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tar-qc_idats.R
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#' Efficiently import idats files mostly using minfi functions.
#'
#' @param directory A `character`. Location of IDAT files, default is the current working directory.
#' @param csv_file A `character`. Path to the sample sheet (csv files) or
#' name of the sample sheet in `directory`.
#' @param meth_value_type A `character`. Indicates whether you prefer m-values (`"M"`)
#' or beta-values (`"B"`). Default is `"B"`.
#' @param array_name A `character`. Choose microarray type, eiyther `"450K"` or `"EPIC"`.
#' Default is `"EPIC"`.
#' @param annotation_version A `character`. Version of the annotation package that should be used.
#' Default is `"ilm10b4.hg19"` for the `"EPIC"` array
#' @param n_cores An `integer`. The number of cores to use,
#' i.e., at most how many child processes will be run simultaneously.
#' @param rgSet A `RGChannelSet` object.
#' @param echo A `logical`. Should messages be displayed?
#' @inheritParams qc_idats
#'
#' @return A `list`.
#'
#' @export
#' @import data.table
#' @import ChAMPdata
#' @import ENmix
#' @import minfi
#' @import utils
read_idats <- function(
directory = getwd(),
csv_file = "csv$",
meth_value_type = "B",
filter_beads = TRUE,
bead_cutoff = 0.05,
filter_non_cpg = TRUE,
filter_snps = FALSE,
population = NULL,
filter_multihit = TRUE,
filter_xy = TRUE,
detection_pvalues = 0.01,
filter_callrate = TRUE,
callrate_samples = 0.99,
callrate_probes = 1,
norm_background = "oob",
norm_dye = "RELIC",
norm_quantile = "quantile1",
array_name = c("EPIC", "450k"),
annotation_version = c("ilm10b5.hg38", "ilm10b4.hg19", "ilmn12.hg19"),
n_cores = 1,
rgSet = NULL,
echo = FALSE
) {
old_r_libs <- Sys.getenv("R_LIBS")
r_libs <- unique(c(
.libPaths()[1],
unlist(strsplit(Sys.getenv("R_LIBS"), .Platform$path.sep))
))
Sys.setenv(R_LIBS = paste(r_libs, collapse = .Platform$path.sep))
on.exit(Sys.setenv(R_LIBS = old_r_libs))
if (echo) {
message(
"======================\n",
"Reading IDAT files ...\n",
"======================",
appendLF = TRUE
)
}
array_name <- array_name[1]
annotation_version <- annotation_version[1]
stopifnot(suppressPackageStartupMessages(
nchar(system.file(package = "minfi")) > 0 &
switch(
EXPR = array_name,
"450k" = nchar(system.file(package = "IlluminaHumanMethylation450kmanifest")) > 0,
"EPIC" = nchar(system.file(package = "IlluminaHumanMethylationEPICmanifest")) > 0
)
))
if (is.null(rgSet) | !inherits(rgSet, "RGChannelSet")) {
sample_sheet <- read_sample_sheet(directory = directory, csv_file = csv_file, echo = echo)
rgSet <- read_metharray_exp(sample_sheet = sample_sheet, n_cores = n_cores)
}
rgSet@annotation <- switch(
EXPR = array_name,
"450k" = c(array = "IlluminaHumanMethylation450k", annotation = annotation_version),
"EPIC" = c(array = "IlluminaHumanMethylationEPIC", annotation = annotation_version)
)
data_detP <- minfi::detectionP(rgSet)
data_detP[is.na(data_detP)] <- 1
if (filter_callrate) {
good_detection <- data_detP < detection_pvalues
call_rate_samples <- colSums(good_detection) / nrow(good_detection)
bad_samples <- names(which(call_rate_samples < callrate_samples))
good_detection <- good_detection[, setdiff(colnames(good_detection), bad_samples)]
call_rate_cpg <- rowSums(good_detection) / ncol(good_detection)
bad_cpgs <- names(which(call_rate_cpg < callrate_probes))
} else {
bad_samples <- NULL
bad_cpgs <- NULL
}
mset_raw <- mset <- minfi::preprocessRaw(rgSet)
if (filter_beads) {
bc <- get_beadcount(rgSet)
bc2 <- bc[rowSums(is.na(bc)) < bead_cutoff * (ncol(bc)), ]
mset_f2 <- mset[minfi::featureNames(mset) %in% rownames(bc2), ]
n_beads_discarded <- format(dim(mset)[1] - dim(mset_f2)[1], big.mark = ",", digits = 1L, nsmall = 0L)
mset <- mset_f2
}
if (filter_non_cpg) {
mset_f2 <- minfi::dropMethylationLoci(mset, dropCH = TRUE)
n_non_cpg_discarded <- format(dim(mset)[1] - dim(mset_f2)[1], big.mark = ",", digits = 1L, nsmall = 0L)
mset <- mset_f2
}
if (filter_snps) {
ref_population <- c(
"AFR", "EAS", "EUR",
"SAS", "AMR", "GWD", "YRI", "TSI", "IBS",
"CHS", "PUR", "JPT", "GIH", "CHB", "STU",
"ITU", "LWK", "KHV", "FIN", "ESN", "CEU",
"PJL", "ACB", "CLM", "CDX", "GBR", "BEB",
"PEL", "MSL", "MXL", "ASW"
)
if (is.null(population) || !(population %in% ref_population)) {
manifest_hg19 <- switch(
EXPR = array_name,
"450k" = get(utils::data("hm450.manifest.hg19", package = "ChAMPdata")),
"EPIC" = get(utils::data("EPIC.manifest.hg19", package = "ChAMPdata"))
)
which_population <- which(manifest_hg19$MASK_general)
} else {
manifest_hg19 <- switch(
EXPR = array_name,
"450k" = get(utils::data("hm450.manifest.pop.hg19", package = "ChAMPdata")),
"EPIC" = get(utils::data("EPIC.manifest.pop.hg19", package = "ChAMPdata"))
)
which_population <- which(manifest_hg19[, paste("MASK_general", population, sep = "_")])
}
maskname <- rownames(manifest_hg19)[which_population]
mset_f2 <- mset[!minfi::featureNames(mset) %in% maskname, ]
n_snps_discarded <- format(dim(mset)[1] - dim(mset_f2)[1], big.mark = ",", digits = 1L, nsmall = 0L)
mset <- mset_f2
}
if (filter_multihit) {
multi_hit <- get(utils::data("multi.hit", package = "ChAMPdata"))
mset_f2 <- mset[!minfi::featureNames(mset) %in% multi_hit$TargetID, ]
n_multihit_discarded <- format(dim(mset)[1] - dim(mset_f2)[1], big.mark = ",", digits = 1L, nsmall = 0L)
mset <- mset_f2
}
if (filter_xy) {
switch(
EXPR = array_name,
"450k" = utils::data("probe.features", package = "ChAMPdata"),
"EPIC" = utils::data("probe.features.epic", package = "ChAMPdata")
)
probe_features <- get("probe.features")
autosomes <- probe_features[!probe_features$CHR %in% c("X", "Y"), ]
mset_f2 <- mset[minfi::featureNames(mset) %in% rownames(autosomes), ]
n_xy_discarded <- format(dim(mset)[1] - dim(mset_f2)[1], big.mark = ",", digits = 1L, nsmall = 0L)
mset <- mset_f2
}
mset <- ENmix::preprocessENmix(
rgSet = rgSet,
bgParaEst = norm_background,
dyeCorr = norm_dye,
QCinfo = NULL,
exQCsample = FALSE,
exQCcpg = FALSE,
exSample = bad_samples,
exCpG = unique(c(bad_cpgs, setdiff(minfi::featureNames(mset_raw), minfi::featureNames(mset)))),
nCores = n_cores
)
mset <- ENmix::norm.quantile(mdat = mset, method = norm_quantile)
methylation_matrix <- switch(meth_value_type,
"B" = minfi::getBeta(mset, "Illumina"),
"M" = minfi::getM(mset),
stop('Methylation value type not defined. Only "B" or "M" are available.')
)
if (min(methylation_matrix, na.rm = TRUE) <= 0) {
methylation_matrix[methylation_matrix <= 0] <- min(methylation_matrix[methylation_matrix > 0], na.rm = TRUE)
}
if (max(methylation_matrix, na.rm = TRUE) >= 1) {
methylation_matrix[methylation_matrix >= 1] <- max(methylation_matrix[methylation_matrix < 1], na.rm = TRUE)
}
colnames(methylation_matrix) <- minfi::pData(mset)[["Sample_ID"]]
mset@metadata[[meth_value_type]] <- methylation_matrix
tmp_phenotypes <- data.table::as.data.table(minfi::pData(rgSet))
tmp_phenotypes[, "Sample_ID" := lapply(.SD, as.character), .SDcols = "Sample_ID"]
tmp_means <- colMeans(data_detP)[tmp_phenotypes[["Sample_ID"]]]
tmp_phenotypes[, "mean_detection_pvalue" := tmp_means]
tmp_callrate <- (colSums(data_detP < detection_pvalues) / nrow(data_detP))[tmp_phenotypes[["Sample_ID"]]]
tmp_phenotypes[, "call_rate" := tmp_callrate]
mset@metadata[["phenotypes"]] <- tmp_phenotypes
log_msg <- character(0)
if (filter_callrate) {
log_msg <- c(log_msg, paste0(
"Filtering samples with call rate below ",
paste(format(callrate_samples * 100, digits = 1, nsmall = 1), "%"), ":\n",
" - ", format(length(bad_samples), big.mark = ",", digits = 1L, nsmall = 0L), " samples were discarded."
))
log_msg <- c(log_msg, paste0(
"Filtering probes with call rate below ",
paste(format(callrate_probes * 100, digits = 1, nsmall = 1), "%"), ":\n",
" - ", format(length(bad_cpgs), big.mark = ",", digits = 1L, nsmall = 0L), " probes were discarded."
))
}
if (filter_beads) {
log_msg <- c(log_msg, paste0(
"Filtering probes with a beadcount lower than three in at least ",
paste(format(bead_cutoff * 100, digits = 1, nsmall = 1), "%"), " of samples:\n",
" - ", n_beads_discarded, " probes were discarded."
))
}
if (filter_non_cpg) {
log_msg <- c(log_msg, paste0(
"Filtering non-cg probes:\n",
" - ", n_non_cpg_discarded, " probes were discarded."
))
}
if (filter_snps) {
log_msg <- c(log_msg, paste0(
"Filtering probes with SNPs (Zhou et al., 2016; doi:10.1093/nar/gkw967):\n",
" - ", n_snps_discarded, " probes were discarded."
))
}
if (filter_multihit) {
log_msg <- c(log_msg, paste0(
"Filtering probes that align to multiple locations ",
"(Nordlund et al., 2013; doi:10.1186/gb-2013-14-9-r105):\n",
" - ", n_multihit_discarded, " probes were discarded."
))
}
if (filter_xy) {
log_msg <- c(log_msg, paste0(
"Filtering probes on the X or Y chromosome:\n",
" - ", n_xy_discarded, " probes were discarded."
))
}
log_msg <- c(log_msg,
"Zeros have been replaced with smallest value over zero.",
"Ones have been replaced with largest value below one.",
paste0(
"Data contains:\n",
" - ", format(dim(methylation_matrix)[1], big.mark = ",", digits = 1L, nsmall = 0L), " probes.\n",
" - ", format(dim(methylation_matrix)[2], big.mark = ",", digits = 1L, nsmall = 0L), " samples.\n",
" - ", format(sum(is.na(methylation_matrix)), big.mark = ",", digits = 1L, nsmall = 0L), " missing values."
)
)
if (echo) {
message(paste(log_msg, collapse = "\n"), appendLF = TRUE)
}
list(mset = mset, rgset = rgSet, log = log_msg)
}
#' read_sample_sheet
#' @import data.table
read_sample_sheet <- function(
directory = ".",
csv_file = "csv$",
ignore.case = TRUE,
recursive = TRUE,
full.names = TRUE,
echo = FALSE
) {
if (file.exists(suppressWarnings(normalizePath(csv_file)))) {
list_files <- normalizePath(csv_file)
} else {
list_files <- list.files(
path = directory,
pattern = csv_file,
full.names = full.names,
ignore.case = ignore.case,
recursive = recursive
)
if (length(list_files) > 1) {
warning("More than one CSV file have been found!")
list_files <- list.files[1]
if (echo) message("File '", list.files, "' will be used.", appendLF = TRUE)
}
}
data_header <- grep("^\\[DATA\\]", readLines(list_files), ignore.case = TRUE)
if (length(data_header) == 0) data_header <- 0
col_names <- colnames(data.table::fread(file = list_files, skip = data_header, nrows = 1))
default_cols <- c("Sample_ID", "Sentrix_ID", "Sentrix_Position")
cols_missing <- default_cols[!default_cols %in% col_names]
if (length(cols_missing) != 0) {
stop(
"Sample Sheet must contains the following missing columns:\n",
" - ", paste(cols_missing, collapse = "\n - ")
)
}
sample_sheet <- data.table::fread(file = list_files, skip = data_header)
data.table::setnames(
x = sample_sheet,
old = c("Sample_ID", "Slide", "Array", "Sample_Plate", "Sample_Well"),
new = c("Sample_ID", "Sentrix_ID", "Sentrix_Position", "Sample_Plate", "Sample_Well"),
skip_absent = TRUE
)
basenames <- sub("_Grn\\.idat.*", "", sapply(
X = paste0(sample_sheet[["Sentrix_ID"]], "_", sample_sheet[["Sentrix_Position"]], "_Grn.idat"),
FUN = grep,
x = list.files(path = directory, recursive = recursive, full.names = TRUE),
value = TRUE,
USE.NAMES = FALSE
), ignore.case = TRUE)
sample_sheet[, "Basename" := basenames]
sample_sheet
}
#' read_metharray
#' @import illuminaio
#' @import minfi
#' @import future.apply
read_metharray <- function(files, n_cores) {
Grn <- Red <- NULL # to avoid note from global variable check
basenames <- unique(sub("_Grn\\.idat.*|_Red\\.idat.*", "", files))
for (ichannel in c("Grn", "Red")) {
i_files <- paste0(basenames, "_", ichannel, ".idat")
names(i_files) <- basename(basenames)
i_files_exists <- file.exists(i_files)
if (!all(i_files_exists)) {
i_filesgz_exists <- file.exists(paste0(i_files, ".gz"))
if (!all(i_filesgz_exists)) {
stop(
"The following specified files do not exist:\n",
" - ", paste(i_files[!i_files_exists], collapse = "\n - ")
)
}
i_files <- paste0(i_files, ".gz")
}
suppressWarnings({
i_idats <- future.apply::future_lapply(
X = i_files,
future.globals = FALSE,
future.packages = "illuminaio",
FUN = illuminaio::readIDAT
)
})
assign(x = ichannel, value = i_idats)
}
G_idats <- lapply(X = Grn, FUN = `[[`, "Quants")
R_idats <- lapply(X = Red, FUN = `[[`, "Quants")
common_rows <- as.character(Reduce("intersect", lapply(X = G_idats, FUN = rownames)))
out <- minfi::RGChannelSetExtended(
Red = do.call("cbind", lapply(X = R_idats, y = common_rows, FUN = function(x, y) x[y, "Mean"])),
Green = do.call("cbind", lapply(X = G_idats, y = common_rows, FUN = function(x, y) x[y, "Mean"])),
RedSD = do.call("cbind", lapply(X = R_idats, y = common_rows, FUN = function(x, y) x[y, "SD"])),
GreenSD = do.call("cbind", lapply(X = G_idats, y = common_rows, FUN = function(x, y) x[y, "SD"])),
NBeads = do.call("cbind", lapply(X = G_idats, y = common_rows, FUN = function(x, y) x[y, "NBeads"]))
)
rownames(out) <- common_rows
out
}
#' read_metharray_exp
#' @import methods
#' @import minfi
read_metharray_exp <- function(
directory = NULL,
sample_sheet = NULL,
ignore.case = TRUE,
recursive = TRUE,
full.names = TRUE,
n_cores = 1
) {
if (is.null(sample_sheet)) {
if (is.null(directory)) directory <- "."
green_files <- list.files(
path = directory,
pattern = "_Grn.idat.*",
recursive = recursive,
ignore.case = ignore.case,
full.names = full.names
)
red_files <- list.files(
path = directory,
pattern = "_Red.idat.*",
recursive = recursive,
ignore.case = ignore.case,
full.names = full.names
)
if (length(green_files) == 0 || length(red_files) == 0) {
stop("IDAT files must be provided.")
}
common_files <- intersect(sub("_Grn.idat.*", "", green_files), sub("_Red.idat.*", "", red_files))
if (length(common_files) == 0) {
stop('"Grn" and "Red" idats files must be provided.')
}
common_files_green <- paste0(common_files, "_Grn.idat")
if (!setequal(common_files_green, green_files)) {
warning(
"The following files only exists for the green channel:\n",
" - ", paste(setdiff(green_files, common_files_green), collapse = "\n - ")
)
}
common_files_red <- paste0(common_files, "_Red.idat")
if (!setequal(common_files_red, red_files)) {
warning(
"The following files only exists for the red channel:\n",
" - ", paste(setdiff(red_files, common_files_red), collapse = "\n - ")
)
}
rgSet <- read_metharray(common_files, n_cores)
} else {
if (all(!grepl("Basename", names(sample_sheet)))) {
stop('"Basename" must be provided as a column of "sample_sheet".')
}
if (is.null(directory)) {
files <- sample_sheet[["Basename"]]
} else {
files <- file.path(directory, basename(sample_sheet[["Basename"]]))
}
rgSet <- read_metharray(files, n_cores)
pD <- methods::as(sample_sheet, "DataFrame")
pD[["filenames"]] <- files
rownames(pD) <- colnames(rgSet)
rgSet@colData <- pD
}
minfi::sampleNames(rgSet) <- rgSet[["Sample_ID"]]
rgSet
}
#' get_beadcount
#' @import minfi
get_beadcount <- function(x) {
nb <- minfi::getNBeads(x)
typeI <- minfi::getProbeInfo(x, type = "I")
typeII <- minfi::getProbeInfo(x, type = "II")
locus_names <- minfi::getManifestInfo(x, "locusNames")
bc_temp <- matrix(
data = NA_real_,
ncol = ncol(x),
nrow = length(locus_names),
dimnames = list(locus_names, minfi::sampleNames(x))
)
bc_temp[typeII$Name, ] <- nb[typeII$AddressA, ]
bc_temp[typeI$Name, ] <- nb[typeI$AddressB, ]
bc_temp[typeI$Name, ] <- nb[typeI$AddressA, ]
bc_temp[which(nb[typeI$AddressA, ] < 3 | nb[typeI$AddressB, ] < 3)] <- NA
data.frame(bc_temp)
}
#' qc_idats
#' @import data.table
qc_idats <- function(params) {
sample_sheet <- data.table::fread(params[["csv_file"]], skip = "Sample_Name")
sample_sheet[
j = Sample_ID := {
x <- as.character(seq_len(.N))
data.table::fifelse(x == "1", as.character(Sample_Name), paste(Sample_Name, x, sep = "_"))
},
by = "Sample_Name"
]
if (any(grepl("^[0-9]", sample_sheet[["Sample_ID"]]))) {
sample_sheet[
j = Sample_ID := paste0("ID_", Sample_ID)
]
}
data.table::setcolorder(sample_sheet, neworder = "Sample_ID")
if (!is.null(params[["sex_colname"]])) {
sample_sheet[
j = qc_observed_sex := c(
"1" = 1, "2" = 2, "M" = 1, "F" = 2, "0" = 2
)[as.character(get(params[["sex_colname"]]))]
]
}
data.table::fwrite(x = sample_sheet, file = file.path(tempdir(), "sample_sheet.csv"))
read_idats(
directory = params[["data_directory"]],
csv_file = file.path(tempdir(), "sample_sheet.csv"),
meth_value_type = "B",
filter_beads = params[["filter_beads"]],
bead_cutoff = params[["bead_cutoff"]],
filter_non_cpg = params[["filter_non_cpg"]],
filter_snps = params[["filter_snps"]],
population = params[["population"]],
filter_multihit = params[["filter_multihit"]],
filter_xy = params[["filter_xy"]],
detection_pvalues = params[["detection_pvalues"]],
filter_callrate = params[["filter_callrate"]],
callrate_samples = params[["callrate_samples"]],
callrate_probes = params[["callrate_probes"]],
norm_background = params[["norm_background"]],
norm_dye = params[["norm_dye"]],
norm_quantile = params[["norm_quantile"]],
array_name = sub(".* ", "", params[["array"]]),
annotation_version = params[["annotation"]],
n_cores = min(c(nrow(sample_sheet), 25, detectCores()))
)
}
#' estimate_cell_composition
#' @import FlowSorted.Blood.EPIC
#' @import minfi
#' @import RefFreeEWAS
#' @import stats
#' @import utils
#' @import future.apply
estimate_cell_composition <- function(data_rgset, data_mset, cell_tissue, array, n_cores) {
switch(
EXPR = tolower(cell_tissue),
"blood" = {
idol_cpgs <- switch(array,
"450k" = FlowSorted.Blood.EPIC::IDOLOptimizedCpGs450klegacy,
"EPIC" = FlowSorted.Blood.EPIC::IDOLOptimizedCpGs
)
out <- FlowSorted.Blood.EPIC::estimateCellCounts2(
rgSet = data_rgset,
compositeCellType = "Blood",
processMethod = "preprocessNoob",
probeSelect = "IDOL",
cellTypes = c("CD8T", "CD4T", "NK", "Bcell", "Mono", "Neu"),
referencePlatform = paste0("IlluminaHumanMethylation", array),
IDOLOptimizedCpGs = idol_cpgs,
returnAll = FALSE,
meanPlot = FALSE,
verbose = FALSE
)$counts
colnames(out) <- paste0("CellT_", colnames(out))
out
},
"cordbloodlegacy" = {
out <- FlowSorted.Blood.EPIC::estimateCellCounts2(
rgSet = data_rgset,
compositeCellType = "CordBlood",
processMethod = "preprocessNoob",
probeSelect = "auto",
cellTypes = c("Bcell", "CD4T", "CD8T", "Gran", "Mono", "NK", "nRBC"),
referencePlatform = "IlluminaHumanMethylation450k",
IDOLOptimizedCpGs = NULL,
returnAll = FALSE,
meanPlot = FALSE,
verbose = FALSE
)$counts
colnames(out) <- paste0("CellT_", colnames(out))
out
},
"cordblood" = {
if (nchar(system.file(package = "FlowSorted.CordBloodCombined.450k")) > 0) {
idol_cpgs <- get(utils::data("IDOLOptimizedCpGsCordBlood", package = "FlowSorted.CordBloodCombined.450k"))
} else {
idol_cpgs <- NULL
}
# FlowSorted.CordBloodCombined.450k <- FlowSorted.CordBloodCombined.450k::FlowSorted.CordBloodCombined.450k()
out <- FlowSorted.Blood.EPIC::estimateCellCounts2(
rgSet = data_rgset,
compositeCellType = "CordBloodCombined",
processMethod = "preprocessNoob",
probeSelect = if (is.null(idol_cpgs)) "auto" else "IDOL",
cellTypes = c("Bcell", "CD4T", "CD8T", "Gran", "Mono", "NK", "nRBC"),
referencePlatform = paste0("IlluminaHumanMethylation", array),
# referenceset = "FlowSorted.CordBloodCombined.450k",
IDOLOptimizedCpGs = idol_cpgs,
returnAll = FALSE,
meanPlot = FALSE,
verbose = FALSE
)$counts
colnames(out) <- paste0("CellT_", colnames(out))
out
},
{
estimate_k_cluster <- function(Rmat, max_k = 25, n_cores = 1) {
svdRmat <- RefFreeEWAS::svdSafe(Rmat)
tmp <- do.call("rbind", future.apply::future_lapply(
X = 0:max_k,
future.globals = FALSE,
mc_Rmat = Rmat,
mc_svdRmat = svdRmat,
FUN = function(Ktest, mc_Rmat, mc_svdRmat) {
N1 <- dim(mc_Rmat)[1]
N2 <- dim(mc_Rmat)[2]
if (Ktest == 0) {
tmpRminLU <- mc_Rmat
} else {
tmpRminLU <- mc_Rmat - mc_svdRmat$u[, seq_len(Ktest)] %*%
(mc_svdRmat$d[seq_len(Ktest)] * t(mc_svdRmat$v[, seq_len(Ktest)]))
}
tmpSigSq <- rowSums(tmpRminLU * tmpRminLU) / N2
c(
K = Ktest,
AIC = 2 * (N1 + Ktest * (N1 + N2)) +
N1 * N2 +
N2 * sum(log(tmpSigSq)),
BIC = log(N2) * (N1 + Ktest * (N1 + N2)) +
N1 * N2 +
N2 * sum(log(tmpSigSq))
)
}))
list(
icTable = tmp,
best = tmp[c(AIC = which.min(tmp[, "AIC"]), BIC = which.min(tmp[, "BIC"])), "K"],
custom_best = tmp[c(
AIC = which.max(abs(diff(tmp[, "AIC"])[-1])) + 1,
BIC = which.max(abs(diff(tmp[, "BIC"])[-1])) + 1
), "K"]
)
}
beta_matrix <- stats::na.exclude(minfi::getBeta(data_mset))
max_k <- min(ncol(beta_matrix), 25)
k_estimated <- min(estimate_k_cluster(
Rmat = beta_matrix,
max_k = max_k,
n_cores = min(n_cores, max_k)
)$best)
mu0 <- RefFreeEWAS::RefFreeCellMixInitialize(
Y = beta_matrix,
K = k_estimated,
Y.Distance = NULL,
Y.Cluster = NULL,
largeOK = TRUE,
dist.method = "euclidean"
)
RefFreeCellMixObj <- RefFreeEWAS::RefFreeCellMix(
Y = beta_matrix,
mu0 = mu0,
K = NULL,
iters = 10,
Yfinal = NULL,
verbose = FALSE
)
out <- RefFreeCellMixObj[["Omega"]]
colnames(out) <- paste0("CellT_", sqe_len(ncol(out)))
out
}
)
}
#' compute_sex_threshold
#' @import minfi
#' @import stats
compute_sex_threshold <- function(data_rgset, sex_threshold) {
if (is.null(sex_threshold)) {
sex_predicted <- minfi::getSex(minfi::mapToGenome(data_rgset), cutoff = -2)
sex_density <- stats::density(sex_predicted$yMed - sex_predicted$xMed, n = 100000)
min_diff_xy <- which(diff(sign(diff(sex_density$y))) == 2)
min_diff_xy <- min_diff_xy[which.min(sex_density$y[min_diff_xy])]
sex_threshold <- round(x = sex_density$x[min_diff_xy], digits = 3)
}
sex_threshold
}
#' check_sex
#' @import data.table
#' @import minfi
check_sex <- function(data_rgset, sex_threshold) {
sex_predicted <- data.table::as.data.table(
minfi::getSex(minfi::mapToGenome(data_rgset), cutoff = sex_threshold)
)[j = Sample_ID := minfi::sampleNames(data_rgset)]
data.table::setnames(
x = sex_predicted,
old = c("xMed", "yMed", "predictedSex"),
new = paste0("qc_", c("xmedian", "ymedian", "predicted_sex"))
)
sex_predicted
}
#' compute_phenotypes
#' @import data.table
compute_phenotypes <- function(data_mset, cell, sex_predicted) {
raw_phenotypes <- data.table::as.data.table(data_mset@metadata[["phenotypes"]])[
j = c("Sample_ID", "Sample_Plate", "Sentrix_ID") := lapply(.SD, as.character),
.SDcols = c("Sample_ID", "Sample_Plate", "Sentrix_ID")
]
if (inherits(cell, c("data.table", "data.frame", "matrix"))) {
raw_phenotypes <- merge(
x = raw_phenotypes,
y = data.table::as.data.table(cell, keep.rownames = "Sample_ID"),
by = "Sample_ID"
)
}
if (inherits(sex_predicted, c("data.table", "data.frame", "matrix"))) {
raw_phenotypes <- merge(
x = raw_phenotypes,
y = sex_predicted[
j = c("Sample_ID", "qc_predicted_sex") := list(
as.character(Sample_ID),
c("M" = 1, "F" = 2)[qc_predicted_sex]
)
],
by = "Sample_ID"
)[j = qc_sex_discrepancy := is.na(qc_observed_sex) | qc_observed_sex != qc_predicted_sex]
raw_phenotypes[
j = c("qc_predicted_sex", "qc_observed_sex") :=
lapply(.SD, factor, levels = c(1, 2), labels = c("Male", "Female")),
.SDcols = c("qc_predicted_sex", "qc_observed_sex")
]
}
raw_phenotypes
}
#' normalise_mset
#' @import ENmix
#' @import minfi
#' @import sva
normalise_mset <- function(data_mset, phenotypes) {
norm_beta <- ENmix::rcp(mdat = data_mset)
if (length(unique(minfi::pData(data_mset)[["Sentrix_ID"]])) > 1) {
norm_beta <- sva::ComBat(
dat = norm_beta,
batch = factor(minfi::pData(data_mset)[["Sentrix_ID"]])
)[rownames(data_mset), ]
}
colnames(norm_beta) <- minfi::pData(data_mset)[["Sample_ID"]]
if (min(norm_beta, na.rm = TRUE) <= 0) {
norm_beta[norm_beta <= 0] <- min(norm_beta[norm_beta > 0])
}
if (max(norm_beta, na.rm = TRUE) >= 1) {
norm_beta[norm_beta >= 1] <- max(norm_beta[norm_beta < 1])
}
data_mset@metadata[grep("_values", names(data_mset@metadata))] <- NULL
data_mset@metadata[["norm_beta_values"]] <- norm_beta
data_mset@metadata[["phenotypes"]] <- phenotypes
data_mset
}
#' mset_pca_plot
#' @import data.table
#' @import flashpcaR
#' @import ggplot2
#' @import ggtext
#' @import minfi
#' @import patchwork
#' @import scales
#' @import stats
#' @import utils
mset_pca_plot <- function(data, normalised_mset, pca_vars) {
phenotypes <- normalised_mset@metadata[["phenotypes"]]
pca_vars <- intersect(colnames(phenotypes), unique(c(pca_vars, "qc_observed_sex")))
lapply(
X = list(
"Raw β-values" = `colnames<-`(minfi::getBeta(data$rgset), minfi::pData(data$rgset)[, "Sample_ID"]),
"Normalised β-values" = normalised_mset@metadata[["norm_beta_values"]]
),
FUN = function(data_batch) {
pca_methylation <- data_batch
pca_methylation <- pca_methylation[rowSums(is.na(pca_methylation)) == 0, ]
pca_phenotypes <- phenotypes[Sample_ID %in% colnames(pca_methylation)]
n_comp <- min(c(10, max(c(3, ncol(pca_methylation) - 5))))
fig_n_comp <- min(c(3, n_comp))
keep_technical <- names(which(sapply(pca_phenotypes[
j = lapply(.SD, function(x) {
(data.table::uniqueN(x) > 1 & data.table::uniqueN(x) < length(x)) | is.numeric(x)
}),
.SDcols = pca_vars
], isTRUE)))
variables_excluded <- setdiff(pca_vars, keep_technical)
if (length(variables_excluded) != 0) {
message(paste(
"The following variables have been excluded (null variances or confounding with samples):\n",
paste("+", variables_excluded),
"\n",
sep = "\n"
))
}
if (length(keep_technical) == 0) return(NULL)
pca_res <- flashpcaR::flashpca(
X = t(pca_methylation),
stand = "sd",
ndim = n_comp
)
pca_dfxy <- data.table::as.data.table(pca_res[["vectors"]], keep.rownames = "Sample_ID")
data.table::setnames(
x = pca_dfxy,
old = setdiff(names(pca_dfxy), "Sample_ID"),
new = sprintf("PC%02d", as.numeric(gsub("V", "", setdiff(names(pca_dfxy), "Sample_ID"))))
)
pca_dfxy <- merge(x = pca_dfxy, y = pca_phenotypes, by = "Sample_ID")
p_inertia <- ggplot2::ggplot(
data = data.table::data.table(
y = pca_res[["pve"]],
x = sprintf("PC%02d", seq_along(pca_res[["pve"]]))
)[x %in% sprintf("PC%02d", seq_len(fig_n_comp))]
) +
ggplot2::aes(
x = paste0(
x,
"<br><i style='font-size:8pt;'>(",
scales::percent_format(accuracy = 0.01, suffix = " %")(y),
")</i>"
),
y = y
) +
ggplot2::geom_col(
width = 1,
colour = "white",
fill = scales::viridis_pal(begin = 0.5, end = 0.5)(1),
na.rm = TRUE
) +
ggplot2::scale_y_continuous(
labels = scales::percent_format(accuracy = 0.1, suffix = " %"),
expand = ggplot2::expansion(mult = c(0, 0.05))
) +
ggplot2::labs(
x = "Principal Components",
y = "Variance Explained"
)
asso_dt <- data.table::melt(
data = pca_dfxy,
measure.vars = grep("^PC[0-9]+$", names(pca_dfxy), value = TRUE),
variable.name = "pc",
value.name = "values"
)[pc %in% sprintf("PC%02d", seq_len(n_comp))][
j = {
m <- stats::model.matrix(
object = stats::as.formula(
object = paste0("values ~ ", paste(sprintf("`%s`", keep_technical), collapse = " + "))
),
data = .SD
)
if (qr(m)$rank == ncol(m)) {
out <- data.table::as.data.table(
stats::anova(
stats::lm(
formula = stats::as.formula(
object = paste0("values ~ ", paste(sprintf("`%s`", keep_technical), collapse = " + "))
),
data = .SD
)
),
keep.rownames = "term"
)[term != "Residuals"]
} else {
out <- data.table::rbindlist(
lapply(X = sprintf("`%s`", keep_technical), .data = .SD, FUN = function(.x, .data) {
data.table::as.data.table(
stats::anova(
stats::lm(
formula = stats::as.formula(paste0("values ~ ", .x)),
data = .SD
)
),
keep.rownames = "term"
)[term != "Residuals"]
})
)
}
out[j = full_rank := qr(m)$rank == ncol(m)][j = term := gsub("`", "", term)]
},
by = "pc"
]
p_association <- ggplot2::ggplot(data = asso_dt) +
ggplot2::aes(
x = factor(.data[["pc"]]),
y = factor(
x = .data[["term"]],
levels = data.table::setorderv(
x = data.table::dcast(
data = asso_dt[j = list(pc, term, `Pr(>F)` = data.table::fifelse(`Pr(>F)` <= 0.1, `Pr(>F)`, NA_real_))],
formula = term ~ pc,
value.var = "Pr(>F)"
),
cols = levels(asso_dt[["pc"]])[seq_len(n_comp)],
order = -1
)[["term"]]
),
fill = .data[["Pr(>F)"]]
) +
ggplot2::geom_tile(colour = "white", na.rm = TRUE) +
ggtext::geom_richtext(
mapping = ggplot2::aes(
label = gsub(
pattern = "(.*)e([-+]*)0*(.*)",
replacement = "\\1<br>×<br>10<sup>\\2\\3</sup>",
x = format(.data[["Pr(>F)"]], digits = 2, nsmall = 2, scientific = TRUE)
)
),
colour = "white",
fill = NA,
label.colour = NA,
size = 4,
na.rm = TRUE
) +
ggplot2::scale_fill_viridis_c(na.value = "grey85", end = 0.75, limits = c(0, 0.1)) +
ggplot2::theme(panel.grid = ggplot2::element_blank()) +
ggplot2::scale_x_discrete(
expand = c(0, 0),
labels = function(x) {
paste0(
x,
"<br><i style='font-size:8pt;'>(",
format(
x = pca_res[["pve"]][as.numeric(gsub("PC", "", x))] * 100,
digits = 2,
nsmall = 2
),
" %)</i>"
)
}
) +
ggplot2::scale_y_discrete(expand = c(0, 0), labels = toupper) +
ggplot2::labs(
x = "Principal Components",
y = "Variables",
title = "Association Tests Between Variables And Principal Components",
caption = ifelse(
test = all(asso_dt[["full_rank"]]),
yes = "Variables are tested against principal components using ANOVA.",
no = paste(
"Variables are independently tested against principal components using ANOVA",
"(*i.e.*, model matrix is not full rank)."
)
),
fill = "P-Value"
)
c(
p_association = list(p_association),
lapply(stats::setNames(keep_technical, keep_technical), function(ivar) {
patchwork::wrap_plots(
c(
apply(
X = utils::combn(sprintf("PC%02d", seq_len(fig_n_comp)), 2),
MARGIN = 2,
FUN = function(x) {
ggplot2::ggplot(data = pca_dfxy[j = .SD, .SDcols = c(ivar, x)]) +
ggplot2::aes(x = .data[[x[1]]], y = .data[[x[2]]], colour = .data[[ivar]]) +
ggplot2::geom_hline(yintercept = 0, linetype = 2, na.rm = TRUE) +
ggplot2::geom_vline(xintercept = 0, linetype = 2, na.rm = TRUE) +
ggplot2::geom_point(na.rm = TRUE) +
{
if (is.numeric(pca_dfxy[[ivar]])) {
ggplot2::scale_colour_viridis_c(
name = NULL,
begin = 0,
end = 0.75
)
} else {
list(
ggplot2::stat_ellipse(type = "norm", na.rm = TRUE, show.legend = FALSE),
ggplot2::scale_colour_viridis_d(
name = NULL,
begin = if (pca_dfxy[j = data.table::uniqueN(.SD), .SDcols = ivar] == 2) 0.25 else 0,
end = 0.75,
guide = ggplot2::guide_legend(override.aes = list(size = 4))