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 in Flow. 

We recommend performing the normalization on a single cell raw count data node. Select SCTransfrom task in Normalization and scaling section on the pop-up menu to invoke the dialog (Figure 1).

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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.

Clip results: If this is set to No, outliers might have big effect and the transformed data can be very large for some features, usually the ones with few non-zero counts. When set to Yes, the range to clip the transformed data is between -sqrt(n/30) and sqrt(n/30), where n is the number of cells.

Random seed: use the same random seed to reproduce the results. 

Data has been log transformed with base: specify the input data is logged or not.

The data in the output node 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 default is unchecked, it uses the original sctransform model (v1).

In addition to the previous output of ‘SC scaled data’ data node that are still used to perform integration, PCA, etc. It is a matrix of normalized values (residuals) that by default has the same size as the input data set.  The range of normalized values is roughly between -4 and 4. SCTransform v2 outputs a second data node named ‘SC corrected data’. ‘SC corrected data’ is equivalent to the ‘corrected counts’ that are stored in the data slot of the SCT assay. To ensure the fixed value is set properly,  the ‘PrepSCTFindMarkers’ task has also been run under the hood in Flow.  When perform DE analysis with Hurdle,  the 'shrinkage of error term variance' option might need to turn off depending on the dataset. Similarly, the 'Lognormal with shrinkage/voom' option needs to turn off when run DE with GSA.

References

1. Christoph Hafemeister, Rahul Satija Normalization 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