# SCTransform

SC transform task performs the variance stabilizing normalization proposed in \[1]. The task's interface follows that of SCTransform() function in R \[2]. SCTransform v2 \[3] provides the ability to perform downstream differential expression analyses besides the improvements on running speed and memory consumption. v2 is the default method.

We recommend performing the normalization on a single cell raw count data node. Select **SCTransform** task in *Normalization and scaling* section on the pop-up menu to invoke the dialog.

<div align="left"><figure><img src="/files/DVShsGGiphKRJE5wzLNM" alt=""><figcaption></figcaption></figure></div>

By default, it will generate report on all the input features. Unchecking the *Report all features*, user can limit the results to a certain number of features with highest variance.

In Advanced options, users can the click **Configure** to change the default settings.

<div align="left"><figure><img src="/files/hiBCxOqopiNrT8RvIDYE" alt=""><figcaption></figcaption></figure></div>

*Scale results:* Whether to scale residuals to have unit variance; default is FALSE

*Center results*: When set to Yes, center all the transformed features to have zero mean expression. Default is TRUE.

*VST v2:* Default is TRUE. When set to 'v2', it sets method = glmGamPoi\_offset, n\_cells=2000, and exclude\_poisson = TRUE which causes the model to learn theta and intercept only besides excluding poisson genes from learning and regularization; If this option is unchecked, it uses the original sctransform model (v1), it will only generate SC scaled data node.

There are two data nodes generated from this task (if *VST v2* option is checked as default):

**SC scaled data:** it is a matrix of normalized values (residuals) that by default has the same size as the input data set. This data node is used to perform downstream exploratory analysis e.g. PCA, Seurat3 integration etc., this data node is not recommend to use for differential analysis.

**SC corrected data:** is equivalent to the ‘corrected counts’ in data slot generated after PrepSCTFindMarkers task in the SCT assay in Seurat object. It is used for downstream differential expression(DE) analyses.

<figure><img src="/files/kxBJFVcg2BmqnwkbCmoT" alt=""><figcaption></figcaption></figure>

References

1. Christoph Hafemeister, Rahul Satija. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. <https://doi.org/10.1101/576827>
2. SCTransform() documentation <https://www.rdocumentation.org/packages/Seurat/versions/3.1.4/topics/SCTransform>
3. Saket Choudhary, Rahul Satija. Comparison and evaluation of statistical error models for scRNA-seq. <https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02584-9>


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