Suppl.Fig1.Rmd

---
title: "Suppl.Fig1"
output: html_document
---

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

# loading 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)
```


# Supplimentary Figure 1.A
```{r message=FALSE}
comb_imp_foldchnge <- read.csv("./Data/comb_imp_foldchnge.csv")

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

colnames(comb_imp_foldchnge) <- c("Random Forest", "LLS", "KNNmethod",
                                  "BPCA", "SVDmethod", "minProb", "minDet",
                                  "Zero", "None", "Ideal")

corr_foldchnge_imp <- cor(comb_imp_foldchnge)
p.imp <- cor_pmat(comb_imp_foldchnge)

corr_foldchnge_imp <- as.matrix(corr_foldchnge_imp)
p.foldchnge_imp <- cor_pmat(comb_imp_foldchnge)
col_cor_scale <- c('#f1eef6','#bdc9e1','#74a9cf','#0570b0')


corrplot(corr_foldchnge_imp, type="upper", addrect = 3, 
         rect.col = "red", tl.col="black", tl.srt=45,
         col = col_cor_scale,
         addCoef.col = "black",
         p.mat = p.foldchnge_imp, sig.level = 0.0001,
         cl.lim = c(-0.2, 1), is.corr = FALSE,
         number.cex=2, tl.cex = 2)
```

# Supplimentary Figure 1.C
```{r message=FALSE}
library(colorspace)

peptide <- read.csv("./Data/pept_gather.csv", stringsAsFactors = F)

peptide <- as.data.frame(peptide[,-1], stringsAsFactors = F)
colnames(peptide) <- c("load", "KNNmethod", "LLS", "Random Forest", "Ideal")

cor_pept <- cor(peptide[,c(2:5)])

p.pept <- cor_pmat(peptide[,c(2:5)])

oryel <- sequential_hcl(9, "YlOrRd", rev = T)

corrplot(cor_pept, type="upper", addrect = 3, rect.col = "red", tl.col="black", tl.srt=45,
         addCoef.col = "white",
         col = oryel,
         p.mat = p.pept, sig.level = 0.001,
         cl.lim = c(0.7, 1), is.corr = FALSE,
         number.cex=1.65, tl.cex = 1.65)
```

# Supplimentary Figure 1.D
```{r message=FALSE}
protein <- read.csv("./Data/prot_gather.csv", stringsAsFactors = F)

protein <- as.data.frame(protein[,-1], stringsAsFactors = F)
colnames(protein) <- c("load", "KNNmethod", "LLS", "Random Forest", "Ideal")

cor_prot <- cor(protein[,c(2:5)])

p.prot <- cor_pmat(protein[,c(2:5)])

oryel <- sequential_hcl(9, "YlOrRd", rev = T)

corrplot(cor_prot, type="upper", addrect = 3, rect.col = "red", tl.col="black", tl.srt=45,
         addCoef.col = "white",
         col = oryel,
         p.mat = p.prot, sig.level = 0.001,
         cl.lim = c(0.7, 1), is.corr = FALSE,
         number.cex=1.65, tl.cex = 1.65)
```

# Supplimentary Figure 1.E
```{r message=FALSE}

rsr_pept <- read.csv("./Data/rsr_pept_imp.csv")


label_pept <- rsr_pept$rsr
label_pept <- round(label_pept, digits = 2)

colnames(rsr_pept) <- c("X", "rsr", "Imputation")

rsr_pept$Imputation <- c("Ideal", "KNNmethod", "LLS", "Random Forest")

ggplot(rsr_pept, aes(x = Imputation, y = rsr, fill = Imputation)) +
  geom_col(color = "black") +
  scale_fill_manual(values = c("black", '#a65628','#f781bf','#999999')) +
  theme_bw() +
  labs(x = "Imputation Methods", y = "RMSE/SD") +
  geom_text(aes(label=label_pept), position=position_dodge(width=0.9), vjust=-0.25,
            size = 14) +
  theme(text = element_text(size = 40),
        axis.text.x = element_blank()) 

```

# Supplimentary Figure 1.F
```{r message=FALSE}
rsr_prot <- read.csv("./Data/rsr_prot_imp.csv")


label_prot <- rsr_prot$rsr
label_prot <- round(label_prot, digits = 2)

colnames(rsr_prot) <- c("X", "rsr", "Imputation")

rsr_prot$Imputation <- c("Ideal", "KNNmethod", "LLS", "Random Forest")

ggplot(rsr_prot, aes(x = Imputation, y = rsr, fill = Imputation)) +
  geom_col(color = "black") +
  scale_fill_manual(values = c("black", '#a65628','#f781bf','#999999')) +
  theme_bw() +
  labs(x = "Imputation Methods", y = "RMSE/SD") +
  geom_text(aes(label=label_prot), position=position_dodge(width=0.9), vjust=-0.25,
            size = 14) +
  theme(text = element_text(size = 40),
        axis.text.x = element_blank())
```