priya_presentation_0730.Rmd

---
title: "Priya Presentation v5 Figures"
author: "D. Ford Hannum Jr."
date: "7/30/2020"
output: 
        html_document:
                toc: true
                toc_depth: 3
                number_sections: false
                theme: united
                highlight: tango
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, message = FALSE)
library(Seurat)
library(ggplot2)
library(data.table)
library(MAST)
```

```{r loading data}
wbm <- readRDS('./data/wbm_clustered_filtered_named.rds')
#DimPlot(wbm, reduction = 'umap', label = T, repel = T) + NoLegend()
```

```{r changing the levels of the data}
new_levels <- c('Granulocyte','Granulocyte','Granulocyte', 'B-cell','Progenitor', 
        'Granulocyte', 'Granulocyte','Monocyte','Macrophage','Erythroid','B-cell',
        'T-cell/NK', 'MK')
names(new_levels)  <- levels(wbm)
#new_levels
wbm <- RenameIdents(wbm, new_levels)
```

The fifth version of the figures.

# Figure 3

## a) UMAP Projection of Data

A UMAP projection of the data, split into four panes for the four different conditions (Migr1/Mpl, WBM/enrWBM).

```{r 3a}

color_pal <- c("#0072B2", "#CC79A7", "#009E73", "#56B4E9","#D55E00",
               "#E69F00","#999999", "black")

wbm@meta.data$state <- factor(wbm@meta.data$state, levels = levels(as.factor(wbm@meta.data$state))[c(3,4,1,2)])

# DimPlot(wbm, reduction = 'umap', split.by = 'state', cols = color_pal, ncol = 2) +
#         theme_bw() + 
#         theme(axis.text = element_blank(), strip.text = element_blank(),
#               text = element_text(size = 10, family = 'sans'))

wbm$identity <- ifelse(wbm$condition == 'mut', 'Mpl', 'Migr1')

DimPlot(wbm, reduction = 'umap', split.by = 'identity', 
        group.by = 'seurat_clusters', pt.size = .01) + 
        theme_bw() +
        theme(text = element_text(size = 10, family = 'sans'))

DimPlot(wbm, reduction = 'umap', split.by = 'identity', 
        cols = color_pal, ncol = 2, pt.size = .01) +
        theme_bw() +
        theme(text = element_text(size = 10, family = 'sans'))

DimPlot(wbm, reduction = 'umap', split.by = 'state')
```

**Figure Legend** a) A UMAP projection of the data, split into four panes for the different experimental states.

## b) Bar Quantification of Cell Counts

A bar graph for each condition (Migr1/Mpl) and the distribution of cells within each cluster

### Looking at only WBM cells

```{r 3b setup}
wbm$new_cluster_IDs <- wbm@meta.data$cluster_IDs
levels(wbm@meta.data$new_cluster_IDs) <- new_levels
wbm_wbm <- wbm@meta.data[wbm@meta.data$celltype == 'WBM',]

tbl <- as.data.frame(table(wbm_wbm$condition, wbm_wbm$new_cluster_IDs))
colnames(tbl) <- c('condition','cell_type','count')
tbl$condition_count <- ifelse(tbl$condition == 'control', sum(tbl[tbl$condition == 'control',]$count),
                              sum(tbl[tbl$condition == 'mut',]$count))
tbl$perc <- round(tbl$count/tbl$condition_count,4)*100
#tbl

comb_counts <- c()
cnt <- 1
for (i in levels(tbl$cell_type)){
        #print(i)
        comb_counts[cnt] <- sum(tbl[tbl$cell_type == i,]$perc)
        cnt <- cnt + 1
}
tbl$comb_perc <- rep(comb_counts, each = 2)
tbl <- tbl[order(tbl$comb_perc, decreasing = T),]
tbl$condition <- ifelse(tbl$condition == 'control', 'Control','Mpl')
tbl$`Cell Type` <- tbl$cell_type
#tbl
```

```{r 3b}
tbl$Condition <- ifelse(tbl$condition == 'Mpl', 'Mpl', 'Migr1')
ggplot(data = tbl, aes(x = Condition, y = perc, fill = `Cell Type`)) +
        geom_bar(stat = 'identity') + 
        ylim(0,100) +
        scale_fill_manual(values = color_pal) +
        theme_bw() +
        ylab('Percentage of Cells') + xlab('Condition') +
        NoLegend()+
        scale_y_continuous(position = 'right') +
        theme(text = element_text(size = 10, family = 'sans')) 

```

**Figure Legend** b) Percentage o cells distributed to each cluster for the two conditions (Migr1, Mpl). There is a clear increase in the percentage of cells coming from the granulocyte cluster (blue) in Mpl (90%) compared to Migr1 (42%). There is also a decrease in the number of B-cells (pink) in Migr1 (37%) to Mpl (0.5%), and T-cells/NKs in Migr1 (6%) to Mpl (0.4%).

### Looking at WBM and enriched WBM


```{r 3b-2 setup}
wbm$new_cluster_IDs <- wbm@meta.data$cluster_IDs
levels(wbm@meta.data$new_cluster_IDs) <- new_levels
wbm_wbm <- wbm@meta.data
tbl <- as.data.frame(table(wbm_wbm$condition, wbm_wbm$new_cluster_IDs))
colnames(tbl) <- c('condition','cell_type','count')
tbl$condition_count <- ifelse(tbl$condition == 'control', sum(tbl[tbl$condition == 'control',]$count),
                              sum(tbl[tbl$condition == 'mut',]$count))
tbl$perc <- round(tbl$count/tbl$condition_count,4)*100
#tbl

comb_counts <- c()
cnt <- 1
for (i in levels(tbl$cell_type)){
        #print(i)
        comb_counts[cnt] <- sum(tbl[tbl$cell_type == i,]$perc)
        cnt <- cnt + 1
}
tbl$comb_perc <- rep(comb_counts, each = 2)
tbl <- tbl[order(tbl$comb_perc, decreasing = T),]
tbl$condition <- ifelse(tbl$condition == 'control', 'Control','Mpl')
tbl$`Cell Type` <- tbl$cell_type
#tbl
```

```{r 3b-2}
tbl$Condition <- ifelse(tbl$condition == 'Mpl', 'Mpl', 'Migr1')
ggplot(data = tbl, aes(x = Condition, y = perc, fill = `Cell Type`)) +
        geom_bar(stat = 'identity') + 
        ylim(0,100) +
        scale_fill_manual(values = color_pal) +
        theme_bw() +
        ylab('Percentage of Cells') + xlab('Condition') +
        NoLegend()+
        scale_y_continuous(position = 'right') +
        theme(text = element_text(size = 10, family = 'sans')) 

```

# Marker Genes

```{r marker dot plot}
marker.genes <- c('Itga2b','Vwf','Gata3','Alas2','Vcam1','Cd68','Gata2','Ighd', 'Ngp')

DotPlot(wbm, features = marker.genes) +
        ylab ('Cell Cluster') + xlab ('Marker Genes') +
        theme(text = element_text(size = 10, family = 'sans'),
              axis.text.x = element_text(angle = 45, vjust = .5, size = 10, family = 'sans'),
              axis.text.y = element_text(family = 'sans', size = 10),
              axis.title = element_text(family = 'sans', size = 12))

VlnPlot(wbm, marker.genes[1:3], pt.size = 0)
VlnPlot(wbm, marker.genes[4:6], pt.size = 0)
VlnPlot(wbm, marker.genes[7:9], pt.size = 0)
```


# Extended Markers

## MEP markers

```{r mep markers}
mep.genes <- c('Gata1','Gata2','Runx1','Tal1')

VlnPlot(wbm, mep.genes, pt.size = 0)
```
## Kit

```{r wbm kit}

VlnPlot(wbm, features = 'Kit', split.by = 'seurat_clusters', pt.size = 0)

```


# Subclustering of Progenitor Cluster

```{r prog subclustering}
prog <- readRDS('./data/MK-MEP_subclustering.rds')

DimPlot(prog, reduction = 'umap')

DimPlot(prog, reduction = 'umap', split.by = 'condition')
```

## Markers within Progenitor Subclusters



```{r markers within progenitors}
genes <- c('Mpo', 'Hba-a2', 'Car2', 'Ank1','Gata2', "Gp9")

VlnPlot(prog, genes, pt.size = 0)
```