A single-cell RNA-seq Training and Analysis Suite using the Galaxy Framework

Abstract

Background The vast ecosystem of single-cell RNA-seq tools has until recently been plagued by an excess of diverging analysis strategies, inconsistent le formats, and compatibility issues between di erent software suites. The uptake of 10x Genomics datasets has begun to calm this diversity, and the bioinformatics community leans once more towards the large computing requirements and the statistically-driven methods needed to process and understand these ever-growing datasets. Results Here we outline several Galaxy work ows and learning resources for scRNA-seq, with the aim of providing a comprehensive analysis environment paired with a thorough user learning experience that bridges the knowledge gap between the computational methods and the underlying cell biology. The Galaxy reproducible bioinformatics framework provides tools, work ows and trainings that not only enable users to perform one-click 10x preprocessing, but also empowers them to demultiplex raw sequencing data manually. The downstream analysis supports a wide range of high-quality interoperable suites separated into common stages of analysis: inspection, ltering, normalization, confounder removal and clustering. The teaching resources cover an assortment of di erent concepts from computer science to cell biology. Access to all resources is provided at the singlecell.usegalaxy.eu portal. Conclusions The reproducible and training-oriented Galaxy framework provides a sustainable HPC environment for users to run exible analyses on both 10x and alternatively derived datasets. The tutorials from the Galaxy Training Network along with the frequent training workshops hosted by the Galaxy Community provide a means for users to learn, publish and teach scRNA-seq analysis.

From bench to bedside: Single-cell analysis for cancer immunotherapy

“…Implementing these analysis pipelines can require extensive computer resources and computer programming experience. To make these pipelines more generally accessible, platforms such as Galaxy (Tekman et al, 2020) and GenePattern Notebook (Reich et al, 2017) provide these methods in interactive, user-friendly interfaces for single-cell analysis with direct access to cloud computing. Further improvements in creating user-friendly databases, such as the developing CellxGene platform (Megill et al, 2021), remain an active area of development for the single-cell community.…”

Using Galaxy to Perform Large‐Scale Interactive Data Analyses—An Update

“…However, using the isolated tools described in this article allows an understanding of the flexibility of single cell analyses, and demonstrates the ability of Galaxy to create modular workflows with several tools from disparate sources. The Galaxy Training Network has several other in-depth single cell sequencing tutorials, and a public Galaxy server specializing in single cell analysis is available (Tekman et al, 2020). Please visit the 719 transcriptomics section (https:// training.galaxyproject.org/ trainingmaterial/ topics/ transcriptomics/ ) of the Galaxy Training network for more training in single-cell analysis.…”

Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State-of-the-Practice

“…We highlight three recent DS specialisations of Galaxy which are publicly available online. Vandenbrouck et al developed a Galaxy instance for proteomics research [109], Tekman et al provide one for "single cell omics" [104], and Gu et al offer a ML focus [49]. These specialisations each offer targeted DS tools, workflows, and computational resources, allowing domain experts to quickly develop workflows.…”