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analysis_ppca.Rmd
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---
title: "Analysis of distances"
author: "Léopold Guyot"
date: "`r Sys.Date()`"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
#Packages
```{r, message=FALSE}
library(scp)
library(scpdata)
library(pcaMethods)
library(tidyverse)
library(ggplot2)
library(patchwork)
library(car)
```
# Data
```{r}
leduc <- leduc2022()
prots <- getWithColData(leduc,
"proteins_norm2")
```
# Finding the best subset
```{r}
prots_df <- as.data.frame(assay(prots))
prots_tib <- as_tibble(prots_df)
sub_no_na_2 <- prots_tib %>%
select(where(~sum(is.na(.x))/length(.x) < 0.59))
sub_no_na_2 <- sub_no_na_2[complete.cases(sub_no_na_2),]
sub_no_na_3 <- prots_tib %>%
select(where(~sum(is.na(.x))/length(.x) < 0.6))
sub_no_na_3 <- sub_no_na_3[complete.cases(sub_no_na_3),]
```
# NA_generation
```{r}
set.seed(1)
source("na_generation.R")
matrix_3 <- as.matrix(sub_no_na_3)
list_3 <- lapply(seq(0, 0.9, by = 0.05), na_generation, x = matrix_3)
```
# ppca
```{r}
ppca_wraper <- function(matrix){
pca <- pca(t(matrix), nPcs = 15, method = "ppca", scale = "uv", center = TRUE)
return(pca)
}
ppca_3 <- lapply(list_3, ppca_wraper)
```
# Distances
```{r}
dist_3_ppca <- dist(scores(ppca_3[[1]]))
dim(as.matrix(dist_3_ppca))
dist_list_3_ppca <- lapply(ppca_3, function(x)dist(scores(x)))
dist_list_norm_3_ppca <- lapply(ppca_3, function(x)dist(scores(x)%*%diag(1/x@sDev)))
cor_list_3_ppca <- lapply(ppca_3, function(x)cor(t(scores(x))))
dist_proccessed_3_ppca <- lapply(dist_list_3_ppca, as.vector)
dist_norm_proccessed_3_ppca <- lapply(dist_list_norm_3_ppca, as.vector)
cor_proccessed_3_ppca <- lapply(cor_list_3_ppca, function(x) x[lower.tri(x)])
```
# PCA plots
```{r}
ppca_na_plots <- list()
for (ppca in ppca_3) {
ppca_df <- as.data.frame(scores(ppca))
ppca_df$st <- colData(leduc)[rownames(ppca_df), "SampleType"]
ppca_na_plots[[length(ppca_na_plots)+1]] <- ggplot(data.frame(ppca_df), aes(x = PC1, PC2, color = st))+
geom_point(size = 0.5)+labs(title = as.character((sum(ppca@missing)/length(ppca@missing))))+theme(legend.position = "none", plot.title = element_text(size=9))
}
patchwork::wrap_plots(ppca_na_plots)
?patchwork::wrap_plots
```
# PCA plots for presentation
```{r}
scores_ppca_0_na <- as.data.frame(scores(ppca_3[[1]]))
scores_ppca_0_na$st <- colData(leduc)[rownames(scores_ppca_0_na), "SampleType"]
scores_ppca_30_na <- as.data.frame(scores(ppca_3[[7]]))
scores_ppca_30_na$st <- colData(leduc)[rownames(scores_ppca_30_na), "SampleType"]
scores_ppca_60_na <- as.data.frame(scores(ppca_3[[13]]))
scores_ppca_60_na$st <- colData(leduc)[rownames(scores_ppca_60_na), "SampleType"]
scores_ppca_90_na <- as.data.frame(scores(ppca_3[[19]]))
scores_ppca_90_na$st <- colData(leduc)[rownames(scores_ppca_90_na), "SampleType"]
first_ppca <- ggplot(scores_ppca_0_na, aes(x = PC1, PC2, color = st))+
geom_point()+labs(color = "Sample type", title = "PCA plot for the 0% NA subset")+theme(plot.title = element_text(size=8))+
xlab(paste("PC1 (", round(ppca_3[[1]]@R2[1], 3), ")", sep = ""))+
ylab(paste("PC2 (", round(ppca_3[[1]]@R2[2], 3), ")", sep = ""))
second_ppca <- ggplot(scores_ppca_30_na, aes(x = -PC1, -PC2, color = st))+
geom_point()+labs(color = "Sample type", title = "PCA plot for the 30% NA subset")+theme(plot.title = element_text(size=8))+
xlab(paste("PC1 (", round(ppca_3[[7]]@R2[1], 3), ")", sep = ""))+
ylab(paste("PC2 (", round(ppca_3[[7]]@R2[2], 3), ")", sep = ""))
third_ppca <- ggplot(scores_ppca_60_na, aes(x = PC1, PC2, color = st))+
geom_point()+labs(color = "Sample type", title = "PCA plot for the 60% NA subset")+theme(plot.title = element_text(size=8))+
xlab(paste("PC1 (", round(ppca_3[[13]]@R2[1], 3), ")", sep = ""))+
ylab(paste("PC2 (", round(ppca_3[[13]]@R2[2], 3), ")", sep = ""))
fourth_ppca <- ggplot(scores_ppca_90_na, aes(x = -PC1, PC2, color = st))+
geom_point()+labs(color = "Sample type", title = "PCA plot for the 90% NA subset")+theme(plot.title = element_text(size=8))+
xlab(paste("PC1 (", round(ppca_3[[19]]@R2[1], 3), ")", sep = ""))+
ylab(paste("PC2 (", round(ppca_3[[19]]@R2[2], 3), ")", sep = ""))
(first_ppca/second_ppca/third_ppca/fourth_ppca)+plot_layout(guides = "collect")
ggsave("figures/pca_plots_presentation_ppca.pdf", width = 4, height = 10)
```
# Graphs
```{r}
l_dist_ppca <- length(dist_proccessed_3_ppca[[1]])
df3_ppca <- data.frame("rate_na" = sort(rep(seq(0, 0.9, by = 0.05), l_dist_ppca)),
"dist" = unlist(dist_proccessed_3_ppca), "cor" = unlist(cor_proccessed_3_ppca), "norm_dist" = unlist(dist_norm_proccessed_3_ppca))
df3_ppca$factor_na <- as.factor(df3_ppca$rate_na)
ggplot(df3_ppca, aes(x = factor_na, y = dist, color = factor_na))+geom_violin()
ggplot(df3_ppca, aes(x = factor_na, y = norm_dist, color = factor_na))+geom_violin()
ggplot(df3_ppca, aes(x = factor_na, y = cor, color = factor_na))+geom_boxplot()
model <- lm(dist~factor_na, df3_ppca)
model_conti <- lm(dist~rate_na, df3_ppca)
summary(model)
summary(model_conti)
```
# Difference with the reference (0%)
```{r}
compare <- function(dist, ref){
return(mapply(sum, dist, -c(ref)))
}
x <- compare(dist = dist_proccessed_3_ppca[[5]],ref = dist_proccessed_3_ppca[[1]] )
delta_dist_ppca <- lapply(dist_proccessed_3_ppca[2:19], compare, ref = dist_proccessed_3_ppca[[1]])
delta_cor_ppca <- lapply(cor_proccessed_3_ppca[2:19], compare, ref = cor_proccessed_3_ppca[[1]])
delta_dist_ppca_norm <- lapply(dist_norm_proccessed_3_ppca[2:19], compare, ref = dist_norm_proccessed_3_ppca[[1]])
l_delta_ppca <- length(delta_dist_ppca[[1]])
df3_ppca_delta <- data.frame("rate_na" = sort(rep(seq(0.05, 0.9, by = 0.05), l_delta_ppca)),
"delta_dist_ppca" = unlist(delta_dist_ppca), "delta_dist_ppca_norm" = unlist(delta_dist_ppca_norm), "delta_cor" = unlist(delta_cor_ppca))
df3_ppca_delta$factor_na <- as.factor(df3_ppca_delta$rate_na)
ggplot(df3_ppca_delta, aes(x = factor_na, y = delta_dist_ppca_norm, color = factor_na))+geom_violin()
leveneTest(delta_cor~factor_na, data = df3_ppca_delta)
bartlett.test(delta_cor~factor_na, data = df3_ppca_delta)
```
# dist and cor plots for presentations
```{r}
dist_plot <- ggplot(df3_ppca, aes(x = factor_na, y = dist))+geom_violin()+theme(legend.position = "none",axis.text.x=element_blank(),
axis.ticks.x=element_blank(), axis.title.x = element_blank())+ylab("Distance")
norm_dist_plot <- ggplot(df3_ppca, aes(x = factor_na, y = norm_dist))+geom_violin()+theme(legend.position = "none",axis.text.x=element_blank(),
axis.ticks.x=element_blank(), axis.title.x = element_blank())+ylab("Normalized distance")
cor_plot <- ggplot(df3_ppca, aes(x = factor_na, y = cor))+geom_violin()+theme(legend.position = "none")+ylab("Correlation")+xlab("NA rates")
delta_dist <- ggplot(df3_ppca_delta, aes(x = factor_na, y = delta_dist_ppca_norm))+geom_violin()+theme(legend.position = "none",axis.text.x=element_blank(),
axis.ticks.x=element_blank(), axis.title.x = element_blank())+ylab("Delta normalized distance")
delta_cor <- ggplot(df3_ppca_delta, aes(x = factor_na, y = delta_cor))+geom_violin()+theme(legend.position = "none")+ylab("Delta correlation")+xlab("NA rates")
dist_plot/norm_dist_plot/cor_plot/delta_dist/delta_cor
ggsave("figures/dist_cor_plot_ppca.pdf", width = 8, height = 10)
```
#acc_ppca, totss_ppca/betss_ppca, and mse_ppca
```{r}
mse_ppca_compute <- function(matrix, reference){
result <- mean((scores(reference)[,1] - scores(matrix)[,1])^2+(scores(reference)[,2] - scores(matrix)[,2])^2)
return(result)
}
acc_ppcauracy <- function(kmn){
conf_table <- table(colData(leduc)[names(kmn$cluster), "SampleType"], kmn$cluster)
acc_ppcas <- c((conf_table[1,1]+conf_table[2,2])/sum(conf_table), (conf_table[1,2]+conf_table[2,1])/sum(conf_table))
return(max(acc_ppcas))
}
kmn_list_ppca <- lapply(ppca_3, function(x) kmeans(scores(x), centers = 2,iter.max = 100))
totss_ppca <- lapply(kmn_list_ppca, function(x) x$tot.withinss)
betss_ppca <- lapply(kmn_list_ppca, function(x) x$betweenss)
mse_ppca <- lapply(ppca_3[2:19], mse_ppca_compute, ref = ppca_3[[1]])
acc_ppca <- lapply(kmn_list_ppca[1:19], acc_ppcauracy)
df_kmn_ppca <- data.frame("rate_na" = seq(0, 0.9, by = 0.05),
"totss_ppca" = unlist(totss_ppca), "betss_ppca" = unlist(betss_ppca), "bet_tot" = unlist(betss_ppca)/unlist(totss_ppca), "acc_ppca" = unlist(acc_ppca), "mse_ppca" = c(NA, unlist(mse_ppca)))
ggplot(df_kmn_ppca, aes(x = rate_na, y = bet_tot))+ geom_point()
ggplot(df_kmn_ppca, aes(x = rate_na, y = acc_ppca))+ geom_point()
ggplot(df_kmn_ppca, aes(x = rate_na, y = mse_ppca))+ geom_point()
```