R/class_dmSQTLprecision.R
In gosianow/DRIMSeq: Differential transcript usage and tuQTL analyses with Dirichlet-multinomial model in RNA-seq
#' @include class_dmSQTLdata.R
NULL
###############################################################################
### dmSQTLprecision class
###############################################################################
#' dmSQTLprecision object
#'
#' dmSQTLprecision extends the \code{\linkS4class{dmSQTLdata}} by adding the
#' precision estimates of Dirichlet-multinomial distribution used to model the
#' feature (e.g., transcript, exon, exonic bin) counts for each gene-SNP pair in
#' the QTL analysis. Result of \code{\link{dmPrecision}}.
#'
#' @return
#'
#' \itemize{ \item \code{mean_expression(x)}: Get a data frame with mean gene
#' expression. \item \code{common_precision(x)}: Get common precision. \item
#' \code{genewise_precision(x)}: Get a data frame with gene-wise precision.}
#'
#' @param x dmSQTLprecision object.
#'
#' @slot mean_expression Numeric vector of mean gene expression.
#' @slot common_precision Numeric value of estimated common precision.
#' @slot genewise_precision List of estimated gene-wise precisions. Each element
#' of this list is a vector of precisions estimated for all the genotype
#' blocks assigned to a given gene.
#'
#' @examples
#' # --------------------------------------------------------------------------
#' # Create dmSQTLdata object
#' # --------------------------------------------------------------------------
#' # Use subsets of data defined in the GeuvadisTranscriptExpr package
#'
#' library(GeuvadisTranscriptExpr)
#' \donttest{
#' geuv_counts <- GeuvadisTranscriptExpr::counts
#' geuv_genotypes <- GeuvadisTranscriptExpr::genotypes
#' geuv_gene_ranges <- GeuvadisTranscriptExpr::gene_ranges
#' geuv_snp_ranges <- GeuvadisTranscriptExpr::snp_ranges
#'
#' colnames(geuv_counts)[c(1,2)] <- c("feature_id", "gene_id")
#' colnames(geuv_genotypes)[4] <- "snp_id"
#' geuv_samples <- data.frame(sample_id = colnames(geuv_counts)[-c(1,2)])
#'
#' d <- dmSQTLdata(counts = geuv_counts, gene_ranges = geuv_gene_ranges,
#' genotypes = geuv_genotypes, snp_ranges = geuv_snp_ranges,
#' samples = geuv_samples, window = 5e3)
#'
#' # --------------------------------------------------------------------------
#' # sQTL analysis - simple group comparison
#' # --------------------------------------------------------------------------
#'
#' ## Filtering
#' d <- dmFilter(d, min_samps_gene_expr = 70, min_samps_feature_expr = 5,
#' minor_allele_freq = 5, min_gene_expr = 10, min_feature_expr = 10)
#'
#' plotData(d)
#'
#' ## To make the analysis reproducible
#' set.seed(123)
#' ## Calculate precision
#' d <- dmPrecision(d)
#'
#' plotPrecision(d)
#' }
#' @author Malgorzata Nowicka
#' @seealso \code{\linkS4class{dmSQTLdata}}, \code{\linkS4class{dmSQTLfit}},
#' \code{\linkS4class{dmSQTLtest}}
setClass("dmSQTLprecision",
contains = "dmSQTLdata",
representation(mean_expression = "numeric",
common_precision = "numeric",
genewise_precision = "list"))
# -----------------------------------------------------------------------------
setValidity("dmSQTLprecision", function(object){
# Has to return TRUE when valid object!
out <- TRUE
if(length(object@mean_expression) > 0){
if(length(object@mean_expression) == length(object@counts)){
if(all(names(object@mean_expression) == names(object@counts)))
out <- TRUE
else
return("Different names of 'counts' and 'mean_expression'")
}
else
return("Unequal length of 'counts' and 'mean_expression'")
}
if(length(object@genewise_precision) > 0){
if(length(object@genewise_precision) == length(object@counts)){
if(all(lapply(object@genewise_precision, length) ==
elementNROWS(object@genotypes)))
out <- TRUE
else
return("Different numbers of blocks in 'genotypes' and in
'genewise_precision'")
}
else
return("Unequal number of genes in 'counts' and in 'genewise_precision'")
}
if(length(object@common_precision) > 0){
if(length(object@common_precision) == 1)
out <- TRUE
else
return("'common_precision' must be a vector of length 1")
}
return(out)
})
################################################################################
### accessing methods
################################################################################
#' @rdname dmSQTLprecision-class
#' @export
setMethod("mean_expression", "dmSQTLprecision", function(x){
data.frame(gene_id = names(x@mean_expression),
mean_expression = x@mean_expression,
stringsAsFactors = FALSE, row.names = NULL)
})
#' @rdname dmSQTLprecision-class
#' @export
setMethod("common_precision", "dmSQTLprecision", function(x)
x@common_precision )
#' @rdname dmSQTLprecision-class
#' @export
setMethod("genewise_precision", "dmSQTLprecision", function(x){
data.frame(gene_id = rep(names(x@genewise_precision),
sapply(x@genewise_precision, length)),
block_id = unlist(lapply(x@genewise_precision, names)),
genewise_precision = unlist(x@genewise_precision),
stringsAsFactors = FALSE, row.names = NULL)
})
################################################################################
### show methods
################################################################################
setMethod("show", "dmSQTLprecision", function(object){
callNextMethod(object)
cat(" mean_expression(), common_precision(), genewise_precision()\n")
})
################################################################################
### dmPrecision
################################################################################
#' @rdname dmPrecision
#' @param speed Logical. If \code{FALSE}, precision is calculated per each
#' gene-block. Such calculation may take a long time, since there can be
#' hundreds of SNPs/blocks per gene. If \code{TRUE}, there will be only one
#' precision calculated per gene and it will be assigned to all the blocks
#' matched with this gene.
#' @export
setMethod("dmPrecision", "dmSQTLdata", function(x, mean_expression = TRUE,
common_precision = TRUE, genewise_precision = TRUE,
prec_adjust = TRUE, prec_subset = 0.1,
prec_interval = c(0, 1e+3), prec_tol = 1e+01,
prec_init = 100, prec_grid_length = 21, prec_grid_range = c(-10, 10),
prec_moderation = "none", prec_prior_df = 0, prec_span = 0.1,
one_way = TRUE, speed = TRUE,
prop_mode = "constrOptim", prop_tol = 1e-12,
coef_mode = "optim", coef_tol = 1e-12,
verbose = 0, BPPARAM = BiocParallel::SerialParam()){
### Parameter checks:
stopifnot(is.logical(mean_expression))
stopifnot(is.logical(common_precision))
stopifnot(is.logical(genewise_precision))
stopifnot(is.logical(prec_adjust))
stopifnot(length(prec_subset) == 1)
stopifnot(is.numeric(prec_subset) && prec_subset > 0 && prec_subset <= 1)
stopifnot(length(prec_interval) == 2)
stopifnot(prec_interval[1] < prec_interval[2])
stopifnot(length(prec_tol) == 1)
stopifnot(is.numeric(prec_tol) && prec_tol > 0)
stopifnot(length(prec_init) == 1)
stopifnot(is.numeric(prec_init))
stopifnot(prec_grid_length > 2)
stopifnot(length(prec_grid_range) == 2)
stopifnot(prec_grid_range[1] < prec_grid_range[2])
stopifnot(length(prec_moderation) == 1)
stopifnot(prec_moderation %in% c("none", "common", "trended"))
stopifnot(length(prec_prior_df) == 1)
stopifnot(is.numeric(prec_prior_df) && prec_prior_df >= 0)
stopifnot(length(prec_span) == 1)
stopifnot(is.numeric(prec_span) && prec_span > 0 && prec_span < 1)
stopifnot(is.logical(one_way))
stopifnot(is.logical(speed))
stopifnot(length(prop_mode) == 1)
stopifnot(prop_mode %in% c("constrOptim"))
stopifnot(length(prop_tol) == 1)
stopifnot(is.numeric(prop_tol) && prop_tol > 0)
stopifnot(length(coef_mode) == 1)
stopifnot(coef_mode %in% c("optim", "nlminb", "nlm"))
stopifnot(length(coef_tol) == 1)
stopifnot(is.numeric(coef_tol) && coef_tol > 0)
stopifnot(verbose %in% 0:2)
if(mean_expression || (genewise_precision &&
prec_moderation == "trended")){
mean_expression <- dm_estimateMeanExpression(counts = x@counts,
verbose = verbose)
}else{
mean_expression <- numeric()
}
if(common_precision){
if(prec_subset < 1){
message(paste0("! Using a subset of ", prec_subset,
" genes to estimate common precision !\n"))
genes2keep <- sample(1:length(x@counts),
max(round(prec_subset * length(x@counts)), 1), replace = FALSE)
}else{
genes2keep <- 1:length(x@counts)
}
### Use only one SNP per gene and a null model to make computation faster
genotypes_null <- new("MatrixList",
unlistData = matrix(1, nrow = length(genes2keep),
ncol = ncol(x@genotypes)),
partitioning = split(1:length(genes2keep),
factor(names(x@genotypes[genes2keep, ]),
levels = names(x@genotypes[genes2keep, ]))))
common_precision <- dmSQTL_estimateCommonPrecision(
counts = x@counts[genes2keep, ], genotypes = genotypes_null,
prec_adjust = prec_adjust,
prec_interval = prec_interval, prec_tol = prec_tol,
one_way = one_way, group_formula = ~ 1,
prop_mode = prop_mode, prop_tol = prop_tol,
coef_mode = coef_mode, coef_tol = coef_tol,
verbose = verbose, BPPARAM = BPPARAM)
}else{
common_precision <- numeric()
}
if(genewise_precision){
if(length(common_precision)){
message("! Using common_precision = ", round(common_precision, 4),
" as prec_init !")
prec_init <- common_precision
}
if(speed){
### Use only one SNP per gene and a null model to make computation faster
G <- length(x@genotypes)
inds <- 1:G
genotypes_null <- new( "MatrixList",
unlistData = matrix(1, nrow = G, ncol = ncol(x@genotypes)),
partitioning = split(inds, factor(names(x@genotypes),
levels = names(x@genotypes))) )
genewise_precision <- dmSQTL_estimateTagwisePrecision(counts = x@counts,
genotypes = genotypes_null, mean_expression = mean_expression,
prec_adjust = prec_adjust, prec_init = prec_init,
prec_grid_length = prec_grid_length, prec_grid_range = prec_grid_range,
prec_moderation = prec_moderation, prec_prior_df = prec_prior_df,
prec_span = prec_span, one_way = one_way, group_formula = ~ 1,
prop_mode = prop_mode, prop_tol = prop_tol,
coef_mode = coef_mode, coef_tol = coef_tol,
verbose = verbose, BPPARAM = BPPARAM)
### Replicate the values for all the snps
genewise_precision <- relist(rep(unlist(genewise_precision),
times = elementNROWS(x@genotypes)), x@genotypes@partitioning)
}else{
genewise_precision <- dmSQTL_estimateTagwisePrecision(counts = x@counts,
genotypes = x@genotypes, mean_expression = mean_expression,
prec_adjust = prec_adjust, prec_init = prec_init,
prec_grid_length = prec_grid_length, prec_grid_range = prec_grid_range,
prec_moderation = prec_moderation, prec_prior_df = prec_prior_df,
prec_span = prec_span, one_way = one_way, group_formula = ~ group,
prop_mode = prop_mode, prop_tol = prop_tol,
coef_mode = coef_mode, coef_tol = coef_tol,
verbose = verbose, BPPARAM = BPPARAM)
}
}else{
genewise_precision <- list()
}
return(new("dmSQTLprecision", mean_expression = mean_expression,
common_precision = common_precision,
genewise_precision = genewise_precision,
counts = x@counts, genotypes = x@genotypes, blocks = x@blocks,
samples = x@samples))
})
###############################################################################
### plotPrecision
###############################################################################
#' @rdname plotPrecision
#' @export
setMethod("plotPrecision", "dmSQTLprecision", function(x){
if(!length(x@genewise_precision) == length(x@counts))
stop("Genewise precision must be estimated for each gene!")
if(!length(x@genewise_precision) == length(x@mean_expression))
stop("Mean expression must be estimated for each gene!")
w <- sapply(x@genewise_precision, length)
mean_expression <- rep(x@mean_expression, w)
nr_features <- rep(elementNROWS(x@counts), w)
genewise_precision <- unlist(x@genewise_precision)
if(length(x@common_precision) == 0){
common_precision <- NULL
}else{
common_precision <- x@common_precision
}
ggp <- dm_plotPrecision(genewise_precision = genewise_precision,
mean_expression = mean_expression, nr_features = nr_features,
common_precision = common_precision)
return(ggp)
})
gosianow/DRIMSeq documentation built on Aug. 8, 2020, 10:29 a.m.
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#' @include class_dmSQTLdata.R
NULL
###############################################################################
### dmSQTLprecision class
###############################################################################
#' dmSQTLprecision object
#'
#' dmSQTLprecision extends the \code{\linkS4class{dmSQTLdata}} by adding the
#' precision estimates of Dirichlet-multinomial distribution used to model the
#' feature (e.g., transcript, exon, exonic bin) counts for each gene-SNP pair in
#' the QTL analysis. Result of \code{\link{dmPrecision}}.
#'
#' @return
#'
#' \itemize{ \item \code{mean_expression(x)}: Get a data frame with mean gene
#' expression. \item \code{common_precision(x)}: Get common precision. \item
#' \code{genewise_precision(x)}: Get a data frame with gene-wise precision.}
#'
#' @param x dmSQTLprecision object.
#'
#' @slot mean_expression Numeric vector of mean gene expression.
#' @slot common_precision Numeric value of estimated common precision.
#' @slot genewise_precision List of estimated gene-wise precisions. Each element
#' of this list is a vector of precisions estimated for all the genotype
#' blocks assigned to a given gene.
#'
#' @examples
#' # --------------------------------------------------------------------------
#' # Create dmSQTLdata object
#' # --------------------------------------------------------------------------
#' # Use subsets of data defined in the GeuvadisTranscriptExpr package
#'
#' library(GeuvadisTranscriptExpr)
#' \donttest{
#' geuv_counts <- GeuvadisTranscriptExpr::counts
#' geuv_genotypes <- GeuvadisTranscriptExpr::genotypes
#' geuv_gene_ranges <- GeuvadisTranscriptExpr::gene_ranges
#' geuv_snp_ranges <- GeuvadisTranscriptExpr::snp_ranges
#'
#' colnames(geuv_counts)[c(1,2)] <- c("feature_id", "gene_id")
#' colnames(geuv_genotypes)[4] <- "snp_id"
#' geuv_samples <- data.frame(sample_id = colnames(geuv_counts)[-c(1,2)])
#'
#' d <- dmSQTLdata(counts = geuv_counts, gene_ranges = geuv_gene_ranges,
#' genotypes = geuv_genotypes, snp_ranges = geuv_snp_ranges,
#' samples = geuv_samples, window = 5e3)
#'
#' # --------------------------------------------------------------------------
#' # sQTL analysis - simple group comparison
#' # --------------------------------------------------------------------------
#'
#' ## Filtering
#' d <- dmFilter(d, min_samps_gene_expr = 70, min_samps_feature_expr = 5,
#' minor_allele_freq = 5, min_gene_expr = 10, min_feature_expr = 10)
#'
#' plotData(d)
#'
#' ## To make the analysis reproducible
#' set.seed(123)
#' ## Calculate precision
#' d <- dmPrecision(d)
#'
#' plotPrecision(d)
#' }
#' @author Malgorzata Nowicka
#' @seealso \code{\linkS4class{dmSQTLdata}}, \code{\linkS4class{dmSQTLfit}},
#' \code{\linkS4class{dmSQTLtest}}
setClass("dmSQTLprecision",
contains = "dmSQTLdata",
representation(mean_expression = "numeric",
common_precision = "numeric",
genewise_precision = "list"))
# -----------------------------------------------------------------------------
setValidity("dmSQTLprecision", function(object){
# Has to return TRUE when valid object!
out <- TRUE
if(length(object@mean_expression) > 0){
if(length(object@mean_expression) == length(object@counts)){
if(all(names(object@mean_expression) == names(object@counts)))
out <- TRUE
else
return("Different names of 'counts' and 'mean_expression'")
}
else
return("Unequal length of 'counts' and 'mean_expression'")
}
if(length(object@genewise_precision) > 0){
if(length(object@genewise_precision) == length(object@counts)){
if(all(lapply(object@genewise_precision, length) ==
elementNROWS(object@genotypes)))
out <- TRUE
else
return("Different numbers of blocks in 'genotypes' and in
'genewise_precision'")
}
else
return("Unequal number of genes in 'counts' and in 'genewise_precision'")
}
if(length(object@common_precision) > 0){
if(length(object@common_precision) == 1)
out <- TRUE
else
return("'common_precision' must be a vector of length 1")
}
return(out)
})
################################################################################
### accessing methods
################################################################################
#' @rdname dmSQTLprecision-class
#' @export
setMethod("mean_expression", "dmSQTLprecision", function(x){
data.frame(gene_id = names(x@mean_expression),
mean_expression = x@mean_expression,
stringsAsFactors = FALSE, row.names = NULL)
})
#' @rdname dmSQTLprecision-class
#' @export
setMethod("common_precision", "dmSQTLprecision", function(x)
x@common_precision )
#' @rdname dmSQTLprecision-class
#' @export
setMethod("genewise_precision", "dmSQTLprecision", function(x){
data.frame(gene_id = rep(names(x@genewise_precision),
sapply(x@genewise_precision, length)),
block_id = unlist(lapply(x@genewise_precision, names)),
genewise_precision = unlist(x@genewise_precision),
stringsAsFactors = FALSE, row.names = NULL)
})
################################################################################
### show methods
################################################################################
setMethod("show", "dmSQTLprecision", function(object){
callNextMethod(object)
cat(" mean_expression(), common_precision(), genewise_precision()\n")
})
################################################################################
### dmPrecision
################################################################################
#' @rdname dmPrecision
#' @param speed Logical. If \code{FALSE}, precision is calculated per each
#' gene-block. Such calculation may take a long time, since there can be
#' hundreds of SNPs/blocks per gene. If \code{TRUE}, there will be only one
#' precision calculated per gene and it will be assigned to all the blocks
#' matched with this gene.
#' @export
setMethod("dmPrecision", "dmSQTLdata", function(x, mean_expression = TRUE,
common_precision = TRUE, genewise_precision = TRUE,
prec_adjust = TRUE, prec_subset = 0.1,
prec_interval = c(0, 1e+3), prec_tol = 1e+01,
prec_init = 100, prec_grid_length = 21, prec_grid_range = c(-10, 10),
prec_moderation = "none", prec_prior_df = 0, prec_span = 0.1,
one_way = TRUE, speed = TRUE,
prop_mode = "constrOptim", prop_tol = 1e-12,
coef_mode = "optim", coef_tol = 1e-12,
verbose = 0, BPPARAM = BiocParallel::SerialParam()){
### Parameter checks:
stopifnot(is.logical(mean_expression))
stopifnot(is.logical(common_precision))
stopifnot(is.logical(genewise_precision))
stopifnot(is.logical(prec_adjust))
stopifnot(length(prec_subset) == 1)
stopifnot(is.numeric(prec_subset) && prec_subset > 0 && prec_subset <= 1)
stopifnot(length(prec_interval) == 2)
stopifnot(prec_interval[1] < prec_interval[2])
stopifnot(length(prec_tol) == 1)
stopifnot(is.numeric(prec_tol) && prec_tol > 0)
stopifnot(length(prec_init) == 1)
stopifnot(is.numeric(prec_init))
stopifnot(prec_grid_length > 2)
stopifnot(length(prec_grid_range) == 2)
stopifnot(prec_grid_range[1] < prec_grid_range[2])
stopifnot(length(prec_moderation) == 1)
stopifnot(prec_moderation %in% c("none", "common", "trended"))
stopifnot(length(prec_prior_df) == 1)
stopifnot(is.numeric(prec_prior_df) && prec_prior_df >= 0)
stopifnot(length(prec_span) == 1)
stopifnot(is.numeric(prec_span) && prec_span > 0 && prec_span < 1)
stopifnot(is.logical(one_way))
stopifnot(is.logical(speed))
stopifnot(length(prop_mode) == 1)
stopifnot(prop_mode %in% c("constrOptim"))
stopifnot(length(prop_tol) == 1)
stopifnot(is.numeric(prop_tol) && prop_tol > 0)
stopifnot(length(coef_mode) == 1)
stopifnot(coef_mode %in% c("optim", "nlminb", "nlm"))
stopifnot(length(coef_tol) == 1)
stopifnot(is.numeric(coef_tol) && coef_tol > 0)
stopifnot(verbose %in% 0:2)
if(mean_expression || (genewise_precision &&
prec_moderation == "trended")){
mean_expression <- dm_estimateMeanExpression(counts = x@counts,
verbose = verbose)
}else{
mean_expression <- numeric()
}
if(common_precision){
if(prec_subset < 1){
message(paste0("! Using a subset of ", prec_subset,
" genes to estimate common precision !\n"))
genes2keep <- sample(1:length(x@counts),
max(round(prec_subset * length(x@counts)), 1), replace = FALSE)
}else{
genes2keep <- 1:length(x@counts)
}
### Use only one SNP per gene and a null model to make computation faster
genotypes_null <- new("MatrixList",
unlistData = matrix(1, nrow = length(genes2keep),
ncol = ncol(x@genotypes)),
partitioning = split(1:length(genes2keep),
factor(names(x@genotypes[genes2keep, ]),
levels = names(x@genotypes[genes2keep, ]))))
common_precision <- dmSQTL_estimateCommonPrecision(
counts = x@counts[genes2keep, ], genotypes = genotypes_null,
prec_adjust = prec_adjust,
prec_interval = prec_interval, prec_tol = prec_tol,
one_way = one_way, group_formula = ~ 1,
prop_mode = prop_mode, prop_tol = prop_tol,
coef_mode = coef_mode, coef_tol = coef_tol,
verbose = verbose, BPPARAM = BPPARAM)
}else{
common_precision <- numeric()
}
if(genewise_precision){
if(length(common_precision)){
message("! Using common_precision = ", round(common_precision, 4),
" as prec_init !")
prec_init <- common_precision
}
if(speed){
### Use only one SNP per gene and a null model to make computation faster
G <- length(x@genotypes)
inds <- 1:G
genotypes_null <- new( "MatrixList",
unlistData = matrix(1, nrow = G, ncol = ncol(x@genotypes)),
partitioning = split(inds, factor(names(x@genotypes),
levels = names(x@genotypes))) )
genewise_precision <- dmSQTL_estimateTagwisePrecision(counts = x@counts,
genotypes = genotypes_null, mean_expression = mean_expression,
prec_adjust = prec_adjust, prec_init = prec_init,
prec_grid_length = prec_grid_length, prec_grid_range = prec_grid_range,
prec_moderation = prec_moderation, prec_prior_df = prec_prior_df,
prec_span = prec_span, one_way = one_way, group_formula = ~ 1,
prop_mode = prop_mode, prop_tol = prop_tol,
coef_mode = coef_mode, coef_tol = coef_tol,
verbose = verbose, BPPARAM = BPPARAM)
### Replicate the values for all the snps
genewise_precision <- relist(rep(unlist(genewise_precision),
times = elementNROWS(x@genotypes)), x@genotypes@partitioning)
}else{
genewise_precision <- dmSQTL_estimateTagwisePrecision(counts = x@counts,
genotypes = x@genotypes, mean_expression = mean_expression,
prec_adjust = prec_adjust, prec_init = prec_init,
prec_grid_length = prec_grid_length, prec_grid_range = prec_grid_range,
prec_moderation = prec_moderation, prec_prior_df = prec_prior_df,
prec_span = prec_span, one_way = one_way, group_formula = ~ group,
prop_mode = prop_mode, prop_tol = prop_tol,
coef_mode = coef_mode, coef_tol = coef_tol,
verbose = verbose, BPPARAM = BPPARAM)
}
}else{
genewise_precision <- list()
}
return(new("dmSQTLprecision", mean_expression = mean_expression,
common_precision = common_precision,
genewise_precision = genewise_precision,
counts = x@counts, genotypes = x@genotypes, blocks = x@blocks,
samples = x@samples))
})
###############################################################################
### plotPrecision
###############################################################################
#' @rdname plotPrecision
#' @export
setMethod("plotPrecision", "dmSQTLprecision", function(x){
if(!length(x@genewise_precision) == length(x@counts))
stop("Genewise precision must be estimated for each gene!")
if(!length(x@genewise_precision) == length(x@mean_expression))
stop("Mean expression must be estimated for each gene!")
w <- sapply(x@genewise_precision, length)
mean_expression <- rep(x@mean_expression, w)
nr_features <- rep(elementNROWS(x@counts), w)
genewise_precision <- unlist(x@genewise_precision)
if(length(x@common_precision) == 0){
common_precision <- NULL
}else{
common_precision <- x@common_precision
}
ggp <- dm_plotPrecision(genewise_precision = genewise_precision,
mean_expression = mean_expression, nr_features = nr_features,
common_precision = common_precision)
return(ggp)
})
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