scRNA-seq_online
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hbctraining
Normalization at various steps
I am confused about this, but the idea is whether we are normalizing too much or too little.
We NormalizeData on the filtered+merged object, and presumably that gets stored in seurat@assays$RNA@data
Then before we look at FeaturePlots for each marker gene, we run NormalizeData again. We should look at values currently in data and then see how/if those values change when we run it again. My guess is that it will normalize the counts which are in seurat@assays$RNA@counts and replace what is in data. If so, this step is redundant and not necessary.
Maybe skip the NormalizeData Step altogether, do SCT instead prior to QC, then redo SCT to regress out the covariates.
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 perform better. However, it is slower, and a bit less transparent compared to using the three separate functions.
We should update to SCT
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 the need for anything else