anova-phylogenetique-projet-msv/R/Method_on_realtree.Rmd

---
title: Méthode sur un vrai arbre
output: html_document
---

Ici nous appliquons les méthodes implémentées sur l'arbre de @chen2019.

```{r knitr_options, echo = FALSE}
knitr::opts_knit$set(cache = TRUE)
```

```{r import_modules, echo = FALSE, include=FALSE}
# Utiliser data.trans, ligne 883 voir RMD
require(phylotools)
require(phytools)
require(phylolm)
require(limma)
require(edgeR)
require(here)
require(ggplot2)
require(dplyr)
require(tidyr)
require(UpSetR)
require(evemodel)

source("utils.R")
```

```{r import_donnees, echo = FALSE}
### Data import
cdata <- readRDS(here("data", "data_TER", "data", "chen2019_rodents_cpd.rds"))
is.valid <- compcodeR:::check_phyloCompData(cdata)
if (!(is.valid == TRUE)) stop("Not a valid phyloCompData object.")

# Design
design_formula <- as.formula(~condition)
design_data <- compcodeR:::sample.annotations(cdata)[, "condition", drop = FALSE]
design_data$condition <- factor(design_data$condition)
design <- model.matrix(design_formula, design_data)

# Normalisation
nf <- edgeR::calcNormFactors(compcodeR:::count.matrix(cdata) / compcodeR:::length.matrix(cdata), method = "TMM")
lib.size <- colSums(compcodeR:::count.matrix(cdata) / compcodeR:::length.matrix(cdata)) * nf
data.norm <- sweep((compcodeR:::count.matrix(cdata) + 0.5) / compcodeR:::length.matrix(cdata), 2, lib.size + 1, "/")
data.norm <- data.norm * 1e6

# Transformation
data.trans <- log2(data.norm)
rownames(data.trans) <- rownames(compcodeR:::count.matrix(cdata))
```

```{r calcul_pvaleurs, echo = FALSE}
### Pvalues computation
pvalues_data = "chen2019pvalues.Rds"
if (!file.exists(here("data",pvalues_data))){
#  computing pvalues vec for all genes
    pvalue_vec_vanilla <- sapply(seq(1, nrow(data.trans)), function(row_id) {
        trait <- data.trans[row_id, ]
        fit_phylo <- phylolm(trait ~ design_data$condition, phy = cdata@tree, measurement_error = TRUE)
        compute_vanilla_pvalue(fit_phylo)
    })

    pvalue_vec_vanilla <- setNames(pvalue_vec_vanilla, rownames(data.trans))

    pvalue_vec_vanilla_adj <- p.adjust(pvalue_vec_vanilla, method = "BH")

    pvalue_vec_satterthwaite <- sapply(seq(1, nrow(data.trans)), function(row_id) {
        trait <- data.trans[row_id, ]
        fit_phylo <- phylolm(trait ~ design_data$condition, phy = cdata@tree, measurement_error = TRUE)
        compute_satterthwaite_pvalue(fit_phylo, tree = cdata@tree)
    })

    pvalue_vec_satterthwaite <- setNames(pvalue_vec_satterthwaite, rownames(data.trans))

    pvalue_vec_satterthwaite_adj <- p.adjust(pvalue_vec_satterthwaite, method = "BH")


    pvalue_vec_lrt <- sapply(seq(1, nrow(data.trans)), function(row_id) {
        trait <- data.trans[row_id, ]
        fit_phylo <- phylolm(trait ~ design_data$condition, phy = cdata@tree, measurement_error = TRUE)
        compute_lrt_pvalue(fit_phylo, tree = cdata@tree)
    })

    pvalue_vec_lrt <- setNames(pvalue_vec_lrt, rownames(data.trans))
    pvalue_vec_lrt_adj <- p.adjust(pvalue_vec_lrt, method = "BH")

    # REML
    pvalue_vec_satterthwaite.REML <- sapply(seq(1, nrow(data.trans)), function(row_id) {
        trait <- data.trans[row_id, ]
        fit_phylo <- phylolm(trait ~ design_data$condition, phy = cdata@tree, measurement_error = TRUE)
        compute_satterthwaite_pvalue(fit_phylo, tree = cdata@tree, REML = TRUE)
    })

    pvalue_vec_satterthwaite.REML <- setNames(pvalue_vec_satterthwaite.REML, rownames(data.trans))

    pvalue_vec_satterthwaite_adj.REML <- p.adjust(pvalue_vec_satterthwaite.REML, method = "BH")


## Préparation du dataframe
    pvalues_dataframe <- data.frame(
    gene = rep(rownames(data.trans), 8),
    pvalue = c(pvalue_vec_vanilla, pvalue_vec_vanilla_adj, 
        pvalue_vec_satterthwaite, pvalue_vec_satterthwaite_adj, 
        pvalue_vec_lrt, pvalue_vec_lrt_adj, pvalue_vec_satterthwaite.REML, 
        pvalue_vec_satterthwaite_adj.REML),
    test_method = rep(c("Vanilla", "VanillaAdj", "Satterthwaite", 
    "SatterthwaiteAdj", "LRT", "LRTAdj", "SatterthwaiteREML", 
    "SatterthwaiteAdjREML"), each = nrow(data.trans))
)
    pvalues_dataframe$test_method <- as.factor(pvalues_dataframe$test_method)
    pvalues_dataframe <- pvalues_dataframe %>% mutate(selected = ifelse(pvalue < 0.05, 1, 0))
    save(pvalues_dataframe, file = here("data", pvalues_data))
} else {
    load(here("data", pvalues_data))
}
```

```{r graphique_all_pvalues, echo = FALSE}
## Graphiques
ggplot(pvalues_dataframe) +
    aes(x = gene, y = pvalue, fill = test_method) +
    geom_bar(stat = "identity", position = "dodge") +
    facet_wrap(~test_method) +
    theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
```

Ici on réalise un pivot_wider pour montrer les gènes sélectionnées par méthodes.

```{r wide_data, echo = FALSE}
pvalues_dataframe_wide <- pvalues_dataframe %>% 
    pivot_wider(id_cols = gene, 
    names_from = test_method, 
    values_from = selected) %>%
    data.frame()
```

```{r upset_selection, echo = FALSE}
upset(pvalues_dataframe_wide, 
    nsets = 8,
    mainbar.y.label = "Nombre de gènes en commun",
    sets.x.label = "Nombre de gènes sélectionnés")
```

```{r , echo = FALSE}
# TODO comparer avec le package evemodel, twothetatest
# Comparer avec OU lrt
# Arbre sans les replicats et les genes data
# remotes::install_gitlab("sandve-lab/evemodel")
# TODO Utiliser les infos de la ligne 83 du Rmd

cdata <- readRDS(here("data", "data_TER", "data", "chen2019_rodents_cpd.rds"))
is.valid <- compcodeR:::check_phyloCompData(cdata)
if (!(is.valid == TRUE)) stop('Not a valid phyloCompData object.')
tree_rep <- compcodeR:::getTree(cdata)
tree_norep <- compcodeR:::getTreeEVE(cdata)
theta_2_vec <- compcodeR:::getIsTheta2edge(cdata, tree_norep)
#col_species <- tree_norep$tip.label[compcodeR:::sample.annotations(cdata)$id.species]
col_species <- tree_norep$tip.label[cumsum(!duplicated(compcodeR:::sample.annotations(cdata)$id.species))]

# Normalisation
nf <- edgeR::calcNormFactors(compcodeR:::count.matrix(cdata) / compcodeR:::length.matrix(cdata), method = 'TMM')
lib.size <- colSums(compcodeR:::count.matrix(cdata) / compcodeR:::length.matrix(cdata)) * nf
data.norm <- sweep((compcodeR:::count.matrix(cdata) + 0.5) / compcodeR:::length.matrix(cdata), 2, lib.size + 1, '/')
data.norm <- data.norm * 1e6

# Transformation
data.trans <- log2(data.norm)
rownames(data.trans) <- rownames(compcodeR:::count.matrix(cdata))

# Analysis with EVE
evemodel.results_list <- evemodel::twoThetaTest(tree = tree_norep, gene.data = data.trans, isTheta2edge = theta_2_vec, colSpecies = col_species, upperBound  =  c(theta = Inf, sigma2 = Inf, alpha = log(2)/0.001/1))

result.table <- data.frame(pvalue = pchisq(evemodel.results_list$LRT, df = 1, lower.tail = FALSE), logFC = compcodeR:::getlogFCEVE(evemodel.results_list$twoThetaRes, theta_2_vec, tree_norep))
result.table$score <- 1 - result.table$pvalue
result.table$adjpvalue <- p.adjust(result.table$pvalue, 'BH')

# Save the results
rownames(result.table) <- rownames(compcodeR:::count.matrix(cdata))
compcodeR:::result.table(cdata) <- result.table
compcodeR:::package.version(cdata) <- paste('evemodel,', packageVersion('evemodel'))
compcodeR:::package.version(cdata) <- paste('edgeR,', packageVersion('edgeR'))
compcodeR:::analysis.date(cdata) <- date()
compcodeR:::method.names(cdata) <- list('short.name' = 'evetwotheta', 'full.name' = 'evemodel0.0.0.9008.TMM.lengthNorm.TPM.dataTrans.log2.empNull.FALSE')
is.valid <- compcodeR:::check_compData_results(cdata)
if (!(is.valid == TRUE)) stop('Not a valid phyloCompData result object.')

# TODO Afficher avec UpSetR les genes differentiellement exprimées et
# voir les diagrammes de Venn


# Appliquer notre méthode autant de fois que de gène et corriger les pvalues
# obtenues par la correction pour obtenir

# Vérifier que la F stat = T stat ^ 2
```