mick Watson
Affymetrix, Microarrays, R
Beginners
PhD students
Researchers
2014-01-14
2017-10-09

Simple plotting in R
This quick and simple tutorial demonstrates some of the easy plotting tools in the R core software.
the data are in the "data" directory and come from an old two-colour microarray experiment where spots on the array were printed with different buffers, in different concentrations and with...

** Keywords: **Bioinformatics, Microarray data analysis, Plotting data, R, Visualisation

Simple plotting in R
https://www.mygoblet.org/training-portal/materials/simple-plotting-r
http://tess.elixir-uk.org/materials/simple-plotting-in-r
This quick and simple tutorial demonstrates some of the easy plotting tools in the R core software.
the data are in the "data" directory and come from an old two-colour microarray experiment where spots on the array were printed with different buffers, in different concentrations and with different pins. Also, both channels Cy5 and Cy3 were spotted identically, and so the fold ratio for all spots should be 1 - this helps us see bias in the data
Mick Watson
Bioinformatics, Microarray data analysis, Plotting data, R, Visualisation
Beginners
PhD students
Researchers
Scientists
2014-01-14
2017-10-09

Flow Cytometry 2013 Module 2 - Exploring FCM data in R
Loading a single or groups of FCS files into R
flowFrame and flowSet objects and their attributes
Exploring sample annotation and keywords stored within the FCS file format, searching for specic samples (such as controls) using grep, highlighting the importance of correct sample annotation...

** Keywords: **Fcs files, Plotting data, R

Flow Cytometry 2013 Module 2 - Exploring FCM data in R
https://www.mygoblet.org/training-portal/materials/flow-cytometry-2013-module-2-exploring-fcm-data-r
http://tess.elixir-uk.org/materials/flow-cytometry-2013-module-2-exploring-fcm-data-in-r
Loading a single or groups of FCS files into R
flowFrame and flowSet objects and their attributes
Exploring sample annotation and keywords stored within the FCS file format, searching for specic samples (such as controls) using grep, highlighting the importance of correct sample annotation at point of acquisition
Simple dot plots and density plots
Michelle Brazas
Fcs files, Plotting data, R
2013-06-26
2017-10-09