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Hello,
Thank you so much for these wonderful workshops.
I am trying to use your pipeline and have the question:
In "seurat_01_qc", before predicting the doublet, we need to run NormalizeData, FindVariableFeatures and ScaleData,
in "seurat_02_dim_reduction", we run FindVariableFeatures and ScaleData before the reduction of dimension,
in "seurat_03_integration", before the integration, we run again NormalizeData, FindVariableFeatures and after the integration, ScaleData.
Until now, I thought it was sufficient to run NormalizeData, FindVariableFeatures and ScaleData only once after QC and data filtering, but it seems that my conclusion was not correct.
If we use SCTransform for normalization, should we run it before doublet prediction, before dimension reduction, and before integration as well?
I would also like to know if there is any rules for choosing the appropriate slot of Seurat object for each analysis?
Thank you so much.
The text was updated successfully, but these errors were encountered:
Hello,
Thank you so much for these wonderful workshops.
I am trying to use your pipeline and have the question:
In "seurat_01_qc", before predicting the doublet, we need to run
NormalizeData
,FindVariableFeatures
andScaleData
,in "seurat_02_dim_reduction", we run
FindVariableFeatures
andScaleData
before the reduction of dimension,in "seurat_03_integration", before the integration, we run again
NormalizeData
,FindVariableFeatures
and after the integration,ScaleData
.Until now, I thought it was sufficient to run
NormalizeData, FindVariableFeatures and ScaleData
only once after QC and data filtering, but it seems that my conclusion was not correct.If we use
SCTransform
for normalization, should we run it before doublet prediction, before dimension reduction, and before integration as well?I would also like to know if there is any rules for choosing the appropriate slot of Seurat object for each analysis?
Thank you so much.
The text was updated successfully, but these errors were encountered: