Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1093/bioinformatics/btz348 | BIOINFORMATICS |
Field | DocType | Volume |
Data science,Data mining,Data analysis,Computer science | Journal | 35 |
Issue | ISSN | Citations |
14 | 1367-4803 | 0 |
PageRank | References | Authors |
0.34 | 0 | 13 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Stražar | 1 | 19 | 2.49 |
| Lan Zagar | 2 | 93 | 5.39 |
| Jaka Kokosar | 3 | 0 | 0.34 |
| Vesna Tanko | 4 | 0 | 0.34 |
| Ales Erjavec | 5 | 87 | 4.79 |
| Pavlin G. Policar | 6 | 0 | 0.68 |
| Anze Staric | 7 | 0 | 0.34 |
| Janez Demšar | 8 | 4276 | 156.75 |
| Gad Shaulsky | 9 | 120 | 11.98 |
| Vilas Menon | 10 | 14 | 2.15 |
| Andrew Lemire | 11 | 0 | 0.34 |
| Anup Parikh | 12 | 10 | 1.17 |
| Blaz Zupan | 13 | 32 | 2.83 |