I'm reading up on the Seurat user guide:  https://satijalab.org/seurat/v3.1/pbmc3k_tutorial.html
And they mention for QC utilizing 

**The number of unique genes detected in each cell** and **The total number of molecules detected within a cell** 

They then refer to them as nCount_RNA and nFeature_RNA, but I'm not sure which is which.  So my question is:

1.)  What are the nCount_RNA and what are the nFeature_FNA
2.)  Later in the pipeline, when you're normalizing the data, it says they "normalizes the feature expression measurements for each cell by the total expression."  Can anybody explain that? 
`nFeature_RNA` is **the number of genes detected in each cell**. `nCount_RNA` is **the total number of molecules detected within a cell**. Low `nFeature_RNA` for a cell indicates that it may be dead/dying or an empty droplet. High `nCount_RNA` and/or `nFeature_RNA` indicates that the "cell" may in fact be a doublet (or multiplet). In combination with %mitochondrial reads, removing outliers from these groups removes most doublets/dead cells/empty droplets, hence why filtering is a common pre-processing step.

The `NormalizeData` step is basically just ensuring expression values across cells are on a comparable scale. By default, it will divide counts for each gene by the total counts in the cell, multiply that value for each gene by the `scale.factor` (10,000 by default), and then natural log-transform them.

 