Suppl.Fig2.Rmd

---
title: "Figure4_imputation"
output: html_document
---

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

# Libraries
```{r message=FALSE}
library(plyr)
library(dplyr)
library(tidyr)
library(tibble)
library(ggplot2)
#library(imputeLCMD)
#library(pcaMethods)
#library(missForest)
library(ComplexHeatmap)
set.seed(4242)
library(hydroGOF)
library(ggcorrplot)
library(corrplot)
library(circlize)
library(RColorBrewer)
library(viridis)
library(readxl)
library(colorspace)
```

# Figure 4.A
```{r}

prot_rfimp <- read.csv("./Data/protein_quant_og_fragment_ms_rf.csv")

prot_rfimp <- as.data.frame(prot_rfimp[-1,-1])

rownames(prot_rfimp) <- NULL
prot_rfimp <- column_to_rownames(prot_rfimp, "Protein")

# plotting heatmap

metadata_heatmap <- read.csv("./Data/metadata_heatmap.csv")
rownames(metadata_heatmap) <- metadata_heatmap$X

metadata_heatmap <- subset(metadata_heatmap, select = -c(X))
metadata_heatmap_rfimp <- metadata_heatmap


all(rownames(metadata_heatmap_rfimp) == colnames(prot_rfimp))

colnames(metadata_heatmap_rfimp) <- "Sample Concentration"

condition_colors <- list(`Sample Concentration` = c("#313695", "#f6e8c3", "#f46d43"))
names(condition_colors$`Sample Concentration`) <- c("2ug", "4ug", "8ug")


colAnn <- HeatmapAnnotation(df=metadata_heatmap_rfimp, which="col", 
                            col=condition_colors, annotation_width=unit(c(2, 8), "cm"), 
                            gap=unit(3, "mm"), annotation_name_gp= gpar(fontsize = 15),
                            annotation_legend_param = list(
                              `Sample Concentration` = list(
                                title_position = "leftcenter-rot"
                              )
                            ))

s9 <- sequential_hcl(9, "Reds", rev = T)

hmap <- Heatmap(
  prot_rfimp,
  name = "Protein Intensity(log2)",
  show_row_names = FALSE,
  show_column_names = FALSE,
  cluster_rows = TRUE,
  cluster_columns = TRUE,
  show_column_dend = TRUE,
  show_row_dend = FALSE,
  row_dend_reorder = TRUE,
  column_dend_reorder = TRUE,
  clustering_method_rows = "ward.D2",
  clustering_method_columns = "ward.D2",
  width = unit(100, "mm"), top_annotation=colAnn,
  col = s9,
  heatmap_legend_param = list(
    title = "Protein Intensity(log2)",
    title_position = "leftcenter-rot"))

draw(hmap, heatmap_legend_side="right", annotation_legend_side="right")


```

# Figure 4.B
```{r}
cor_rf <- cor(prot_rfimp) 

p.rf <- cor_pmat(prot_rfimp)


colfunc <-colorRampPalette(c("white", "yellow", "red"))


corrplot(cor_rf, type="upper", addrect = 3, 
         rect.col = "red", tl.col="black", tl.srt=45,
         col = colfunc(150),
         addCoef.col = "black",
         p.mat = p.rf, sig.level = 0.0001,
         cl.lim = c(0.9, 1), is.corr = FALSE,
         number.cex=1.65, tl.cex = 1.65)
```


# Figure 4.C
```{r}

log_fold_rfimp <- read.csv("./Data/log_fold_rf_imp.csv")

log_fold_rfimp <- as.data.frame(log_fold_rfimp[,-1])

log_fold_rfimp$Load_ug <- factor(log_fold_rfimp$Load_ug, levels = rev(unique(log_fold_rfimp$Load_ug)), ordered = TRUE)


ggplot(log_fold_rfimp, aes(x = Load_ug, y = log2_Intensity, fill = load)) +
  geom_violin() +
  geom_boxplot(outlier.shape = NA, width = 0.1) +
  scale_fill_manual(values = c("#313695", "#f6e8c3", "#f46d43")) +
  labs(x = "Load input replicates", y = "log2(Protein intensity)",
       fill = "Sample Concentration") +
  theme_bw() +
  theme(text = element_text(size = 35),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        legend.position = "bottom") +
  scale_y_continuous(breaks=seq(5, 18, 2))


```


# Figure 4.D
```{r}

coeff_plot_rfimp <- read.csv("./Data/coeff_rf.csv")

coeff_plot_rfimp <- as.data.frame(coeff_plot_rfimp[,-1])

coeff_plot_rfimp$load <- factor(coeff_plot_rfimp$load, levels = rev(unique(coeff_plot_rfimp$load)), ordered = TRUE)




ggplot(coeff_plot_rfimp, aes(x = load, y = Coeff_var, fill = load_ug)) +
  geom_violin() +
  scale_fill_manual(values = c("#313695", "#f6e8c3", "#f46d43")) +
  theme_bw() +
  theme(text = element_text(size = 35),
        legend.position = "bottom",
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank()) +
  labs(x = "Load inputs for dilution series", y = "Coefficient of variance",
       fill = "Sample Concentration")


```