In MassDynamics/lfq_processing: Processes Proteomics Files for Quantification.

knitr::opts_chunk$set(fig.cap = NULL, fig.path = params$output_figure)

library(knitr)
library(data.table)
library(ggplot2)
library(ggrepel)
library(GGally)
library(umap)
library(FactoMineR)
library(factoextra)
library(corrplot)
library(viridis)
library(ggpubr)
library(Hmisc)
library(plotly)
library(stringr)
library(bit64)

#assert that all the stuff we need is there. 
stopifnot(exists("expdes"))
stopifnot(exists("prot"))
stopifnot(exists("prot_int"))

expdes <- expdes[,c("condition", "experiment", "reporter_channel", "replicate")]

p <- ggplot(prot_int, aes(x=interaction(reporter_channel, experiment), 
                                      y=log2NIntNorm, fill=condition, colour=condition)) +
  geom_boxplot() +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.2),
        panel.grid.major.y = element_blank(),
        panel.border = element_blank(),
        axis.ticks.y = element_blank()
  ) +
  scale_x_discrete("Channel.Run") +
  scale_y_continuous("Normalized Log2 Reporter Intensity") +
  ggtitle("Proteins - Normalised measurements distributions")

fig <- ggplotly(p, tooltip = c("y"))%>% config(displayModeBar = T, 
                                        modeBarButtons = list(list('toImage')),
                                        displaylogo = F)


fig$x$data <- lapply(fig$x$data, FUN = function(x){
  x$marker$outliercolor = x$line$color # When creating plot p with ggplot if you specify fill = cut use x$fill$color instead of $line$color
  x$marker$color = x$line$color # When creating plot p with ggplot if you specify fill = cut use x$fill$color instead $line$color
  x$marker$line = x$line$color # When creating plot p with ggplot if you specify fill = cut use x$fill$color instead $line$color
  return(x)
})


fig