Abstract
BackgroundThe vast ecosystem of single-cell RNA-sequencing tools has until recently been plagued by an excess of diverging analysis strategies, inconsistent file formats, and compatibility issues between different 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.ResultsHere we outline several Galaxy workflows and learning resources for single-cell RNA-sequencing, 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, workflows, and trainings that not only enable users to perform 1-click 10x preprocessing but also empower them to demultiplex raw sequencing from custom tagged and full-length sequencing protocols. The downstream analysis supports a range of high-quality interoperable suites separated into common stages of analysis: inspection, filtering, normalization, confounder removal, and clustering. The teaching resources cover concepts from computer science to cell biology. Access to all resources is provided at the singlecell.usegalaxy.eu portal.ConclusionsThe reproducible and training-oriented Galaxy framework provides a sustainable high-performance computing environment for users to run flexible analyses on both 10x and alternative platforms. 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 single-cell RNA-sequencing analysis.
Highlights
The vast ecosystem of single-cell RNA-sequencing tools has until recently been plagued by an excess of diverging analysis strategies, inconsistent file formats, and compatibility issues between different software suites
Here we outline several Galaxy workflows and learning resources for single-cell RNA-sequencing, 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
Single-cell Galaxy workshops based on these materials have been given at the Single-Reproducible cloud-based analysis
Summary
The continuing rise in single-cell technologies has led to previously unprecedented levels of analysis into cell heterogeneity within tissue samples, providing new insights into developmental and differentiation pathways for a wide range of disciplines. The burgeoning software ecosystem Since its conception, several different packages and many pipelines have been developed to assist researchers in the analysis of single-cell RNA sequencing (scRNA-seq) [5, 6] Most of these packages were written for the R programming language because many of the novel normalization methods developed to handle the dropout events depended on statistical packages that were primarily R-based [7]. A crucial quality control step upstream, such as filtering or the removal of unwanted variability, can propagate forward into the downstream sections to yield wildly different results on the same data This uncertainty, and the statistically driven methods to overcome it, leaves a wide knowledge gap for researchers trying to understand the underlying dynamics of cell identity. The GTN has grown rapidly since its conception and gains new volunteers every year, who each contribute and coordinate training and teaching events, maintain topics and subtopics, translate tutorials into multiple languages, and provide peer review on new material [20]
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