Partek Flow Documentation

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SubtitleTextWhen a data node containing a single cell count matrix is selected, SCTransform is available in the toolbox
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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.

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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 (Figure 3). It , 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.   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’ This data node that are used to perform downstream exploratory analysis e.g. PCA, Seurat3 integration etc (Figure 3), this data node is not recommend to use for differential analysis.

SC corrected data: is equivalent to the ‘corrected counts’ that are stored in the data slot of generated after PrepSCTFindMarkers task in the SCT assay for differential in Seurat object. It is used for downstream differential expression(DE) analyses (Figure 3). To ensure the fixed value is set properly,  the ‘PrepSCTFindMarkers’ task has also been run under the hood in Flow. 


Note: 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.


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SubtitleTextDownstream analysis after SCTransform v2 task in Flow.
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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

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