scOrange—a tool for hands-on training of concepts from single-cell data analytics

Abstract

Motivation Single-cell RNA sequencing allows us to simultaneously profile the transcriptomes of thousands of cells and to indulge in exploring cell diversity, development and discovery of new molecular mechanisms. Analysis of scRNA data involves a combination of non-trivial steps from statistics, data visualization, bioinformatics and machine learning. Training molecular biologists in single-cell data analysis and empowering them to review and analyze their data can be challenging, both because of the complexity of the methods and the steep learning curve. Results We propose a workshop-style training in single-cell data analytics that relies on an explorative data analysis toolbox and a hands-on teaching style. The training relies on scOrange, a newly developed extension of a data mining framework that features workflow design through visual programming and interactive visualizations. Workshops with scOrange can proceed much faster than similar training methods that rely on computer programming and analysis through scripting in R or Python, allowing the trainer to cover more ground in the same time-frame. We here review the design principles of the scOrange toolbox that support such workshops and propose a syllabus for the course. We also provide examples of data analysis workflows that instructors can use during the training. Availability and implementation scOrange is an open-source software. The software, documentation and an emerging set of educational videos are available at http://singlecell.biolab.si.

Quasar: Easy Machine Learning for Biospectroscopy

“…Additionally, Orange provides a well-documented programming interface for adding new components and modules. More than a dozen specialized modules currently exist, including text processing, gene expression data analysis [ 15 ], image analytics [ 14 ], and time series analysis.…”

Interactive analysis of single-cell epigenomic landscapes with ChromSCape

“…In addition, two softwares with graphic interface, dedicated to the analysis of single-cell data, “scOrange” 13 and “SCRAT” 14 , were also tested on the same set of cells both with “default” parameters and manually “optimized” parameters. default’ for scOrange corresponds to using the template called “Loading data from 10× protocols”, a workflow meant for analyzing scRNA-seq of bone marrow cells, replacing the input by our matrices of selected cells in 50kbp bins.…”

“…These tools, initially dedicated to scATAC-seq and without graphic interface, require some scripting skills. Biologists with limited computational training can manipulate and analyze scRNA-seq and scATAC-seq datasets using applications such as “scOrange” 13 and ‘“SCRAT” 14 .…”

Embedding to Reference t-SNE Space Addresses Batch Effects in Single-Cell Classification

“…into Orange, an interactive, data-exploration environment [43,44]. Orange is based on workflows, where the user can build complex, data-analysis pipelines using combinations of simpler components, called widgets.…”