> For the complete documentation index, see [llms.txt](https://help.connected.illumina.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.connected.illumina.com/dragen-stratamap/temporary.md).

# Temporary

#### 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."\[2]. It allows accurate cell typing in scRNAseq and spatial transcriptome data \[3].

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 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 and the attribute from the drop-down, by default a Leiden clustering node will be selected (if it exists).
* 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.

<figure><img src="/files/gmlhP684SmDHMdnUBGxI" alt=""><figcaption><p>ScType task setup</p></figcaption></figure>

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**

\[2] 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.

\[3] 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.


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