02_immune_cons.Rmd

---
title: "Cluster Analysis in Integrated Data"
author: "hongc2@ccf.org"
date: "1/16/2020"
output: html_document
---

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

### Setup
Let us setup the experiment by loading the previous R environment files and R libraries if not exist.
```{r load.rlib}
library(data.table)
library(ggplot2)
library(Seurat)

load('out/01_immune_combined.rd') #immune.combined
```

### Identify conserved cell type markers
To identify canonical cell type marker genes that are conserved across conditions, we provide the `FindConservedMarkers` function. This function performs differential gene expression testing for each dataset/group and combines the p-values using meta-analysis methods from the `MetaDE` R package. For example, we can calculate the genes that are conserved markers irrespective of stimulation condition in a cluster labeled by 'NK' cells.

```{r findConserved}
DefaultAssay(immune.combined) <- "RNA"
Idents(immune.combined) <- "seurat_annotations"

message("This run will take 5+ min ...")
nk.markers <- FindConservedMarkers(immune.combined, ident.1 = "NK", grouping.var = "stim", verbose = FALSE) #default slot: 'data'
head(nk.markers)
```

Furthermore, we can explore the following marker genes for each cell type to verify if the clusters have specific cell types.

```{r 2.featuremap}
marker_genes <- c("CD3D", "SELL", "CREM", "CD8A", "GNLY", "CD79A", "FCGR3A", "CCL2", "PPBP")

FeaturePlot(immune.combined, features = marker_genes, min.cutoff = "q9")
```

The `DotPlot` function with the `split.by` can be useful for viewing conserved cell type markers across conditions, showing both the expression level and the percentage of cells in a cluster expressing any given gene. Here we plot 2-3 strong marker genes for each of our 13 clusters.

```{r 2.dotplot}

markers.to.plot <- c("CD3D", "CREM", "HSPH1", "SELL", "GIMAP5", "CACYBP", "GNLY", "NKG7", "CCL5", "CD8A", "MS4A1", "CD79A", "MIR155HG", "NME1", "FCGR3A", "VMO1", "CCL2", "S100A9", "HLA-DQA1", "GPR183", "PPBP", "GNG11", "HBA2", "HBB", "TSPAN13", "IL3RA", "IGJ")

DotPlot(immune.combined,
        features = rev(markers.to.plot), 
        cols = c("blue", "red"), 
        dot.scale = 8, 
        split.by = "stim") + RotatedAxis()
```

Let us save the R variable so that we can continue to work.
```{r save.ce_integ.rd}
save(immune.combined, file = 'out/02_immune_cons.rd',compress = TRUE)
```

### At this point, you should know
- When conserved gene are useful?
- In Seurat object,
    - What is assay?
    - What is slot?
    - Why there are multiple slots?