--- title: "03 - Unite CpGs and sample structure" output: html_document --- ```{r setup, include=FALSE} knitr::opts_chunk$set(eval = FALSE, message = FALSE, warning = FALSE) library(methylKit); library(data.table); library(pheatmap) ``` # Unite: common CpGs across all 24 samples ```{r unite} filt <- readRDS("methylRaw_filtered.rds") meth <- unite(filt, destrand = FALSE) # covered in ALL samples saveRDS(meth, "meth_united.rds") nrow(meth) # expect 1,953,620 ``` NOTE: a relaxed `unite(min.per.group = 4L)` can exceed a 48 GB vector limit on a genome this size. The strict all-sample set is the primary object; per-contrast subsets are re-united inside the differential-methylation notebooks. # Correlation, clustering, PCA ```{r structure} meta <- fread("sample_metadata.csv") pm <- percMethylation(meth) # CpG x sample matrix colnames(pm) <- meta$sampleID # pairwise correlation cc <- cor(pm, use = "pairwise.complete.obs") # Pearson saveRDS(cc, "sample_correlation.rds") # PCA on the 200k most-variable CpGs v <- matrixStats::rowVars(pm) top <- order(v, decreasing = TRUE)[1:200000] pca <- prcomp(t(pm[top, ]), center = TRUE, scale. = TRUE) ve <- round(100 * pca$sdev^2 / sum(pca$sdev^2), 1) pca_dt <- data.table(sampleID = meta$sampleID, meta[, .(ploidy, pH, group)], PC1 = pca$x[,1], PC2 = pca$x[,2], PC3 = pca$x[,3], PC4 = pca$x[,4]) fwrite(pca_dt, "pca_coordinates.csv") ve[1:4] # variance explained; expect ~13.2, 7.8, 6.9, 5.0 ``` Samples are NOT expected to cluster by ploidy or pH; PC1 explains only ~13% of variance. This is the motivation for per-CpG testing: treatment effects are localized, not genome-wide. ```{r heatmap} ann <- data.frame(ploidy = meta$ploidy, pH = meta$pH, row.names = meta$sampleID) ann_cols <- list(ploidy = c("2N"="#8CB369","3N"="#5B5F97"), pH = c("high"="#F4A259","low"="#BC4B51")) pheatmap(cc, annotation_col = ann, annotation_colors = ann_cols, clustering_method = "ward.D2", filename = "sample_correlation.png", width = 8, height = 7) ```