R/wrappers.R
In selinj/chromswitch: An R package to detect chromatin state switches from epigenomic data
Defines functions
callBinary
callSummary
Documented in
callBinary
callSummary
# ---------------------------------------------------------------------------- #
#
# Wrapper functions:
# These combine all the steps in the analysis in order to provide a simple
# way to apply the method to a genomic region for one mark
#
# ---------------------------------------------------------------------------- #
#' callSummary
#'
#' One of two main functions in the \code{chromswitch} package, this function
#' detects a switch in chromatin state in one or
#' more regions given ChIP-seq peak calls for one mark, executing the entire
#' algorithm from preprocessing to evaluating the clustering results,
#' using the summary strategy.
#'
#' This strategy constructs a sample-by-feature matrix to use as input for
#' hierarchical clustering by computing, for each sample, a vector of summary
#' statistics based on that sample's peaks in the query region. The summary
#' statistics are generally based on the enrichment statistics associated with
#' each peak as returned by the peak calling too, which might include, for
#' example, a p value and fold change.
#'
#' @param query GRanges list containing one or more genomic regions of interest
#' in which to call a switch. The output dataframe will contain one row per
#' region in \code{query}.
#' @param metadata A dataframe with at least two columns: "Sample" which stores
#' the sample IDs, "Condition", which stores the biological condition labels
#' of the samples
#' @param peaks List of GRanges objects storing peak calls for each sample,
#' where element names correspond to sample IDs
#' @param mark Character specifying the histone mark or ChIP-target,
#' for example, "H3K4me3"
#' @param filter (Optional) logical value, filter peaks based on thresholds on
#' peak statistics? Default: FALSE. The filter step is described in
#' \code{\link{filterPeaks}}.
#' @param filter_columns If \code{filter} is TRUE, a chracter vector
#' corresponding to names of columns in the peak metadata by which to filter
#' peaks. If \code{filter} is FALSE, not used.
#' @param filter_thresholds If \code{filter} is TRUE, a numeric vector
#' corresponding to lower cutoffs applied to metadata columns in order to filter
#' peaks. Provide one per column specified in \code{filter_columns}, in the same
#' order. If \code{filter} is FALSE, not used.
#' @param normalize (Optional) logical value, normalize peak statistics
#' genome-wide for each sample? Default: TRUE if \code{summarize_columns} or
#' \code{normalize_columns} is specified, FALSE, otherwise.
#' @param normalize_columns If \code{normalize} is TRUE, a character vector
#' corresponding to names of columns in the peak metadata to normalize
#' genome-wide for each sample. If \code{normalize} is FALSE, not used.
#' @param tail (Optional) if \code{normalize} is TRUE, specifies the fraction
#' of extreme values in each tail to bound during normalization. More details at
#' \code{\link{normalizePeaks}}.
#' @param summarize_columns Character vector of column names on which to compute
#' summary statistics during feature matrix construction. These statistics
#' become the features of the matrix.
#' @param fraction (Optional) Logical value, during feature matrix construction,
#' compute the fraction of the region overlapped by peaks? Default: TRUE
#' @param n (Optional) Logical value, during feature matrix construction,
#' compute the number of peaks in the region? Default: FALSE
#' @param heatmap (Optional) Logical value, plot the heatmap corresponding to
#' the hierarchical clustering result? Default: FALSE
#' @param titles (Optional) if \code{heatmap} is TRUE, a character vector
#' of the same length as \code{query}, specifying the title to use when plotting
#' each heatmap (e.g. a gene name), also reused as the
#' prefix of the name of the file where the heatmap is saved. By default, the
#' title is the genomic coordinates of the region in the form "chrN:start-end"
#' @param outdir (Optional) if \code{heatmap} is TRUE, the name of the directory
#' where heatmaps should be saved
#' @param optimal_clusters (Optional) Logical value indicate whether to cluster
#' samples into two groups, or to find the optimal clustering solution by
#' choosing the set of clusters which maximizes the Average Silhouette width.
#' Default: TRUE
#' @param estimate_state (Optional) Logical value indicating whether to include
#' a column "state" in the output specifying the estimated chromatin state of
#' a test condition. The state will be on of "ON", "OFF", or NA, where the
#' latter results if a binary switch between the conditions is unclear.
#' Default: FALSE.
#' @param signal_col (Optional) If \code{estimate_state} is TRUE, string
#' specifying the name of the column in the original peak files which
#' corresponds to the level of enrichment in the region, e.g. fold change
#' @param test_condition (Optional) If \code{estimate_state} is TRUE, string
#' specifying one of the two biological condtions in \code{metadata$Condition}
#' for which to estimate chromatin state.
#' @param BPPARAM (Optional) instance of \code{BiocParallel:BiocParallelParam}
#' used to determine the back-end used for parallel computations when performing
#' the analysis on more than one region.
#'
#' @return Data frame with one row per region in \code{query}. Contains the
#' coordinates of the region, the number of inferred clusters, the computed
#' cluster validity statistics, and the cluster assignment for each sample.
#'
#' @examples
#'
#' samples <- c("E068", "E071", "E074", "E101", "E102", "E110")
#' bedfiles <- system.file("extdata", paste0(samples, ".H3K4me3.bed"),
#' package = "chromswitch")
#' Conditions <- c(rep("Brain", 3), rep("Other", 3))
#'
#' metadata <- data.frame(Sample = samples,
#' H3K4me3 = bedfiles,
#' Condition = Conditions,
#' stringsAsFactors = FALSE)
#'
#' regions <- GRanges(seqnames = c("chr19", "chr19"),
#' ranges = IRanges(start = c(54924104, 54874318),
#' end = c(54929104, 54877536)))
#'
#' callSummary(query = regions,
#' metadata = metadata,
#' peaks = H3K4me3,
#' normalize_columns = c("qValue", "pValue", "signalValue"),
#' mark = "H3K4me3",
#' summarize_columns = c("pValue", "qValue", "signalValue"),
#' heatmap = FALSE,
#' BPPARAM = BiocParallel::SerialParam())
#'
#' @export
callSummary <- function(query, metadata, peaks, mark,
filter = FALSE, filter_columns = summarize_columns,
filter_thresholds = NULL,
summarize_columns = NULL,
normalize_columns = summarize_columns, tail = 0.005,
normalize = ifelse(is.null(normalize_columns) && is.null(summarize_columns), FALSE, TRUE),
fraction = TRUE, n = FALSE,
heatmap = FALSE, titles = NULL, outdir = NULL,
optimal_clusters = TRUE,
estimate_state = FALSE,
signal_col = NULL,
test_condition = NULL,
BPPARAM = bpparam()) {
# Preprocessing
if (filter) {
if (is.null(filter_thresholds))
stop("Please specify thresholds to use for filtering columns. ",
"By default, filters are applied to the columns passed to ",
"summarize_columns. Specific columns to filter can be ",
"passed to filter_columns.",
"Provide one threshold per column, in the same order.")
peaks <- filterPeaks(peaks,
columns = filter_columns,
thresholds = filter_thresholds)
}
if (normalize) {
peaks <- normalizePeaks(peaks,
columns = normalize_columns,
tail = tail)
}
if(is.null(summarize_columns) && fraction == FALSE && n == FALSE) {
stop("No columns specified, and none of fraction, or
n set to TRUE, therefore cannot construct a feature matrix.")
}
# Check titles
if ((!is.null(titles)) && length(query) != length(titles))
stop("Please provide one title per query region.")
# Retrieve peaks: get a LocalPeaks object for each query region
lpks <- bplapply(as(query, "GRangesList"), function(region)
retrievePeaks(peaks, metadata, region),
BPPARAM = BPPARAM)
# Construct feature matrices for each query region
matrices <- bplapply(lpks, summarizePeaks, mark,
summarize_columns, fraction, n,
BPPARAM = BPPARAM)
# Convert the queries into a GRangesList in order to be able to Map over
queries <- as(query, "GRangesList")
# Cluster the feature matrices
if (!isTRUE(heatmap)) {
results <- Map(f = function(ft_mat, query)
cluster(ft_mat, metadata, query,
heatmap = FALSE,
title = NULL,
outdir = NULL,
n_features = FALSE,
optimal_clusters = optimal_clusters,
estimate_state = estimate_state,
method = "summary",
signal_col = signal_col,
test_condition = test_condition,
mark = mark),
matrices, queries)
} else if (isTRUE(heatmap)) {
if (is.null(titles)) titles <- unlist(lapply(queries, GRangesToCoord))
results <- bpmapply(FUN = function(ft_mat, query, title)
cluster(ft_mat, metadata, query,
heatmap = heatmap,
title = title,
outdir = outdir,
n_features = FALSE,
optimal_clusters = optimal_clusters,
estimate_state = estimate_state,
method = "summary",
signal_col = signal_col,
test_condition = test_condition,
mark = mark),
matrices, queries, titles,
SIMPLIFY = FALSE, BPPARAM = BPPARAM)
}
return(data.frame(dplyr::bind_rows(results)))
}
#' callBinary
#'
#' One of two main functions in the \code{chromswitch} package, this function
#' detects a switch in chromatin state in one or
#' more regions given ChIP-seq peak calls for one mark, executing the entire
#' algorithm from preprocessing to evaluating the clustering results,
#' using the binary strategy.
#'
#' This strategy constructs a sample-by-feature matrix to use as input for
#' hierarchical clustering by first assembling the set of unique peaks observed
#' in the region across samples. Then for each unique peak, we model the
#' presence or absence of that peak in each sample, resulting in a binary
#' feature matrix.
#'
#' @param query GRanges list containing one or more genomic regions of interest
#' in which to call a switch. The output dataframe will contain one row per
#' region in \code{query}.
#' @param metadata A dataframe with at least two columns: "Sample" which stores
#' the sample IDs, "Condition", which stores the biological condition labels
#' of the samples
#' @param peaks List of GRanges objects storing peak calls for each sample,
#' where element names correspond to sample IDs
#' @param filter (Optional) logical value, filter peaks based on thresholds on
#' peak statistics? Default: FALSE. The filter step is described in
#' \code{\link{filterPeaks}}.
#' @param filter_columns If \code{filter} is TRUE, a chracter vector
#' corresponding to names of columns in the peak metadata by which to filter
#' peaks. If \code{filter} is FALSE, not used.
#' @param filter_thresholds If \code{filter} is TRUE, a numeric vector
#' corresponding to lower cutoffs applied to metadata columns in order to filter
#' peaks. Provide one per column specified in \code{filter_columns}, in the same
#' order. If \code{filter} is FALSE, not used.
#' @param reduce (Optional) logical value, if TRUE, reduce gaps between nearby
#' peaks in the same sample. See more at \code{\link{reducePeaks}}.
#' Default: TRUE
#' @param gap (Optional) If \code{reduce} is TRUE, numeric value,
#' specifying the threshold distance for merging.
#' Peaks in the same sample which are within this many bp of each other will
#' be merged. Default: 300
#' @param p Numeric value in [0, 1] giving the fraction of reciprocal overlap
#' to require. Default: 0.4
#' @param n_features (Optional) Logical value indicating whether to include
#' a column "n_features" in the output storing the number of features in the
#' feature matrix constructed for the region, which may be useful for
#' understanding the behaviour of the binary strategy for constructing
#' feature matrices. Default: FALSE
#' @param heatmap (Optional) Logical value, plot the heatmap corresponding to
#' the hierarchical clustering result? Default: FALSE
#' @param titles (Optional) if \code{heatmap} is TRUE, a character vector
#' of the same length as \code{query}, specifying the title to use when plotting
#' each heatmap (e.g. a gene name), also reused as the
#' prefix of the name of the file where the heatmap is saved. By default, the
#' title is the genomic coordinates of the region in the form "chrN:start-end"
#' @param outdir (Optional) if \code{heatmap} is TRUE, the name of the directory
#' where heatmaps should be saved
#' @param optimal_clusters (Optional) Logical value indicate whether to cluster
#' samples into two groups, or to find the optimal clustering solution by
#' choosing the set of clusters which maximizes the Average Silhouette width.
#' Default: TRUE.
#' @param estimate_state (Optional) Logical value indicating whether to include
#' a column "state" in the output specifying the estimated chromatin state of
#' a test condition. The state will be on of "ON", "OFF", or NA, where the
#' latter results if a binary switch between the conditions is unclear.
#' Default: FALSE.
#' @param test_condition (Optional) If \code{estimate_state} is TRUE, string
#' specifying one of the two biological condtions in \code{metadata$Condition}
#' for which to estimate chromatin state.
#' @param BPPARAM (Optional) instance of \code{BiocParallel:BiocParallelParam}
#' used to determine the back-end used for parallel computations when performing
#' the analysis on more than one region.
#'
#' @return Data frame with one row per region in \code{query}. Contains the
#' coordinates of the region, the number of inferred clusters, the computed
#' cluster validity statistics, and the cluster assignment for each sample.
#'
#' @examples
#'
#' samples <- c("E068", "E071", "E074", "E101", "E102", "E110")
#' bedfiles <- system.file("extdata", paste0(samples, ".H3K4me3.bed"),
#' package = "chromswitch")
#' Conditions <- c(rep("Brain", 3), rep("Other", 3))
#'
#' metadata <- data.frame(Sample = samples,
#' H3K4me3 = bedfiles,
#' Condition = Conditions,
#' stringsAsFactors = FALSE)
#'
#' regions <- GRanges(seqnames = c("chr19", "chr19"),
#' ranges = IRanges(start = c(54924104, 54874318),
#' end = c(54929104, 54877536)))
#'
#' callBinary(query = regions, metadata = metadata, peaks = H3K4me3,
#' BPPARAM = BiocParallel::SerialParam())
#'
#' @export
callBinary <- function(query, metadata, peaks,
filter = FALSE, filter_columns = NULL,
filter_thresholds = NULL, reduce = TRUE,
gap = 300, p = 0.4, n_features = FALSE,
heatmap = FALSE, titles = NULL, outdir = NULL,
optimal_clusters = TRUE, estimate_state = FALSE,
test_condition = NULL,
BPPARAM = bpparam()) {
# Preprocessing
if (filter) {
if (is.null(filter_columns) || is.null(filter_thresholds))
stop("Please provide names of columns to filter and specify
thresholds to use.")
peaks <- filterPeaks(peaks,
columns = filter_columns,
thresholds = filter_thresholds)
}
# Retrieve peaks: get a LocalPeaks object for each query region
lpks <- bplapply(as(query, "GRangesList"), function(region)
retrievePeaks(peaks, metadata, region), BPPARAM = BPPARAM)
if (reduce) {
lpks <- bplapply(lpks, reducePeaks, gap, BPPARAM = BPPARAM)
}
# Construct feature matrices for each query region
matrices <- bplapply(lpks, binarizePeaks, p, BPPARAM = BPPARAM)
# Convert the queries into a GRangesList in order to be able to Map over
queries <- as(query, "GRangesList")
# Cluster the feature matrices
if (!isTRUE(heatmap)) {
results <- bpmapply(FUN = function(ft_mat, query)
cluster(ft_mat, metadata, query,
heatmap = FALSE,
title = NULL,
outdir = NULL,
optimal_clusters = optimal_clusters,
n_features = n_features,
estimate_state = estimate_state,
method = "binary",
test_condition = test_condition),
matrices, queries,
SIMPLIFY = FALSE, BPPARAM = BPPARAM)
} else if (isTRUE(heatmap)) {
if (is.null(titles)) titles <- unlist(lapply(queries, GRangesToCoord))
results <- bpmapply(FUN = function(ft_mat, query, title)
cluster(ft_mat, metadata, query,
heatmap = TRUE,
title = title,
outdir = outdir,
optimal_clusters = optimal_clusters,
n_features = n_features,
estimate_state = estimate_state,
method = "binary",
test_condition = test_condition),
matrices, queries, titles,
SIMPLIFY = FALSE, BPPARAM = BPPARAM)
}
return(data.frame(dplyr::bind_rows(results)))
}
selinj/chromswitch documentation built on Jan. 27, 2024, 12:36 p.m.
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Defines functions callBinary callSummary
Documented in callBinary callSummary
# ---------------------------------------------------------------------------- #
#
# Wrapper functions:
# These combine all the steps in the analysis in order to provide a simple
# way to apply the method to a genomic region for one mark
#
# ---------------------------------------------------------------------------- #
#' callSummary
#'
#' One of two main functions in the \code{chromswitch} package, this function
#' detects a switch in chromatin state in one or
#' more regions given ChIP-seq peak calls for one mark, executing the entire
#' algorithm from preprocessing to evaluating the clustering results,
#' using the summary strategy.
#'
#' This strategy constructs a sample-by-feature matrix to use as input for
#' hierarchical clustering by computing, for each sample, a vector of summary
#' statistics based on that sample's peaks in the query region. The summary
#' statistics are generally based on the enrichment statistics associated with
#' each peak as returned by the peak calling too, which might include, for
#' example, a p value and fold change.
#'
#' @param query GRanges list containing one or more genomic regions of interest
#' in which to call a switch. The output dataframe will contain one row per
#' region in \code{query}.
#' @param metadata A dataframe with at least two columns: "Sample" which stores
#' the sample IDs, "Condition", which stores the biological condition labels
#' of the samples
#' @param peaks List of GRanges objects storing peak calls for each sample,
#' where element names correspond to sample IDs
#' @param mark Character specifying the histone mark or ChIP-target,
#' for example, "H3K4me3"
#' @param filter (Optional) logical value, filter peaks based on thresholds on
#' peak statistics? Default: FALSE. The filter step is described in
#' \code{\link{filterPeaks}}.
#' @param filter_columns If \code{filter} is TRUE, a chracter vector
#' corresponding to names of columns in the peak metadata by which to filter
#' peaks. If \code{filter} is FALSE, not used.
#' @param filter_thresholds If \code{filter} is TRUE, a numeric vector
#' corresponding to lower cutoffs applied to metadata columns in order to filter
#' peaks. Provide one per column specified in \code{filter_columns}, in the same
#' order. If \code{filter} is FALSE, not used.
#' @param normalize (Optional) logical value, normalize peak statistics
#' genome-wide for each sample? Default: TRUE if \code{summarize_columns} or
#' \code{normalize_columns} is specified, FALSE, otherwise.
#' @param normalize_columns If \code{normalize} is TRUE, a character vector
#' corresponding to names of columns in the peak metadata to normalize
#' genome-wide for each sample. If \code{normalize} is FALSE, not used.
#' @param tail (Optional) if \code{normalize} is TRUE, specifies the fraction
#' of extreme values in each tail to bound during normalization. More details at
#' \code{\link{normalizePeaks}}.
#' @param summarize_columns Character vector of column names on which to compute
#' summary statistics during feature matrix construction. These statistics
#' become the features of the matrix.
#' @param fraction (Optional) Logical value, during feature matrix construction,
#' compute the fraction of the region overlapped by peaks? Default: TRUE
#' @param n (Optional) Logical value, during feature matrix construction,
#' compute the number of peaks in the region? Default: FALSE
#' @param heatmap (Optional) Logical value, plot the heatmap corresponding to
#' the hierarchical clustering result? Default: FALSE
#' @param titles (Optional) if \code{heatmap} is TRUE, a character vector
#' of the same length as \code{query}, specifying the title to use when plotting
#' each heatmap (e.g. a gene name), also reused as the
#' prefix of the name of the file where the heatmap is saved. By default, the
#' title is the genomic coordinates of the region in the form "chrN:start-end"
#' @param outdir (Optional) if \code{heatmap} is TRUE, the name of the directory
#' where heatmaps should be saved
#' @param optimal_clusters (Optional) Logical value indicate whether to cluster
#' samples into two groups, or to find the optimal clustering solution by
#' choosing the set of clusters which maximizes the Average Silhouette width.
#' Default: TRUE
#' @param estimate_state (Optional) Logical value indicating whether to include
#' a column "state" in the output specifying the estimated chromatin state of
#' a test condition. The state will be on of "ON", "OFF", or NA, where the
#' latter results if a binary switch between the conditions is unclear.
#' Default: FALSE.
#' @param signal_col (Optional) If \code{estimate_state} is TRUE, string
#' specifying the name of the column in the original peak files which
#' corresponds to the level of enrichment in the region, e.g. fold change
#' @param test_condition (Optional) If \code{estimate_state} is TRUE, string
#' specifying one of the two biological condtions in \code{metadata$Condition}
#' for which to estimate chromatin state.
#' @param BPPARAM (Optional) instance of \code{BiocParallel:BiocParallelParam}
#' used to determine the back-end used for parallel computations when performing
#' the analysis on more than one region.
#'
#' @return Data frame with one row per region in \code{query}. Contains the
#' coordinates of the region, the number of inferred clusters, the computed
#' cluster validity statistics, and the cluster assignment for each sample.
#'
#' @examples
#'
#' samples <- c("E068", "E071", "E074", "E101", "E102", "E110")
#' bedfiles <- system.file("extdata", paste0(samples, ".H3K4me3.bed"),
#' package = "chromswitch")
#' Conditions <- c(rep("Brain", 3), rep("Other", 3))
#'
#' metadata <- data.frame(Sample = samples,
#' H3K4me3 = bedfiles,
#' Condition = Conditions,
#' stringsAsFactors = FALSE)
#'
#' regions <- GRanges(seqnames = c("chr19", "chr19"),
#' ranges = IRanges(start = c(54924104, 54874318),
#' end = c(54929104, 54877536)))
#'
#' callSummary(query = regions,
#' metadata = metadata,
#' peaks = H3K4me3,
#' normalize_columns = c("qValue", "pValue", "signalValue"),
#' mark = "H3K4me3",
#' summarize_columns = c("pValue", "qValue", "signalValue"),
#' heatmap = FALSE,
#' BPPARAM = BiocParallel::SerialParam())
#'
#' @export
callSummary <- function(query, metadata, peaks, mark,
filter = FALSE, filter_columns = summarize_columns,
filter_thresholds = NULL,
summarize_columns = NULL,
normalize_columns = summarize_columns, tail = 0.005,
normalize = ifelse(is.null(normalize_columns) && is.null(summarize_columns), FALSE, TRUE),
fraction = TRUE, n = FALSE,
heatmap = FALSE, titles = NULL, outdir = NULL,
optimal_clusters = TRUE,
estimate_state = FALSE,
signal_col = NULL,
test_condition = NULL,
BPPARAM = bpparam()) {
# Preprocessing
if (filter) {
if (is.null(filter_thresholds))
stop("Please specify thresholds to use for filtering columns. ",
"By default, filters are applied to the columns passed to ",
"summarize_columns. Specific columns to filter can be ",
"passed to filter_columns.",
"Provide one threshold per column, in the same order.")
peaks <- filterPeaks(peaks,
columns = filter_columns,
thresholds = filter_thresholds)
}
if (normalize) {
peaks <- normalizePeaks(peaks,
columns = normalize_columns,
tail = tail)
}
if(is.null(summarize_columns) && fraction == FALSE && n == FALSE) {
stop("No columns specified, and none of fraction, or
n set to TRUE, therefore cannot construct a feature matrix.")
}
# Check titles
if ((!is.null(titles)) && length(query) != length(titles))
stop("Please provide one title per query region.")
# Retrieve peaks: get a LocalPeaks object for each query region
lpks <- bplapply(as(query, "GRangesList"), function(region)
retrievePeaks(peaks, metadata, region),
BPPARAM = BPPARAM)
# Construct feature matrices for each query region
matrices <- bplapply(lpks, summarizePeaks, mark,
summarize_columns, fraction, n,
BPPARAM = BPPARAM)
# Convert the queries into a GRangesList in order to be able to Map over
queries <- as(query, "GRangesList")
# Cluster the feature matrices
if (!isTRUE(heatmap)) {
results <- Map(f = function(ft_mat, query)
cluster(ft_mat, metadata, query,
heatmap = FALSE,
title = NULL,
outdir = NULL,
n_features = FALSE,
optimal_clusters = optimal_clusters,
estimate_state = estimate_state,
method = "summary",
signal_col = signal_col,
test_condition = test_condition,
mark = mark),
matrices, queries)
} else if (isTRUE(heatmap)) {
if (is.null(titles)) titles <- unlist(lapply(queries, GRangesToCoord))
results <- bpmapply(FUN = function(ft_mat, query, title)
cluster(ft_mat, metadata, query,
heatmap = heatmap,
title = title,
outdir = outdir,
n_features = FALSE,
optimal_clusters = optimal_clusters,
estimate_state = estimate_state,
method = "summary",
signal_col = signal_col,
test_condition = test_condition,
mark = mark),
matrices, queries, titles,
SIMPLIFY = FALSE, BPPARAM = BPPARAM)
}
return(data.frame(dplyr::bind_rows(results)))
}
#' callBinary
#'
#' One of two main functions in the \code{chromswitch} package, this function
#' detects a switch in chromatin state in one or
#' more regions given ChIP-seq peak calls for one mark, executing the entire
#' algorithm from preprocessing to evaluating the clustering results,
#' using the binary strategy.
#'
#' This strategy constructs a sample-by-feature matrix to use as input for
#' hierarchical clustering by first assembling the set of unique peaks observed
#' in the region across samples. Then for each unique peak, we model the
#' presence or absence of that peak in each sample, resulting in a binary
#' feature matrix.
#'
#' @param query GRanges list containing one or more genomic regions of interest
#' in which to call a switch. The output dataframe will contain one row per
#' region in \code{query}.
#' @param metadata A dataframe with at least two columns: "Sample" which stores
#' the sample IDs, "Condition", which stores the biological condition labels
#' of the samples
#' @param peaks List of GRanges objects storing peak calls for each sample,
#' where element names correspond to sample IDs
#' @param filter (Optional) logical value, filter peaks based on thresholds on
#' peak statistics? Default: FALSE. The filter step is described in
#' \code{\link{filterPeaks}}.
#' @param filter_columns If \code{filter} is TRUE, a chracter vector
#' corresponding to names of columns in the peak metadata by which to filter
#' peaks. If \code{filter} is FALSE, not used.
#' @param filter_thresholds If \code{filter} is TRUE, a numeric vector
#' corresponding to lower cutoffs applied to metadata columns in order to filter
#' peaks. Provide one per column specified in \code{filter_columns}, in the same
#' order. If \code{filter} is FALSE, not used.
#' @param reduce (Optional) logical value, if TRUE, reduce gaps between nearby
#' peaks in the same sample. See more at \code{\link{reducePeaks}}.
#' Default: TRUE
#' @param gap (Optional) If \code{reduce} is TRUE, numeric value,
#' specifying the threshold distance for merging.
#' Peaks in the same sample which are within this many bp of each other will
#' be merged. Default: 300
#' @param p Numeric value in [0, 1] giving the fraction of reciprocal overlap
#' to require. Default: 0.4
#' @param n_features (Optional) Logical value indicating whether to include
#' a column "n_features" in the output storing the number of features in the
#' feature matrix constructed for the region, which may be useful for
#' understanding the behaviour of the binary strategy for constructing
#' feature matrices. Default: FALSE
#' @param heatmap (Optional) Logical value, plot the heatmap corresponding to
#' the hierarchical clustering result? Default: FALSE
#' @param titles (Optional) if \code{heatmap} is TRUE, a character vector
#' of the same length as \code{query}, specifying the title to use when plotting
#' each heatmap (e.g. a gene name), also reused as the
#' prefix of the name of the file where the heatmap is saved. By default, the
#' title is the genomic coordinates of the region in the form "chrN:start-end"
#' @param outdir (Optional) if \code{heatmap} is TRUE, the name of the directory
#' where heatmaps should be saved
#' @param optimal_clusters (Optional) Logical value indicate whether to cluster
#' samples into two groups, or to find the optimal clustering solution by
#' choosing the set of clusters which maximizes the Average Silhouette width.
#' Default: TRUE.
#' @param estimate_state (Optional) Logical value indicating whether to include
#' a column "state" in the output specifying the estimated chromatin state of
#' a test condition. The state will be on of "ON", "OFF", or NA, where the
#' latter results if a binary switch between the conditions is unclear.
#' Default: FALSE.
#' @param test_condition (Optional) If \code{estimate_state} is TRUE, string
#' specifying one of the two biological condtions in \code{metadata$Condition}
#' for which to estimate chromatin state.
#' @param BPPARAM (Optional) instance of \code{BiocParallel:BiocParallelParam}
#' used to determine the back-end used for parallel computations when performing
#' the analysis on more than one region.
#'
#' @return Data frame with one row per region in \code{query}. Contains the
#' coordinates of the region, the number of inferred clusters, the computed
#' cluster validity statistics, and the cluster assignment for each sample.
#'
#' @examples
#'
#' samples <- c("E068", "E071", "E074", "E101", "E102", "E110")
#' bedfiles <- system.file("extdata", paste0(samples, ".H3K4me3.bed"),
#' package = "chromswitch")
#' Conditions <- c(rep("Brain", 3), rep("Other", 3))
#'
#' metadata <- data.frame(Sample = samples,
#' H3K4me3 = bedfiles,
#' Condition = Conditions,
#' stringsAsFactors = FALSE)
#'
#' regions <- GRanges(seqnames = c("chr19", "chr19"),
#' ranges = IRanges(start = c(54924104, 54874318),
#' end = c(54929104, 54877536)))
#'
#' callBinary(query = regions, metadata = metadata, peaks = H3K4me3,
#' BPPARAM = BiocParallel::SerialParam())
#'
#' @export
callBinary <- function(query, metadata, peaks,
filter = FALSE, filter_columns = NULL,
filter_thresholds = NULL, reduce = TRUE,
gap = 300, p = 0.4, n_features = FALSE,
heatmap = FALSE, titles = NULL, outdir = NULL,
optimal_clusters = TRUE, estimate_state = FALSE,
test_condition = NULL,
BPPARAM = bpparam()) {
# Preprocessing
if (filter) {
if (is.null(filter_columns) || is.null(filter_thresholds))
stop("Please provide names of columns to filter and specify
thresholds to use.")
peaks <- filterPeaks(peaks,
columns = filter_columns,
thresholds = filter_thresholds)
}
# Retrieve peaks: get a LocalPeaks object for each query region
lpks <- bplapply(as(query, "GRangesList"), function(region)
retrievePeaks(peaks, metadata, region), BPPARAM = BPPARAM)
if (reduce) {
lpks <- bplapply(lpks, reducePeaks, gap, BPPARAM = BPPARAM)
}
# Construct feature matrices for each query region
matrices <- bplapply(lpks, binarizePeaks, p, BPPARAM = BPPARAM)
# Convert the queries into a GRangesList in order to be able to Map over
queries <- as(query, "GRangesList")
# Cluster the feature matrices
if (!isTRUE(heatmap)) {
results <- bpmapply(FUN = function(ft_mat, query)
cluster(ft_mat, metadata, query,
heatmap = FALSE,
title = NULL,
outdir = NULL,
optimal_clusters = optimal_clusters,
n_features = n_features,
estimate_state = estimate_state,
method = "binary",
test_condition = test_condition),
matrices, queries,
SIMPLIFY = FALSE, BPPARAM = BPPARAM)
} else if (isTRUE(heatmap)) {
if (is.null(titles)) titles <- unlist(lapply(queries, GRangesToCoord))
results <- bpmapply(FUN = function(ft_mat, query, title)
cluster(ft_mat, metadata, query,
heatmap = TRUE,
title = title,
outdir = outdir,
optimal_clusters = optimal_clusters,
n_features = n_features,
estimate_state = estimate_state,
method = "binary",
test_condition = test_condition),
matrices, queries, titles,
SIMPLIFY = FALSE, BPPARAM = BPPARAM)
}
return(data.frame(dplyr::bind_rows(results)))
}
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