SCtype

ScType is "a fully-automated and ultra-fast cell-type identification based solely on a given scRNA-seq data, along with a comprehensive cell marker database as background information."[1]. It allows accurate cell typing in scRNAseq and spatial transcriptome data[2].

The task can be called from any non-normalised counts node, in the Classification menu on the right.

• Click on the Counts node.

• Click on Classification> ScType cell classification in the toolbox.

• Select the marker database from the SC Type database drop-down

• Select the marker database from the drop-down, the original ScType database is provided by default.

• Use the checkboxes to the select the appropriate tissues.

• The task uses an existing attribute for the classification, select the node contains the attribute to use for cell clustering. Choose the specific attribute from the Choose attribute drop-down list.

• Click on Configure in Advanced option to modify the advance parameter

• Select the significance threshold in the Advanced options. This number indicates the fraction of cells, higher numbers indicate a less strict significance threshold (see paper for method details).

• The task allows for selection of positive and/or negative biomarkers, in the Advanced options.

• Click Finish to run the task.

The task outputs an annotation for each cell in the dataset. If a cell does not pass the significance threshold it will be classified as N/A. The annotation is saved as 'sctype', and is available from the node. You can use Annotation/Metadata>Publish cell attributes to project in the toolbox to make the attribute available at the Analysis level.

References

[1] Ianevski, A., Giri, A. K., & Aittokallio, T. (2022). Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data. Nature communications, 13(1), 1246.

[2] Nader, K., Tasci, M., Ianevski, A., Erickson, A., Verschuren, E. W., Aittokallio, T., & Miihkinen, M. (2024). ScType enables fast and accurate cell type identification from spatial transcriptomics data. Bioinformatics, 40(7), btae426.