---
title: "Analysis of a cohort of Major Depressive Disorder (MDD) with RGCCA"
author: "Jimmy Vandel & Arnaud Gloaguen"
date: "`r date()`"
bibliography: references.bib
link-citations: true
output: 
  html_document:
    toc: true
    toc_float: true
    toc_depth: 4
    number_sections: false
    code_folding: show
    highlight: zenburn
params:
  work_dir: "~/IFB_SIB/TP/MDD_RGCCA/"
editor_options: 
  chunk_output_type: console
---

```{r setup, include=FALSE}
knitr::opts_knit$set(root.dir=params$work_dir)
knitr::opts_chunk$set(echo = TRUE)
options(knitr.table.format="html")
setwd(params$work_dir)
```

# 1. Unsupervised One-block analysis with Principal Component Analysis (PCA) 

## 1.1 Library installation/loading

```{r libraries_loading, warning=FALSE, message=FALSE, results='hide'}

# Load libraries
library(RGCCA)
library(FactoMineR)
library(factoextra)
library(ggpubr)
library(corrplot)
```


## 1.2 Data Introduction

**Slides 2 -> 5 of "RGCCA - Theory"**.

## 1.3 Preliminary observation and manipulations

Starts by doing a simple PCA on gene expression matrix.

```{r, fig.height=8, fig.width=10, warning=FALSE, message=FALSE}
##############

# Starts from here 
data.train = readRDS(file = "data.train")
DNAm_covariates = readRDS("pd_mdd_bin.RDS")
Sex = as.factor(data.train$cli$Sex)
levels(Sex) = c("Female", "Male")

# PCA on mRNA with full data-set
pca_results <- PCA(data.train$mRNA, scale.unit=TRUE, ncp=25, graph=F) 

fviz_pca_ind(pca_results, axes = c(1,2))+ 
  geom_point(aes(colour = Sex)) +
  guides(colour = guide_legend(title = "color"))

```

==> There is a strong "Sex" effect. Several methods exist in the literature to correct for Sex (or other batch) effects (ex: ComBat @Johnson2006)

For the sake of simplicity, we will limit ourselves to the gender with the highest number of samples --> Females.

```{r, fig.height=12, fig.width=10, warning=FALSE, message=FALSE}
# Function taken from CHAMP to help interpret factors
drawheatmap <- function(svdPV.m, title = NULL, display_legend = FALSE) {
  myPalette <- c("darkred", "red", "orange", "pink", "white")
  breaks.v <- c(-10000, -10, -5, -2, log10(0.05), 0)
  image(x = 1:nrow(svdPV.m), y = 1:ncol(svdPV.m), z = log10(svdPV.m),
        col = myPalette, breaks = breaks.v, xlab = "", ylab = "",
        axes = FALSE, main = paste0("Interpretation of the Components-", title))
  axis(1, at = 1:nrow(svdPV.m), labels = paste("Comp-",
                                               1:nrow(svdPV.m), sep = ""), las = 2)
  suppressWarnings(axis(2, at = 1:ncol(svdPV.m), labels = colnames(svdPV.m),
                        las = 2))
  if(display_legend){
    legend(x = 17, y = 10, legend = c(expression("p < 1x" ~ 10^{-10}),
                                              expression("p < 1x" ~ 10^{-5}),
                                              "p < 0.01", "p < 0.05", "p > 0.05"),
         fill = c("darkred","red", "orange", "pink", "white"), par("usr")[2],
         par("usr")[4], xpd = NA)
  }
}

check_association_CHAMP <- function(x, y){
  if(!is.numeric(y)){
    return(kruskal.test(x ~ as.factor(y))$p.value)
  }else{
    return(summary(lm(x ~ y))$coeff[2, 4])
  }
}
```
 
## 1.4 Interpretation of the PCA components

Once we are limited to Females, let's look back at PCA. Let us start with only two blocks (messenger RNA (mRNA) and DNA methylation (DNAm)). In order to better understand what the different PCA components represent, lets us borrow a technique from the R package Chip Analysis Methylation Pipeline (ChAMP) (@Morris2013 ; function "champ.SVD"; this function allows to test the association between PCA components (continuous) and either continuous (F-test) or discrete (Kruskal-Wallis test) covariates; **see slides 7 -> 8 of "RGCCA - Theory"**).

**Figure below**: Components are interpreted in three cases : mRNA, DNAm and the concatenation of mRNA & DNAm. The last case is to see what a naïve integration can achieve and what are the limitations. Multiple remarks : 

  - mRNA : Main effect of interest : the link with "Sample_group" (Patient(MDD)/Control) appears mainly in components 6 & 7. Though not the strongest link (for these components) each time.
  - DNAm : Not any Principal Component (PC) is associated with "Sample_group".
  - mRNA & DNAm : Association with "Sample_group" stays in PC7 and seems strengthened (it is the strongest link for this component!), which encourages integration. Drawbacks: 
    (i) several strong other links are also described for this component and 
    (ii) there isn't any control over the influence of each omic to contribute for a component. Indeed lets us look at the contribution of each variables.

```{r, fig.height=12, fig.width=10, warning=FALSE, message=FALSE}
# Keep only female samples
data.train.female = lapply(data.train, 
                           function(x) x[which(data.train$cli$Sex == F), ])

DNAm_covariates_female = DNAm_covariates[match(rownames(data.train.female$mRNA), 
                                               DNAm_covariates$Sample_ID), ]

covariates_explored = c("Sample_Group", "BMI", "BMI.bin", "Age", "Age.bin", 
                        "Array", "Slide", "CD4", "CD8", "MO", "B", "NK", "GR")

DNAm_covariates_explored_female = 
  as.data.frame(DNAm_covariates_female[, covariates_explored])

# mRNA
pca_female_mRNA <- PCA(data.train.female$mRNA, scale.unit=TRUE, ncp=20, graph=F) 

interp_pca_female_mRNA = apply(pca_female_mRNA$ind$coord, 2,
                               function(x) 
                                 sapply(DNAm_covariates_explored_female, 
                                        function(y) 
                                          check_association_CHAMP(x, y)))

# DNAm
pca_female_DNAm <- PCA(data.train.female$DNAm, scale.unit=TRUE, ncp=20, graph=F) 

interp_pca_female_DNAm = apply(pca_female_DNAm$ind$coord, 2,
                               function(x) 
                                 sapply(DNAm_covariates_explored_female, 
                                        function(y) 
                                          check_association_CHAMP(x, y)))

# mRNA & DNAm
pca_female_mRNA_DNAm <- PCA(cbind(data.train.female$mRNA, 
                                  data.train.female$DNAm), scale.unit=TRUE, 
                            ncp=20, graph=F) 

interp_pca_female_mRNA_DNAm = apply(pca_female_mRNA_DNAm$ind$coord, 2,
                               function(x) 
                                 sapply(DNAm_covariates_explored_female, 
                                        function(y) 
                                          check_association_CHAMP(x, y)))

par(mfrow = c(3, 1), omi = c(0, 0.7, 0, 0))
drawheatmap(t(interp_pca_female_mRNA), title = "Block mRNA")
drawheatmap(t(interp_pca_female_DNAm), title = "Block DNAm", display_legend = T)
drawheatmap(t(interp_pca_female_mRNA_DNAm), title = "Block mRNA & DNAm")
```

**Figure below**: Contribution of variables for Principal Component 7 for either the mRNA block or the concatenation of the mRNA and DNAm blocks. Remarks:

  - If we focus on the Top 100 contributions, it is dominated by mRNA in the case of the concatenation of the mRNA and DNAm blocks.
  - The components of interest (PC7) explains very little percentage of the total variance.
  
=> It might be interesting to look for methods that treat each omic as their own and allow to separately interpret the influence of each omics contributing to a "signal" of interest.

```{r, fig.height=12, fig.width=10, warning=FALSE, message=FALSE}
# "var" = variables contributions.
weights_pca_female_mRNA_DNAm = fviz_contrib(pca_female_mRNA_DNAm, 
                                            choice = "var", axes = 7, 
                                            top = 100) + 
  labs(title =paste0("Block mRNA & DNAm - Contribution of variables to PC7", 
                     "\nVariance Explained : ", pca_female_mRNA_DNAm$eig[7, 2]))
weights_pca_female_mRNA = fviz_contrib(pca_female_mRNA, choice = "var", 
                                            axes = 7, top = 100) + 
  labs(title =paste0("Block mRNA - Contribution of variables to PC7", 
                     "\nVariance Explained : ", pca_female_mRNA$eig[7, 2]))
ggarrange(weights_pca_female_mRNA, weights_pca_female_mRNA_DNAm,
                  ncol = 1)

```

**Figure below**: A Scree plot allows to visualize the variance explained by each component. Below are two Scree plots, one for the mRNA and one for the DNAm block. We can see that, apart from the first component, all principal components of the DNAm block explain a very low percentage of the total variance which is diffused across all components. Whereas for the mRNA block, the first 10 components seem to bear a signal. This might explain the fact that when the mRNA and DNAm blocks are concatenated, the influence of the mRNA block is greater.

```{r, fig.height=6, fig.width=10, warning=FALSE, message=FALSE}
par(mfrow = c(2, 1), omi = c(0, 0, 0, 0))
barplot(pca_female_mRNA$eig[, 2], names.arg=1:nrow(pca_female_mRNA$eig), 
        main = "mRNA - Scree Plot",
        xlab = "Principal Components",
        ylab = "Percentage of variances",
        col ="steelblue", ylim = c(0, 25))
# Add connected line segments to the plot
lines(x = 1:nrow(pca_female_mRNA$eig), pca_female_mRNA$eig[, 2], 
      type="b", pch=19, col = "red")

barplot(pca_female_DNAm$eig[, 2], names.arg=1:nrow(pca_female_DNAm$eig), 
        main = "DNAm - Scree Plot",
        xlab = "Principal Components",
        ylab = "Percentage of variances",
        col ="steelblue", ylim = c(0, 25))
# Add connected line segments to the plot
lines(x = 1:nrow(pca_female_DNAm$eig), pca_female_DNAm$eig[, 2], 
      type="b", pch=19, col = "red")

```


## 1.5 Establishing the Optimization Criterion of PCA in order to go further

**Slides 9 -> 12 of "RGCCA - Theory"**.

# 2. Unsupervised analysis of $2$-blocks with Partial Least Squares (PLS) and Canonical Correlation Analysis (CCA) 
## 2.1 From PCA to PLS and CCA

**Slides 13 -> 16 of "RGCCA - Theory"**.

## 2.2 Application on the MDD dataset

So Now, lets us apply PLS and CCA that we have just learnt about to the MDD data-set.

**Figure below**: For both PLS and CCA, 10 components are extracted for each block. Similarly to PCA, we can interpret the influence of each computed components:

  - mRNA components extracted with PLS.
  - DNAm components extracted with PLS.
  - mRNA components extracted with CCA.
  - DNAm components extracted with CCA.
  
=> The interpretation of the components for CCA seems really alike, components must be really correlated.

```{r, fig.height=12, fig.width=10, warning=FALSE, message=FALSE}
set.seed(27) #favorite number
resampling = caret::createDataPartition(data.train.female$cli$Group, list = TRUE, p = 0.2)$Resample1
data.validation.female = lapply(data.train.female, function(x) x[-resampling, ])
data.test.female = lapply(data.train.female, function(x) x[resampling, ])


PLS_female_mRNA_DNAm = rgcca(blocks = list(mRNA = data.train.female$mRNA, 
                                           DNAm = data.train.female$DNAm), 
                             connection = matrix(1, 2, 2) - diag(2), 
                             tau = c(1, 1), 
                             scheme = "horst", verbose = F, ncomp = 10)

CCA_female_mRNA_DNAm = rgcca(blocks = list(mRNA = data.validation.female$mRNA, 
                                           DNAm = data.validation.female$DNAm), 
                             connection = matrix(1, 2, 2) - diag(2), 
                             tau = c(0, 0), 
                             scheme = "horst", verbose = F, ncomp = 10)


interp_PLS_female_mRNA = apply(PLS_female_mRNA_DNAm$Y$mRNA, 2,
                               function(x) 
                                 sapply(DNAm_covariates_explored_female, 
                                        function(y) 
                                          check_association_CHAMP(x, y)))
interp_PLS_female_DNAm = apply(PLS_female_mRNA_DNAm$Y$DNAm, 2,
                               function(x) 
                                 sapply(DNAm_covariates_explored_female, 
                                        function(y) 
                                          check_association_CHAMP(x, y)))
interp_CCA_female_mRNA = apply(CCA_female_mRNA_DNAm$Y$mRNA, 2,
                               function(x) 
                                 sapply(DNAm_covariates_explored_female[-resampling, ], 
                                        function(y) 
                                          check_association_CHAMP(x, y)))
interp_CCA_female_DNAm = apply(CCA_female_mRNA_DNAm$Y$DNAm, 2,
                               function(x) 
                                 sapply(DNAm_covariates_explored_female[-resampling, ], 
                                        function(y) 
                                          check_association_CHAMP(x, y)))

par(mfrow = c(2, 2), omi = c(0, 0.7, 0, 0))
drawheatmap(t(interp_PLS_female_mRNA), title = "PLS - Block mRNA")
drawheatmap(t(interp_PLS_female_DNAm), title = "PLS - Block DNAm")
drawheatmap(t(interp_CCA_female_mRNA), title = "CCA - Block mRNA")
drawheatmap(t(interp_CCA_female_DNAm), title = "CCA - Block DNAm")
```

## 2.3 The need for regularization

**Figure below**: Correlation between components for CCA between the DNAm and mRNA block for a train set (left) and a test set (right). On the train set, components are perfectly pairwise correlated (and uncorrelated to their unmatching components), though this is not the case at all on the test set. 

=> The model is overfitting !!

```{r, fig.height=8, fig.width=15, warning=FALSE, message=FALSE}
# a = plot(CCA_female_mRNA_DNAm, type = "samples", comp = 1, bloc = 1, cex = 1.5)
# b = plot(CCA_female_mRNA_DNAm, type = "samples", comp = 2, bloc = 1, cex = 1.5)
# c = plot(CCA_female_mRNA_DNAm, type = "samples", comp = 3, bloc = 1, cex = 1.5)
# d = plot(CCA_female_mRNA_DNAm, type = "samples", comp = 4, bloc = 1, cex = 1.5)
# ggarrange(a, b, c, d , ncol = 2, nrow = 2)



CCA_female_mRNA_DNAm_validation = rgcca_transform(rgcca_res = CCA_female_mRNA_DNAm, 
                                                  blocks_test = list(mRNA = data.test.female$mRNA, 
                                                                     DNAm = data.test.female$DNAm))
colnames(CCA_female_mRNA_DNAm_validation$mRNA) = 
  colnames(CCA_female_mRNA_DNAm$Y$mRNA) = 
  paste0("mRNA - comp", 1:dim(CCA_female_mRNA_DNAm_validation$mRNA)[2])
colnames(CCA_female_mRNA_DNAm_validation$DNAm) = 
  colnames(CCA_female_mRNA_DNAm$Y$DNAm) = 
  paste0("DNAm - comp", 1:dim(CCA_female_mRNA_DNAm_validation$mRNA)[2])

par(mfrow = c(1, 2), omi = c(0, 0, 0, 0))
corrplot(Reduce(cor, CCA_female_mRNA_DNAm$Y), method = "number", main = "\n\n\n\nTrain Set")
corrplot(Reduce(cor, CCA_female_mRNA_DNAm_validation), method = "number",  main = "\n\n\n\nTest Set")
```

## 2.4 Regularized-CCA (R-CCA)

**Slides 17 -> 18 of "RGCCA - Theory"**.

=> Now let us apply R-CCA to the data-set, with an arbitrary low value of $\tau$ (0.1) for each block.

**Figure below**: Correlation between each mRNA component and each DNAm component, for both TRAIN and TEST sets and for CCA/R-CCA/PLS. As mentioned before, CCA suffers from overfitting. PLS and R-CCA also but their first component seems to be spared, which is important as it is the first signal captured, so the main one. 

```{r, fig.height=18, fig.width=10, warning=FALSE, message=FALSE}

PLS_female_mRNA_DNAm_2 = 
  rgcca(blocks = list(mRNA = data.validation.female$mRNA, 
                      DNAm = data.validation.female$DNAm), 
        connection = matrix(1, 2, 2) - diag(2), 
        tau = c(1, 1), 
        scheme = "horst", verbose = F, ncomp = 10)

CCA_female_mRNA_DNAm_regu_2 = 
  rgcca(blocks = list(mRNA = data.validation.female$mRNA, 
                      DNAm = data.validation.female$DNAm), 
        connection = matrix(1, 2, 2) - diag(2), 
        tau = c(0.1, 0.1), 
        scheme = "horst", verbose = F, ncomp = 10)


PLS_female_mRNA_DNAm_validation = 
  rgcca_transform(rgcca_res = PLS_female_mRNA_DNAm_2, 
                  blocks_test = list(mRNA = data.test.female$mRNA, 
                                     DNAm = data.test.female$DNAm))

CCA_female_mRNA_DNAm_validation_regu = 
  rgcca_transform(rgcca_res = CCA_female_mRNA_DNAm_regu_2, 
                  blocks_test = list(mRNA = data.test.female$mRNA, 
                                     DNAm = data.test.female$DNAm))
colnames(PLS_female_mRNA_DNAm_validation$mRNA) = 
  colnames(PLS_female_mRNA_DNAm_2$Y$mRNA) = 
  paste0("mRNA - comp", 1:dim(PLS_female_mRNA_DNAm_validation$mRNA)[2])
colnames(PLS_female_mRNA_DNAm_validation$DNAm) = 
  colnames(PLS_female_mRNA_DNAm_2$Y$DNAm) = 
  paste0("DNAm - comp", 1:dim(PLS_female_mRNA_DNAm_validation$mRNA)[2])

colnames(CCA_female_mRNA_DNAm_validation_regu$mRNA) = 
  colnames(CCA_female_mRNA_DNAm_regu_2$Y$mRNA) = 
  paste0("mRNA - comp", 1:dim(CCA_female_mRNA_DNAm_validation_regu$mRNA)[2])
colnames(CCA_female_mRNA_DNAm_validation_regu$DNAm) = 
  colnames(CCA_female_mRNA_DNAm_regu_2$Y$DNAm) = 
  paste0("DNAm - comp", 1:dim(CCA_female_mRNA_DNAm_validation_regu$mRNA)[2])

par(mfrow = c(3, 2), omi = c(0, 0, 0, 0))
corrplot(Reduce(cor, CCA_female_mRNA_DNAm$Y), method = "number", main = "\nTrain Set - CCA")
corrplot(Reduce(cor, CCA_female_mRNA_DNAm_validation), method = "number",  main = "\nTest Set - CCA")
corrplot(Reduce(cor, CCA_female_mRNA_DNAm_regu_2$Y), method = "number", main = "\nTrain Set - RCCA")
corrplot(Reduce(cor, CCA_female_mRNA_DNAm_validation_regu), method = "number",  main = "\nTest Set - RCCA")
corrplot(Reduce(cor, PLS_female_mRNA_DNAm_2$Y), method = "number", main = "\nTrain Set - PLS")
corrplot(Reduce(cor, PLS_female_mRNA_DNAm_validation), method = "number",  main = "\nTest Set - PLS")
```

## 2.5 Comparison of PLS and R-CCA on the MDD data-set

**Figure below**: For both PLS and R-CCA, 10 components are extracted for each block. Similarly to PCA, we can interpret the influence of each computed components:

  - mRNA components extracted with PLS.
  - DNAm components extracted with PLS.
  - mRNA components extracted with R-CCA.
  - DNAm components extracted with R-CCA.
  
=> Is there any differences ? -> This is not obvious but as expected, R-CCA leads to components bearing slightly more the same meaning; whereas for PLS we have information specific to each bloc (this is light, but differences are essentially in components 4/5/6)

=> GOOD NEWS: the DNAm now seems to bear a component that explains also the Patient/Control effect. Furthermore, this component seems to be more specific to the group effect than before (associated with only 2 other covariates, whereas before it was 3). Though, it is still only the 7th component (see next figure to see the corresponding % of variance explained by this component).

```{r, fig.height=12, fig.width=12, warning=FALSE, message=FALSE}
CCA_female_mRNA_DNAm_regu = rgcca(blocks = list(mRNA = data.train.female$mRNA, 
                                                DNAm = data.train.female$DNAm), 
                                  connection = matrix(1, 2, 2) - diag(2), 
                                  tau = c(0.1, 0.1), 
                                  scheme = "horst", verbose = F, ncomp = 10)

interp_CCA_female_mRNA_regu = apply(CCA_female_mRNA_DNAm_regu$Y$mRNA, 2,
                                    function(x) 
                                      sapply(DNAm_covariates_explored_female, 
                                             function(y) 
                                               check_association_CHAMP(x, y)))
interp_CCA_female_DNAm_regu = apply(CCA_female_mRNA_DNAm_regu$Y$DNAm, 2,
                                    function(x) 
                                      sapply(DNAm_covariates_explored_female, 
                                             function(y) 
                                               check_association_CHAMP(x, y)))
par(mfrow = c(2, 2), omi = c(0, 0.7, 0, 0))
drawheatmap(t(interp_PLS_female_mRNA), title = "PLS - Block mRNA")
drawheatmap(t(interp_PLS_female_DNAm), title = "PLS - Block DNAm")
drawheatmap(t(interp_CCA_female_mRNA_regu), title = "R-CCA - Block mRNA")
drawheatmap(t(interp_CCA_female_DNAm_regu), title = "R-CCA - Block DNAm")
```

**Figure below**: Let us look closer at what is inside component 7. For both methods, we can display the sample space (here component 7 of the mRNA block against component 7 of the DNAM block). We can see that these two components are quite correlated and indeed seems do be catching a group effect. Then we can also display the contribution (block-weight vector) of each variable to the component of its block. For the sake of space, we only display the top 10 contributing variable for each method and each block. This helps interpreting what variables were used to compute these components. Then it is possible to try going deeper into the interpretation. For example "ENSG00000153234" seems interesting (see below information taken from https://www.uniprot.org):

```
Protein: Nuclear receptor subfamily 4 group A member 2

Gene: NR4A2

Function: Transcriptional regulator which is important for the differentiation and maintenance of meso-diencephalic dopaminergic (mdDA) neurons during development. It is crucial for expression of a set of genes such as SLC6A3, SLC18A2, TH and DRD2 which are essential for development of mdDA neurons (By similarity).
```
This gene was already present in top genes of PCA.

==> But how can we add the micro-RNA (miRNA) block now ?

```{r, fig.height=12, fig.width=12, warning=FALSE, message=FALSE, fig.show='hide'}

a = plot(PLS_female_mRNA_DNAm, type = "samples", response = data.train.female$cli$Group, cex = 1.5, comp = 7) + ylim(c(-1, 1)) + xlim(c(-1.5, 1.5)) + ggtitle("PLS\nSample space")
b = plot(CCA_female_mRNA_DNAm_regu, type = "samples", response = data.train.female$cli$Group, cex = 1.5, comp = 7)+ ylim(c(-1, 1))+ xlim(c(-1.5, 1.5)) + ggtitle("R-CCA\nSample space")
c = plot(PLS_female_mRNA_DNAm, type = "weights", comp = 7, bloc = 1, cex = 1.5, n = 10)
d = plot(CCA_female_mRNA_DNAm, type = "weights", comp = 7, bloc = 1, cex = 1.5, n = 10)
e = plot(PLS_female_mRNA_DNAm, type = "weights", comp = 7, bloc = 2, cex = 1.5, n = 10)
f = plot(CCA_female_mRNA_DNAm, type = "weights", comp = 7, bloc = 2, cex = 1.5, n = 10)
```


```{r, fig.height=18, fig.width=16, warning=FALSE, message=FALSE}

ggarrange(a, b, c, d, e, f, ncol = 2, nrow = 3)

```

# 3. Unsupervised analysis of $L$-blocks with Regularized Generalized Canonical Correlation Analysis (RGCCA) 


## 3.1 RGCCA 

**Slides 19 -> 22 of "RGCCA - Theory"**.

## 3.2 Application of RGCCA to the MDD dataset

### 3.2.1 Impact of $\tau$

Now let us apply RGCCA to our data-set. In order to be as close as possible to the methods above, let us apply it when: 

  - For each block $\tau = 0.1$ (close to CCA) or $1$ (PLS)
  - Function $g(x) = x$ (a.k.a Horst scheme)
  - All blocks are connected (a.k.a. Complete design)

**Figure below**: Differences: once again RGCCA $\tau = 0.1$ seems to have components describing the same information across all blocks. This is particularly true for the miRNA blocks that seems to have specific information when $\tau = 1$ (located mainly on the first components and the covariates describing cell composition).

=> GOOD NEWS : The components mainly linked to the group effect has raised in position (3 for $\tau = 1$ - 4 and 9 for $\tau = 0.1$). This may be interpreted as the fact that this group effect is indeed common to each block and as such, is sooner/better catch by RGCCA. This has to be mitigated by the fact that a quite strong link to covariate "MO" is also catch by this component in the mRNA block. Furthermore see next figure for the pourcentage of explained variance by each component.

```{r, fig.height=12, fig.width=10, warning=FALSE, message=FALSE}
RGCCA_PLS_female = rgcca(blocks = list(mRNA = data.train.female$mRNA, 
                                  miRNA = data.train.female$miRNA, 
                                  DNAm = data.train.female$DNAm), 
                    connection = matrix(1, 3, 3) - diag(3), 
                    tau = c(1,1,1), scheme = "horst", verbose = F, ncomp = 10)

interp_RGCCA_PLS_female_mRNA = apply(RGCCA_PLS_female$Y$mRNA, 2,
                               function(x) 
                                 sapply(DNAm_covariates_explored_female, 
                                        function(y) 
                                          check_association_CHAMP(x, y)))
interp_RGCCA_PLS_female_miRNA = apply(RGCCA_PLS_female$Y$miRNA, 2,
                               function(x) 
                                 sapply(DNAm_covariates_explored_female, 
                                        function(y) 
                                          check_association_CHAMP(x, y)))
interp_RGCCA_PLS_female_DNAm = apply(RGCCA_PLS_female$Y$DNAm, 2,
                               function(x) 
                                 sapply(DNAm_covariates_explored_female, 
                                        function(y) 
                                          check_association_CHAMP(x, y)))

RGCCA_CCA_female = rgcca(blocks = list(mRNA = data.train.female$mRNA, 
                                  miRNA = data.train.female$miRNA, 
                                  DNAm = data.train.female$DNAm), 
                    connection = matrix(1, 3, 3) - diag(3), 
                    tau = c(0.1,0.1,0.1), scheme = "horst", verbose = F, ncomp = 10)

interp_RGCCA_CCA_female_mRNA = apply(RGCCA_CCA_female$Y$mRNA, 2,
                               function(x) 
                                 sapply(DNAm_covariates_explored_female, 
                                        function(y) 
                                          check_association_CHAMP(x, y)))
interp_RGCCA_CCA_female_miRNA = apply(RGCCA_CCA_female$Y$miRNA, 2,
                               function(x) 
                                 sapply(DNAm_covariates_explored_female, 
                                        function(y) 
                                          check_association_CHAMP(x, y)))
interp_RGCCA_CCA_female_DNAm = apply(RGCCA_CCA_female$Y$DNAm, 2,
                               function(x) 
                                 sapply(DNAm_covariates_explored_female, 
                                        function(y) 
                                          check_association_CHAMP(x, y)))

par(mfrow = c(3, 2), omi = c(0, 0.7, 0, 0))
drawheatmap(t(interp_RGCCA_PLS_female_mRNA), title = "\nRGCCA - tau = 1\nBlock mRNA")
drawheatmap(t(interp_RGCCA_CCA_female_mRNA), title = "\nRGCCA - tau = 0.1\nBlock mRNA")
drawheatmap(t(interp_RGCCA_PLS_female_miRNA), title = "Block miRNA")
drawheatmap(t(interp_RGCCA_CCA_female_miRNA), title = "Block miRNA")
drawheatmap(t(interp_RGCCA_PLS_female_DNAm), title = "Block DNAm")
drawheatmap(t(interp_RGCCA_CCA_female_DNAm), title = "Block DNAm")
```

**Figure below**: Though raised in position, the components of interest, i.e. associated with Patient/Control (3 for $\tau = 1$ - 4 and 9 for $\tau = 0.1$), still explain a low percentage of the total variance.

```{r, fig.height=12, fig.width=10, warning=FALSE, message=FALSE, fig.show='hide'}
a = plot(RGCCA_PLS_female, type = "ave", comp = 4) + ggtitle("RGCCA - tau = 1\nAverage Variance Explained")
b = plot(RGCCA_CCA_female, type = "ave", comp = 4) + ggtitle("RGCCA - tau = 0.1\nAverage Variance Explained")
```


```{r, fig.height=12, fig.width=14, warning=FALSE, message=FALSE}

ggarrange(a, b, nrow = 2, ncol  = 1)

```

### 3.2.2 Impact of the connection matrix

Until now, all blocks were connected (a.k.a. Complete design). It might be too constraining. Indeed, each block might bear the group effect but not in the same way. Let us try a second design matrix where all blocks are connected to the mRNA bock only (referred to as a Hierarchical design).

**Figure below**: Interpretation of the components for each block in the Hierarchical (left) or Complete (right) design. Observations:

  - The component 3 is less clearly linked to the group effect for all blocks in the Hierarchical design.
  - In the Hierarchical design, block are less constrained to be linked (especially block miRNA and DNAm) and they start having specific information that are extracted (see first component of DNAm and component 6 of miRNA). This seems to have impacted the group effect in a bad way (less commonly retrieve across all blocks.)

```{r, fig.height=12, fig.width=10, warning=FALSE, message=FALSE}
RGCCA_PLS_H_female = rgcca(blocks = list(mRNA = data.train.female$mRNA, 
                                  miRNA = data.train.female$miRNA, 
                                  DNAm = data.train.female$DNAm), 
                    connection = matrix(c(0, 1, 1, 1, 0, 0, 1, 0, 0), ncol = 3, byrow = T), 
                    tau = c(1,1,1), scheme = "horst", verbose = F, ncomp = 10)

interp_RGCCA_PLS_H_female_mRNA = apply(RGCCA_PLS_H_female$Y$mRNA, 2,
                               function(x) 
                                 sapply(DNAm_covariates_explored_female, 
                                        function(y) 
                                          check_association_CHAMP(x, y)))
interp_RGCCA_PLS_H_female_miRNA = apply(RGCCA_PLS_H_female$Y$miRNA, 2,
                               function(x) 
                                 sapply(DNAm_covariates_explored_female, 
                                        function(y) 
                                          check_association_CHAMP(x, y)))
interp_RGCCA_PLS_H_female_DNAm = apply(RGCCA_PLS_H_female$Y$DNAm, 2,
                               function(x) 
                                 sapply(DNAm_covariates_explored_female, 
                                        function(y) 
                                          check_association_CHAMP(x, y)))

RGCCA_PLS_C_female = rgcca(blocks = list(mRNA = data.train.female$mRNA, 
                                  miRNA = data.train.female$miRNA, 
                                  DNAm = data.train.female$DNAm), 
                    connection = matrix(1, 3, 3) - diag(3), 
                    tau = c(1,1,1), scheme = "horst", verbose = F, ncomp = 10)

interp_RGCCA_PLS_C_female_mRNA = apply(RGCCA_PLS_C_female$Y$mRNA, 2,
                               function(x) 
                                 sapply(DNAm_covariates_explored_female, 
                                        function(y) 
                                          check_association_CHAMP(x, y)))
interp_RGCCA_PLS_C_female_miRNA = apply(RGCCA_PLS_C_female$Y$miRNA, 2,
                               function(x) 
                                 sapply(DNAm_covariates_explored_female, 
                                        function(y) 
                                          check_association_CHAMP(x, y)))
interp_RGCCA_PLS_C_female_DNAm = apply(RGCCA_PLS_C_female$Y$DNAm, 2,
                               function(x) 
                                 sapply(DNAm_covariates_explored_female, 
                                        function(y) 
                                          check_association_CHAMP(x, y)))

par(mfrow = c(3, 2), omi = c(0, 0.7, 0, 0))
drawheatmap(t(interp_RGCCA_PLS_H_female_mRNA), title = "\nRGCCA - tau = 1 - Hierarchical\nBlock mRNA")
drawheatmap(t(interp_RGCCA_PLS_C_female_mRNA), title = "\nRGCCA - tau = 1 - Complete\nBlock mRNA")
drawheatmap(t(interp_RGCCA_PLS_H_female_miRNA), title = "Block miRNA")
drawheatmap(t(interp_RGCCA_PLS_C_female_miRNA), title = "Block miRNA")
drawheatmap(t(interp_RGCCA_PLS_H_female_DNAm), title = "Block DNAm")
drawheatmap(t(interp_RGCCA_PLS_C_female_DNAm), title = "Block DNAm")

```

### 3.2.3 Impact of the scheme function

Lets us compare two schemes: 
  - when $g(x) = x$ (a.k.a. Horst scheme), this is the scheme used since the beginning.
  - when $g(x) = x^2$  (a.k.a. Factorial scheme). 
  
The particular interest of the Factorial scheme is that it allows to catch high effect that are positively or negatively correlated between blocks (Horst allows only positively). It also have the effect of decreasing low links (covariance below 1) and increasing higher ones (covariance above 1).

**Figure below**: Interpretation of the components for each block with the Horst (left) or the Factorial (right) scheme. Observations:

  - The component 3 is less clearly linked to the group effect for all blocks in the Factorial scheme.
  - There are less pinks squares (lowest level of significance) for the Factorial scheme (43 for Factorial vs 56 for Horst).


```{r, fig.height=12, fig.width=10, warning=FALSE, message=FALSE}
RGCCA_PLS_C_Horst_female = rgcca(blocks = list(mRNA = data.train.female$mRNA, 
                                  miRNA = data.train.female$miRNA, 
                                  DNAm = data.train.female$DNAm), 
                    connection = matrix(1, 3, 3) - diag(3), 
                    tau = c(1,1,1), scheme = "horst", verbose = F, ncomp = 10)

interp_RGCCA_PLS_C_Horst_female_mRNA = apply(RGCCA_PLS_C_Horst_female$Y$mRNA, 2,
                               function(x) 
                                 sapply(DNAm_covariates_explored_female, 
                                        function(y) 
                                          check_association_CHAMP(x, y)))
interp_RGCCA_PLS_C_Horst_female_miRNA = apply(RGCCA_PLS_C_Horst_female$Y$miRNA, 2,
                               function(x) 
                                 sapply(DNAm_covariates_explored_female, 
                                        function(y) 
                                          check_association_CHAMP(x, y)))
interp_RGCCA_PLS_C_Horst_female_DNAm = apply(RGCCA_PLS_C_Horst_female$Y$DNAm, 2,
                               function(x) 
                                 sapply(DNAm_covariates_explored_female, 
                                        function(y) 
                                          check_association_CHAMP(x, y)))

RGCCA_PLS_C_Factorial_female = rgcca(blocks = list(mRNA = data.train.female$mRNA, 
                                  miRNA = data.train.female$miRNA, 
                                  DNAm = data.train.female$DNAm), 
                    connection = matrix(1, 3, 3) - diag(3), 
                    tau = c(1,1,1), scheme = "factorial", verbose = F, ncomp = 10)

interp_RGCCA_PLS_C_Factorial_female_mRNA = apply(RGCCA_PLS_C_Factorial_female$Y$mRNA, 2,
                               function(x) 
                                 sapply(DNAm_covariates_explored_female, 
                                        function(y) 
                                          check_association_CHAMP(x, y)))
interp_RGCCA_PLS_C_Factorial_female_miRNA = apply(RGCCA_PLS_C_Factorial_female$Y$miRNA, 2,
                               function(x) 
                                 sapply(DNAm_covariates_explored_female, 
                                        function(y) 
                                          check_association_CHAMP(x, y)))
interp_RGCCA_PLS_C_Factorial_female_DNAm = apply(RGCCA_PLS_C_Factorial_female$Y$DNAm, 2,
                               function(x) 
                                 sapply(DNAm_covariates_explored_female, 
                                        function(y) 
                                          check_association_CHAMP(x, y)))

par(mfrow = c(3, 2), omi = c(0, 0.7, 0, 0))
drawheatmap(t(interp_RGCCA_PLS_C_Horst_female_mRNA), title = "\nRGCCA - tau = 1 - Complete - Horst\nBlock mRNA")
drawheatmap(t(interp_RGCCA_PLS_C_Factorial_female_mRNA), title = "\nRGCCA - tau = 1 - Complete - Factorial\nBlock mRNA")
drawheatmap(t(interp_RGCCA_PLS_C_Horst_female_miRNA), title = "Block miRNA")
drawheatmap(t(interp_RGCCA_PLS_C_Factorial_female_miRNA), title = "Block miRNA")
drawheatmap(t(interp_RGCCA_PLS_C_Horst_female_DNAm), title = "Block DNAm")
drawheatmap(t(interp_RGCCA_PLS_C_Factorial_female_DNAm), title = "Block DNAm")

```

## 3.3 Tunning parameters in an unsupervised analysis

**Slides 23 of "RGCCA - Theory"**.

Now, let us apply this permutation to our situation in order to tune the parameter $\tau$ (the RGCCA package also allows to optimize for the number of component). We set the RGCCA model to the Complete design with the Factorial scheme (Horst scheme was giving better results but we believe in the necessity to retrieve both highly positively or negatively correlated events). We choose to explore 20 values for $\tau$ in the interval [1e-4; 0.5], sampled in a logspace manner. For the sake of computational time, each block and each component are associated with the same value of tau (though one value of $\tau$ could have been specified for each combination). Furthermore, for the same reason, we limit ourselves to 6 components, as it seems that the relevant information is catch in these components. 30 permutations are performed 

**Figure below**: Best set of parameters is:

```
      mRNA      miRNA       DNAm 
0.05315656 0.05315656 0.05315656 

```

```{r, warning=FALSE, message=FALSE}
# Not run
# perm_test_PLS_3B = rgcca_permutation(blocks = list(mRNA = data.train.female$mRNA, 
#                                                    miRNA = data.train.female$miRNA, 
#                                                    DNAm = data.train.female$DNAm), 
#                                      connection = matrix(c(0, 1, 1, 1, 0, 1, 1, 1, 0), ncol = 3, byrow = T), 
#                                      par_value = set_tau, 
#                                      n_perms = 30, n_cores = 20, ncomp = 6)


permutation_RGCCA_C_Factorial_female = readRDS("permutation_RGCCA_C_Factorial_female")
class(permutation_RGCCA_C_Factorial_female) = "permutation"

set_tau = sapply(data.train.female[1:3], function(x) pracma:::logseq(x1 = 1e-4, x2 = 0.5, n = 20))

plot(permutation_RGCCA_C_Factorial_female)
```

## 3.4 Bootstrapping: a way of assessing the robustness of the tuned model

Now that we optimized the set of parameters for our model, we wish to evaluate its robustness. This is going to be performed through bootstrapping.

**Slides 23 of "RGCCA - Theory"**.

**Figure below**: 2000 bootstrap samples were generated. We are looking at the first 100 weights (black dots: they were estimated on the regular cohort, without resampling) with their estimated Confidence Interval (grey lines) for components 1/3/4 (the last two because it is where the association with the group effect is supposed to be). 

=> Nothing is significant at all.

```{r, fig.height=12, fig.width=10, warning=FALSE, message=FALSE, fig.show='hide'}
# NOT RUN
# RGCCA_C_Factorial_female_bestPerm = rgcca(blocks = list(mRNA = data.train.female$mRNA, 
#                                                         miRNA = data.train.female$miRNA, 
#                                                         DNAm = data.train.female$DNAm), 
#                                           connection = matrix(c(0, 1, 1, 1, 0, 1, 1, 1, 0), ncol = 3, byrow = T), 
#                                           tau = permutation_RGCCA_C_Factorial_female$best_params, scheme = "factorial", 
#                                           verbose = F, ncomp = 6)
# 
# boot_test_PLS_3B = rgcca_bootstrap(RGCCA_C_Factorial_female_bestPerm , n_cores = 20, n_boot = 2000)

bootstrap_RGCCA_C_Factorial_female_bestPerm = readRDS("bootstrap_RGCCA_C_Factorial_female_bestPerm")
class(bootstrap_RGCCA_C_Factorial_female_bestPerm) = "bootstrap"

a = plot(bootstrap_RGCCA_C_Factorial_female_bestPerm, n = 100, bloc = 1, comp = 1)
b = plot(bootstrap_RGCCA_C_Factorial_female_bestPerm, n = 100, bloc = 2, comp = 1)
c = plot(bootstrap_RGCCA_C_Factorial_female_bestPerm, n = 100, bloc = 3, comp = 1)
d = plot(bootstrap_RGCCA_C_Factorial_female_bestPerm, n = 100, bloc = 1, comp = 3)
e = plot(bootstrap_RGCCA_C_Factorial_female_bestPerm, n = 100, bloc = 2, comp = 3)
f = plot(bootstrap_RGCCA_C_Factorial_female_bestPerm, n = 100, bloc = 3, comp = 3)
g = plot(bootstrap_RGCCA_C_Factorial_female_bestPerm, n = 100, bloc = 1, comp = 4)
h = plot(bootstrap_RGCCA_C_Factorial_female_bestPerm, n = 100, bloc = 2, comp = 4)
i = plot(bootstrap_RGCCA_C_Factorial_female_bestPerm, n = 100, bloc = 3, comp = 4)
```


```{r, fig.height=18, fig.width=16, warning=FALSE, message=FALSE}

ggarrange(a, b, c, d, e, f, g, h, i, nrow = 3, ncol = 3)

```

=> What can we do to get out of this situation. Actually a lot of things: 

  - I was really quick on the pre-processing and drop down all variables with NAs... actually, RGCCA can handle  missing values, it would be interesting to try with them.
  - In the pre-processing steps, I kept only the first 2000 variables for DNAm and mRNA according to the Median Absolute Deviation (MAD). At least I could have respected the ratio of variables across blocks (so take more variables for DNAm as it is originally composed of ~700.000 CpGs against ~16.000 genes for the mRNA block).
  - Parameters can be better better tuned. Here I forced them to be the same across all components and blocks.
  - Try extensions of RGCCA that will be evoked at the end (especially Sparse GCCA to perform variable selection).
  - I could have removed batch effects and/or unwanted effects. Here we can see strong association with GR, Age, BMI... This is not taken into account.
  - And... we can supervise the model.
  
  
# 4. Supervised analysis of $L$-blocks with RGCCA

Here, our goal is to predict for Patient vs. Control, so we will supervise accordingly.

**Slides 26 of "RGCCA - Theory"**.

## 4.1 Cross-Validation

In such situation, it is required to perform Cross-Validation in order to tune parameters to obtain the model with the best generalization capacity.

We optimize for the parameter $\tau$ (for the same set of values as before). The prediction model used in the second step is a Linear Discriminant Analysis (LDA).

We still select the Factorial scheme. Here, as we supervise, we are not forced to extract a high number of components and we can limit ourselves to 3 components per bloc (it is also less time consuming). Cross Validation was performed on the F1-score with 5 repetitions of a 5-folds Cross-Validation (CV). Furthermore, before performing the CV, we extracted 20% of the data-set as a test set to validate our model optimized on the train set (optimized with CV). The resampling to separated train and test was performed with stratification (in order for each sample to be representative of the original proportion of patients vs controls).

**Figure below**: the best set of extracted parameters is:

```
 mRNA miRNA  DNAm group 
 
1e-04 1e-04 1e-04 0e+00
```

=> Actually this set is on the borders of the grid... It would be interesting to explore lower values.

```{r, warning=FALSE, message=FALSE}
resampling = readRDS(file = "resampling4CV")

data.validation.female = lapply(data.train.female, function(x) x[-resampling, ])
data.test.female = lapply(data.train.female, function(x) x[resampling, ])
  
# NOT RUN
# crossValidation_RGCCA_HiearchicalSupervised_Factorial_female <- rgcca_cv(blocks = list(mRNA = data.validation.female$mRNA, 
#                                                                                        miRNA = data.validation.female$miRNA, 
#                                                                                        DNAm = data.validation.female$DNAm, 
#                                                                                        group = data.validation.female$cli$Group),
#                                                                          response = 4, method = "rgcca",
#                                                                          par_type = "tau", par_value = cbind(set_tau, 0),
#                                                                          prediction_model = "lda", #caret::modelLookup()
#                                                                          metric = "F1",
#                                                                          k=5, n_run = 5,
#                                                                          verbose = TRUE, n_cores = 20, ncomp = c(3, 3, 3, 1))

crossValidation_RGCCA_HiearchicalSupervised_Factorial_female = readRDS("crossValidation_RGCCA_HiearchicalSupervised_Factorial_female.RDS")
class(crossValidation_RGCCA_HiearchicalSupervised_Factorial_female) = "cval"

plot(crossValidation_RGCCA_HiearchicalSupervised_Factorial_female)
```

The Best model is learnt and then applied on the test set. The F1 score on the test set is rather high, "0.7272727", though, overfitting may still be present as the F1 score on the train set is "1".

```{r, fig.height=12, fig.width=10, warning=FALSE, message=FALSE}
RGCCA_HiearchicalSupervised_Factorial_female_bestCV = rgcca(crossValidation_RGCCA_HiearchicalSupervised_Factorial_female)

predictOnTest_RGCCA_HiearchicalSupervised_Factorial_female_bestCV =
  rgcca_predict(rgcca_res = RGCCA_HiearchicalSupervised_Factorial_female_bestCV, 
                blocks_test = list(mRNA = data.test.female$mRNA, 
                                   miRNA = data.test.female$miRNA, 
                                   DNAm = data.test.female$DNAm, 
                                   group = data.test.female$cli$Group), 
                prediction_model = "lda", metric = "F1")
print(predictOnTest_RGCCA_HiearchicalSupervised_Factorial_female_bestCV$metric)
# pairs(Reduce(cbind, predictOnTest_RGCCA_HiearchicalSupervised_Factorial_female_bestCV$projection), col = as.factor(data.test.female$cli$Group), pch = 15)
```

## 4.2 Interpretation of the extracted components 

**Figure below**: interpretation of the components for each bloc and on TRAIN or TEST sets. As expected by the supervision effect, the first component is highly linked to the group effect and this is the only effect explained. On the TEST set, this illustrates the overfitting. Though, as expected by the fact that the F1 score was high on the test set, there are still some relevant information extracted from the TRAIN set. The learnt model still seems to bear a certain generalization power (not as strong as expected on the TRAIN set obviously). Especially for the mRNA bloc, a little for the miRNA block and not at all for the DNAm block.

```{r, fig.height=12, fig.width=10, warning=FALSE, message=FALSE}
interp_RGCCA_HiearchicalSupervised_Factorial_female_bestCV_mRNA = 
  apply(RGCCA_HiearchicalSupervised_Factorial_female_bestCV$Y$mRNA, 2,
        function(x) 
          sapply(DNAm_covariates_explored_female[-resampling, ], 
                 function(y) 
                   check_association_CHAMP(x, y)))
interp_RGCCA_HiearchicalSupervised_Factorial_female_bestCV_miRNA =
  apply(RGCCA_HiearchicalSupervised_Factorial_female_bestCV$Y$miRNA, 2,
        function(x) 
          sapply(DNAm_covariates_explored_female[-resampling, ], 
                 function(y) 
                   check_association_CHAMP(x, y)))
interp_RGCCA_HiearchicalSupervised_Factorial_female_bestCV_DNAm =
  apply(RGCCA_HiearchicalSupervised_Factorial_female_bestCV$Y$DNAm, 2,
        function(x) 
          sapply(DNAm_covariates_explored_female[-resampling, ], 
                 function(y) 
                   check_association_CHAMP(x, y)))
interp_predictOnTest_RGCCA_HiearchicalSupervised_Factorial_female_bestCV_mRNA =
  apply(predictOnTest_RGCCA_HiearchicalSupervised_Factorial_female_bestCV$projection$mRNA, 2,
        function(x) 
          sapply(DNAm_covariates_explored_female[resampling, ], 
                 function(y) 
                   check_association_CHAMP(x, y)))
interp_predictOnTest_RGCCA_HiearchicalSupervised_Factorial_female_bestCV_miRNA =
  apply(predictOnTest_RGCCA_HiearchicalSupervised_Factorial_female_bestCV$projection$miRNA, 2,
        function(x) 
          sapply(DNAm_covariates_explored_female[resampling, ], 
                 function(y) 
                   check_association_CHAMP(x, y)))
interp_predictOnTest_RGCCA_HiearchicalSupervised_Factorial_female_bestCV_DNAm =
  apply(predictOnTest_RGCCA_HiearchicalSupervised_Factorial_female_bestCV$projection$DNAm, 2,
        function(x) 
          sapply(DNAm_covariates_explored_female[resampling, ], 
                 function(y) 
                   check_association_CHAMP(x, y)))

par(mfrow = c(3, 2), omi = c(0, 0.7, 0, 0))
drawheatmap(t(interp_RGCCA_HiearchicalSupervised_Factorial_female_bestCV_mRNA), title = "\nTRAIN SET _ RGCCA - tau Opt CV - Complete - Factorial\nBlock mRNA")
drawheatmap(t(interp_predictOnTest_RGCCA_HiearchicalSupervised_Factorial_female_bestCV_mRNA), title = "\nTEST SET _ RGCCA - tau Opt CV - Complete - Factorial\nBlock mRNA")
drawheatmap(t(interp_RGCCA_HiearchicalSupervised_Factorial_female_bestCV_miRNA), title = "Block miRNA")
drawheatmap(t(interp_predictOnTest_RGCCA_HiearchicalSupervised_Factorial_female_bestCV_miRNA), title = "Block miRNA")
drawheatmap(t(interp_RGCCA_HiearchicalSupervised_Factorial_female_bestCV_DNAm), title = "Block DNAm")
drawheatmap(t(interp_predictOnTest_RGCCA_HiearchicalSupervised_Factorial_female_bestCV_DNAm), title = "Block DNAm")

```

## 4.3 Bootstrap

We can still perform a bootstrap procedure in order to evaluate the robustness of the block-weight vectors and identify robust variables contributing to each component for each block. 

```{r, fig.height=12, fig.width=10, warning=FALSE, message=FALSE, fig.show='hide'}
bootstrap_RGCCA_HiearchicalSupervised_Factorial_female_bestCV = readRDS("bootstrap_RGCCA_HiearchicalSupervised_Factorial_female_bestCV")
class(bootstrap_RGCCA_HiearchicalSupervised_Factorial_female_bestCV) = "bootstrap"


a = plot(bootstrap_RGCCA_HiearchicalSupervised_Factorial_female_bestCV, n = 100, bloc = 1, comp = 1)
b = plot(bootstrap_RGCCA_HiearchicalSupervised_Factorial_female_bestCV, n = 100, bloc = 2, comp = 1)
c = plot(bootstrap_RGCCA_HiearchicalSupervised_Factorial_female_bestCV, n = 100, bloc = 3, comp = 1)
d = plot(bootstrap_RGCCA_HiearchicalSupervised_Factorial_female_bestCV, n = 100, bloc = 1, comp = 2)
e = plot(bootstrap_RGCCA_HiearchicalSupervised_Factorial_female_bestCV, n = 100, bloc = 2, comp = 2)
f = plot(bootstrap_RGCCA_HiearchicalSupervised_Factorial_female_bestCV, n = 100, bloc = 3, comp = 2)
g = plot(bootstrap_RGCCA_HiearchicalSupervised_Factorial_female_bestCV, n = 100, bloc = 1, comp = 3)
h = plot(bootstrap_RGCCA_HiearchicalSupervised_Factorial_female_bestCV, n = 100, bloc = 2, comp = 3)
i = plot(bootstrap_RGCCA_HiearchicalSupervised_Factorial_female_bestCV, n = 100, bloc = 3, comp = 3)
```


```{r, fig.height=18, fig.width=15, warning=FALSE, message=FALSE}

ggarrange(a, b, c, d, e, f, g, h, i, nrow = 3, ncol = 3)

```

Bellow are the significant variables for each bock and each component. We can now try to perform enrichment analysis on each list associated with each component.

```{r, fig.height=12, fig.width=10, warning=FALSE, message=FALSE}

weights_idx = which(bootstrap_RGCCA_HiearchicalSupervised_Factorial_female_bestCV$stats$type == "weights")

print("#################   Component 1   #################")
bootstrap_RGCCA_HiearchicalSupervised_Factorial_female_bestCV$stats$var[which((p.adjust(bootstrap_RGCCA_HiearchicalSupervised_Factorial_female_bestCV$stats$pval[weights_idx], method = "fdr")<=0.05) & (bootstrap_RGCCA_HiearchicalSupervised_Factorial_female_bestCV$stats$comp == 1))]

print("#################   Component 2   #################")
bootstrap_RGCCA_HiearchicalSupervised_Factorial_female_bestCV$stats$var[which((p.adjust(bootstrap_RGCCA_HiearchicalSupervised_Factorial_female_bestCV$stats$pval[weights_idx], method = "fdr")<=0.05) & (bootstrap_RGCCA_HiearchicalSupervised_Factorial_female_bestCV$stats$comp == 2))]

print("#################   Component 3   #################")
bootstrap_RGCCA_HiearchicalSupervised_Factorial_female_bestCV$stats$var[which((p.adjust(bootstrap_RGCCA_HiearchicalSupervised_Factorial_female_bestCV$stats$pval[weights_idx], method = "fdr")<=0.05) & (bootstrap_RGCCA_HiearchicalSupervised_Factorial_female_bestCV$stats$comp == 3))]
```

# 5. Going futher with RGCCA

**Slides 28 -> ? of "RGCCA - Theory"**.


# 6. MOFA

Slides to present theory behind MOFA.

```{r}
library(data.table)#
library(ggplot2)#
library(gage)#
library(MOFA2)#
# library(MOFAdata)#
library(DT)#
library(msigdbr)#
```


## Create MOFA object

```{r MOFAobject_creation, message=FALSE}
##––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––##
##                          Create the MOFA object.                           ##
##––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––##

#select only women
selectedPatients=data.train$cli$Name[which(data.train$cli$Sex=="FALSE")]

multiview.norm.mat=list()
multiview.norm.mat$clinical=NULL
multiview.norm.mat$methylation=t(data.train$DNAm[selectedPatients,])
multiview.norm.mat$mirna=t(data.train$miRNA[selectedPatients,])
multiview.norm.mat$mrna=t(data.train$mRNA[selectedPatients,])

#they will be renamed
MOFAobject <- create_mofa(multiview.norm.mat)


#Plot overview of the input data, including the number of samples, views, features, and the missing assays.
plot_data_overview(MOFAobject) # everything is matching here
```


## MOFA options {.tabset}

First, we have to define the options, they are classified in 3 categories :

* Data options
* Model options
* Training options


### Data options

Data options, important arguments:

* scale_views: logical indicating whether to scale views to have the same unit variance. As long as the scale differences between the views is not too high, this is not required. Default is FALSE.
* scale_groups: logical indicating whether to scale groups to have the same unit variance. As long as the scale differences between the groups is not too high, this is not required. Default is FALSE. (Groups are introduced in MOFA+)

You can also change the name of views and groups through the 'data_options' field of the MOFA object.

```{r MOFA_dataoptions}
##––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––##
##                                 MOFA options                               ##
##––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––##
## Data options
###################################
data_opts <- get_default_data_options(MOFAobject)
# data_opts$scale_views <- TRUE
data_opts
```

### Model options

Model options,  important arguments:

* likelihoods: type of data per view (options are “gaussian”, “poisson”, “bernoulli”). 
'gaussian' is for continuous data, 'bernoulli' for binary data and 'poisson' for count/discrete data. By default they are inferred automatically... normally.
* num_factors: number of factors

You can also specified through 'model_options' the use of sparsity priors on the weight and factor matrices (W and H). And which type of prior to use (ARD, spike-and-slab, or both). In the original MOFA approach, sparsity is only applied on W (ARD + spike-and-slab). In MOFA+ sparsity prior is also applied to the factor matrix H considering several groups. Corresponding fields are : spikeslab_factors, spikeslab_weights, ard_factors and ard_weights.

$~$

The model flexibly handles different data types; it is important to choose the likelihoods corresponding to the data (you can visually check data distribution).

For the number of factors, you can start with default value ~10 and further use dedicated packages such as ButchR method to define this number based on statistical criterion. If this number is too high, MOFA will remove unnecessary factors.

```{r MOFA_modeloptions}
## Model options
###################################
model_opts <- get_default_model_options(MOFAobject)
model_opts$spikeslab_weights <- TRUE
model_opts$num_factors <- 10  
model_opts
```


**Visual inspection of mRNA data**

```{r MOFA_countdistribmRNA}
hist(MOFAobject@data$mrna$group1, breaks = 100, main = "Histogram of mRNA values stored in MOFA object")
```

**Visual inspection of miRNA data**

```{r MOFA_countdistribmiRNA}
hist(MOFAobject@data$mirna$group1 , breaks = 100, main = "Histogram of miRNA values stored in MOFA object")
```

**Visual inspection of methylation data**

```{r MOFA_countdistribMethylation}
hist(MOFAobject@data$methylation$group1 , breaks = 100, main = "Histogram of methylation values stored in MOFA object")
```

*Open question: Should we model all these data with a gaussian distribution?*

*to change this option, you can use model_opts$likelihoods <- "gaussian" or "poisson" or "bernoulli"*
```{r MOFA_changeLikelihood}
#for example
#model_opts$likelihoods["methylation"] <- "bernoulli"
```
### Training options

Training important options:

* maxiter: maximum number of iterations (default is 1000). 

* convergence_mode: "fast", "medium", "slow". For exploration, the fast mode is good enough.

* startELBO: initial iteration to compute the ELBO (the objective function used to assess convergence)

* freqELBO: frequency of computations of the ELBO (the objective function used to assess convergence)

* drop_factor_threshold: minimum fraction of explained variance criteria to drop factors while training (eg 0.01 for 1%). Default is -1 (no dropping of factors). 
*One can change it to discard factors that explain a low percentage of variance in all views.*

* stochastic: logical indicating whether to use stochastic variational inference (only required for very big data sets, introduced in MOFA+, default is FALSE).

* seed : numeric indicating the seed for reproducibility (default is 42).


```{r MOFA_trainingoptions}
## Training options
###################################
train_opts <- get_default_training_options(MOFAobject)
train_opts$convergence_mode <- "slow"
train_opts$freqELBO <- 1
train_opts$seed <- 42
#train_opts$drop_factor_threshold <- 0.001

train_opts
```


## Prepare the object and run MOFA

```{r run_MOFA, warning=FALSE}
##––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––##
##                          Prepare the MOFA object                           ##
##––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––##
MOFAobject <- prepare_mofa(MOFAobject,
  data_options = data_opts,
  model_options = model_opts,
  training_options = train_opts
)


##––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––##
##                                  Run MOFA                                  ##
##––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––##

MOFAobject <- run_mofa(MOFAobject, outfile="MOFA2_train.hdf5", use_basilisk = TRUE) 
```

You can load a precomputed MOFA model with the load_Model() function with an hdf5 file.


# Results interpretation

## Get familiar with the MOFA object

### Slots of MOFA object

MOFAobject is a S4 class used to store all relevant data to analyse a MOFA model. 
Get available slots

```{r available_slots}
slotNames(MOFAobject)
```

Object slots are accessible using @ or $

* data : input data (either a list of matrices or a MultiAssayExperiment)
* intercepts : feature intercepts
* imputed_data: imputed data (filled by running impute on the object, which is not necessary)
* samples_ and features_metadata (basically groups)
* expectations: expectations of the factors and weights ($Z and $W matrices)
* training_stats: statistics about iterative training process (time, elbo..)
* data_options: data processing options (given as parameters)
* model_options: model options (given as parameters)
* training_options: training options (given as parameters)
* stochastic_options: training options (given as parameters is stochastic option is TRUE in training_options)
* dimensions : dimensions for the number of views (M), groups (G), samples (N), features per view (D) and factors (K)
* dim_red : non-linear dimension reduction manifolds.
* cache : variance explained by factors.
* on_disk : logical indicating whether data is loaded from disk.
* status : auxiliary variable indicating whether the model has been trained.
* mefisto_options, covariates, covariates_warped and interpolated_Z are optional slot to store sample covariate only for training in MEFISTO

### Functions to get infos from MOFA object

Some interesting functions : 

* factors_names(): to get or set factor names
* features_names(): to get or set feature names
* samples_names(): to get or set sample names
* views_names(): to get or set view names
* get_dimensions(): to get dimensions (number of samples, features, etc.)
* get_factors(): to get model factors
* get_weights(): to get model weights

**Check some slots of the trained MOFA model**

```{r}
###data
views_names(MOFAobject)
get_dimensions(MOFAobject)

#Add sample metadata to the model
#temp_metadata <- readRDS("pd_mdd_bin.RDS",row.names = 1)
temp_metadata <- DNAm_covariates
colnames(temp_metadata)[2]="sample" #one metadata column should contains IDs of samples matching with input data and labelled as "sample"
temp_metadata <- temp_metadata[which(temp_metadata[,2] %in% colnames(MOFAobject@data$mrna$group1)),]
which(temp_metadata[,2]=="302-1")#two sample with the same number but different name in men+women cohort
#temp_metadata <- temp_metadata[-95,] 
samples_metadata(MOFAobject) <- temp_metadata
```

## Results

To interpret these data it is important to have information about the samples. Go to read the publication associated to your dataset.


### General factors plots {.tabset}

#### Factors correlation plot

Correlation plot between factors with plot_factor_cor function.

Normally, they should be uncorrelated, or maybe you choose too many factors.

```{r}
#Check that the Factors are uncorrelated
plot_factor_cor(MOFAobject)
```



#### Variance decomposition

Percentage of variance explained by factors in each view with the plot_variance_explained function.
This is a key plot of MOFA and should always be done before inspecting factors or weights.

The latent factors inferred by MOFA represent the underlying principal sources of variation among the samples. Some of these factors may be shared across multiple data types, while others may be specific to a particular kind of data.

```{r , warning=FALSE}
#variance decomposition analysis. 
plot_variance_explained(MOFAobject, max_r2=35,)
#plot_variance_explained(MOFAobject, plot_total = T)[[1]]

MOFAobject@cache$variance_explained$r2_per_factor
```

In our case, Factor 2 captures a source of variability that is present across all data modalities, but a stronger association with mRNA data. 
Factors 1, 3 and 4 captures a very strong source of variation for the miRNA data.

You can compare those factors with RGCCA factors.


We can ask whether the model provides a good fit to the data. To check it we can plot the total variance explained (using all factors) with the plot_variance_explained function. The resulting values will depend on the nature of the data set, the number of samples, the number of factors, etc...:
 
* Noisy datasets will result in a lower amounts of variance explained (<10%).
* Higher the number of samples --> smaller the total variance explained.
* Higher the number of factors --> higher the total variance explained.

The objective is not to reach 100%, even with an infinity of factors.

Explaining ~50% for a view using a linear model is already good.


```{r , warning=FALSE}
d <- plot_variance_explained(MOFAobject, plot_total = T)[[2]]
#d$data
d
```


We can also access the variance explained per sample when all modalities are gaussian, to detect potential outliers with the calculate_variance_explained_per_sample function:

```{r , warning=FALSE}
calculate_variance_explained(MOFAobject)
calculate_variance_explained_per_sample(MOFAobject) #only with pure gaussian data
```


#### Summary plot of factors

Test the association between MOFA factors and some metadata with the correlate_factors_with_covariates function.
```{r , fig.width = 10, warning=FALSE}
correlate_factors_with_covariates(MOFAobject, 
  covariates = c("CD4","CD8","MO","B","NK","GR","Slide","Array","Age","BMI","Sample_Group"),
  plot="log_pval"
)
```

Factor2 is significantly correlated with GR, CD4, CD8, B and NK.
Sample_group variable seems to be correlated with Factor3, 5 and 6.

Using those correlations, try to identify some similarities between Factors extracted by RGCCA and MOFA .


CD4 is correlated with Factor2 in the previous figure, try to identify with the plot_factor function this association. Not so obvious visually. 
Display factor values for each sample colored following a metadata variable. 
```{r , fig.width = 10, warning=FALSE}
# Plot
p <- plot_factor(MOFAobject, 
  factors = c(1:10),
  color_by = "CD4",
  dot_size = 3,        # change dot size
  dodge = T,           # dodge points with different colors
  legend = T,          # remove legend
#  add_violin = T,      # add violin plots (here we have too few points to make violin, let's do boxplots)
#  violin_alpha = 0.25  # transparency of violin plots
  add_boxplot = T
)

# The output of plot_factor is a ggplot2 object that we can edit
print(p)
```


#### Combinations of factors

You can compute the pairwise combination of factors with the plot_factors function (up to factor 7 here). You can chose a metadata variable to color samples.
 
```{r, , fig.width = 10, fig.height = 9, warning = FALSE, message=FALSE}
plot_factors(MOFAobject, 
  factors = 1:7,
  color_by = "GR"
)
```

Which factors seem to be correlated ?

### Explore individual factors {.tabset}

#### Useful functions

**To explore loadings**

* plot_top_weights(): plot top loadings for a given factor/view
* plot_weights(): plot all loadings for a given factor/view
* plot_weights_heatmap(): plot a heatmap of the loadings from multiple factors of a given view

**To explore data**

* plot_data_heatmap(): heatmap of the training data using only top features of a given factor
* plot_data_scatter(): scatterplot of the data using only top features of a given factor

Functions usage are shown in the following sections.

#### Table of weights/factors

The weights measure how strong each feature relates to each factor : 

*no association = 0 ; 
*strong association = high absolute values

The sign of the weight gives the direction of the effect (e.g. a positive weight indicates that the gene has higher levels in the cells with positive factor values).

**Table of mRNA weigths**
```{r}
datatable(MOFAobject@expectations$W$mrna[order(MOFAobject@expectations$W$mrna[,1], decreasing = TRUE ),]) %>% formatRound(c(1,c(1:dim(MOFAobject@expectations$W$mrna)[2])))
```

**Table of miRNA weigths**
```{r}
datatable(MOFAobject@expectations$W$mirna[order(MOFAobject@expectations$W$mirna[,1], decreasing = TRUE),]) %>% formatRound(c(1,c(1:dim(MOFAobject@expectations$W$mirna)[2])))
```

**Table of Methylation weigths**
```{r}
datatable(MOFAobject@expectations$W$methylation[order(MOFAobject@expectations$W$methylation[,1], decreasing = TRUE),]) %>% formatRound(c(1,c(1:dim(MOFAobject@expectations$W$methylation)[2])))
```

#### Factor6

Factor 6 seems to be the more associated to Sample_Group variable (Factors 3 and 5 also but less).

**Plot Factor6 values for the different samples groups**

We can first check it with the plot_factor function only for Factor6.

```{r}
plot_factor(MOFAobject, 
  factors = 6, 
  color_by = "Factor6",
  group_by = "Sample_Group"
) 
```

Factor6 is clearly associated with Sample_Group.

$~$
$~$

**Find top genes involved in Factor6 (mRNA view)**

* Visualize weight distribution for Factor6 with plot_weights function.

```{r}
plot_weights(MOFAobject,
 view = "mrna",
 factor = 6,
 nfeatures = 10,     # Top number of features to highlight
 scale = T           # Scale weights from -1 to 1
)
```


* Zoom on top 10 absolute weight for Factor6 with plot_top_weights function 
```{r}
plot_top_weights(MOFAobject,
 view = "mrna",
 factor = 6,
 nfeatures = 10,     # Top number of features to highlight
 scale = T           # Scale weights from -1 to 1
)
```

We plot the 10 genes having the highest absolute weight. The weights measure how strong each feature/gene relates to each factor (no association = 0 ; strong association = high absolute values). The sign of the weight gives the direction of the effect (e.g. a positive weight indicates that the gene has higher levels in the cells with positive factor values).


$~$
$~$


**Check expression level of genes involved in Factor6**

We can plot a heatmap to display expression of genes with higher absolute value of the weight for Factor6 across sample with plot_data_heatmap function.

```{r}
plot_data_heatmap(MOFAobject, 
  view = "mrna",
  factor = 6,  
  features = 25,
  cluster_rows = FALSE, cluster_cols = FALSE,
  show_rownames = TRUE, show_colnames = TRUE,
  denoise=TRUE,
  scale = "row"
)
```

Individual expression plot of the top 4 genes having positive weight with plot_data_scatter function.

```{r}
plot_data_scatter(MOFAobject, 
  view = "mrna",
  factor = 6,  
  features = 4,
  sign = "positive",
  color_by = "Sample_Group",
) + labs(y="RNA expression")
```

$~$
$~$


Individual plot of the top 4 genes having negative factor weight.

```{r}
plot_data_scatter(MOFAobject, 
  view = "mrna",
  factor = 6,  
  features = 4,
  sign = "negative",
  color_by = "Sample_Group",
) + labs(y="RNA expression")
```

$~$
$~$


**Find top miRNA involved in Factor6**

* Visualize weight distribution for Factor6
```{r}
plot_weights(MOFAobject,
 view = "mirna",
 factor = 6,
 nfeatures = 10,     # Top number of features to highlight
 scale = T           # Scale weights from -1 to 1
)
```


* Zoom on top 10 absolute weight for Factor6
```{r}
plot_top_weights(MOFAobject,
 view = "mirna",
 factor = 6,
 nfeatures = 10,     # Top number of features to highlight
 scale = T           # Scale weights from -1 to 1
)
```

We plot the 10 miRNA having the highest absolute weight. The weights measure how strong each feature/methylation site relates to each factor (no association = 0 ; strong association = high absolute values). The sign of the weight gives the direction of the effect (e.g. a positive weight indicates that the miRNA levels in the sample with positive factor values).

We can plot heatmap also.

```{r}
plot_data_heatmap(MOFAobject, 
  view = "mirna",
  factor = 6,  
  features = 25,
  cluster_rows = FALSE, cluster_cols = FALSE,
  show_rownames = TRUE, show_colnames = TRUE,
  scale = "row"
)
```

Individual plot of the top 4 miRNA having positive factor values

```{r}
plot_data_scatter(MOFAobject, 
  view = "mirna",
  factor = 6,  
  features = 4,
  sign = "positive",
  color_by = "Sample_Group",
) + labs(y="mirna")
```

$~$
$~$


Individual plot of the top 4 miRNA having negative factor values

```{r}
plot_data_scatter(MOFAobject, 
  view = "mirna",
  factor = 6,  
  features = 4,
  sign = "negative",
  color_by = "Sample_Group",
) + labs(y="mirna")
```

You can do the same for methylation view, keeping in mind that methylation view is not well represented by the 10 factors.

# 6. References