Statistics and machine learning

Statistical Analyses for omics data and machine learning using Galaxy tools

You can view the tutorial materials in different languages by clicking the dropdown icon next to the slides () and tutorial () buttons below.

Requirements

Before diving into this topic, we recommend you to have a look at:

Material

Lesson Slides Hands-on Recordings Input dataset Workflows
A Docker-based interactive Jupyterlab powered by GPU for artificial intelligence in Galaxy
interactive-tools machine-learning deep-learning jupyter-lab image-segmentation protein-3D-structure
Age prediction using machine learning
Basics of machine learning
Classification in Machine Learning
Clustering in Machine Learning
Deep Learning (Part 1) - Feedforward neural networks (FNN)
Deep Learning (Part 2) - Recurrent neural networks (RNN)
Deep Learning (Part 3) - Convolutional neural networks (CNN)
Image classification in Galaxy with fruit 360 dataset
Interval-Wise Testing for omics data
Introduction to Machine Learning using R
interactive-tools
Introduction to deep learning
Machine learning: classification and regression
PAPAA PI3K_OG: PanCancer Aberrant Pathway Activity Analysis
Machine learning Pan-cancer cancer biomarkers oncogenes and tumor suppressor genes
Regression in Machine Learning
Supervised Learning with Hyperdimensional Computing
Text-mining with the SimText toolset
interactive-tools

Galaxy instances

You can use a public Galaxy instance which has been tested for the availability of the used tools. They are listed along with the tutorials above.

You can also use the following Docker image for these tutorials:

docker run -p 8080:80 quay.io/galaxy/statistics-training

NOTE: Use the -d flag at the end of the command if you want to automatically download all the data-libraries into the container.

It will launch a flavored Galaxy instance available on http://localhost:8080. This instance will contain all the tools and workflows to follow the tutorials in this topic. Login as admin with password password to access everything.

Frequently Asked Questions

Common questions regarding this topic have been collected on a dedicated FAQ page . Common questions related to specific tutorials can be accessed from the tutorials themselves.

Editorial Board

This material is reviewed by our Editorial Board:

AvatarMarzia A Cremonaorcid logoAvatarFabio CumboAvatarAnup Kumar

For any question related to this topic and the content, you can contact them or visit our Gitter channel.

Contributors

This material was contributed to by:

AvatarSimon BrayAvatarKaivan Kamaliorcid logoAvatarMarie GrammAvatarDennis Lal grouporcid logoAvatarBérénice Batutorcid logoAvatarAlireza Khanteymooriorcid logoAvatarFabio CumboAvatarAnup KumarAvatarVijayAvatarEkaterina PolkhAvatarMarzia A Cremonaorcid logoAvatarFotis E. Psomopoulosorcid logoAvatarDaniel Blankenberg

Funders

This material was funded by:

AvatarErasmus+ Programme