r crest_methylation.r

Posted 2021-05-24

library(DeepBlueR)
library(gplots)
library(RColorBrewer)
library(matrixStats)
library(stringr)

#crest_data <- deepblue_list_experiments(project = "CREST", genome="grch38", epigenetic_mark = "DNA Methylation")

#crest_data <- crest_data[grep("_call", deepblue_extract_names(crest_data)),]

grid_experiments = c( "JKU004_call.wig",
                      "JKU001_call.wig",
                      "JKU002_call.wig",
                      "JKU003_call.wig",
                      "JKU015_call.wig",
                      "JKU008_call.wig",
                      "JKU007_call.wig",
                      "JKU006_call.wig",
                      "JKU005_call.wig",
                      "JKU011_call.wig",
                      "JKU009_call.wig",
                      "JKU013_call.wig",
                      "JKU014_call.wig" )
crest_data <- deepblue_name_to_id(grid_experiments, "experiments")

exp_columns <- deepblue_select_column(crest_data, "VALUE")


# http://www.ensembl.org/info/genome/funcgen/regulatory_build.html
blueprint_regulatory_regions <- deepblue_select_annotations(
  annotation_name = "Blueprint Ensembl Regulatory Build",
  genome = "GRCh38")

request_id <- deepblue_score_matrix(
  experiments_columns = exp_columns,
  aggregation_function = "mean",
  aggregation_regions_id = blueprint_regulatory_regions)


score_matrix <- deepblue_download_request_data(request_id = request_id)

getPalette <- colorRampPalette(brewer.pal(9, "Set1"))
experiments_info <- deepblue_info(deepblue_extract_ids(crest_data))
biosource <- unlist(lapply(experiments_info, function(x){ x$sample_info$biosource_name}))

color_map <- data.frame(biosource = unique(biosource),
                        color = getPalette(length(unique(biosource))))


exp_names <- unlist(lapply(experiments_info, function(x){ x$name}))

biosource_colors <- data.frame(name = exp_names, biosource = biosource)
biosource_colors <- dplyr::left_join(biosource_colors, color_map, by = "biosource")
color_vector <- as.character(biosource_colors$color)
names(color_vector) <-  biosource_colors$biosource


filtered_score_matrix <- as.matrix(score_matrix[,-c(1:3), with=FALSE])
head(filtered_score_matrix[,1:3])


message("regions before: ", nrow(filtered_score_matrix))
filtered_score_matrix <- filtered_score_matrix[which(complete.cases(filtered_score_matrix)),]
message("regions after: ", nrow(filtered_score_matrix))


message("regions before: ", nrow(filtered_score_matrix))
filtered_score_matrix_rowVars <- rowVars(filtered_score_matrix, na.rm = TRUE)
filtered_score_matrix <- filtered_score_matrix[which(filtered_score_matrix_rowVars > 0.10),]
message("regions after: ", nrow(filtered_score_matrix))

filtered_score_matrix <- filtered_score_matrix[,exp_names]

par(oma=c(10,4,4,2))
par(mar=c(10,4,4,2))

heatmap.2(filtered_score_matrix,
          trace = "none", ColSideColors = color_vector,
          hclust=function(x) hclust(x,method="complete"),
          distfun=function(x) as.dist(1-cor(t(x), method = "pearson")),
          Rowv = TRUE, dendrogram = "column",
          key.xlab = "beta value", denscol = "black", keysize = 1.5)


legend("left",
       legend = color_map$biosource,
       col = as.character(color_map$color),
       text.width = 0.25,
       lty= 1,
       lwd = 6,
       cex = 0.7,
       y.intersp = 0.7,
       x.intersp = 0.7,
       inset=c(0.0,0.0))