sCAFfold

Guiding trough sCAFfold

Get guided trough the different features of the query, visualization tool and the omics integration.

Knowledgebase Search

The Knowledgebase Search allows you to identify publications and experiments that match your selected parameters. You can customize the view by adding or modifying tabs, making it easy to focus on the experimental features most relevant to your question. Each publication contains expandable experiment entries, giving you access to the full set of curated parameters.

How to use (step by step)

  1. Open the tool and select the parameters you want to include. You can also exclude conditions using the OR/NOT options.
  2. Run the search to retrieve all publications matching your defined criteria.
  3. Add or adjust tabs to display the parameters you want to inspect directly.
  4. Click the PMID number to open the publication.
  5. Expand the publication using the icon to view all associated experiments.
  6. Expand individual experiments to explore the complete set of curated experimental parameters.

Data Visualization

The Data Visualization tool provides an interactive way to explore the knowledgebase. After applying your filters, you can generate dynamic plots for a predefined set of parameters and examine trends, distributions, and relationships across the dataset.

How to use (step by step)

  1. Open the tool and build your filter by selecting parameters of interest, or start by exploring the full knowledgebase.
  2. Click Visualize to open the interactive visualization interface.
  3. Refine or expand your active filters using the left hand panel.
  4. Select the parameter you want to visualize (right dropdown) and choose your preferred plot type (left dropdown).
  5. Hover over the plot to view exact values.
  6. Click the search icon query_logo on the right to return to the Knowledgebase Search with your current filters applied.

Omics Exploration

(Under construction)

Reference

sCAFfold icon

Felix De Vuyst, sCAFfold Consortium, Olivier De Wever (2026). sCAFfold: A systematic framework for open learning and discovery of cancer-associated fibroblast experimental practices.
(in preparation)