VennDiagrams.Rmd

---
title: "VennDiagrams"
author: "Nikeisha Caruana"
date: "21 February 2017"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
rm(list=ls())
library(VennDiagram)
library(splitstackshape)
library(stringr)
library(dplyr)
```

Venn Diagram for Maxquant vs PROTXML glycoproteins

```{r}
#read in files
glyco_MQ <-  read.csv("raw_data/Glycosylated_MQ.csv", sep='\t', header = FALSE)
glyco_PROT <-  read.csv("raw_data/Glycosylated_PROTXML.csv", sep='\t', header = TRUE)
#split Maxquant to compare proteins
#glyco_MQ <- cSplit(glyco_MQ, "V1", ";","long")
#extract protein IDs
glyco_MQ <- str_extract(glyco_MQ$V1,"[A-Z]*_[A-Z]{2}[0-9]{0,6}_c[0-9]{1,2}_g[0-9]{1}_i[0-9]{1}")

#make venn diagram
vd<-venn.diagram(list(Maxquant=(glyco_MQ),PROTXML=(glyco_PROT$x)),NULL,main="Identified glycosylated proteins",cat.just,
                 fill = c("slateblue2", "turquoise"), fontfamily='sans', cat.fontfamily= "sans", main.fontfamily = "sans",
                 cat.dist = c(0.03, 0.03),cat.pos = c(-14, 14))
grid.draw(vd)

```

Venn Diagram for PNGase F vs non-PNGase F Proteins in MQ

```{r}
#read in data
MQ_data = read.table("raw_data/proteinGroups_PNGaseFSL.txt",sep="\t",header=TRUE, row.names = 6)
iBAQdata = MQ_data[c(68,69,70,71,72,73,74,75)]
iBAQdata = subset(iBAQdata, !(iBAQ.1_160309183003 == 0 & iBAQ.2 == 0 & iBAQ.3 == 0 & iBAQ.4 == 0 & iBAQ.1P == 0 & iBAQ.2P == 0 & iBAQ.3P == 0 & iBAQ.4P == 0))

#subset iBAQ data
MQdata_Psub <- subset(iBAQdata, iBAQ.1P > 0 | iBAQ.2P > 0 | iBAQ.3P > 0 | iBAQ.4P > 0)
MQdata_Psub2 <- subset(MQdata_Psub, iBAQ.1_160309183003 == 0 & iBAQ.2 == 0 & iBAQ.3 == 0 & iBAQ.4 == 0)

MQdata_NPsub <- subset(iBAQdata, iBAQ.1_160309183003 > 0  | iBAQ.2 > 0 | iBAQ.3 > 0 | iBAQ.4 > 0)
MQdata_NPsub2 <- subset(MQdata_NPsub, iBAQ.1P == 0 & iBAQ.2P == 0 & iBAQ.3P == 0 & iBAQ.4P == 0 )
```

```{r}
#makes sure the row names are their own column called ID 
editiBAQdata<- cbind(ID = rownames(iBAQdata), iBAQdata)
row.names(editiBAQdata) <- NULL 

editNP<- cbind(ID = rownames(MQdata_NPsub2), MQdata_NPsub2)
row.names(editNP) <- NULL 

editP<- cbind(ID = rownames(MQdata_Psub2), MQdata_Psub2)
row.names(editP) <- NULL 
```

```{r}
#anti merge the whole iBAQ data with the subset of NP to get rid of it from the data
merged <- iBAQdata[!(editiBAQdata$ID %in% editNP$ID),]

merged<- cbind(ID = rownames(merged), merged)
row.names(merged) <- NULL 

#antimerge the P and iBAQ data -> so now you only have the iBAQ data that doesnt have NP or P alone (i.e the middle of the diagram)
merged <- merged[!(merged$ID %in% editP$ID),]
```

```{r}
#add the iBAQ data and the NP data then the same with the P data 
datasetNP <- rbind(merged,editNP)
datasetNP <- filter(datasetNP, grepl("TRINITY",ID))
datasetP <- rbind(merged,editP)
datasetP <- filter(datasetP, grepl("TRINITY",ID))
#make venn diagram
vd<-venn.diagram(list(NonPNGaseF=(datasetNP$ID),PNGaseF=(datasetP$ID)),NULL,main="Proteins in S. lineolata slime",cat.just,fill = c("cornflowerblue", "seagreen3"), fontfamily='sans', cat.fontfamily= "sans", main.fontfamily = "sans", cat.dist = c(0.03, 0.03),cat.pos = c(-14, 14))
grid.draw(vd)

```


Venn Diagram for PNGase F vs non-PNGase F Proteins in the TPP


```{r}
#read in data

TPPP_data = read.table("raw_data/observed_peptides_PNGaseF.gff3",sep="\t",header=FALSE)
TPPNP_data = read.table("raw_data/observed_peptideNPs.gff3",sep="\t",header=FALSE)

datasetTPPP <- TPPP_data$V1
datasetTPPP <- as.data.frame(datasetTPPP)
datasetTPPP <- datasetTPPP %>% filter(!grepl("decoy",datasetTPPP)) %>% filter(!grepl("sp",datasetTPPP))
datasetTPPP <- unique(datasetTPPP)

datasetTPPNP <- TPPNP_data$V1
datasetTPPNP <- as.data.frame(datasetTPPNP)
datasetTPPNP <- datasetTPPNP %>% filter(!grepl("decoy",datasetTPPNP)) %>% filter(!grepl("sp",datasetTPPNP))
datasetTPPNP <-unique(datasetTPPNP)
```

```{r}
#make venn diagram
vd<-venn.diagram(list(NonPNGaseF=(datasetTPPNP$datasetNP),PNGaseF=(datasetTPPP$datasetP)),NULL,main="Proteins identified by the Trans-Proteomic Pipeline in S. lineolata slime",cat.just,fill = c("cornflowerblue", "seagreen3"), fontfamily='sans', cat.fontfamily= "sans", main.fontfamily = "sans", cat.dist = c(0.03, 0.03),cat.pos = c(-14, 14))
grid.draw(vd)
```


```{r checking overall number of proteins PNGase F and Non-PNGase in both search engines}
#pngasef
MQPNGase <- datasetP$ID
TPPPNGase <- datasetTPPP$datasetTPPP
MQPNGase <- str_match(MQPNGase,"TRINITY_DN[0-9]*_c[0-9]*_g[0-9]*_i[0-9]")
MQPNGase <- na.omit(MQPNGase)
intersect <- intersect(TPPPNGase, MQPNGase)

#nonpngasef

MQNPNGase <- datasetNP$ID
TPPNPNGase <- datasetTPPNP$datasetTPPNP
MQNPNGase <- str_match(MQNPNGase,"TRINITY_DN[0-9]*_c[0-9]*_g[0-9]*_i[0-9]")
MQNPNGase <- na.omit(MQNPNGase)
intersectNP <- intersect(TPPNPNGase, MQNPNGase)

#onlyinTPPP

TPPPonly <- setdiff(TPPPNGase, MQPNGase)

```