```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

Question #1
============

How many clusters are found in our seurat_integrated object with the following resolutions:

Resolution 0.4:
Resolution 0.6:
Resolution 0.8:
Resolution 1.0:
Resolution 1.4:
Resolution 2.0:


Question #2
============
Given the "known markers" from the lecture:
|            B cells           |         CD79A, MS4A1         |
|            T cells           |             CD3D             |
|         CD4+ T cells         |       CD3D, IL7R, CCR7       |
|         CD8+ T cells         |          CD3D, CD8A          |
|           NK cells           |          GNLY, NKG7          |
|        Megakaryocytes        |             PPBP             |
|         Erythrocytes         |           HBB, HBA2          |

Which cluster numbers correspond to the following cell types

# B cells	
# T cells	
# CD8+ T cells
# NK cells
# Megakaryocytes
# Erythrocytes


Question #3
============

Using the muscData::Kang18_8vs8() from our differential expression section
subset to "Dendritic cells", "FCGR3A+ Monocytes", "CD14+ Monocytes", instead of
"CD4 T cells", "B cells", "NK cells".

Compare `cellCD14+ Monocytes` - `cellFCGR3A+ Monocytes` in the pseudobulk and identify
the number of differentially expressed genes.

Do the same for the non pseudobulk cell counts.

How many genes are differentially expressed in each, why is one higher than the other?