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R/createTADdata.R
In preciseTAD: preciseTAD: A machine learning framework for precise TAD boundary prediction
Defines functions
createTADdata
Documented in
createTADdata
#' Function to create a data matrix used for building a predictive model to
#' classify boundary regions from functional genomic elements
#'
#' @param bounds.GR a GRanges object with chromosomal coordinates of TAD
#' boundaries used to identify positive cases (can be obtained using
#' \code{\link{extractBoundaries}}). Required.
#' @param resolution Numeric, the width to bin the genome at, should match the
#' resolution that TADs were called at. Required.
#' @param genomicElements.GR a GRangesList object containing GRanges objects
#' for each ChIP-seq data to leverage in the random forest model (can be
#' obtained using the \code{\link{bedToGRangesList}}). Required.
#' @param featureType Character, controls how the feature space is constructed
#' (one of either "binary" (overlap yes/no), "oc" (overlap counts, the number
#' of overlaps), "op" (overlap percent, the percent of bin width covered by the
#' genomic annotation), or "distance" (log2-transformed distance from the center
#' of the nearest genomic annotation to the center of the bin); default is
#' "distance"). Required.
#' @param resampling Character, controls if and how the data should be
#' resampled to create balanced classes of boundary vs. nonboundary regions (one
#' of either "none" - no re-sampling, "ros" - Random Over-Sampling, "rus" -
#' Random Under-Sampling, or "smote" - Synthetic Minority Over-sampling
#' TEchnique). Required.
#' @param trainCHR Character vector of chromosomes to use to build the binned
#' data matrix for training. Required.
#' @param predictCHR Character vector of chromosomes to use to build the binned
#' data matrix for testing. Default in NULL, indicating no test data is created.
#' If trainCHR=predictCHR then a 7:3 split is created.
#'
#' @return A list object containing two data.frames: 1) the training data, 2)
#' the test data (only if predictCHR is not NULL, otherwise it is NA). "y" is
#' an indicator whether the corresponding bin is a TAD boundary, and the
#' subsequent columns have the association measures between bins and the
#' genomic annotations
#' @export
#'
#' @import IRanges GenomicRanges DMwR
#'
#' @examples
#' # Create training data for CHR21 and testing data for CHR22 with
#' # 5 kb binning, oc-type predictors from 26 different transcription factor
#' # binding sites from the GM12878 cell line, and random under-sampling
#'
#' # Read in ARROWHEAD-called TADs at 5kb
#' data(arrowhead_gm12878_5kb)
#'
#' #Extract unique boundaries
#' bounds.GR <- extractBoundaries(domains.mat = arrowhead_gm12878_5kb,
#' preprocess = FALSE,
#' CHR = c("CHR21", "CHR22"),
#' resolution = 5000)
#'
#' # Read in GRangesList of 26 TFBS
#' data(tfbsList)
#'
#' tadData <- createTADdata(bounds.GR = bounds.GR,
#' resolution = 5000,
#' genomicElements.GR = tfbsList,
#' featureType = "oc",
#' resampling = "rus",
#' trainCHR = "CHR21",
#' predictCHR = "CHR22")
createTADdata <- function(bounds.GR, resolution, genomicElements.GR, featureType = "distance",
resampling, trainCHR, predictCHR = NULL) {
resolution = as.integer(resolution)
#ESTABLISHING CHROMOSOME-SPECIFIC SEQINFO#
#LOADING CHROMOSOME LENGTHS#
hg19 <- preciseTAD:::hg19
#ESTABLISHING DATA MATRIX FOR MODELING#
if (is.null(predictCHR)) {
# you are not interested in performance therefore no holdout test data is created
# building training set
bounds.GR.flank <- flank(bounds.GR[which(as.character(seqnames(bounds.GR)) %in%
tolower(trainCHR))], width = (resolution/2), both = TRUE)
data_mat_list <- list()
outcome_list <- list()
train_list <- list()
for (i in seq_len(length(trainCHR))) {
start <- (resolution/2)
chrLength <- hg19$length[hg19$chrom %in% trainCHR][i]
centromereStart <- as.integer(hg19$centromerStart[hg19$chrom==trainCHR[i]])
centromereEnd <- as.integer(hg19$centromerEnd[hg19$chrom==trainCHR[i]])
end <- chrLength - (chrLength %% resolution) + resolution/2
data_mat_list[[i]] <- matrix(nrow=length(seq(start,end-1,resolution)[-which(seq(start,end-1,resolution) %in% c(centromereStart:centromereEnd))]),
ncol=length(genomicElements.GR))
dat_mat_gr <- GRanges(seqnames = tolower(trainCHR[i]),
IRanges(start = seq(start,end-1,resolution)[-which(seq(start,end-1,resolution) %in% c(centromereStart:centromereEnd))],
width=resolution))
if (featureType == "distance") {
# for use of distance type features
dat_mat_gr <- GRanges(seqnames = tolower(trainCHR[i]), IRanges(start = (start(dat_mat_gr) +
end(dat_mat_gr))/2, width = 1))
for (k in seq(length(genomicElements.GR))) {
d <- distance_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- d
}
for (h in seq_len(ncol(data_mat_list[[i]]))) {
data_mat_list[[i]][, h] <- log(data_mat_list[[i]][, h] + 1, base = 2)
}
} else if (featureType == "binary") {
for (k in seq_len(length(genomicElements.GR))) {
cb <- binary_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- cb
}
} else if (featureType == "oc") {
for (k in seq_len(length(genomicElements.GR))) {
co <- count_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- co
}
} else if (featureType == "op") {
for (k in seq_len(length(genomicElements.GR))) {
cp <- percent_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- cp
}
} else {
cs <- signal_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- cs
}
outcome_list[[i]] <- countOverlaps(GRanges(seqnames = tolower(trainCHR[i]),
IRanges(start = seq(start,end-1,resolution)[-which(seq(start,end-1,resolution) %in% c(centromereStart:centromereEnd))],
width = resolution)), bounds.GR.flank)
outcome_list[[i]] <- ifelse(outcome_list[[i]] >= 1, 1, 0)
train_list[[i]] <- cbind.data.frame(outcome_list[[i]], as.matrix(data_mat_list[[i]]))
names(train_list[[i]]) <- c("y", names(genomicElements.GR))
train_list[[i]]$y <- factor(train_list[[i]]$y)
levels(train_list[[i]]$y) <- c("No", "Yes")
if (resampling == "ros") {
# assign sample indices
sampids.train <- sample(x = which(train_list[[i]]$y == "Yes"), size = length(which(train_list[[i]]$y ==
"No")), replace = TRUE)
train_list[[i]] <- rbind.data.frame(train_list[[i]][which(train_list[[i]]$y ==
"No"), ], train_list[[i]][sampids.train, ])
# Randomly shuffle the data
train_list[[i]] <- train_list[[i]][sample(nrow(train_list[[i]])),
]
} else if (resampling == "rus") {
sampids.train <- sample(x = which(train_list[[i]]$y == "No"), size = length(which(train_list[[i]]$y ==
"Yes")), replace = FALSE)
train_list[[i]] <- rbind.data.frame(train_list[[i]][which(train_list[[i]]$y ==
"Yes"), ], train_list[[i]][sampids.train, ])
# Randomly shuffle the data
train_list[[i]] <- train_list[[i]][sample(nrow(train_list[[i]])),
]
} else if (resampling == "smote") {
train_list[[i]] <- SMOTE(y ~ ., data = train_list[[i]], perc.over = 100,
perc.under = 200)
# Randomly shuffle the data
train_list[[i]] <- train_list[[i]][sample(nrow(train_list[[i]])),
]
} else {
train_list[[i]] = train_list[[i]]
}
}
train_list <- do.call("rbind.data.frame", train_list)
} else if (isTRUE(all.equal(sort(trainCHR), sort(predictCHR)))) {
# you are training on the same chr(s) that you are predicting on so we build the
# full datamatrix (including predictor type) and then split the data into
# training and testing then perform resampling on training set
bounds.GR.flank <- flank(bounds.GR, width = (resolution/2), both = TRUE)
data_mat_list <- list()
outcome_list <- list()
full_list <- list()
train_list <- list()
test_list <- list()
for (i in seq_len(length(trainCHR))) {
start <- (resolution/2)
chrLength <- hg19$length[hg19$chrom %in% trainCHR][i]
centromereStart <- as.integer(hg19$centromerStart[hg19$chrom==trainCHR[i]])
centromereEnd <- as.integer(hg19$centromerEnd[hg19$chrom==trainCHR[i]])
end <- chrLength - (chrLength %% resolution) + resolution/2
data_mat_list[[i]] <- matrix(nrow=length(seq(start,end-1,resolution)[-which(seq(start,end-1,resolution) %in% c(centromereStart:centromereEnd))]),
ncol=length(genomicElements.GR))
dat_mat_gr <- GRanges(seqnames = tolower(trainCHR[i]),
IRanges(start = seq(start,end-1,resolution)[-which(seq(start,end-1,resolution) %in% c(centromereStart:centromereEnd))],
width=resolution))
if (featureType == "distance") {
# for use of distance type features
dat_mat_gr <- GRanges(seqnames = tolower(trainCHR[i]), IRanges(start = (start(dat_mat_gr) +
end(dat_mat_gr))/2, width = 1))
for (k in seq_len(length(genomicElements.GR))) {
d <- distance_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- d
}
for (h in seq_len(ncol(data_mat_list[[i]]))) {
data_mat_list[[i]][, h] <- log(data_mat_list[[i]][, h] + 1, base = 2)
}
} else if (featureType == "binary") {
for (k in seq_len(length(genomicElements.GR))) {
cb <- binary_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- cb
}
} else if (featureType == "oc") {
for (k in seq_len(length(genomicElements.GR))) {
co <- count_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- co
}
} else if (featureType == "op") {
for (k in seq_len(length(genomicElements.GR))) {
cp <- percent_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- cp
}
}
outcome_list[[i]] <- countOverlaps(GRanges(seqnames = tolower(trainCHR[i]),
IRanges(start = seq(start,end-1,resolution)[-which(seq(start,end-1,resolution) %in% c(centromereStart:centromereEnd))],
width = resolution)), bounds.GR.flank)
outcome_list[[i]] <- ifelse(outcome_list[[i]] >= 1, 1, 0)
full_list[[i]] <- cbind.data.frame(outcome_list[[i]], as.matrix(data_mat_list[[i]]))
names(full_list[[i]]) <- c("y", names(genomicElements.GR))
full_list[[i]]$y <- factor(full_list[[i]]$y)
levels(full_list[[i]]$y) <- c("No", "Yes")
inTrainingSet <- sample(length(full_list[[i]]$y), floor(length(full_list[[i]]$y) *
0.7))
train_list[[i]] <- full_list[[i]][inTrainingSet, ]
test_list[[i]] <- full_list[[i]][-inTrainingSet, ]
if (resampling == "ros") {
# assign sample indices
sampids.train <- sample(x = which(train_list[[i]]$y == "Yes"), size = length(which(train_list[[i]]$y ==
"No")), replace = TRUE)
train_list[[i]] <- rbind.data.frame(train_list[[i]][which(train_list[[i]]$y ==
"No"), ], train_list[[i]][sampids.train, ])
# Randomly shuffle the data
train_list[[i]] <- train_list[[i]][sample(nrow(train_list[[i]])),
]
} else if (resampling == "rus") {
sampids.train <- sample(x = which(train_list[[i]]$y == "No"), size = length(which(train_list[[i]]$y ==
"Yes")), replace = FALSE)
train_list[[i]] <- rbind.data.frame(train_list[[i]][which(train_list[[i]]$y ==
"Yes"), ], train_list[[i]][sampids.train, ])
# Randomly shuffle the data
train_list[[i]] <- train_list[[i]][sample(nrow(train_list[[i]])),
]
} else if (resampling == "smote") {
train_list[[i]] <- SMOTE(y ~ ., data = train_list[[i]], perc.over = 100,
perc.under = 200)
# Randomly shuffle the data
train_list[[i]] <- train_list[[i]][sample(nrow(train_list[[i]])),
]
} else {
train_list[[i]] = train_list[[i]]
}
}
train_list <- do.call("rbind.data.frame", train_list)
test_list <- do.call("rbind.data.frame", test_list)
} else {
# you are training on one set of chr(s) and are predicting on another set of
# chr(s) first build training set on the trainCHR, implement resampling technique
# then build testing set from the predictCHR, with no resampling technique
# building training set
bounds.GR.flank <- flank(bounds.GR[which(as.character(seqnames(bounds.GR)) %in%
tolower(trainCHR))], width = (resolution/2), both = TRUE)
data_mat_list <- list()
outcome_list <- list()
train_list <- list()
for (i in seq_len(length(trainCHR))) {
start <- (resolution/2)
chrLength <- hg19$length[hg19$chrom %in% trainCHR][i]
centromereStart <- as.integer(hg19$centromerStart[hg19$chrom==trainCHR[i]])
centromereEnd <- as.integer(hg19$centromerEnd[hg19$chrom==trainCHR[i]])
end <- chrLength - (chrLength %% resolution) + resolution/2
data_mat_list[[i]] <- matrix(nrow=length(seq(start,end-1,resolution)[-which(seq(start,end-1,resolution) %in% c(centromereStart:centromereEnd))]),
ncol=length(genomicElements.GR))
dat_mat_gr <- GRanges(seqnames = tolower(trainCHR[i]),
IRanges(start = seq(start,end-1,resolution)[-which(seq(start,end-1,resolution) %in% c(centromereStart:centromereEnd))],
width=resolution))
if (featureType == "distance") {
# for use of distance type features
dat_mat_gr <- GRanges(seqnames = tolower(trainCHR[i]), IRanges(start = (start(dat_mat_gr) +
end(dat_mat_gr))/2, width = 1))
for (k in seq_len(length(genomicElements.GR))) {
d <- distance_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- d
}
for (h in seq_len(ncol(data_mat_list[[i]]))) {
data_mat_list[[i]][, h] <- log(data_mat_list[[i]][, h] + 1, base = 2)
}
} else if (featureType == "binary") {
for (k in seq_len(length(genomicElements.GR))) {
cb <- binary_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- cb
}
} else if (featureType == "oc") {
for (k in seq_len(length(genomicElements.GR))) {
co <- count_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- co
}
} else if (featureType == "op") {
for (k in seq_len(length(genomicElements.GR))) {
cp <- percent_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- cp
}
}
outcome_list[[i]] <- countOverlaps(GRanges(seqnames = tolower(trainCHR[i]),
IRanges(start = seq(start,end-1,resolution)[-which(seq(start,end-1,resolution) %in% c(centromereStart:centromereEnd))],
width = resolution)), bounds.GR.flank)
outcome_list[[i]] <- ifelse(outcome_list[[i]] >= 1, 1, 0)
train_list[[i]] <- cbind.data.frame(outcome_list[[i]], as.matrix(data_mat_list[[i]]))
names(train_list[[i]]) <- c("y", names(genomicElements.GR))
train_list[[i]]$y <- factor(train_list[[i]]$y)
levels(train_list[[i]]$y) <- c("No", "Yes")
if (resampling == "ros") {
# assign sample indices
sampids.train <- sample(x = which(train_list[[i]]$y == "Yes"), size = length(which(train_list[[i]]$y ==
"No")), replace = TRUE)
train_list[[i]] <- rbind.data.frame(train_list[[i]][which(train_list[[i]]$y ==
"No"), ], train_list[[i]][sampids.train, ])
# Randomly shuffle the data
train_list[[i]] <- train_list[[i]][sample(nrow(train_list[[i]])),
]
} else if (resampling == "rus") {
sampids.train <- sample(x = which(train_list[[i]]$y == "No"), size = length(which(train_list[[i]]$y ==
"Yes")), replace = FALSE)
train_list[[i]] <- rbind.data.frame(train_list[[i]][which(train_list[[i]]$y ==
"Yes"), ], train_list[[i]][sampids.train, ])
# Randomly shuffle the data
train_list[[i]] <- train_list[[i]][sample(nrow(train_list[[i]])),
]
} else if (resampling == "smote") {
train_list[[i]] <- SMOTE(y ~ ., data = train_list[[i]], perc.over = 100,
perc.under = 200)
# Randomly shuffle the data
train_list[[i]] <- train_list[[i]][sample(nrow(train_list[[i]])),
]
} else {
train_list[[i]] = train_list[[i]]
}
}
train_list <- do.call("rbind.data.frame", train_list)
# building testing set
bounds.GR.flank.pred <- flank(bounds.GR[which(as.character(seqnames(bounds.GR)) %in%
tolower(predictCHR))], width = (resolution/2), both = TRUE)
data_mat_list <- list()
outcome_list <- list()
test_list <- list()
for (i in seq_len(length(predictCHR))) {
start <- (resolution/2)
chrLength <- hg19$length[hg19$chrom %in% predictCHR][i]
centromereStart <- as.integer(hg19$centromerStart[hg19$chrom==predictCHR[i]])
centromereEnd <- as.integer(hg19$centromerEnd[hg19$chrom==predictCHR[i]])
end <- chrLength - (chrLength %% resolution) + resolution/2
data_mat_list[[i]] <- matrix(nrow=length(seq(start,end-1,resolution)[-which(seq(start,end-1,resolution) %in% c(centromereStart:centromereEnd))]),
ncol=length(genomicElements.GR))
dat_mat_gr <- GRanges(seqnames = tolower(predictCHR[i]),
IRanges(start = seq(start,end-1,resolution)[-which(seq(start,end-1,resolution) %in% c(centromereStart:centromereEnd))],
width=resolution))
if (featureType == "distance") {
# for use of distance type features
dat_mat_gr <- GRanges(seqnames = tolower(predictCHR[i]), IRanges(start = (start(dat_mat_gr) +
end(dat_mat_gr))/2, width = 1))
for (k in seq_len(length(genomicElements.GR))) {
d <- distance_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- d
}
for (h in seq_len(ncol(data_mat_list[[i]]))) {
data_mat_list[[i]][, h] <- log(data_mat_list[[i]][, h] + 1, base = 2)
}
} else if (featureType == "binary") {
for (k in seq_len(length(genomicElements.GR))) {
cb <- binary_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- cb
}
} else if (featureType == "oc") {
for (k in seq_len(length(genomicElements.GR))) {
co <- count_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- co
}
} else if (featureType == "op") {
for (k in seq_len(length(genomicElements.GR))) {
cp <- percent_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- cp
}
}
outcome_list[[i]] <- countOverlaps(GRanges(seqnames = tolower(predictCHR[i]),
IRanges(start = seq(start,end-1,resolution)[-which(seq(start,end-1,resolution) %in% c(centromereStart:centromereEnd))],
width = resolution)), bounds.GR.flank.pred)
outcome_list[[i]] <- ifelse(outcome_list[[i]] >= 1, 1, 0)
test_list[[i]] <- cbind.data.frame(outcome_list[[i]], as.matrix(data_mat_list[[i]]))
names(test_list[[i]]) <- c("y", names(genomicElements.GR))
test_list[[i]]$y <- factor(test_list[[i]]$y)
levels(test_list[[i]]$y) <- c("No", "Yes")
}
test_list <- do.call("rbind.data.frame", test_list)
}
if (is.null(predictCHR)) {
TADdataList <- list(train_list, NA)
} else {
TADdataList <- list(train_list, test_list)
}
return(TADdataList)
}
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Defines functions createTADdata
Documented in createTADdata
#' Function to create a data matrix used for building a predictive model to
#' classify boundary regions from functional genomic elements
#'
#' @param bounds.GR a GRanges object with chromosomal coordinates of TAD
#' boundaries used to identify positive cases (can be obtained using
#' \code{\link{extractBoundaries}}). Required.
#' @param resolution Numeric, the width to bin the genome at, should match the
#' resolution that TADs were called at. Required.
#' @param genomicElements.GR a GRangesList object containing GRanges objects
#' for each ChIP-seq data to leverage in the random forest model (can be
#' obtained using the \code{\link{bedToGRangesList}}). Required.
#' @param featureType Character, controls how the feature space is constructed
#' (one of either "binary" (overlap yes/no), "oc" (overlap counts, the number
#' of overlaps), "op" (overlap percent, the percent of bin width covered by the
#' genomic annotation), or "distance" (log2-transformed distance from the center
#' of the nearest genomic annotation to the center of the bin); default is
#' "distance"). Required.
#' @param resampling Character, controls if and how the data should be
#' resampled to create balanced classes of boundary vs. nonboundary regions (one
#' of either "none" - no re-sampling, "ros" - Random Over-Sampling, "rus" -
#' Random Under-Sampling, or "smote" - Synthetic Minority Over-sampling
#' TEchnique). Required.
#' @param trainCHR Character vector of chromosomes to use to build the binned
#' data matrix for training. Required.
#' @param predictCHR Character vector of chromosomes to use to build the binned
#' data matrix for testing. Default in NULL, indicating no test data is created.
#' If trainCHR=predictCHR then a 7:3 split is created.
#'
#' @return A list object containing two data.frames: 1) the training data, 2)
#' the test data (only if predictCHR is not NULL, otherwise it is NA). "y" is
#' an indicator whether the corresponding bin is a TAD boundary, and the
#' subsequent columns have the association measures between bins and the
#' genomic annotations
#' @export
#'
#' @import IRanges GenomicRanges DMwR
#'
#' @examples
#' # Create training data for CHR21 and testing data for CHR22 with
#' # 5 kb binning, oc-type predictors from 26 different transcription factor
#' # binding sites from the GM12878 cell line, and random under-sampling
#'
#' # Read in ARROWHEAD-called TADs at 5kb
#' data(arrowhead_gm12878_5kb)
#'
#' #Extract unique boundaries
#' bounds.GR <- extractBoundaries(domains.mat = arrowhead_gm12878_5kb,
#' preprocess = FALSE,
#' CHR = c("CHR21", "CHR22"),
#' resolution = 5000)
#'
#' # Read in GRangesList of 26 TFBS
#' data(tfbsList)
#'
#' tadData <- createTADdata(bounds.GR = bounds.GR,
#' resolution = 5000,
#' genomicElements.GR = tfbsList,
#' featureType = "oc",
#' resampling = "rus",
#' trainCHR = "CHR21",
#' predictCHR = "CHR22")
createTADdata <- function(bounds.GR, resolution, genomicElements.GR, featureType = "distance",
resampling, trainCHR, predictCHR = NULL) {
resolution = as.integer(resolution)
#ESTABLISHING CHROMOSOME-SPECIFIC SEQINFO#
#LOADING CHROMOSOME LENGTHS#
hg19 <- preciseTAD:::hg19
#ESTABLISHING DATA MATRIX FOR MODELING#
if (is.null(predictCHR)) {
# you are not interested in performance therefore no holdout test data is created
# building training set
bounds.GR.flank <- flank(bounds.GR[which(as.character(seqnames(bounds.GR)) %in%
tolower(trainCHR))], width = (resolution/2), both = TRUE)
data_mat_list <- list()
outcome_list <- list()
train_list <- list()
for (i in seq_len(length(trainCHR))) {
start <- (resolution/2)
chrLength <- hg19$length[hg19$chrom %in% trainCHR][i]
centromereStart <- as.integer(hg19$centromerStart[hg19$chrom==trainCHR[i]])
centromereEnd <- as.integer(hg19$centromerEnd[hg19$chrom==trainCHR[i]])
end <- chrLength - (chrLength %% resolution) + resolution/2
data_mat_list[[i]] <- matrix(nrow=length(seq(start,end-1,resolution)[-which(seq(start,end-1,resolution) %in% c(centromereStart:centromereEnd))]),
ncol=length(genomicElements.GR))
dat_mat_gr <- GRanges(seqnames = tolower(trainCHR[i]),
IRanges(start = seq(start,end-1,resolution)[-which(seq(start,end-1,resolution) %in% c(centromereStart:centromereEnd))],
width=resolution))
if (featureType == "distance") {
# for use of distance type features
dat_mat_gr <- GRanges(seqnames = tolower(trainCHR[i]), IRanges(start = (start(dat_mat_gr) +
end(dat_mat_gr))/2, width = 1))
for (k in seq(length(genomicElements.GR))) {
d <- distance_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- d
}
for (h in seq_len(ncol(data_mat_list[[i]]))) {
data_mat_list[[i]][, h] <- log(data_mat_list[[i]][, h] + 1, base = 2)
}
} else if (featureType == "binary") {
for (k in seq_len(length(genomicElements.GR))) {
cb <- binary_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- cb
}
} else if (featureType == "oc") {
for (k in seq_len(length(genomicElements.GR))) {
co <- count_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- co
}
} else if (featureType == "op") {
for (k in seq_len(length(genomicElements.GR))) {
cp <- percent_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- cp
}
} else {
cs <- signal_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- cs
}
outcome_list[[i]] <- countOverlaps(GRanges(seqnames = tolower(trainCHR[i]),
IRanges(start = seq(start,end-1,resolution)[-which(seq(start,end-1,resolution) %in% c(centromereStart:centromereEnd))],
width = resolution)), bounds.GR.flank)
outcome_list[[i]] <- ifelse(outcome_list[[i]] >= 1, 1, 0)
train_list[[i]] <- cbind.data.frame(outcome_list[[i]], as.matrix(data_mat_list[[i]]))
names(train_list[[i]]) <- c("y", names(genomicElements.GR))
train_list[[i]]$y <- factor(train_list[[i]]$y)
levels(train_list[[i]]$y) <- c("No", "Yes")
if (resampling == "ros") {
# assign sample indices
sampids.train <- sample(x = which(train_list[[i]]$y == "Yes"), size = length(which(train_list[[i]]$y ==
"No")), replace = TRUE)
train_list[[i]] <- rbind.data.frame(train_list[[i]][which(train_list[[i]]$y ==
"No"), ], train_list[[i]][sampids.train, ])
# Randomly shuffle the data
train_list[[i]] <- train_list[[i]][sample(nrow(train_list[[i]])),
]
} else if (resampling == "rus") {
sampids.train <- sample(x = which(train_list[[i]]$y == "No"), size = length(which(train_list[[i]]$y ==
"Yes")), replace = FALSE)
train_list[[i]] <- rbind.data.frame(train_list[[i]][which(train_list[[i]]$y ==
"Yes"), ], train_list[[i]][sampids.train, ])
# Randomly shuffle the data
train_list[[i]] <- train_list[[i]][sample(nrow(train_list[[i]])),
]
} else if (resampling == "smote") {
train_list[[i]] <- SMOTE(y ~ ., data = train_list[[i]], perc.over = 100,
perc.under = 200)
# Randomly shuffle the data
train_list[[i]] <- train_list[[i]][sample(nrow(train_list[[i]])),
]
} else {
train_list[[i]] = train_list[[i]]
}
}
train_list <- do.call("rbind.data.frame", train_list)
} else if (isTRUE(all.equal(sort(trainCHR), sort(predictCHR)))) {
# you are training on the same chr(s) that you are predicting on so we build the
# full datamatrix (including predictor type) and then split the data into
# training and testing then perform resampling on training set
bounds.GR.flank <- flank(bounds.GR, width = (resolution/2), both = TRUE)
data_mat_list <- list()
outcome_list <- list()
full_list <- list()
train_list <- list()
test_list <- list()
for (i in seq_len(length(trainCHR))) {
start <- (resolution/2)
chrLength <- hg19$length[hg19$chrom %in% trainCHR][i]
centromereStart <- as.integer(hg19$centromerStart[hg19$chrom==trainCHR[i]])
centromereEnd <- as.integer(hg19$centromerEnd[hg19$chrom==trainCHR[i]])
end <- chrLength - (chrLength %% resolution) + resolution/2
data_mat_list[[i]] <- matrix(nrow=length(seq(start,end-1,resolution)[-which(seq(start,end-1,resolution) %in% c(centromereStart:centromereEnd))]),
ncol=length(genomicElements.GR))
dat_mat_gr <- GRanges(seqnames = tolower(trainCHR[i]),
IRanges(start = seq(start,end-1,resolution)[-which(seq(start,end-1,resolution) %in% c(centromereStart:centromereEnd))],
width=resolution))
if (featureType == "distance") {
# for use of distance type features
dat_mat_gr <- GRanges(seqnames = tolower(trainCHR[i]), IRanges(start = (start(dat_mat_gr) +
end(dat_mat_gr))/2, width = 1))
for (k in seq_len(length(genomicElements.GR))) {
d <- distance_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- d
}
for (h in seq_len(ncol(data_mat_list[[i]]))) {
data_mat_list[[i]][, h] <- log(data_mat_list[[i]][, h] + 1, base = 2)
}
} else if (featureType == "binary") {
for (k in seq_len(length(genomicElements.GR))) {
cb <- binary_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- cb
}
} else if (featureType == "oc") {
for (k in seq_len(length(genomicElements.GR))) {
co <- count_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- co
}
} else if (featureType == "op") {
for (k in seq_len(length(genomicElements.GR))) {
cp <- percent_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- cp
}
}
outcome_list[[i]] <- countOverlaps(GRanges(seqnames = tolower(trainCHR[i]),
IRanges(start = seq(start,end-1,resolution)[-which(seq(start,end-1,resolution) %in% c(centromereStart:centromereEnd))],
width = resolution)), bounds.GR.flank)
outcome_list[[i]] <- ifelse(outcome_list[[i]] >= 1, 1, 0)
full_list[[i]] <- cbind.data.frame(outcome_list[[i]], as.matrix(data_mat_list[[i]]))
names(full_list[[i]]) <- c("y", names(genomicElements.GR))
full_list[[i]]$y <- factor(full_list[[i]]$y)
levels(full_list[[i]]$y) <- c("No", "Yes")
inTrainingSet <- sample(length(full_list[[i]]$y), floor(length(full_list[[i]]$y) *
0.7))
train_list[[i]] <- full_list[[i]][inTrainingSet, ]
test_list[[i]] <- full_list[[i]][-inTrainingSet, ]
if (resampling == "ros") {
# assign sample indices
sampids.train <- sample(x = which(train_list[[i]]$y == "Yes"), size = length(which(train_list[[i]]$y ==
"No")), replace = TRUE)
train_list[[i]] <- rbind.data.frame(train_list[[i]][which(train_list[[i]]$y ==
"No"), ], train_list[[i]][sampids.train, ])
# Randomly shuffle the data
train_list[[i]] <- train_list[[i]][sample(nrow(train_list[[i]])),
]
} else if (resampling == "rus") {
sampids.train <- sample(x = which(train_list[[i]]$y == "No"), size = length(which(train_list[[i]]$y ==
"Yes")), replace = FALSE)
train_list[[i]] <- rbind.data.frame(train_list[[i]][which(train_list[[i]]$y ==
"Yes"), ], train_list[[i]][sampids.train, ])
# Randomly shuffle the data
train_list[[i]] <- train_list[[i]][sample(nrow(train_list[[i]])),
]
} else if (resampling == "smote") {
train_list[[i]] <- SMOTE(y ~ ., data = train_list[[i]], perc.over = 100,
perc.under = 200)
# Randomly shuffle the data
train_list[[i]] <- train_list[[i]][sample(nrow(train_list[[i]])),
]
} else {
train_list[[i]] = train_list[[i]]
}
}
train_list <- do.call("rbind.data.frame", train_list)
test_list <- do.call("rbind.data.frame", test_list)
} else {
# you are training on one set of chr(s) and are predicting on another set of
# chr(s) first build training set on the trainCHR, implement resampling technique
# then build testing set from the predictCHR, with no resampling technique
# building training set
bounds.GR.flank <- flank(bounds.GR[which(as.character(seqnames(bounds.GR)) %in%
tolower(trainCHR))], width = (resolution/2), both = TRUE)
data_mat_list <- list()
outcome_list <- list()
train_list <- list()
for (i in seq_len(length(trainCHR))) {
start <- (resolution/2)
chrLength <- hg19$length[hg19$chrom %in% trainCHR][i]
centromereStart <- as.integer(hg19$centromerStart[hg19$chrom==trainCHR[i]])
centromereEnd <- as.integer(hg19$centromerEnd[hg19$chrom==trainCHR[i]])
end <- chrLength - (chrLength %% resolution) + resolution/2
data_mat_list[[i]] <- matrix(nrow=length(seq(start,end-1,resolution)[-which(seq(start,end-1,resolution) %in% c(centromereStart:centromereEnd))]),
ncol=length(genomicElements.GR))
dat_mat_gr <- GRanges(seqnames = tolower(trainCHR[i]),
IRanges(start = seq(start,end-1,resolution)[-which(seq(start,end-1,resolution) %in% c(centromereStart:centromereEnd))],
width=resolution))
if (featureType == "distance") {
# for use of distance type features
dat_mat_gr <- GRanges(seqnames = tolower(trainCHR[i]), IRanges(start = (start(dat_mat_gr) +
end(dat_mat_gr))/2, width = 1))
for (k in seq_len(length(genomicElements.GR))) {
d <- distance_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- d
}
for (h in seq_len(ncol(data_mat_list[[i]]))) {
data_mat_list[[i]][, h] <- log(data_mat_list[[i]][, h] + 1, base = 2)
}
} else if (featureType == "binary") {
for (k in seq_len(length(genomicElements.GR))) {
cb <- binary_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- cb
}
} else if (featureType == "oc") {
for (k in seq_len(length(genomicElements.GR))) {
co <- count_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- co
}
} else if (featureType == "op") {
for (k in seq_len(length(genomicElements.GR))) {
cp <- percent_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- cp
}
}
outcome_list[[i]] <- countOverlaps(GRanges(seqnames = tolower(trainCHR[i]),
IRanges(start = seq(start,end-1,resolution)[-which(seq(start,end-1,resolution) %in% c(centromereStart:centromereEnd))],
width = resolution)), bounds.GR.flank)
outcome_list[[i]] <- ifelse(outcome_list[[i]] >= 1, 1, 0)
train_list[[i]] <- cbind.data.frame(outcome_list[[i]], as.matrix(data_mat_list[[i]]))
names(train_list[[i]]) <- c("y", names(genomicElements.GR))
train_list[[i]]$y <- factor(train_list[[i]]$y)
levels(train_list[[i]]$y) <- c("No", "Yes")
if (resampling == "ros") {
# assign sample indices
sampids.train <- sample(x = which(train_list[[i]]$y == "Yes"), size = length(which(train_list[[i]]$y ==
"No")), replace = TRUE)
train_list[[i]] <- rbind.data.frame(train_list[[i]][which(train_list[[i]]$y ==
"No"), ], train_list[[i]][sampids.train, ])
# Randomly shuffle the data
train_list[[i]] <- train_list[[i]][sample(nrow(train_list[[i]])),
]
} else if (resampling == "rus") {
sampids.train <- sample(x = which(train_list[[i]]$y == "No"), size = length(which(train_list[[i]]$y ==
"Yes")), replace = FALSE)
train_list[[i]] <- rbind.data.frame(train_list[[i]][which(train_list[[i]]$y ==
"Yes"), ], train_list[[i]][sampids.train, ])
# Randomly shuffle the data
train_list[[i]] <- train_list[[i]][sample(nrow(train_list[[i]])),
]
} else if (resampling == "smote") {
train_list[[i]] <- SMOTE(y ~ ., data = train_list[[i]], perc.over = 100,
perc.under = 200)
# Randomly shuffle the data
train_list[[i]] <- train_list[[i]][sample(nrow(train_list[[i]])),
]
} else {
train_list[[i]] = train_list[[i]]
}
}
train_list <- do.call("rbind.data.frame", train_list)
# building testing set
bounds.GR.flank.pred <- flank(bounds.GR[which(as.character(seqnames(bounds.GR)) %in%
tolower(predictCHR))], width = (resolution/2), both = TRUE)
data_mat_list <- list()
outcome_list <- list()
test_list <- list()
for (i in seq_len(length(predictCHR))) {
start <- (resolution/2)
chrLength <- hg19$length[hg19$chrom %in% predictCHR][i]
centromereStart <- as.integer(hg19$centromerStart[hg19$chrom==predictCHR[i]])
centromereEnd <- as.integer(hg19$centromerEnd[hg19$chrom==predictCHR[i]])
end <- chrLength - (chrLength %% resolution) + resolution/2
data_mat_list[[i]] <- matrix(nrow=length(seq(start,end-1,resolution)[-which(seq(start,end-1,resolution) %in% c(centromereStart:centromereEnd))]),
ncol=length(genomicElements.GR))
dat_mat_gr <- GRanges(seqnames = tolower(predictCHR[i]),
IRanges(start = seq(start,end-1,resolution)[-which(seq(start,end-1,resolution) %in% c(centromereStart:centromereEnd))],
width=resolution))
if (featureType == "distance") {
# for use of distance type features
dat_mat_gr <- GRanges(seqnames = tolower(predictCHR[i]), IRanges(start = (start(dat_mat_gr) +
end(dat_mat_gr))/2, width = 1))
for (k in seq_len(length(genomicElements.GR))) {
d <- distance_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- d
}
for (h in seq_len(ncol(data_mat_list[[i]]))) {
data_mat_list[[i]][, h] <- log(data_mat_list[[i]][, h] + 1, base = 2)
}
} else if (featureType == "binary") {
for (k in seq_len(length(genomicElements.GR))) {
cb <- binary_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- cb
}
} else if (featureType == "oc") {
for (k in seq_len(length(genomicElements.GR))) {
co <- count_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- co
}
} else if (featureType == "op") {
for (k in seq_len(length(genomicElements.GR))) {
cp <- percent_func(dat_mat_gr, genomicElements.GR[[k]])
data_mat_list[[i]][, k] <- cp
}
}
outcome_list[[i]] <- countOverlaps(GRanges(seqnames = tolower(predictCHR[i]),
IRanges(start = seq(start,end-1,resolution)[-which(seq(start,end-1,resolution) %in% c(centromereStart:centromereEnd))],
width = resolution)), bounds.GR.flank.pred)
outcome_list[[i]] <- ifelse(outcome_list[[i]] >= 1, 1, 0)
test_list[[i]] <- cbind.data.frame(outcome_list[[i]], as.matrix(data_mat_list[[i]]))
names(test_list[[i]]) <- c("y", names(genomicElements.GR))
test_list[[i]]$y <- factor(test_list[[i]]$y)
levels(test_list[[i]]$y) <- c("No", "Yes")
}
test_list <- do.call("rbind.data.frame", test_list)
}
if (is.null(predictCHR)) {
TADdataList <- list(train_list, NA)
} else {
TADdataList <- list(train_list, test_list)
}
return(TADdataList)
}
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