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111 lines
No EOL
4 KiB
R
111 lines
No EOL
4 KiB
R
# TODO appliquer Satterthwaite, et calcule les pvalues pour les 5000 genes
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# avec Correction Bonferroni et Benjamini-Hochberg (voir Livre Christophe Giraud)
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### Import et fonctions utiles
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# Repartir du fichier d'analyse Rmd
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# Utiliser data.trans, ligne 883 voir RMD
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require(phylotools)
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require(phytools)
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require(phylolm)
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require(limma)
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require(edgeR)
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require(here)
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require(ggplot2)
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require(dplyr)
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require(tidyr)
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source("R/utils.R")
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### Data import
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cdata <- readRDS(here("data", "data_TER", "data", "chen2019_rodents_cpd.rds"))
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is.valid <- compcodeR:::check_phyloCompData(cdata)
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if (!(is.valid == TRUE)) stop("Not a valid phyloCompData object.")
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# Design
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design_formula <- as.formula(~condition)
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design_data <- compcodeR:::sample.annotations(cdata)[, "condition", drop = FALSE]
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design_data$condition <- factor(design_data$condition)
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design <- model.matrix(design_formula, design_data)
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# Normalisation
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nf <- edgeR::calcNormFactors(compcodeR:::count.matrix(cdata) / compcodeR:::length.matrix(cdata), method = "TMM")
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lib.size <- colSums(compcodeR:::count.matrix(cdata) / compcodeR:::length.matrix(cdata)) * nf
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data.norm <- sweep((compcodeR:::count.matrix(cdata) + 0.5) / compcodeR:::length.matrix(cdata), 2, lib.size + 1, "/")
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data.norm <- data.norm * 1e6
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# Transformation
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data.trans <- log2(data.norm)
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rownames(data.trans) <- rownames(compcodeR:::count.matrix(cdata))
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### Pvalues computation
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# computing pvalues vec for all genes
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pvalue_vec_vanilla <- sapply(seq(1, nrow(data.trans)), function(row_id) {
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trait <- data.trans[row_id, ]
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fit_phylo <- phylolm(trait ~ design_data$condition, phy = cdata@tree, measurement_error = TRUE)
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compute_vanilla_pvalue(fit_phylo)
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})
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pvalue_vec_vanilla <- setNames(pvalue_vec_vanilla, rownames(data.trans))
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pvalue_vec_vanilla_adj <- p.adjust(pvalue_vec_vanilla, method = "BH")
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pvalue_vec_satterthwaite <- sapply(seq(1, nrow(data.trans)), function(row_id) {
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trait <- data.trans[row_id, ]
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fit_phylo <- phylolm(trait ~ design_data$condition, phy = cdata@tree, measurement_error = TRUE)
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compute_satterthwaite_pvalue(fit_phylo, tree = cdata@tree)
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})
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pvalue_vec_satterthwaite <- setNames(pvalue_vec_satterthwaite, rownames(data.trans))
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pvalue_vec_satterthwaite_adj <- p.adjust(pvalue_vec_satterthwaite, method = "BH")
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pvalue_vec_lrt <- sapply(seq(1, nrow(data.trans)), function(row_id) {
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trait <- data.trans[row_id, ]
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fit_phylo <- phylolm(trait ~ design_data$condition, phy = cdata@tree, measurement_error = TRUE)
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compute_lrt_pvalue(fit_phylo, tree = cdata@tree)
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})
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pvalue_vec_lrt <- setNames(pvalue_vec_lrt, rownames(data.trans))
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pvalue_vec_lrt_adj <- p.adjust(pvalue_vec_lrt, method = "BH")
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# REML
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pvalue_vec_satterthwaite.REML <- sapply(seq(1, nrow(data.trans)), function(row_id) {
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trait <- data.trans[row_id, ]
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fit_phylo <- phylolm(trait ~ design_data$condition, phy = cdata@tree, measurement_error = TRUE)
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compute_satterthwaite_pvalue(fit_phylo, tree = cdata@tree, REML = TRUE)
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})
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pvalue_vec_satterthwaite.REML <- setNames(pvalue_vec_satterthwaite.REML, rownames(data.trans))
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pvalue_vec_satterthwaite_adj.REML <- p.adjust(pvalue_vec_satterthwaite.REML, method = "BH")
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# TODO Histogramme des pvalues
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# TODO utiliser UpSetR pour diagramme de Venn
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# TODO comparer avec le package evemodel, twothetatest
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# Comparer avec OU lrt
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# Arbre sans les replicats et les genes data
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remotes::install_gitlab("sandve-lab/evemodel")
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# computing pvalues vec for all genes
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pvalue_vec_vanilla <- sapply(seq(1,nrow(data.trans)), function(row_id) {
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trait <- data.trans[row_id,]
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fit_phylo <- phylolm(trait ~ design_data$condition, phy = cdata@tree,
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measurement_error = TRUE, model = "OUfixedRoot")
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compute_vanilla_pvalue(fit_phylo)
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})
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# TODO Utiliser les infos de la ligne 83 du Rmd
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# TODO Afficher avec UpSetR les genes differentiellement exprimées et
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# voir les diagrammes de Venn
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# Appliquer notre méthode autant de fois que de gène et corriger les pvalues
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# obtenues par la correction pour obtenir
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# Vérifier que la F stat = T stat ^ 2 |