Nothing
R/plotOverrep.R
In ngsReports: Load FastqQC reports and other NGS related files
#' @title Plot a summary of Over-represented Sequences
#'
#' @description Plot a summary of Over-represented Sequences for a set of
#' FASTQC reports
#'
#' @details Percentages are obtained by simply summing those within a report.
#' Any possible double counting by FastQC is ignored for the purposes of a
#' simple approximation.
#'
#' Plots generated from a \code{FastqcData} object will show the top \code{n}
#' sequences grouped by their predicted source & coloured by whether the
#' individual sequence would cause a WARN/FAIL.
#'
#' Plots generated from a \code{FastqcDataList} group sequences by predicted
#' source and summarise as a percentage of the total reads.
#'
#' @param x Can be a \code{FastqcData}, \code{FastqcDataList} or file paths
#' @param usePlotly \code{logical} Default \code{FALSE} will render using
#' ggplot. If \code{TRUE} plot will be rendered with plotly
#' @param labels An optional named factor of labels for the file names.
#' All filenames must be present in the names.
#' File extensions are dropped by default.
#' @param n The number of sequences to plot from an individual file
#' @param pwfCols Object of class \code{\link{PwfCols}} containing the colours
#' for PASS/WARN/FAIL
#' @param cluster \code{logical} default \code{FALSE}. If set to \code{TRUE},
#' fastqc data will be clustered using hierarchical clustering
#' @param dendrogram \code{logical} redundant if \code{cluster} is \code{FALSE}
#' if both \code{cluster} and \code{dendrogram} are specified as \code{TRUE}
#' then the dendrogram will be displayed.
#' @param ... Used to pass additional attributes to theme() and between methods
#' @param expand.x,expand.y Output from \code{expansion()} or numeric
#' vectors of length 4. Passed to \code{scale_*_continuous()}
#' @param paletteName Name of the palette for colouring the possible sources
#' of the overrepresented sequences. Must be a palette name from
#' \code{RColorBrewer}
#'
#' @return A standard ggplot2 object
#'
#' @examples
#'
#' # Get the files included with the package
#' packageDir <- system.file("extdata", package = "ngsReports")
#' fl <- list.files(packageDir, pattern = "fastqc.zip", full.names = TRUE)
#'
#' # Load the FASTQC data as a FastqcDataList object
#' fdl <- FastqcDataList(fl)
#'
#' # Another example which isn't ideal
#' plotOverrep(fdl)
#'
#' @docType methods
#'
#' @importFrom tidyr spread
#' @importFrom plotly layout ggplotly
#' @importFrom grDevices rgb
#' @import ggplot2
#'
#' @name plotOverrep
#' @rdname plotOverrep-methods
#' @export
setGeneric("plotOverrep", function(
x, usePlotly = FALSE, labels, pwfCols, ...){
standardGeneric("plotOverrep")
}
)
#' @rdname plotOverrep-methods
#' @export
setMethod("plotOverrep", signature = "ANY", function(
x, usePlotly = FALSE, labels, pwfCols, ...){
.errNotImp(x)
}
)
#' @rdname plotOverrep-methods
#' @export
setMethod("plotOverrep", signature = "character", function(
x, usePlotly = FALSE, labels, pwfCols, ...){
x <- FastqcDataList(x)
if (length(x) == 1) x <- x[[1]]
plotOverrep(x, usePlotly, labels, pwfCols, ...)
}
)
#' @rdname plotOverrep-methods
#' @export
setMethod("plotOverrep", signature = "FastqcData", function(
x, usePlotly = FALSE, labels, pwfCols, n = 10, ...,
expand.x = expansion(mult = c(0, 0.05)),
expand.y = expansion(0, 0.6)){
df <- getModule(x, "Overrepresented_sequences")
if (!length(df)) {
overPlot <- .emptyPlot("No Overrepresented Sequences")
if (usePlotly) overPlot <- ggplotly(overPlot, tooltip = "")
return(overPlot)
}
## Drop the suffix, or check the alternate labels
labels <- .makeLabels(x, labels, ...)
labels <- labels[names(labels) %in% df$Filename]
df$Filename <- labels[df$Filename]
## Get any arguments for dotArgs that have been set manually
dotArgs <- list(...)
allowed <- names(formals(theme))
keepArgs <- which(names(dotArgs) %in% allowed)
userTheme <- c()
if (length(keepArgs) > 0) userTheme <- do.call(theme, dotArgs[keepArgs])
df <- dplyr::top_n(df, n, Percentage)
df$Status <- cut(
df$Percentage,
breaks = c(0, 0.1, 1, 100),
labels = c("PASS", "WARN", "FAIL")
)
df$Possible_Source <-
gsub(" \\([0-9]*\\% over [0-9]*bp\\)", "", df$Possible_Source)
df$Sequence <- factor(df$Sequence, levels = rev(df$Sequence))
df$Percentage <- round(df$Percentage, 2)
df <- droplevels(df)
## Check the axis expansion
stopifnot(is.numeric(expand.x), length(expand.x) == 4)
stopifnot(is.numeric(expand.y), length(expand.y) == 4)
## Set plotting parameters
xLab <- "Percent of Total Reads (%)"
yLab <- "Overrepresented Sequence"
## Sort out the colours & pass/warn/fail breaks
if (missing(pwfCols)) pwfCols <- getColours(ngsReports::pwf)
pwfCols <- pwfCols[names(pwfCols) %in% levels(df$Status)]
overPlot <- ggplot(
df,
aes_string(
x = "Sequence", y = "Percentage", fill = "Status",
label = "Possible_Source"
)
) +
geom_bar(stat = "identity") +
labs(y = xLab, x = yLab) +
scale_y_continuous(expand = expand.x, labels = .addPercent) +
scale_x_discrete(expand = expand.y) +
theme_bw() +
coord_flip() +
scale_fill_manual(values = pwfCols)
## Only facet is using ggplot. They look bad under plotly
if (!usePlotly) overPlot <- overPlot +
facet_grid(Possible_Source~., scales = "free_y", space = "free")
## Add the basic customisations
if (!is.null(userTheme)) overPlot <- overPlot + userTheme
if (usePlotly) {
## Add the customisations for plotly
overPlot <- overPlot +
ggtitle(df$Filename[1]) +
theme(
axis.ticks.y = element_blank(),
axis.text.y = element_blank(),
plot.title = element_text(hjust = 0.5),
legend.position = "none"
)
## Add the empty plot to align in the shiny app
overPlot <- suppressMessages(
suppressWarnings(
plotly::subplot(
plotly::plotly_empty(),
overPlot,
widths = c(0.14,0.86)
)
)
)
}
overPlot
}
)
#' @rdname plotOverrep-methods
#' @export
setMethod("plotOverrep", signature = "FastqcDataList", function(
x, usePlotly = FALSE, labels, pwfCols, cluster = FALSE, dendrogram = FALSE,
..., paletteName = "Set1", expand.x = expansion(mult = c(0, 0.05)),
expand.y = expansion(0, 0)){
df <- getModule(x, "Overrepresented_sequences")
if (!length(df)) {
overPlot <- .emptyPlot("No Overrepresented Sequences")
if (usePlotly) overPlot <- ggplotly(overPlot, tooltip = "")
return(overPlot)
}
if (missing(pwfCols)) pwfCols <- pwf
## Drop the suffix, or check the alternate labels
labels <- .makeLabels(x, labels, ...)
labels <- labels[names(labels) %in% df$Filename]
## Get any arguments for dotArgs that have been set manually
dotArgs <- list(...)
allowed <- names(formals(theme))
keepArgs <- which(names(dotArgs) %in% allowed)
userTheme <- c()
if (length(keepArgs) > 0) userTheme <- do.call(theme, dotArgs[keepArgs])
Possible_Source <- c() # Here to avoid a NOTE in R CMD check...
df$Possible_Source <-
gsub(" \\([0-9]*\\% over [0-9]*bp\\)", "", df$Possible_Source)
df <- dplyr::group_by(df, Filename, Possible_Source)
df <- dplyr::summarise(df, Percentage = sum(Percentage), .groups = "keep")
df <- dplyr::ungroup(df)
df$Percentage <- round(df$Percentage, 2)
lev <- unique(dplyr::arrange(df, Percentage)$Possible_Source)
df$Possible_Source <- factor(df$Possible_Source, levels = lev)
if (dendrogram && !cluster) {
message("cluster will be set to TRUE when dendrogram = TRUE")
cluster <- TRUE
}
## Now define the order for a dendrogram if required
key <- names(labels)
if (cluster) {
cols <- c("Filename", "Possible_Source", "Percentage")
clusterDend <- .makeDendro(
df[cols], "Filename", "Possible_Source", "Percentage")
key <- labels(clusterDend)
}
## Now set everything as factors
df$Filename <- factor(labels[df$Filename], levels = labels[key])
maxChar <- max(nchar(levels(df$Filename)))
## Check the axis expansion
stopifnot(is.numeric(expand.x), length(expand.x) == 4)
stopifnot(is.numeric(expand.y), length(expand.y) == 4)
## Define the palette
paletteName <-
match.arg(paletteName, rownames(RColorBrewer::brewer.pal.info))
nMax <- RColorBrewer::brewer.pal.info[paletteName, "maxcolors"]
nSource <- length(levels(df$Possible_Source))
pal <- RColorBrewer::brewer.pal(nMax, paletteName)
if (nSource > nMax) {
pal <- colorRampPalette(pal)(nSource)
}
else {
pal <- pal[seq_len(nSource)]
}
names(pal) <- levels(df$Possible_Source)
## Set the axis label
xLab <- "Overrepresented Sequences (% of Total)"
overPlot <- ggplot(
df,
aes_string("Filename", "Percentage", fill = "Possible_Source")
) +
geom_bar(stat = "identity") +
labs(y = xLab, fill = "Possible Source") +
scale_y_continuous(expand = expand.x, labels = .addPercent) +
scale_x_discrete(expand = expand.y) +
scale_fill_manual(values = pal) +
theme_bw() +
coord_flip()
## Add the basic customisations
if (!is.null(userTheme)) overPlot <- overPlot + userTheme
if (usePlotly) {
## Remove annotations before sending to plotly
overPlot <- overPlot +
theme(
legend.position = "none",
axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.ticks.y = element_blank()
)
## Prepare the sidebar
status <- getSummary(x)
status <- subset(status, Category == "Overrepresented sequences")
status$Filename <- labels[status$Filename]
status$Filename <-
factor(status$Filename, levels = levels(df$Filename))
status <- dplyr::right_join(
status,
dplyr::distinct(df, Filename),
by = "Filename"
)
sideBar <- .makeSidebar(status, key, pwfCols)
## Prepare the dendrogram
dendro <- plotly::plotly_empty()
if (dendrogram) {
dx <- ggdendro::dendro_data(clusterDend)
dendro <- .renderDendro(dx$segments)
}
## The final interactive plot
overPlot <- suppressWarnings(
suppressMessages(
plotly::subplot(
dendro,
sideBar,
overPlot,
margin = 0.001,
widths = c(0.08,0.08,0.84)
)
)
)
}
overPlot
}
)
Try the ngsReports package in your browser
Any scripts or data that you put into this service are public.
ngsReports documentation built on Nov. 23, 2020, 2:01 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#' @title Plot a summary of Over-represented Sequences
#'
#' @description Plot a summary of Over-represented Sequences for a set of
#' FASTQC reports
#'
#' @details Percentages are obtained by simply summing those within a report.
#' Any possible double counting by FastQC is ignored for the purposes of a
#' simple approximation.
#'
#' Plots generated from a \code{FastqcData} object will show the top \code{n}
#' sequences grouped by their predicted source & coloured by whether the
#' individual sequence would cause a WARN/FAIL.
#'
#' Plots generated from a \code{FastqcDataList} group sequences by predicted
#' source and summarise as a percentage of the total reads.
#'
#' @param x Can be a \code{FastqcData}, \code{FastqcDataList} or file paths
#' @param usePlotly \code{logical} Default \code{FALSE} will render using
#' ggplot. If \code{TRUE} plot will be rendered with plotly
#' @param labels An optional named factor of labels for the file names.
#' All filenames must be present in the names.
#' File extensions are dropped by default.
#' @param n The number of sequences to plot from an individual file
#' @param pwfCols Object of class \code{\link{PwfCols}} containing the colours
#' for PASS/WARN/FAIL
#' @param cluster \code{logical} default \code{FALSE}. If set to \code{TRUE},
#' fastqc data will be clustered using hierarchical clustering
#' @param dendrogram \code{logical} redundant if \code{cluster} is \code{FALSE}
#' if both \code{cluster} and \code{dendrogram} are specified as \code{TRUE}
#' then the dendrogram will be displayed.
#' @param ... Used to pass additional attributes to theme() and between methods
#' @param expand.x,expand.y Output from \code{expansion()} or numeric
#' vectors of length 4. Passed to \code{scale_*_continuous()}
#' @param paletteName Name of the palette for colouring the possible sources
#' of the overrepresented sequences. Must be a palette name from
#' \code{RColorBrewer}
#'
#' @return A standard ggplot2 object
#'
#' @examples
#'
#' # Get the files included with the package
#' packageDir <- system.file("extdata", package = "ngsReports")
#' fl <- list.files(packageDir, pattern = "fastqc.zip", full.names = TRUE)
#'
#' # Load the FASTQC data as a FastqcDataList object
#' fdl <- FastqcDataList(fl)
#'
#' # Another example which isn't ideal
#' plotOverrep(fdl)
#'
#' @docType methods
#'
#' @importFrom tidyr spread
#' @importFrom plotly layout ggplotly
#' @importFrom grDevices rgb
#' @import ggplot2
#'
#' @name plotOverrep
#' @rdname plotOverrep-methods
#' @export
setGeneric("plotOverrep", function(
x, usePlotly = FALSE, labels, pwfCols, ...){
standardGeneric("plotOverrep")
}
)
#' @rdname plotOverrep-methods
#' @export
setMethod("plotOverrep", signature = "ANY", function(
x, usePlotly = FALSE, labels, pwfCols, ...){
.errNotImp(x)
}
)
#' @rdname plotOverrep-methods
#' @export
setMethod("plotOverrep", signature = "character", function(
x, usePlotly = FALSE, labels, pwfCols, ...){
x <- FastqcDataList(x)
if (length(x) == 1) x <- x[[1]]
plotOverrep(x, usePlotly, labels, pwfCols, ...)
}
)
#' @rdname plotOverrep-methods
#' @export
setMethod("plotOverrep", signature = "FastqcData", function(
x, usePlotly = FALSE, labels, pwfCols, n = 10, ...,
expand.x = expansion(mult = c(0, 0.05)),
expand.y = expansion(0, 0.6)){
df <- getModule(x, "Overrepresented_sequences")
if (!length(df)) {
overPlot <- .emptyPlot("No Overrepresented Sequences")
if (usePlotly) overPlot <- ggplotly(overPlot, tooltip = "")
return(overPlot)
}
## Drop the suffix, or check the alternate labels
labels <- .makeLabels(x, labels, ...)
labels <- labels[names(labels) %in% df$Filename]
df$Filename <- labels[df$Filename]
## Get any arguments for dotArgs that have been set manually
dotArgs <- list(...)
allowed <- names(formals(theme))
keepArgs <- which(names(dotArgs) %in% allowed)
userTheme <- c()
if (length(keepArgs) > 0) userTheme <- do.call(theme, dotArgs[keepArgs])
df <- dplyr::top_n(df, n, Percentage)
df$Status <- cut(
df$Percentage,
breaks = c(0, 0.1, 1, 100),
labels = c("PASS", "WARN", "FAIL")
)
df$Possible_Source <-
gsub(" \\([0-9]*\\% over [0-9]*bp\\)", "", df$Possible_Source)
df$Sequence <- factor(df$Sequence, levels = rev(df$Sequence))
df$Percentage <- round(df$Percentage, 2)
df <- droplevels(df)
## Check the axis expansion
stopifnot(is.numeric(expand.x), length(expand.x) == 4)
stopifnot(is.numeric(expand.y), length(expand.y) == 4)
## Set plotting parameters
xLab <- "Percent of Total Reads (%)"
yLab <- "Overrepresented Sequence"
## Sort out the colours & pass/warn/fail breaks
if (missing(pwfCols)) pwfCols <- getColours(ngsReports::pwf)
pwfCols <- pwfCols[names(pwfCols) %in% levels(df$Status)]
overPlot <- ggplot(
df,
aes_string(
x = "Sequence", y = "Percentage", fill = "Status",
label = "Possible_Source"
)
) +
geom_bar(stat = "identity") +
labs(y = xLab, x = yLab) +
scale_y_continuous(expand = expand.x, labels = .addPercent) +
scale_x_discrete(expand = expand.y) +
theme_bw() +
coord_flip() +
scale_fill_manual(values = pwfCols)
## Only facet is using ggplot. They look bad under plotly
if (!usePlotly) overPlot <- overPlot +
facet_grid(Possible_Source~., scales = "free_y", space = "free")
## Add the basic customisations
if (!is.null(userTheme)) overPlot <- overPlot + userTheme
if (usePlotly) {
## Add the customisations for plotly
overPlot <- overPlot +
ggtitle(df$Filename[1]) +
theme(
axis.ticks.y = element_blank(),
axis.text.y = element_blank(),
plot.title = element_text(hjust = 0.5),
legend.position = "none"
)
## Add the empty plot to align in the shiny app
overPlot <- suppressMessages(
suppressWarnings(
plotly::subplot(
plotly::plotly_empty(),
overPlot,
widths = c(0.14,0.86)
)
)
)
}
overPlot
}
)
#' @rdname plotOverrep-methods
#' @export
setMethod("plotOverrep", signature = "FastqcDataList", function(
x, usePlotly = FALSE, labels, pwfCols, cluster = FALSE, dendrogram = FALSE,
..., paletteName = "Set1", expand.x = expansion(mult = c(0, 0.05)),
expand.y = expansion(0, 0)){
df <- getModule(x, "Overrepresented_sequences")
if (!length(df)) {
overPlot <- .emptyPlot("No Overrepresented Sequences")
if (usePlotly) overPlot <- ggplotly(overPlot, tooltip = "")
return(overPlot)
}
if (missing(pwfCols)) pwfCols <- pwf
## Drop the suffix, or check the alternate labels
labels <- .makeLabels(x, labels, ...)
labels <- labels[names(labels) %in% df$Filename]
## Get any arguments for dotArgs that have been set manually
dotArgs <- list(...)
allowed <- names(formals(theme))
keepArgs <- which(names(dotArgs) %in% allowed)
userTheme <- c()
if (length(keepArgs) > 0) userTheme <- do.call(theme, dotArgs[keepArgs])
Possible_Source <- c() # Here to avoid a NOTE in R CMD check...
df$Possible_Source <-
gsub(" \\([0-9]*\\% over [0-9]*bp\\)", "", df$Possible_Source)
df <- dplyr::group_by(df, Filename, Possible_Source)
df <- dplyr::summarise(df, Percentage = sum(Percentage), .groups = "keep")
df <- dplyr::ungroup(df)
df$Percentage <- round(df$Percentage, 2)
lev <- unique(dplyr::arrange(df, Percentage)$Possible_Source)
df$Possible_Source <- factor(df$Possible_Source, levels = lev)
if (dendrogram && !cluster) {
message("cluster will be set to TRUE when dendrogram = TRUE")
cluster <- TRUE
}
## Now define the order for a dendrogram if required
key <- names(labels)
if (cluster) {
cols <- c("Filename", "Possible_Source", "Percentage")
clusterDend <- .makeDendro(
df[cols], "Filename", "Possible_Source", "Percentage")
key <- labels(clusterDend)
}
## Now set everything as factors
df$Filename <- factor(labels[df$Filename], levels = labels[key])
maxChar <- max(nchar(levels(df$Filename)))
## Check the axis expansion
stopifnot(is.numeric(expand.x), length(expand.x) == 4)
stopifnot(is.numeric(expand.y), length(expand.y) == 4)
## Define the palette
paletteName <-
match.arg(paletteName, rownames(RColorBrewer::brewer.pal.info))
nMax <- RColorBrewer::brewer.pal.info[paletteName, "maxcolors"]
nSource <- length(levels(df$Possible_Source))
pal <- RColorBrewer::brewer.pal(nMax, paletteName)
if (nSource > nMax) {
pal <- colorRampPalette(pal)(nSource)
}
else {
pal <- pal[seq_len(nSource)]
}
names(pal) <- levels(df$Possible_Source)
## Set the axis label
xLab <- "Overrepresented Sequences (% of Total)"
overPlot <- ggplot(
df,
aes_string("Filename", "Percentage", fill = "Possible_Source")
) +
geom_bar(stat = "identity") +
labs(y = xLab, fill = "Possible Source") +
scale_y_continuous(expand = expand.x, labels = .addPercent) +
scale_x_discrete(expand = expand.y) +
scale_fill_manual(values = pal) +
theme_bw() +
coord_flip()
## Add the basic customisations
if (!is.null(userTheme)) overPlot <- overPlot + userTheme
if (usePlotly) {
## Remove annotations before sending to plotly
overPlot <- overPlot +
theme(
legend.position = "none",
axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.ticks.y = element_blank()
)
## Prepare the sidebar
status <- getSummary(x)
status <- subset(status, Category == "Overrepresented sequences")
status$Filename <- labels[status$Filename]
status$Filename <-
factor(status$Filename, levels = levels(df$Filename))
status <- dplyr::right_join(
status,
dplyr::distinct(df, Filename),
by = "Filename"
)
sideBar <- .makeSidebar(status, key, pwfCols)
## Prepare the dendrogram
dendro <- plotly::plotly_empty()
if (dendrogram) {
dx <- ggdendro::dendro_data(clusterDend)
dendro <- .renderDendro(dx$segments)
}
## The final interactive plot
overPlot <- suppressWarnings(
suppressMessages(
plotly::subplot(
dendro,
sideBar,
overPlot,
margin = 0.001,
widths = c(0.08,0.08,0.84)
)
)
)
}
overPlot
}
)
Try the ngsReports package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.