Meeta Mistry
Meeta Mistry
This is covered in Arpita's slides
We NormalizeData on the filtered+merged object, and presumably that gets stored in seurat@assays$RNA@data Then before we look at FeaturePlots for each marker gene, we run NormalizeData again. We should look...
The functions NormalizeData, VariableFeatures and ScaleData can be replaced by the function SCTransform. The latter uses a more sophisticated way to perform the normalization and scaling, and is [argued to...
Or we can add a note to justify why we use logNormalize - because it is good to observe the data and any trends using a simple transformation and asses...
added a note
In the past, we have used RNA slot because .... "SCT Pearson residuals themselves are not sparse (contain exclusively non-zero elements), so they take a lot of memory to store....
But now with Seurat 4.0 we can "PrepSCTFindmarkers" so maybe we should we doing this? `FindMarkers(immune.combined.sct, assay = "SCT", ident.1 = "B_STIM", ident.2 = "B_CTRL", verbose = FALSE)`
added a note about why we don't use sct and what the values represent. didn't add anything about prepSCTFindMarkers since we have never used it
Also, currently we link to this page for AnnotationHub: https://github.com/hbctraining/Training-modules/blob/master/DGE-functional-analysis/lessons/AnnotationHub.md I think this should be updated to have code that walks through the same dataset used in-class to demonstrate how...
Can take some content from the slide deck and use that as a base: https://www.dropbox.com/s/eue6fc2maks9x6z/Intro-to-scRNAseq-analysis_HBC_2022.pdf?dl=0