R/genetic_diversity.R
In SingerLab/gac: Genetic Analysis of Cells
Defines functions
subset_ci
percent_genome_amplified
percent_genome_gain
percent_genome_loss
avg_num_alleles_per_locus
proportion_of_polymorphic_loci
Documented in
avg_num_alleles_per_locus
proportion_of_polymorphic_loci
#' proportion of polymorphic alleles as subclonal
#'
#' Estimate genome-wide proportion of polymorphic loci in a population
#'
#' @param cnr a cnr bundle
#'
#' @param exclude.chr chromosomes to exclude, default X, Y
#'
#' @param monomorphic.threshold alteration frequency at which a
#' locus is to be considered monomorphic (universally altered).
#' default 0.95
#'
#' @param noise.threshold lower-bound alteration frequency at
#' which alterations are considered technical noise, rather
#' than a true alteration, default 0.1
#'
#' @param chrom.col name of column containing chromosomes
#'
#' @return
#'
#' Single-value with the proportion of polymorphic loci for a given
#' sample population
#'
#' @examples
#' data(cnr)
#'
#' cnr <- get_alteration_frequencies(cnr)
#'
#' proportion_of_polymorphic_loci(cnr)
#'
#' @importFrom assertthat assert_that
#' @export
proportion_of_polymorphic_loci <- function(cnr, exclude.chr = c("X", "Y"),
monomorphic.threshold = 0.95,
noise.threshold = 0.1,
chrom.col = "bin.chrom") {
assertthat::assert_that(monomorphic.threshold > noise.threshold)
ge <- subset_ci(cnr, exclude.chr = exclude.chr, chrom.col = chrom.col)
n_pj <- sum(ge$altFQ <= monomorphic.threshold &
ge$altFQ >= noise.threshold)
n_total <- nrow(ge)
P <- n_pj / n_total
return(P)
}
#' Estimate the estimated number of alleles (copies) per locus
#'
#' Function is `experimental` and requires validation to asses it's value
#'
#' @param cnr a cnr bundle
#'
#' @param exclude.chr chromosomes to exclude, default X, Y
#'
#' @param chrom.col name of column containing chromosomes
#'
#' @return
#' Estimates the number of alleles (copies per locus), across the population.
#'
#' Function is `experimental` and requires validation to asses it's value
#'
#' @examples
#' data(cnr)
#'
#' avg_num_alleles_per_locus(cnr)
#'
#' avg_num_alleles_per_locus(cnr, exclude.chr = c("X", "Y"))
#'
#' @importFrom assertthat assert_that
#'
#' @export
avg_num_alleles_per_locus <- function(cnr, exclude.chr = NULL, chrom.col = "bin.chrom") {
if(is.null(exclude.chr)) {
K <- nrow(cnr$X)
## list of allels per bin
ni <- apply(cnr$X, 1, function(i) length(unique(as.numeric(i))))
} else {
assertthat::assert_that(all(exclude.chr %in% cnr$chromInfo[,chrom.col]))
incl <- !which(cnr$chromInfo$bin.chrom %in% exclude.chr)
K <- sum(incl)
## list of allels per bin
ni <- apply(cnr$X[incl,], 1, function(i) length(unique(as.numeric(i))))
}
## Average number of alleles per locus
n = (1/K) * sum(ni)
return(n)
}
#' calculate the proportion of the genome loss per cell
#'
#' @param cnr a cnr bundle
#'
#' @param by character, estimate percent genome loss at population, clone,
#' or cell levels. Options are NULL, "clone", and "cell". When NULL the
#' estimate is done at the population level using bin alteration frequencies.
#' Using "clone", and "cell", genome loss is performed using the clone profiles
#' (DDRC.df), and cell profiles (X), respectively. Default NULL
#'
#' @param loss.threshold number of copies at which a locus is considered as a loss.
#' Ignored when by = NULL. Default 1
#'
#' @param noise.threshold lower-bound alteration frequency at
#' which alterations are considered technical noise, rather
#' than a true alteration, default 0.1, ignored when by is "clone" or "cell"
#'
#' @param genome.size size of the genome in base pairs.
#' default 3098825702, human genome
#'
#' @param exclude.chr vector, chromosomes to exclude
#'
#' @param chrom.col name of column containing chromosomes
#'
#' @return
#' The value of the percent genome loss across a population of cells.
#'
#' In the analysis by.clone = TRUE, the default is to consider all
#' copy numbers <= 1 as altered. For this, it's considered, at the moment,
#' that chromosomes X and Y have are to have two copies
#'
#' @examples \dontrun{
#'
#' data(cnr)
#'
#' noisy.cells <- cnr$qc$cellID[cnr$qc$qc.status == "FAIL"]
#'
#' ## reduced pipeline to genrate DDRC clone profiles
#' cnr <- excludeCells(cnr, excl = noisy.cells)
#' cnr <- phylo_cnr(cnr, root.cell = "cell0")
#' cnr <- setBrayClusters(cnr)
#' cnr <- run_consensus_clustering(cnr, iters = 20, maxK = 40)
#' cnr <- doKSpectral(cnr)
#' cnr <- setKcc(cnr)
#' cnr <- cluster_heterogeneity(cnr, by = "category1",
#' cluster_column = "ConsensusC")
#' cnr <- get_cluster_profiles(cnr)
#' cnr <- get_alteration_frequencies(cnr)
#'
#' percent_genome_loss(cnr)
#'
#' percent_genome_loss(cnr, by = "cell")
#'
#' percent_genome_loss(cnr, by = "clone")
#'
#' }
#'
#' @importFrom assertthat assert_that
#'
#' @keywords internal
#' @noRd
percent_genome_loss <- function(cnr,
by = NULL,
loss.threshold = 1,
noise.threshold = 0.1,
genome.size = 3098825702,
exclude.chr = NULL,
chrom.col = "bin.chrom") {
ge <- subset_ci(cnr, exclude.chr = exclude.chr, chrom.col = chrom.col)
if(is.null(by)) {
assertthat::assert_that("delFQ" %in% names(cnr$chromInfo),
msg = "alteration frequencies not available in chromInfo. Please run `get_alteration_frequencies`")
pct.loss <- sum(ge[ge$delFQ >= noise.threshold, "bin.length"]) /
genome.size
} else {
if(by == "clone") {
assertthat::assert_that(!is.null(cnr$DDRC.df),
msg = "`cnr$DDRC.df` not found")
ddrc.loss <- cnr$DDRC.df <= loss.threshold
pct.loss <- apply(ddrc.loss, 2, function(i) {
sum(ge[i, "bin.length"]) / genome.size
})
} else {
if(by == "cell") {
cell.loss <- cnr$X <= loss.threshold
pct.loss <- apply(cell.loss, 2, function(i) {
sum(ge[i, "bin.length"]) / genome.size
})
}
}
}
return(pct.loss)
} ## end percent_genome_loss
#' calculate the proportion of the genome with gains
#'
#' @param cnr a cnr bundle
#'
#' @param by character, estimate percent genome loss at population, clone,
#' or cell levels. Options are NULL, "clone", and "cell". When NULL the
#' estimate is done at the population level using bin alteration frequencies.
#' Using "clone", and "cell", genome loss is performed using the clone profiles
#' (DDRC.df), and cell profiles (X), respectively. Default NULL
#'
#' @param gain.thresholds lower and upper bound copy number at which a locus
#' should considered a gain. The threshold is ignored when by = NULL.
#' If only one value is provided, all copy numbers above that value are
#' treated as a gain. Default 3 to 5.
#'
#' @param noise.threshold lower-bound alteration frequency at
#' which alterations are considered technical noise, rather
#' than a true alteration, default 0.1, ignored when by is "clone" or "cell"
#'
#' @param genome.size size of the genome in base pairs.
#' default 3098825702, human genome
#'
#' @param exclude.chr vector, chromosomes to exclude
#'
#' @param chrom.col name of column containing chromosomes
#'
#' @return
#' The value of the percent genome gained across a population of cells.
#' gaines are considered when cell or clone copy numers are CN >= 3 & CN <= 5.
#' CN >= 6, it's considerd an amplification, consistent with \code{\link{callCN}}
#'
#' In the analysis by.clone = TRUE, the default is to consider all
#' copy numbers <= 1 as altered. For this, it's considered, at the moment,
#' that chromosomes X and Y have are to have two copies
#'
#' @examples \dontrun{
#'
#' data(cnr)
#'
#' noisy.cells <- cnr$qc$cellID[cnr$qc$qc.status == "FAIL"]
#'
#' ## reduced pipeline to genrate DDRC clone profiles
#' cnr <- excludeCells(cnr, excl = noisy.cells)
#' cnr <- phylo_cnr(cnr, root.cell = "cell0")
#' cnr <- setBrayClusters(cnr)
#' cnr <- run_consensus_clustering(cnr, iters = 20, maxK = 40)
#' cnr <- doKSpectral(cnr)
#' cnr <- setKcc(cnr)
#' cnr <- cluster_heterogeneity(cnr, by = "category1",
#' cluster_column = "ConsensusC")
#' cnr <- get_cluster_profiles(cnr)
#' cnr <- get_alteration_frequencies(cnr)
#'
#' percent_genome_gain(cnr)
#'
#' percent_genome_gain(cnr, by = "cell")
#'
#' percent_genome_gain(cnr, by = "clone")
#'
#' }
#'
#' @importFrom assertthat assert_that
#'
#' @keywords internal
#' @noRd
percent_genome_gain <- function(cnr,
by = NULL,
gain.thresholds = c(3, 5),
noise.threshold = 0.1,
genome.size = 3098825702,
exclude.chr = NULL,
chrom.col = "bin.chrom") {
ge <- subset_ci(cnr, exclude.chr = exclude.chr, chrom.col = chrom.col)
if(is.null(by)) {
assertthat::assert_that("AmpFQ" %in% names(cnr$chromInfo),
msg = "alteration frequencies not available in chromInfo. Please run `get_alteration_frequencies`")
pct.gain <- sum(ge[ge$AmpFQ >= noise.threshold,"bin.length"]) / genome.size
} else {
if(by == "clone") {
assertthat::assert_that(!is.null(cnr$DDRC.df),
msg = "`cnr$DDRC.df` not found")
if(length(gain.thresholds) == 1) {
ddrc.gain <- cnr$DDRC.df >= gain.thresholds
} else {
ddrc.gain <- cnr$DDRC.df >= gain.thresholds[1] &
cnr$DDRC.df <= gain.thresholds[2]
}
pct.gain <- apply(ddrc.gain, 2, function(i) {
sum(ge[i, "bin.length"]) / genome.size
})
} else {
if(by == "cell")
if(length(gain.thresholds) == 1) {
cell.gain <- cnr$X >= gain.thresholds
} else {
cell.gain <- cnr$X >= gain.thresholds[1] &
cnr$X <= gain.thresholds[2]
}
pct.gain <- apply(cell.gain, 2, function(i) {
sum(ge[i, "bin.length"]) / genome.size
})
}
}
return(pct.gain)
} ## end percent_genome_gain
#' calculate the proportion of the genome amplified
#' @param cnr a cnr bundle
#'
#' @param by character, estimate percent genome loss at population, clone,
#' or cell levels. Options are "clone", and "cell".
#' Using "clone", and "cell", genome loss is performed using the clone profiles
#' (DDRC.df), and cell profiles (X), respectively. Default "cell"
#'
#' @param amplification.threshold number of copies at which a locus is considered as an amplification.
#'
#' @param ... additional parameters to \code{\link{percent_genome_gain}}
#'
#' @examples \dontrun{
#'
#' data(cnr)
#'
#' noisy.cells <- cnr$qc$cellID[cnr$qc$qc.status == "FAIL"]
#'
#' ## reduced pipeline to genrate DDRC clone profiles
#' cnr <- excludeCells(cnr, excl = noisy.cells)
#' cnr <- phylo_cnr(cnr, root.cell = "cell0")
#' cnr <- setBrayClusters(cnr)
#' cnr <- run_consensus_clustering(cnr, iters = 20, maxK = 40)
#' cnr <- doKSpectral(cnr)
#' cnr <- setKcc(cnr)
#' cnr <- cluster_heterogeneity(cnr, by = "category1",
#' cluster_column = "ConsensusC")
#' cnr <- get_cluster_profiles(cnr)
#' cnr <- get_alteration_frequencies(cnr)
#'
#' percent_genome_amplified(cnr)
#'
#' percent_genome_amplified(cnr, by = "cell")
#'
#' percent_genome_amplified(cnr, by = "clone")
#'
#' }
#'
#' @keywords internal
#' @noRd
percent_genome_amplified <- function(cnr, by = "cell",
amplification.threshold = 6,
...) {
if(is.null(by)) {
warning('output is equivalent as running percent_genome_gain
with by = NULL')
pct.amp <- percent_genome_gain(cnr, by = by, ...)
} else {
pct.amp <- percent_genome_gain(
cnr, by = by,
gain.thresholds = amplification.threshold,
...)
}
return(pct.amp)
} ## end percent_genome_amplified
#' subset for chromosomes of interest
#' @param cnr a cnr bundle
#'
#' @param exclude.chr vector, chromosomes to exclude
#'
#' @param chrom.col name of column containing chromosomes
#'
#' @return
#' conditional subset of chromInfo w/o excluded chromosomes
#'
#' @keywords internal
#' @noRd
subset_ci <- function(cnr, exclude.chr = NULL, chrom.col = "bin.chrom") {
if(is.null(exclude.chr)) {
ge <- cnr$chromInfo
} else {
assertthat::assert_that(chrom.col %in% names(cnr$chromInfo))
assertthat::assert_that(all(exclude.chr %in% cnr$chromInfo[,chrom.col]))
ge <- cnr$chromInfo[!cnr$chromInfo[,chrom.col] %in% exclude.chr, ]
}
return(ge)
}
SingerLab/gac documentation built on March 23, 2024, 5:15 a.m.
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Defines functions subset_ci percent_genome_amplified percent_genome_gain percent_genome_loss avg_num_alleles_per_locus proportion_of_polymorphic_loci
Documented in avg_num_alleles_per_locus proportion_of_polymorphic_loci
#' proportion of polymorphic alleles as subclonal
#'
#' Estimate genome-wide proportion of polymorphic loci in a population
#'
#' @param cnr a cnr bundle
#'
#' @param exclude.chr chromosomes to exclude, default X, Y
#'
#' @param monomorphic.threshold alteration frequency at which a
#' locus is to be considered monomorphic (universally altered).
#' default 0.95
#'
#' @param noise.threshold lower-bound alteration frequency at
#' which alterations are considered technical noise, rather
#' than a true alteration, default 0.1
#'
#' @param chrom.col name of column containing chromosomes
#'
#' @return
#'
#' Single-value with the proportion of polymorphic loci for a given
#' sample population
#'
#' @examples
#' data(cnr)
#'
#' cnr <- get_alteration_frequencies(cnr)
#'
#' proportion_of_polymorphic_loci(cnr)
#'
#' @importFrom assertthat assert_that
#' @export
proportion_of_polymorphic_loci <- function(cnr, exclude.chr = c("X", "Y"),
monomorphic.threshold = 0.95,
noise.threshold = 0.1,
chrom.col = "bin.chrom") {
assertthat::assert_that(monomorphic.threshold > noise.threshold)
ge <- subset_ci(cnr, exclude.chr = exclude.chr, chrom.col = chrom.col)
n_pj <- sum(ge$altFQ <= monomorphic.threshold &
ge$altFQ >= noise.threshold)
n_total <- nrow(ge)
P <- n_pj / n_total
return(P)
}
#' Estimate the estimated number of alleles (copies) per locus
#'
#' Function is `experimental` and requires validation to asses it's value
#'
#' @param cnr a cnr bundle
#'
#' @param exclude.chr chromosomes to exclude, default X, Y
#'
#' @param chrom.col name of column containing chromosomes
#'
#' @return
#' Estimates the number of alleles (copies per locus), across the population.
#'
#' Function is `experimental` and requires validation to asses it's value
#'
#' @examples
#' data(cnr)
#'
#' avg_num_alleles_per_locus(cnr)
#'
#' avg_num_alleles_per_locus(cnr, exclude.chr = c("X", "Y"))
#'
#' @importFrom assertthat assert_that
#'
#' @export
avg_num_alleles_per_locus <- function(cnr, exclude.chr = NULL, chrom.col = "bin.chrom") {
if(is.null(exclude.chr)) {
K <- nrow(cnr$X)
## list of allels per bin
ni <- apply(cnr$X, 1, function(i) length(unique(as.numeric(i))))
} else {
assertthat::assert_that(all(exclude.chr %in% cnr$chromInfo[,chrom.col]))
incl <- !which(cnr$chromInfo$bin.chrom %in% exclude.chr)
K <- sum(incl)
## list of allels per bin
ni <- apply(cnr$X[incl,], 1, function(i) length(unique(as.numeric(i))))
}
## Average number of alleles per locus
n = (1/K) * sum(ni)
return(n)
}
#' calculate the proportion of the genome loss per cell
#'
#' @param cnr a cnr bundle
#'
#' @param by character, estimate percent genome loss at population, clone,
#' or cell levels. Options are NULL, "clone", and "cell". When NULL the
#' estimate is done at the population level using bin alteration frequencies.
#' Using "clone", and "cell", genome loss is performed using the clone profiles
#' (DDRC.df), and cell profiles (X), respectively. Default NULL
#'
#' @param loss.threshold number of copies at which a locus is considered as a loss.
#' Ignored when by = NULL. Default 1
#'
#' @param noise.threshold lower-bound alteration frequency at
#' which alterations are considered technical noise, rather
#' than a true alteration, default 0.1, ignored when by is "clone" or "cell"
#'
#' @param genome.size size of the genome in base pairs.
#' default 3098825702, human genome
#'
#' @param exclude.chr vector, chromosomes to exclude
#'
#' @param chrom.col name of column containing chromosomes
#'
#' @return
#' The value of the percent genome loss across a population of cells.
#'
#' In the analysis by.clone = TRUE, the default is to consider all
#' copy numbers <= 1 as altered. For this, it's considered, at the moment,
#' that chromosomes X and Y have are to have two copies
#'
#' @examples \dontrun{
#'
#' data(cnr)
#'
#' noisy.cells <- cnr$qc$cellID[cnr$qc$qc.status == "FAIL"]
#'
#' ## reduced pipeline to genrate DDRC clone profiles
#' cnr <- excludeCells(cnr, excl = noisy.cells)
#' cnr <- phylo_cnr(cnr, root.cell = "cell0")
#' cnr <- setBrayClusters(cnr)
#' cnr <- run_consensus_clustering(cnr, iters = 20, maxK = 40)
#' cnr <- doKSpectral(cnr)
#' cnr <- setKcc(cnr)
#' cnr <- cluster_heterogeneity(cnr, by = "category1",
#' cluster_column = "ConsensusC")
#' cnr <- get_cluster_profiles(cnr)
#' cnr <- get_alteration_frequencies(cnr)
#'
#' percent_genome_loss(cnr)
#'
#' percent_genome_loss(cnr, by = "cell")
#'
#' percent_genome_loss(cnr, by = "clone")
#'
#' }
#'
#' @importFrom assertthat assert_that
#'
#' @keywords internal
#' @noRd
percent_genome_loss <- function(cnr,
by = NULL,
loss.threshold = 1,
noise.threshold = 0.1,
genome.size = 3098825702,
exclude.chr = NULL,
chrom.col = "bin.chrom") {
ge <- subset_ci(cnr, exclude.chr = exclude.chr, chrom.col = chrom.col)
if(is.null(by)) {
assertthat::assert_that("delFQ" %in% names(cnr$chromInfo),
msg = "alteration frequencies not available in chromInfo. Please run `get_alteration_frequencies`")
pct.loss <- sum(ge[ge$delFQ >= noise.threshold, "bin.length"]) /
genome.size
} else {
if(by == "clone") {
assertthat::assert_that(!is.null(cnr$DDRC.df),
msg = "`cnr$DDRC.df` not found")
ddrc.loss <- cnr$DDRC.df <= loss.threshold
pct.loss <- apply(ddrc.loss, 2, function(i) {
sum(ge[i, "bin.length"]) / genome.size
})
} else {
if(by == "cell") {
cell.loss <- cnr$X <= loss.threshold
pct.loss <- apply(cell.loss, 2, function(i) {
sum(ge[i, "bin.length"]) / genome.size
})
}
}
}
return(pct.loss)
} ## end percent_genome_loss
#' calculate the proportion of the genome with gains
#'
#' @param cnr a cnr bundle
#'
#' @param by character, estimate percent genome loss at population, clone,
#' or cell levels. Options are NULL, "clone", and "cell". When NULL the
#' estimate is done at the population level using bin alteration frequencies.
#' Using "clone", and "cell", genome loss is performed using the clone profiles
#' (DDRC.df), and cell profiles (X), respectively. Default NULL
#'
#' @param gain.thresholds lower and upper bound copy number at which a locus
#' should considered a gain. The threshold is ignored when by = NULL.
#' If only one value is provided, all copy numbers above that value are
#' treated as a gain. Default 3 to 5.
#'
#' @param noise.threshold lower-bound alteration frequency at
#' which alterations are considered technical noise, rather
#' than a true alteration, default 0.1, ignored when by is "clone" or "cell"
#'
#' @param genome.size size of the genome in base pairs.
#' default 3098825702, human genome
#'
#' @param exclude.chr vector, chromosomes to exclude
#'
#' @param chrom.col name of column containing chromosomes
#'
#' @return
#' The value of the percent genome gained across a population of cells.
#' gaines are considered when cell or clone copy numers are CN >= 3 & CN <= 5.
#' CN >= 6, it's considerd an amplification, consistent with \code{\link{callCN}}
#'
#' In the analysis by.clone = TRUE, the default is to consider all
#' copy numbers <= 1 as altered. For this, it's considered, at the moment,
#' that chromosomes X and Y have are to have two copies
#'
#' @examples \dontrun{
#'
#' data(cnr)
#'
#' noisy.cells <- cnr$qc$cellID[cnr$qc$qc.status == "FAIL"]
#'
#' ## reduced pipeline to genrate DDRC clone profiles
#' cnr <- excludeCells(cnr, excl = noisy.cells)
#' cnr <- phylo_cnr(cnr, root.cell = "cell0")
#' cnr <- setBrayClusters(cnr)
#' cnr <- run_consensus_clustering(cnr, iters = 20, maxK = 40)
#' cnr <- doKSpectral(cnr)
#' cnr <- setKcc(cnr)
#' cnr <- cluster_heterogeneity(cnr, by = "category1",
#' cluster_column = "ConsensusC")
#' cnr <- get_cluster_profiles(cnr)
#' cnr <- get_alteration_frequencies(cnr)
#'
#' percent_genome_gain(cnr)
#'
#' percent_genome_gain(cnr, by = "cell")
#'
#' percent_genome_gain(cnr, by = "clone")
#'
#' }
#'
#' @importFrom assertthat assert_that
#'
#' @keywords internal
#' @noRd
percent_genome_gain <- function(cnr,
by = NULL,
gain.thresholds = c(3, 5),
noise.threshold = 0.1,
genome.size = 3098825702,
exclude.chr = NULL,
chrom.col = "bin.chrom") {
ge <- subset_ci(cnr, exclude.chr = exclude.chr, chrom.col = chrom.col)
if(is.null(by)) {
assertthat::assert_that("AmpFQ" %in% names(cnr$chromInfo),
msg = "alteration frequencies not available in chromInfo. Please run `get_alteration_frequencies`")
pct.gain <- sum(ge[ge$AmpFQ >= noise.threshold,"bin.length"]) / genome.size
} else {
if(by == "clone") {
assertthat::assert_that(!is.null(cnr$DDRC.df),
msg = "`cnr$DDRC.df` not found")
if(length(gain.thresholds) == 1) {
ddrc.gain <- cnr$DDRC.df >= gain.thresholds
} else {
ddrc.gain <- cnr$DDRC.df >= gain.thresholds[1] &
cnr$DDRC.df <= gain.thresholds[2]
}
pct.gain <- apply(ddrc.gain, 2, function(i) {
sum(ge[i, "bin.length"]) / genome.size
})
} else {
if(by == "cell")
if(length(gain.thresholds) == 1) {
cell.gain <- cnr$X >= gain.thresholds
} else {
cell.gain <- cnr$X >= gain.thresholds[1] &
cnr$X <= gain.thresholds[2]
}
pct.gain <- apply(cell.gain, 2, function(i) {
sum(ge[i, "bin.length"]) / genome.size
})
}
}
return(pct.gain)
} ## end percent_genome_gain
#' calculate the proportion of the genome amplified
#' @param cnr a cnr bundle
#'
#' @param by character, estimate percent genome loss at population, clone,
#' or cell levels. Options are "clone", and "cell".
#' Using "clone", and "cell", genome loss is performed using the clone profiles
#' (DDRC.df), and cell profiles (X), respectively. Default "cell"
#'
#' @param amplification.threshold number of copies at which a locus is considered as an amplification.
#'
#' @param ... additional parameters to \code{\link{percent_genome_gain}}
#'
#' @examples \dontrun{
#'
#' data(cnr)
#'
#' noisy.cells <- cnr$qc$cellID[cnr$qc$qc.status == "FAIL"]
#'
#' ## reduced pipeline to genrate DDRC clone profiles
#' cnr <- excludeCells(cnr, excl = noisy.cells)
#' cnr <- phylo_cnr(cnr, root.cell = "cell0")
#' cnr <- setBrayClusters(cnr)
#' cnr <- run_consensus_clustering(cnr, iters = 20, maxK = 40)
#' cnr <- doKSpectral(cnr)
#' cnr <- setKcc(cnr)
#' cnr <- cluster_heterogeneity(cnr, by = "category1",
#' cluster_column = "ConsensusC")
#' cnr <- get_cluster_profiles(cnr)
#' cnr <- get_alteration_frequencies(cnr)
#'
#' percent_genome_amplified(cnr)
#'
#' percent_genome_amplified(cnr, by = "cell")
#'
#' percent_genome_amplified(cnr, by = "clone")
#'
#' }
#'
#' @keywords internal
#' @noRd
percent_genome_amplified <- function(cnr, by = "cell",
amplification.threshold = 6,
...) {
if(is.null(by)) {
warning('output is equivalent as running percent_genome_gain
with by = NULL')
pct.amp <- percent_genome_gain(cnr, by = by, ...)
} else {
pct.amp <- percent_genome_gain(
cnr, by = by,
gain.thresholds = amplification.threshold,
...)
}
return(pct.amp)
} ## end percent_genome_amplified
#' subset for chromosomes of interest
#' @param cnr a cnr bundle
#'
#' @param exclude.chr vector, chromosomes to exclude
#'
#' @param chrom.col name of column containing chromosomes
#'
#' @return
#' conditional subset of chromInfo w/o excluded chromosomes
#'
#' @keywords internal
#' @noRd
subset_ci <- function(cnr, exclude.chr = NULL, chrom.col = "bin.chrom") {
if(is.null(exclude.chr)) {
ge <- cnr$chromInfo
} else {
assertthat::assert_that(chrom.col %in% names(cnr$chromInfo))
assertthat::assert_that(all(exclude.chr %in% cnr$chromInfo[,chrom.col]))
ge <- cnr$chromInfo[!cnr$chromInfo[,chrom.col] %in% exclude.chr, ]
}
return(ge)
}
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