1 Introduction

#source("https://bioconductor.org/biocLite.R")
#biocLite()
library(ggplot2)
library(dplyr)
#biocLite("limma")
library(limma)
library(GGally)
library(tidyverse)
library(reshape2)

2 Data loading

### To load primary MS data
primaryMassSpecData <- read.table("inputdata/2017-12-21_PG_hotptex03.txt", header = TRUE, sep = "\t")

To ease later analyses, character vectors of columnnames are created.

intensities <- c("Intensity.HotPTex_DDM_NoXL_1",
               "Intensity.HotPTex_DDM_NoXL_2",
               "Intensity.HotPTex_DDM_XL_1",
               "Intensity.HotPTex_DDM_XL_2",
               "Intensity.HotPTex_PBS_NoXL_1",
               "Intensity.HotPTex_PBS_NoXL_2",
               "Intensity.HotPTex_PBS_XL_1",
               "Intensity.HotPTex_PBS_XL_2",
               "Intensity.Input_DDM_NoXL_1",
               "Intensity.Input_DDM_NoXL_2",
               "Intensity.Input_DDM_XL_1",
               "Intensity.Input_DDM_XL_2",
               "Intensity.Input_PBS_NoXL_1",
               "Intensity.Input_PBS_NoXL_2",
               "Intensity.Input_PBS_XL_1",
               "Intensity.Input_PBS_XL_2")
IBAQ <- c("iBAQ.HotPTex_DDM_NoXL_1",
               "iBAQ.HotPTex_DDM_NoXL_2",
               "iBAQ.HotPTex_DDM_XL_1",
               "iBAQ.HotPTex_DDM_XL_2",
               "iBAQ.HotPTex_PBS_NoXL_1",
               "iBAQ.HotPTex_PBS_NoXL_2",
               "iBAQ.HotPTex_PBS_XL_1",
               "iBAQ.HotPTex_PBS_XL_2",
               "iBAQ.Input_DDM_NoXL_1",
               "iBAQ.Input_DDM_NoXL_2",
               "iBAQ.Input_DDM_XL_1",
               "iBAQ.Input_DDM_XL_2",
               "iBAQ.Input_PBS_NoXL_1",
               "iBAQ.Input_PBS_NoXL_2",
               "iBAQ.Input_PBS_XL_1",
               "iBAQ.Input_PBS_XL_2")
LFQ <- c("LFQ.intensity.HotPTex_DDM_NoXL_1",
               "LFQ.intensity.HotPTex_DDM_NoXL_2",
               "LFQ.intensity.HotPTex_DDM_XL_1",
               "LFQ.intensity.HotPTex_DDM_XL_2",
               "LFQ.intensity.HotPTex_PBS_NoXL_1",
               "LFQ.intensity.HotPTex_PBS_NoXL_2",
               "LFQ.intensity.HotPTex_PBS_XL_1",
               "LFQ.intensity.HotPTex_PBS_XL_2",
               "LFQ.intensity.Input_DDM_NoXL_1",
               "LFQ.intensity.Input_DDM_NoXL_2",
               "LFQ.intensity.Input_DDM_XL_1",
               "LFQ.intensity.Input_DDM_XL_2",
               "LFQ.intensity.Input_PBS_NoXL_1",
               "LFQ.intensity.Input_PBS_NoXL_2",
               "LFQ.intensity.Input_PBS_XL_1",
               "LFQ.intensity.Input_PBS_XL_2")

2.1 First filter

In a first filtering step reverse and hits only identified by site are removed

MassSpecData <- primaryMassSpecData[primaryMassSpecData$Reverse != "+",]
MassSpecData <- MassSpecData[MassSpecData$Only.identified.by.site != "+",]

2.2 Transformation

All intensites are log2 transformed.

MassSpecData[c(intensities, IBAQ, LFQ)] <- log2(MassSpecData[c(intensities, IBAQ, LFQ)])
#colnames(MassSpecData)

Infinity values that resulted from log2 transformation are converted to NA:

is.na(MassSpecData[c(intensities,IBAQ,LFQ)]) <- sapply(MassSpecData[c(intensities,IBAQ,LFQ)], is.infinite)

2.3 Normalisation and trypsin check

trypsin = MassSpecData[MassSpecData$Majority.protein.IDs == "CON__P00761",]
#select genes you want to see patterns
ptn = c("CON__P00761")           #Trypsin
#select corresponding colors
cl = c(rgb(0.85,0,0))
for (k in c("intensities", "IBAQ", "LFQ")) {
  
  all <- melt(MassSpecData,id.vars=c("Majority.protein.IDs"), measure.vars=eval(parse(text = k)))
  
  
  tiff(paste("figures/Boxplot_",k,".tiff", sep = ""), width = 800, height = 1000, pointsize = 25)
  
  p1 <- ggplot(all, aes(variable,value), col(Majority.protein.IDs)) +     # Plot boxplot
    geom_boxplot(width = 1) +
    theme(axis.title.x = element_blank(),
          axis.text.x  = element_text(face = "bold", color = "black",
                                      angle=90, 
                                      vjust=0.5,
                                      hjust = 1,
                                      size=25),
          axis.title.y = element_text(face="bold",
                                      size=40,
                                      hjust = 0.5,
                                      vjust = 1.5),
          axis.text.y  = element_text(color = "black", size = 30)) +
    ylab(paste(k," (Log2)", sep = "")) +
    coord_cartesian(ylim = c(12, 40)) +
    scale_y_continuous(breaks=seq(12, 40, 4)) +
    scale_x_discrete(labels= eval(parse(text = k))) +
    scale_color_hue(l=65, c=65) +
    
    #Error: Incompatible lengths for set aesthetics: colour, x, y      <--   this error happens when one of your gene names is identified in more than one protein group (isoforms)
    
    geom_point(data = subset(all,Majority.protein.IDs %in% ptn), aes(x = variable, col = Majority.protein.IDs), size = 4) +
    geom_line(data = subset(all,Majority.protein.IDs %in% ptn), aes(x = variable, col = Majority.protein.IDs, group = Majority.protein.IDs) , size = 2, linetype = 1)
  
  plot(p1)
  
  graphics.off()
  rm(all)
   
}
rm(cl,k,p1)

2.4 Contaminant removal

After calculation of the normalisation factors the contaminants are removed from the data set.

MassSpecData <- MassSpecData[MassSpecData$Potential.contaminant != "+",]

2.5 Incomplete observation removal

We only want to consider those proteins, that were found in 2 out of 3 replicates of all experiments.

norm_MassSpecData <- MassSpecData
#PTex PBS only 
for(exp in c("HotPTex_PBS_noXL","HotPTex_PBS_XL")){  
  
  sub_MassSpecData <- select(norm_MassSpecData, matches(paste("iBAQ", exp, sep = ".")))
  x <- apply(sub_MassSpecData, 1, function(x)sum(!is.na(x))>1) #set to 1 if 2/3 or set to 2 if 3/3
  norm_MassSpecData <- norm_MassSpecData[x,]
}

3 Enrichment analysis

After preparation the fold changes between respective Input/PTex pairs can be calculated and a moderated t-test with following Benjamini-Hochberg correction is used to determine the false discovery rate (FDR).

3.1 Fold Changes

The fold changes are calculated by subtracting the log-transformed LFQ intensity values of the non-crosslinked (-CL) from the crosslinked (+CL) samples.

norm_MassSpecData$FC.Input_PBS.rep1 <- (norm_MassSpecData$iBAQ.Input_PBS_XL_1 - norm_MassSpecData$iBAQ.Input_PBS_NoXL_1)
norm_MassSpecData$FC.Input_PBS.rep2 <- (norm_MassSpecData$iBAQ.Input_PBS_XL_2 - norm_MassSpecData$iBAQ.Input_PBS_NoXL_2)
norm_MassSpecData$FC.Input_DDM.rep1 <- (norm_MassSpecData$iBAQ.Input_DDM_XL_1 - norm_MassSpecData$iBAQ.Input_DDM_NoXL_1)
norm_MassSpecData$FC.Input_DDM.rep2 <- (norm_MassSpecData$iBAQ.Input_DDM_XL_2 - norm_MassSpecData$iBAQ.Input_DDM_NoXL_2)
norm_MassSpecData$FC.PTex_PBS.rep1 <- (norm_MassSpecData$iBAQ.HotPTex_PBS_XL_1 - norm_MassSpecData$iBAQ.HotPTex_PBS_NoXL_1)
norm_MassSpecData$FC.PTex_PBS.rep2 <- (norm_MassSpecData$iBAQ.HotPTex_PBS_XL_2 - norm_MassSpecData$iBAQ.HotPTex_PBS_NoXL_2)
norm_MassSpecData$FC.PTex_DDM.rep1 <- (norm_MassSpecData$iBAQ.HotPTex_DDM_XL_1 - norm_MassSpecData$iBAQ.HotPTex_DDM_NoXL_1)
norm_MassSpecData$FC.PTex_DDM.rep2 <- (norm_MassSpecData$iBAQ.HotPTex_DDM_XL_2 - norm_MassSpecData$iBAQ.HotPTex_DDM_NoXL_2)
norm_MassSpecData$FC.Input_PBS.mean <- rowMeans(norm_MassSpecData[,180:181])
norm_MassSpecData$FC.Input_DDM.mean <- rowMeans(norm_MassSpecData[,182:183])
norm_MassSpecData$FC.PTex_PBS.mean <- rowMeans(norm_MassSpecData[,184:185])
norm_MassSpecData$FC.PTex_DDM.mean <- rowMeans(norm_MassSpecData[,186:187])

3.2 Enrichment analysis

3.2.1 Moderated t-test and Benjamini-Hochberg correction

pval =  eBayes(lmFit(norm_MassSpecData[,grep("FC.Input_PBS.rep",names(norm_MassSpecData))]))
norm_MassSpecData$pval.Input_PBS <- pval$p.value
norm_MassSpecData$padj.Input_PBS <-  p.adjust(norm_MassSpecData$pval.Input_PBS, method="BH")
pval =  eBayes(lmFit(norm_MassSpecData[,grep("FC.Input_DDM.rep",names(norm_MassSpecData))]))
norm_MassSpecData$pval.Input_DDM <- pval$p.value
norm_MassSpecData$padj.Input_DDM <-  p.adjust(norm_MassSpecData$pval.Input_DDM, method="BH")
pval = eBayes(lmFit(norm_MassSpecData[,grep("FC.PTex_PBS.rep",names(norm_MassSpecData))]))
norm_MassSpecData$pval.PTex_PBS <- pval$p.value
norm_MassSpecData$padj.PTex_PBS <- p.adjust(norm_MassSpecData$pval.PTex_PBS, method="BH")
pval = eBayes(lmFit(norm_MassSpecData[,grep("FC.PTex_DDM.rep",names(norm_MassSpecData))]))
norm_MassSpecData$pval.PTex_DDM <- pval$p.value
norm_MassSpecData$padj.PTex_DDM <- p.adjust(norm_MassSpecData$pval.PTex_DDM, method="BH")

3.3 Mean intensities

### iBAQ
norm_MassSpecData$iBAQ.Input_PBS_noCL_mean <- rowMeans(norm_MassSpecData[,c("iBAQ.Input_PBS_NoXL_1", "iBAQ.Input_PBS_NoXL_2")], na.rm = FALSE)
norm_MassSpecData$iBAQ.Input_DDM_noCL_mean <- rowMeans(norm_MassSpecData[,c("iBAQ.Input_DDM_NoXL_1", "iBAQ.Input_DDM_NoXL_2")], na.rm = FALSE)
norm_MassSpecData$iBAQ.Input_PBS_mean <- rowMeans(norm_MassSpecData[,c("iBAQ.Input_PBS_XL_1", "iBAQ.Input_PBS_XL_2")], na.rm = FALSE)
norm_MassSpecData$iBAQ.Input_DDM_mean <- rowMeans(norm_MassSpecData[,c("iBAQ.Input_DDM_XL_1", "iBAQ.Input_DDM_XL_2")], na.rm = FALSE)
norm_MassSpecData$iBAQ.PTex_PBS_noCL_mean <- rowMeans(norm_MassSpecData[,c("iBAQ.HotPTex_PBS_NoXL_1", "iBAQ.HotPTex_PBS_NoXL_2")], na.rm = FALSE)
norm_MassSpecData$iBAQ.PTex_DDM_noCL_mean <- rowMeans(norm_MassSpecData[,c("iBAQ.HotPTex_DDM_NoXL_1", "iBAQ.HotPTex_DDM_NoXL_2")], na.rm = FALSE)
norm_MassSpecData$iBAQ.PTex_PBS_mean <- rowMeans(norm_MassSpecData[,c("iBAQ.HotPTex_PBS_XL_1", "iBAQ.HotPTex_PBS_XL_2")], na.rm = FALSE)
norm_MassSpecData$iBAQ.PTex_DDM_mean <- rowMeans(norm_MassSpecData[,c("iBAQ.HotPTex_DDM_XL_1", "iBAQ.HotPTex_DDM_XL_2")], na.rm = FALSE)
write.table(norm_MassSpecData, file="MassSpecData_Exp3_iBAQ_notrypsin.txt", sep="\t", col.names=TRUE, row.names=FALSE)

3.4 Quality control and results plots

To produce pairwise scatter plots with correlation information we use the ggpairs() function from the GGally package:

3.5 Replicate reproducibility

scatter_theme <- theme(legend.position = "none", 
      panel.grid.major = element_blank(), 
      axis.ticks = element_blank(), 
      panel.border = element_rect(linetype = "solid", colour = "black", fill = NA))
ggplot <- function(...) ggplot2::ggplot(...) + scale_colour_manual(values = c("black","red")) + scale_fill_manual(values = c("black","red"))
unlockBinding("ggplot",parent.env(asNamespace("GGally")))
assign("ggplot",ggplot,parent.env(asNamespace("GGally")))
## Input PBS noCL
input_PBS_nocl_scatter <- ggpairs(norm_MassSpecData[,IBAQ[13:14]], 
        lower = list(continuous = wrap("cor", size = 7)), 
        upper = list(continuous = "smooth"),
        diag = list(continuous = "densityDiag")) + scatter_theme
## Input PBS CL
input_PBS_cl_scatter <- ggpairs(norm_MassSpecData[,IBAQ[15:16]], 
        lower = list(continuous = wrap("cor", size = 7)), 
        upper = list(continuous = "smooth"),
        diag = list(continuous = "densityDiag")) + scatter_theme
## HotPTex PBS noCL
hotptex_PBS_nocl_scatter <- ggpairs(norm_MassSpecData[,IBAQ[5:6]], 
        lower = list(continuous = wrap("cor", size = 7)), 
        upper = list(continuous = "smooth"),
        diag = list(continuous = "densityDiag"))  + scatter_theme
## HotPTex PBS Cl
hotptex_PBS_cl_scatter <- ggpairs(norm_MassSpecData[,IBAQ[7:8]], 
        lower = list(continuous = wrap("cor", size = 7)), 
        upper = list(continuous = "smooth"),
        diag = list(continuous = "densityDiag"))  + scatter_theme
input_PBS_nocl_scatter

input_PBS_cl_scatter

hotptex_PBS_nocl_scatter

hotptex_PBS_cl_scatter

Plots were saved with:

ggsave("figures/input_PBS_nocl_scatter.pdf", plot = input_PBS_nocl_scatter, units = "in", height = 7, width = 12)
ggsave("figures/input_PBS_cl_scatter.pdf", plot = input_PBS_cl_scatter, units = "in", height = 7, width = 12)

ggsave("figures/hotptex_PBS_nocl_scatter.pdf", plot = hotptex_PBS_nocl_scatter, units = "in", height = 7, width = 12)
ggsave("figures/hotptex_PBS_cl_scatter.pdf", plot = hotptex_PBS_cl_scatter, units = "in", height = 7, width = 12)

Tidy up:

rm(list = ls(pattern = "_scatter"))

4 Plots

Plot RBP classes

Safe plot

ggsave("figures/intensity_enrichment.pdf", plot = enr, units = "in", height = 7, width = 12)
---
title: "HotPTex Salmonella"
output:
  html_notebook:
    number_sections: yes
    smart: no
    toc: yes
  pdf_document:
    toc: yes
---

# Introduction

```{r}
#source("https://bioconductor.org/biocLite.R")
#biocLite()

library(ggplot2)
library(dplyr)
#biocLite("limma")
library(limma)
library(GGally)
library(tidyverse)
library(reshape2)
library(RColorBrewer)
```



# Data loading

```{r load primary MS data}
### To load primary MS data
primaryMassSpecData <- read.table("inputdata/2017-12-21_PG_hotptex03.txt", header = TRUE, sep = "\t")
```

To ease later analyses, character vectors of columnnames are created.
```{r colnames intensities}
intensities <- c("Intensity.HotPTex_DDM_NoXL_1",
               "Intensity.HotPTex_DDM_NoXL_2",
               "Intensity.HotPTex_DDM_XL_1",
               "Intensity.HotPTex_DDM_XL_2",
               "Intensity.HotPTex_PBS_NoXL_1",
               "Intensity.HotPTex_PBS_NoXL_2",
               "Intensity.HotPTex_PBS_XL_1",
               "Intensity.HotPTex_PBS_XL_2",
               "Intensity.Input_DDM_NoXL_1",
               "Intensity.Input_DDM_NoXL_2",
               "Intensity.Input_DDM_XL_1",
               "Intensity.Input_DDM_XL_2",
               "Intensity.Input_PBS_NoXL_1",
               "Intensity.Input_PBS_NoXL_2",
               "Intensity.Input_PBS_XL_1",
               "Intensity.Input_PBS_XL_2")
```

```{r colnames iBAQ}
IBAQ <- c("iBAQ.HotPTex_DDM_NoXL_1",
               "iBAQ.HotPTex_DDM_NoXL_2",
               "iBAQ.HotPTex_DDM_XL_1",
               "iBAQ.HotPTex_DDM_XL_2",
               "iBAQ.HotPTex_PBS_NoXL_1",
               "iBAQ.HotPTex_PBS_NoXL_2",
               "iBAQ.HotPTex_PBS_XL_1",
               "iBAQ.HotPTex_PBS_XL_2",
               "iBAQ.Input_DDM_NoXL_1",
               "iBAQ.Input_DDM_NoXL_2",
               "iBAQ.Input_DDM_XL_1",
               "iBAQ.Input_DDM_XL_2",
               "iBAQ.Input_PBS_NoXL_1",
               "iBAQ.Input_PBS_NoXL_2",
               "iBAQ.Input_PBS_XL_1",
               "iBAQ.Input_PBS_XL_2")
```

```{r colnames LFQ}
LFQ <- c("LFQ.intensity.HotPTex_DDM_NoXL_1",
               "LFQ.intensity.HotPTex_DDM_NoXL_2",
               "LFQ.intensity.HotPTex_DDM_XL_1",
               "LFQ.intensity.HotPTex_DDM_XL_2",
               "LFQ.intensity.HotPTex_PBS_NoXL_1",
               "LFQ.intensity.HotPTex_PBS_NoXL_2",
               "LFQ.intensity.HotPTex_PBS_XL_1",
               "LFQ.intensity.HotPTex_PBS_XL_2",
               "LFQ.intensity.Input_DDM_NoXL_1",
               "LFQ.intensity.Input_DDM_NoXL_2",
               "LFQ.intensity.Input_DDM_XL_1",
               "LFQ.intensity.Input_DDM_XL_2",
               "LFQ.intensity.Input_PBS_NoXL_1",
               "LFQ.intensity.Input_PBS_NoXL_2",
               "LFQ.intensity.Input_PBS_XL_1",
               "LFQ.intensity.Input_PBS_XL_2")
```


## First filter

In a first filtering step reverse and hits only identified by site are removed

```{r first filter step}
MassSpecData <- primaryMassSpecData[primaryMassSpecData$Reverse != "+",]
MassSpecData <- MassSpecData[MassSpecData$Only.identified.by.site != "+",]
```



## Transformation

All intensites are log2 transformed.

```{r log2 transformation}
MassSpecData[c(intensities, IBAQ, LFQ)] <- log2(MassSpecData[c(intensities, IBAQ, LFQ)])

#colnames(MassSpecData)

```

Infinity values that resulted from log2 transformation are converted to `NA`:

```{r conversion of infinity values}
is.na(MassSpecData[c(intensities,IBAQ,LFQ)]) <- sapply(MassSpecData[c(intensities,IBAQ,LFQ)], is.infinite)
```




##Normalisation and trypsin check


```{r}

trypsin = MassSpecData[MassSpecData$Majority.protein.IDs == "CON__P00761",]

#select genes you want to see patterns
ptn = c("CON__P00761")           #Trypsin
#select corresponding colors
cl = c(rgb(0.85,0,0))

for (k in c("intensities", "IBAQ", "LFQ")) {
  
  all <- melt(MassSpecData,id.vars=c("Majority.protein.IDs"), measure.vars=eval(parse(text = k)))
  
  
  tiff(paste("figures/Boxplot_",k,".tiff", sep = ""), width = 800, height = 1000, pointsize = 25)
  
  p1 <- ggplot(all, aes(variable,value), col(Majority.protein.IDs)) +     # Plot boxplot
    geom_boxplot(width = 1) +
    theme(axis.title.x = element_blank(),
          axis.text.x  = element_text(face = "bold", color = "black",
                                      angle=90, 
                                      vjust=0.5,
                                      hjust = 1,
                                      size=25),
          axis.title.y = element_text(face="bold",
                                      size=40,
                                      hjust = 0.5,
                                      vjust = 1.5),
          axis.text.y  = element_text(color = "black", size = 30)) +
    ylab(paste(k," (Log2)", sep = "")) +
    coord_cartesian(ylim = c(12, 40)) +
    scale_y_continuous(breaks=seq(12, 40, 4)) +
    scale_x_discrete(labels= eval(parse(text = k))) +
    scale_color_hue(l=65, c=65) +
    
    #Error: Incompatible lengths for set aesthetics: colour, x, y      <--   this error happens when one of your gene names is identified in more than one protein group (isoforms)
    
    geom_point(data = subset(all,Majority.protein.IDs %in% ptn), aes(x = variable, col = Majority.protein.IDs), size = 4) +
    geom_line(data = subset(all,Majority.protein.IDs %in% ptn), aes(x = variable, col = Majority.protein.IDs, group = Majority.protein.IDs) , size = 2, linetype = 1)
  
  plot(p1)
  
  graphics.off()
  rm(all)
   
}

rm(cl,k,p1)
```


## Contaminant removal

After calculation of the normalisation factors the contaminants are removed from the data set.

```{r contaminant removal}
MassSpecData <- MassSpecData[MassSpecData$Potential.contaminant != "+",]
```


## Incomplete observation removal

We only want to consider those proteins, that were found in 2 out of 3 replicates of all experiments.

```{r removal of incomplete observations}

norm_MassSpecData <- MassSpecData

#PTex PBS only 
for(exp in c("HotPTex_PBS_noXL","HotPTex_PBS_XL")){  
  
  sub_MassSpecData <- select(norm_MassSpecData, matches(paste("iBAQ", exp, sep = ".")))
  x <- apply(sub_MassSpecData, 1, function(x)sum(!is.na(x))>1) #set to 1 if 2/3 or set to 2 if 3/3
  norm_MassSpecData <- norm_MassSpecData[x,]
}
```


# Enrichment analysis

After preparation the fold changes between respective Input/PTex pairs can be calculated and a moderated t-test with following Benjamini-Hochberg correction is used to determine the false discovery rate (FDR).

## Fold Changes

The fold changes are calculated by subtracting the log-transformed LFQ intensity values of the non-crosslinked (-CL) from the crosslinked (+CL) samples.

```{r calculation of individual fold changes}
norm_MassSpecData$FC.Input_PBS.rep1 <- (norm_MassSpecData$iBAQ.Input_PBS_XL_1 - norm_MassSpecData$iBAQ.Input_PBS_NoXL_1)
norm_MassSpecData$FC.Input_PBS.rep2 <- (norm_MassSpecData$iBAQ.Input_PBS_XL_2 - norm_MassSpecData$iBAQ.Input_PBS_NoXL_2)


norm_MassSpecData$FC.Input_DDM.rep1 <- (norm_MassSpecData$iBAQ.Input_DDM_XL_1 - norm_MassSpecData$iBAQ.Input_DDM_NoXL_1)
norm_MassSpecData$FC.Input_DDM.rep2 <- (norm_MassSpecData$iBAQ.Input_DDM_XL_2 - norm_MassSpecData$iBAQ.Input_DDM_NoXL_2)


norm_MassSpecData$FC.PTex_PBS.rep1 <- (norm_MassSpecData$iBAQ.HotPTex_PBS_XL_1 - norm_MassSpecData$iBAQ.HotPTex_PBS_NoXL_1)
norm_MassSpecData$FC.PTex_PBS.rep2 <- (norm_MassSpecData$iBAQ.HotPTex_PBS_XL_2 - norm_MassSpecData$iBAQ.HotPTex_PBS_NoXL_2)

norm_MassSpecData$FC.PTex_DDM.rep1 <- (norm_MassSpecData$iBAQ.HotPTex_DDM_XL_1 - norm_MassSpecData$iBAQ.HotPTex_DDM_NoXL_1)
norm_MassSpecData$FC.PTex_DDM.rep2 <- (norm_MassSpecData$iBAQ.HotPTex_DDM_XL_2 - norm_MassSpecData$iBAQ.HotPTex_DDM_NoXL_2)

```

```{r calculation of mean fold changes}
norm_MassSpecData$FC.Input_PBS.mean <- rowMeans(norm_MassSpecData[,180:181])
norm_MassSpecData$FC.Input_DDM.mean <- rowMeans(norm_MassSpecData[,182:183])

norm_MassSpecData$FC.PTex_PBS.mean <- rowMeans(norm_MassSpecData[,184:185])
norm_MassSpecData$FC.PTex_DDM.mean <- rowMeans(norm_MassSpecData[,186:187])

```

##Enrichment analysis

### Moderated t-test and Benjamini-Hochberg correction
```{r mod t-test and BH correction}
pval =  eBayes(lmFit(norm_MassSpecData[,grep("FC.Input_PBS.rep",names(norm_MassSpecData))]))
norm_MassSpecData$pval.Input_PBS <- pval$p.value
norm_MassSpecData$padj.Input_PBS <-  p.adjust(norm_MassSpecData$pval.Input_PBS, method="BH")

pval =  eBayes(lmFit(norm_MassSpecData[,grep("FC.Input_DDM.rep",names(norm_MassSpecData))]))
norm_MassSpecData$pval.Input_DDM <- pval$p.value
norm_MassSpecData$padj.Input_DDM <-  p.adjust(norm_MassSpecData$pval.Input_DDM, method="BH")


pval = eBayes(lmFit(norm_MassSpecData[,grep("FC.PTex_PBS.rep",names(norm_MassSpecData))]))
norm_MassSpecData$pval.PTex_PBS <- pval$p.value
norm_MassSpecData$padj.PTex_PBS <- p.adjust(norm_MassSpecData$pval.PTex_PBS, method="BH")

pval = eBayes(lmFit(norm_MassSpecData[,grep("FC.PTex_DDM.rep",names(norm_MassSpecData))]))
norm_MassSpecData$pval.PTex_DDM <- pval$p.value
norm_MassSpecData$padj.PTex_DDM <- p.adjust(norm_MassSpecData$pval.PTex_DDM, method="BH")


```


## Mean intensities


```{r}

### iBAQ
norm_MassSpecData$iBAQ.Input_PBS_noCL_mean <- rowMeans(norm_MassSpecData[,c("iBAQ.Input_PBS_NoXL_1", "iBAQ.Input_PBS_NoXL_2")], na.rm = FALSE)
norm_MassSpecData$iBAQ.Input_DDM_noCL_mean <- rowMeans(norm_MassSpecData[,c("iBAQ.Input_DDM_NoXL_1", "iBAQ.Input_DDM_NoXL_2")], na.rm = FALSE)
norm_MassSpecData$iBAQ.Input_PBS_mean <- rowMeans(norm_MassSpecData[,c("iBAQ.Input_PBS_XL_1", "iBAQ.Input_PBS_XL_2")], na.rm = FALSE)
norm_MassSpecData$iBAQ.Input_DDM_mean <- rowMeans(norm_MassSpecData[,c("iBAQ.Input_DDM_XL_1", "iBAQ.Input_DDM_XL_2")], na.rm = FALSE)

norm_MassSpecData$iBAQ.PTex_PBS_noCL_mean <- rowMeans(norm_MassSpecData[,c("iBAQ.HotPTex_PBS_NoXL_1", "iBAQ.HotPTex_PBS_NoXL_2")], na.rm = FALSE)
norm_MassSpecData$iBAQ.PTex_DDM_noCL_mean <- rowMeans(norm_MassSpecData[,c("iBAQ.HotPTex_DDM_NoXL_1", "iBAQ.HotPTex_DDM_NoXL_2")], na.rm = FALSE)
norm_MassSpecData$iBAQ.PTex_PBS_mean <- rowMeans(norm_MassSpecData[,c("iBAQ.HotPTex_PBS_XL_1", "iBAQ.HotPTex_PBS_XL_2")], na.rm = FALSE)
norm_MassSpecData$iBAQ.PTex_DDM_mean <- rowMeans(norm_MassSpecData[,c("iBAQ.HotPTex_DDM_XL_1", "iBAQ.HotPTex_DDM_XL_2")], na.rm = FALSE)


write.table(norm_MassSpecData, file="MassSpecData_Exp3_iBAQ_notrypsin.txt", sep="\t", col.names=TRUE, row.names=FALSE)

```

## Quality control and results plots


To produce pairwise scatter plots with correlation information we use the `ggpairs()` function from the `GGally` package:

## Replicate reproducibility
```{r}
scatter_theme <- theme(legend.position = "none", 
      panel.grid.major = element_blank(), 
      axis.ticks = element_blank(), 
      panel.border = element_rect(linetype = "solid", colour = "black", fill = NA))

ggplot <- function(...) ggplot2::ggplot(...) + scale_colour_manual(values = c("black","red")) + scale_fill_manual(values = c("black","red"))
unlockBinding("ggplot",parent.env(asNamespace("GGally")))
assign("ggplot",ggplot,parent.env(asNamespace("GGally")))
```

```{r}
## Input PBS noCL
input_PBS_nocl_scatter <- ggpairs(norm_MassSpecData[,IBAQ[13:14]], 
        lower = list(continuous = wrap("cor", size = 7)), 
        upper = list(continuous = "smooth"),
        diag = list(continuous = "densityDiag")) + scatter_theme


## Input PBS CL
input_PBS_cl_scatter <- ggpairs(norm_MassSpecData[,IBAQ[15:16]], 
        lower = list(continuous = wrap("cor", size = 7)), 
        upper = list(continuous = "smooth"),
        diag = list(continuous = "densityDiag")) + scatter_theme


## HotPTex PBS noCL
hotptex_PBS_nocl_scatter <- ggpairs(norm_MassSpecData[,IBAQ[5:6]], 
        lower = list(continuous = wrap("cor", size = 7)), 
        upper = list(continuous = "smooth"),
        diag = list(continuous = "densityDiag"))  + scatter_theme

## HotPTex PBS Cl
hotptex_PBS_cl_scatter <- ggpairs(norm_MassSpecData[,IBAQ[7:8]], 
        lower = list(continuous = wrap("cor", size = 7)), 
        upper = list(continuous = "smooth"),
        diag = list(continuous = "densityDiag"))  + scatter_theme



```

```{r, fig.width=12, fig.height=7}
input_PBS_nocl_scatter
input_PBS_cl_scatter

hotptex_PBS_nocl_scatter
hotptex_PBS_cl_scatter

```

Plots were saved with:

```{r, eval=FALSE, include=TRUE}
ggsave("figures/input_PBS_nocl_scatter.pdf", plot = input_PBS_nocl_scatter, units = "in", height = 7, width = 12)
ggsave("figures/input_PBS_cl_scatter.pdf", plot = input_PBS_cl_scatter, units = "in", height = 7, width = 12)

ggsave("figures/hotptex_PBS_nocl_scatter.pdf", plot = hotptex_PBS_nocl_scatter, units = "in", height = 7, width = 12)
ggsave("figures/hotptex_PBS_cl_scatter.pdf", plot = hotptex_PBS_cl_scatter, units = "in", height = 7, width = 12)

```

Tidy up:

```{r}
rm(list = ls(pattern = "_scatter"))
```

# Plots


Plot RBP classes

```{r}

mlist <- read.table("PTexSalmonella_master.txt", sep = "\t", header=TRUE)

ggplot2::ggplot(data = mlist, aes(x = iBAQ.PTex_PBS_mean, y = iBAQ.PTex_PBS_noCL_mean, color=known.RNA.interaction)) +
  scale_colour_manual(values = c("orange","grey","red")) + 
  scale_fill_manual(values = c("orange","grey","red")) +
  geom_point(size = 2.5) +
  geom_point(alpha = 0.8) +
  geom_abline(intercept = 0)

#enr <- ggplot2::ggplot(data = mlist, aes(x = iBAQ.PTex_PBS_mean, y = iBAQ.PTex_PBS_noCL_mean, color=Ribosome, group=Ribosome)) +
#  scale_colour_manual(values = c("grey","orange")) + 
#  scale_fill_manual(values = c("grey","orange")) +
#  geom_point(size = 5) +
  #geom_point(alpha = 0.8) +
#  geom_abline(intercept = 0)
  
enr <- ggplot2::ggplot(data = mlist, aes(x = iBAQ.PTex_PBS_mean, y = iBAQ.PTex_PBS_noCL_mean, color=known.RNA.interaction)) +
  scale_colour_manual(values = c("grey","orange")) + 
  scale_fill_manual(values = c("grey","orange")) +
  geom_point(size = 5) +
  #geom_point(alpha = 0.8) +
  geom_abline(intercept = 0)


enr <- ggplot2::ggplot(data = mlist, aes(x = iBAQ.PTex_PBS_mean, y = iBAQ.PTex_PBS_noCL_mean, color=known.RNA.interaction)) +
  scale_colour_manual(values = c("orange","grey","red")) + 
  scale_fill_manual(values = c("orange","grey","red")) +
  geom_point(alpha = 0.8) +
   geom_point(size = 5) +
  geom_abline(intercept = 0) 
  #geom_abline(intercept = 2, lty = 2) +
#  geom_abline(intercept = 4, lty = 3) +
  #geom_abline(intercept = -2, lty = 2)
```
Safe plot
```{r, eval=FALSE, include=TRUE}
ggsave("figures/intensity_enrichment.pdf", plot = enr, units = "in", height = 7, width = 12)


```




