for_revision.Rmd

---
title: "Additional analysis for the revision"
output:
    html_document:
      toc: true
      toc_float: true 
      theme: united
---
Xiyu Peng

```{r setup, include=FALSE}
library(knitr)
knitr::opts_chunk$set(echo = FALSE)
knitr::opts_knit$set(root.dir= normalizePath('..'))
knitr::opts_chunk$set(error = FALSE)
knitr::opts_chunk$set(warning = FALSE, message = FALSE) 
```

```{r}
library(corrplot)
library(tidyverse)
library(pheatmap)
```

* input the data

```{r}
X50_res0.8 <- read.csv("~/flow_cytometry/lda/flow_17162_rerun_cluster_res1.5_and_gating_ratio.csv")
## prepare data matrix
X50_res0.8 %>% select(starts_with("cluster"))->data_matrix
data_matrix<-data_matrix[,1:20]
```

## Hierarchical clustering analysis

* simple hierarchical clustering based on correlation

```{r}
M<-cor(data_matrix)
corrplot(M, method = "circle",order = "AOE")
#hclust on correlation matrix
ComplexHeatmap::pheatmap(M)
M<-cor(data_matrix/rowSums(data_matrix))
ComplexHeatmap::pheatmap(M,heatmap_legend_param = list(title = "Correlation"))
ComplexHeatmap::pheatmap(data_matrix/rowSums(data_matrix),clustering_distance_cols = "correlation")
```

* simple hierarchical clustering based on euclidean distance

```{r}
ComplexHeatmap::pheatmap(data_matrix/rowSums(data_matrix),heatmap_legend_param = list(title = "Abundance"))
```

* simple hierarchical clustering based on euclidean distance (scaled on column)

```{r}
ComplexHeatmap::pheatmap(data_matrix/rowSums(data_matrix),scale = "column")
```

* clustering on different data points
```{r}
ann_colors = list(
  BOR = c(CR = "white",PR = "white", SD = "firebrick",PD = "firebrick"),
  Toxicity = c(N = "white",Y = "darkblue"),
  topics = c(`1` = "#0072B2", `2` = "#009E73",`3` = "#D55E00",Activation = "#0072B2", Naive = "#009E73", Exhaustion = "#D55E00" ),
  Immunotype = c("LAG-" =  "#555599", "LAG+" =  "#66BBBB", PRO = "#DD4444"),
  Group = c(group1 = "#7570B3", group2 = "#E7298A", group3 = "#66A61E",group4 = "#D95F02")
)

```

```{r}
X50_res0.8 %>% select(pt, time, paste0("X.cluster",0:19)) %>% arrange(pt, time)%>%reshape(.,timevar = "time",idvar = "pt",direction = "wide") %>% filter(pt != "17-162-08") ->cluster_freq_wide 
rownames(cluster_freq_wide)<-cluster_freq_wide$pt
pt_meta<-X50_res0.8 %>% select(pt,imtype,tox,BOR) %>% arrange(pt) %>% distinct(pt,.keep_all = TRUE)
colnames(pt_meta)<-c("pt","Immunotype","Toxicity","BOR")
rownames(pt_meta)<-pt_meta$pt
alternative<-colnames(cluster_freq_wide)
alternative<-gsub("A","week0",alternative)
alternative<-gsub("B","week3",alternative)
alternative<-gsub("C","week6",alternative)
alternative<-gsub("X.","",alternative)
colnames(cluster_freq_wide)<-alternative
ComplexHeatmap::pheatmap(cluster_freq_wide[,-1],annotation_row = pt_meta[-6,2:4],annotation_colors = ann_colors,clustering_distance_rows = "correlation",heatmap_legend_param = list(title = "Abundance"))
ComplexHeatmap::pheatmap(cluster_freq_wide[,-1],annotation_row = pt_meta[-6,2:4],annotation_colors = ann_colors,clustering_distance_rows = "correlation",cluster_cols = FALSE)
```


## UMAP based on 27 genes

```{r}
library(Seurat)
pt1716205<-readRDS(file = "C:/Users/pengx1/OneDrive - Memorial Sloan Kettering Cancer Center/Desktop/reports/seurat_objects/seurat_pt17-162-05redo_v2.rds")
Idents(pt1716205)<-"pool_cluster"
cells<-sample(colnames(subset(pt1716205, idents = 0:19)),60000)
DimPlot(pt1716205[,cells],reduction = "umap", label = TRUE)
```


```{r,eval=FALSE}
genes<-rownames(pt1716205)
pt1716205 <- RunUMAP(pt1716205, features = genes,min.dist = 0.3)
pt1716205_newUMAP<-pt1716205
saveRDS(pt1716205_newUMAP,file = "C:/Users/pengx1/OneDrive - Memorial Sloan Kettering Cancer Center/Desktop/seurat_objects/seurat_pt17-162-05redo_v2_newUMAP.rds")
```

```{r}
pt1716205_newUMAP<-readRDS(file = "C:/Users/pengx1/OneDrive - Memorial Sloan Kettering Cancer Center/Desktop/reports/seurat_objects/seurat_pt17-162-05redo_v2_newUMAP.rds")
DimPlot(pt1716205_newUMAP[,cells],reduction = "umap", label = TRUE)
```