Analysis_3DProMet.Rmd

---
title: "Analysis_3Dpromet"
author: "Alexis Hucteau"
date: "27/09/2021"
output: html_document
---

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

library(igraph)
library(dplyr)
library(stringr)
library(DataCombine)
library(VennDiagram)
library(ggvenn)
library(vioplot)
library(pheatmap)
"%ni%" <- Negate("%in%")
```

# Loading signatures

```{r}
TIL10_sig <- read.csv("TIL10_signature.csv")$GeneSymbol
MCP_counter_sig <- read.csv("MCPcounter_signature.csv")$HUGO.symbols
xCells_sig <- read.csv("xCells_signature.csv", check.names = F)[,c(3:202)] %>% c() %>% unlist() %>% unique()
BPRNA <- read.csv("BPRNA.csv", header = F)$V1
BPRNACan <- read.csv("BPRNACan.csv", header = F)$V1
BPRNACan3DProMet <- read.csv("Signatures.csv")$BPRNACan3DProMet %>% unique() %>% .[!.==""]

```

# Compare signatures

```{r}
Diag1 <- list(
  "BPRNA" = BPRNA,
  "BPRNACan3DProMet" = BPRNACan3DProMet,
  "BPRNACan" = BPRNACan
)

Diag2 <- list(
  "TIL10" = TIL10_sig,
  "BPRNACan3DProMet" = BPRNACan3DProMet,
  "BPRNACan" = BPRNACan,
  "MCP" = MCP_counter_sig
)

ggvenn(Diag1, 
       show_percentage = F,
       set_name_size = 4)
ggsave("test.png")

```




```{r}
ggvenn(Diag2,
       show_percentage = F,
       set_name_size = 4)
ggsave("test.png")
```

```{r}
Cibersort_HNSCC_sig <- read.csv("CIBERSORTx_HNSCC_sig.txt", sep = "\t")$NAME
Cibersort_melanoma_sig <- read.csv("CIBERSORTx_melanoma_Sig.txt", sep = "\t")$NAME
Cibersort_NSCL_sig <- read.csv("Fig2ab-NSCLC_PBMCs_scRNAseq_sigmatrix.txt", sep = "\t")$NAME
```


```{r}
Diag_final <- list(
  "Melanoma" = Cibersort_melanoma_sig,
  "BPRNACan3DProMet" = BPRNACan3DProMet,
  "BPRNACan" = BPRNACan,
  "HNSCC" = Cibersort_HNSCC_sig
)

ggvenn(Diag_final,
       show_percentage = F,
       set_name_size = 4)
ggsave("Diag4.png")
```


# 40 new genes

```{r}
Totally_new_genes <- BPRNACan3DProMet[BPRNACan3DProMet %ni% Cibersort_melanoma_sig] %>% .[. %ni% Cibersort_HNSCC_sig] %>% .[. %ni% BPRNACan]

BPRNACan3DProMet_genes_expr <- read.csv("BPRNACan3DProMet.csv", row.names = 1)

Totally_new_genes_expr <- BPRNACan3DProMet_genes_expr[Totally_new_genes,]

vioplot(Totally_new_genes_expr,
        col = 1:ncol(Totally_new_genes_expr), 
        outlier = F,
        ylim = c(0, 2000))

png("test_heatmap.png")
pheatmap(as.matrix(Totally_new_genes_expr),
         scale = "row")
dev.off()
```


```{r}
BPRNACan_expr <- BPRNACan3DProMet_genes_expr[rownames(BPRNACan3DProMet_genes_expr) %in% BPRNACan,]

png("test_heatmap_BPRNACan.png")
pheatmap(as.matrix(BPRNACan_expr),
         scale = "row", 
         cluster_rows =  F, show_rownames = F
         )
dev.off()
```

# Signatures

```{r}
load("pchic.RData")

```


```{r}
pchic <- tidyr::separate_rows(pchic, "baitName", sep = ";") %>% unique()

dplyr::filter(pchic, pchic$baitName %in% M2_unique) %>% .[,c(4,5,9,10)] %>% write.csv(., "~/tmp/M2.csv", quote = F, row.names = F)
```

```{r}
test <- sample(unique(pchic$baitName), size = 93)
dplyr::filter(pchic, pchic$baitName %in% test) %>% .[,c(4,5,9,10)] %>% write.csv(., "~/tmp/test.csv", quote = F, row.names = F)
```

```{r}
library(igraph)
```

```{r}
Signatures <- list()
files <- list.files("signatures/")
for (i in files){
  Signatures[[i]] <- read.csv(paste0("signatures/", i), sep = "\t") %>% .[,1]
}

Other_signatures <- c(Signatures[["BPRNACan.txt"]], 
                      Signatures[["CBSX_LM22.txt"]], 
                      Signatures[["CBSX_melanoma.txt"]], 
                      Signatures[["CBSX_NSCLC.txt"]], 
                      Signatures[["quanTIseq_TIL10.txt"]]) %>% unique()

Totally_new_genes <- Signatures[["BPRNACan3DProMet.txt"]] %>% .[. %ni% Other_signatures]

BPRNACan3DProMet_genes_expr <- read.csv("BPRNACan3DProMet.csv", row.names = 1)

Totally_new_genes_expr <- BPRNACan3DProMet_genes_expr[Totally_new_genes,]

vioplot(Totally_new_genes_expr,
        col = 1:ncol(Totally_new_genes_expr), 
        outlier = F,
        ylim = c(0, 2000))

png("test_heatmap.png")
pheatmap(as.matrix(Totally_new_genes_expr),
         scale = "row", fontsize_row = 5)
dev.off()
pchic <- tidyr::separate_rows(pchic, "baitName", sep = ";") %>% unique()
pchic <- tidyr::separate_rows(pchic, "oeName", sep = ";") %>% unique()

dplyr::filter(pchic, pchic$baitName %in% Totally_new_genes | pchic$oeName %in% Totally_new_genes) %>% .[,c(4,5,9,10)] %>% write.csv(., "~/tmp/fig2.csv", quote = F, row.names = F)

write.csv(Totally_new_genes, "~/tmp/fi2_genes.csv", quote = F)
```