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plotExplanatoryHeatmap.R
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plotExplanatoryHeatmap.R
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#' @importFrom ggplot2 ggplot aes_string theme scale_y_discrete
#' @importFrom ggplot2 geom_segment scale_x_reverse scale_y_continuous unit
heatmapClasses <- function(pl,
x,
threshold,
title,
distanceMeasure,
clusterMethod,
featureNames,
dendrogram,
featureLimit){
pl %>%
map(~{
r <- .
pred <- r$Response[1]
classes <- r$Comparison %>%
unique() %>%
str_split('~') %>%
unlist() %>%
unique()
feat <- r$Feature %>%
unique()
if (length(feat) > featureLimit){
feat <- feat[1:featureLimit]
}
d <- x %>%
keepClasses(cls = pred,
classes = classes) %>%
keepFeatures(features = feat)
d <- d %>%
sinfo() %>%
select(all_of(pred)) %>%
bind_cols(d %>%
dat()) %>%
gather('Feature','Intensity',-1) %>%
group_by_at(c(pred,'Feature')) %>%
summarise(Intensity = mean(Intensity),
.groups = 'drop')
sums <- d %>%
group_by(Feature) %>%
summarise(Total = max(Intensity),.groups = 'drop')
d <- d %>%
left_join(sums,by = c('Feature')) %>%
mutate(`Relative Intensity` = Intensity / Total)
suppressWarnings({
dend <- d %>%
select(-Intensity,-Total) %>%
spread(1,`Relative Intensity`) %>%
data.frame(check.names = FALSE) %>%
set_rownames(.$Feature) %>%
select(-Feature) %>%
dist(distanceMeasure) %>%
hclust(clusterMethod) %>%
dendro_data()
})
clusters <- dend$labels$label
d <- d %>%
mutate(Feature = factor(Feature,levels = clusters)) %>%
mutate_at(pred,factor)
low <- 'white'
high <- "#F21A00"
plo <- d %>%
ggplot(aes_string(x = pred,
y = 'Feature',
fill = '`Relative Intensity`')) +
geom_tile(colour = 'black') +
scale_fill_gradient(low = low, high = high,limits=c(0,1)) +
scale_y_discrete(expand = c(0,0),position = 'right') +
theme_minimal(base_size = 8) +
labs(title = title,
fill = 'Relative\nIntensity')
if (isTRUE(featureNames)) {
plo <- plo +
theme(plot.title = element_text(face = 'bold',
hjust = 0.5),
axis.title = element_text(face = 'bold'),
legend.title = element_text(face = 'bold'),
axis.text.x = element_text(angle = 30,hjust = 1),
panel.grid = element_blank(),
plot.margin = unit(c(0,0,0,0), "pt")
)
} else {
plo <- plo +
theme(plot.title = element_text(face = 'bold',
hjust = 0.5),
axis.title = element_text(face = 'bold'),
legend.title = element_text(face = 'bold'),
axis.text.x = element_text(angle = 30,hjust = 1),
axis.text.y = element_blank(),
panel.grid = element_blank(),
plot.margin = unit(c(0,0,0,0), "pt")
)
}
if (isTRUE(dendrogram)) {
offset <- 1 / length(feat) * 0.5
dend_plot <- ggplot() +
geom_segment(
data = dend$segments,
aes(x = y, y = x, xend = yend, yend = xend)) +
scale_x_reverse() +
scale_y_continuous(breaks = seq_along(dend$labels$label),
labels = dend$labels$label,position = 'right',
expand = c(offset,offset)) +
theme_minimal(base_size = 14) +
theme(axis.text.x = element_blank(),
panel.grid = element_blank(),
plot.margin = unit(c(0,0,0,0), "pt"),
axis.text.y = element_blank()) +
labs(x = NULL,
y = NULL)
plo <- dend_plot + plo + plot_layout(widths = c(1, 2))
}
return(plo)
})
}
#' @importFrom ggplot2 scale_fill_gradient2
heatmapRegression <- function(pl,
x,
threshold,
title,
distanceMeasure,
clusterMethod,
featureNames,
dendrogram,
featureLimit){
pl %>%
map(~{
response <- .x$Response[1]
feat <- .x$Feature %>%
unique()
if (length(feat) > featureLimit){
feat <- feat[1:featureLimit]
}
p <- sym(response)
d <- x %>%
keepFeatures(features = feat)
d <- d %>%
sinfo() %>%
select(all_of(response)) %>%
bind_cols(d %>%
dat()) %>%
rowid_to_column(var = 'Sample') %>%
gather('Feature','Intensity',-1,-2) %>%
group_by(Feature) %>%
summarise(r = cor(!! p,Intensity),.groups = 'drop') %>%
mutate(Response = response)
dend <- d %>%
spread(3,r) %>%
data.frame(check.names = FALSE) %>%
set_rownames(.$Feature) %>%
select(-Feature) %>%
dist(distanceMeasure) %>%
hclust(clusterMethod) %>%
dendro_data()
clusters <- dend$labels$label
d <- d %>%
mutate(Feature = factor(Feature,levels = clusters)) %>%
mutate(Response = factor(Response))
low <- '#00B7FF'
mid <- 'white'
high <- "#F21A00"
plo <- d %>%
ggplot(aes_string(x = 'Response',y = 'Feature',fill = 'r')) +
geom_tile(colour = 'black') +
scale_fill_gradient2(low = low, mid = mid,high = high,limits=c(-1,1)) +
scale_y_discrete(expand = c(0,0),position = 'right') +
theme_minimal(base_size = 8) +
labs(title = title,
fill = 'Relative\nIntensity')
if (isTRUE(featureNames)) {
plo <- plo +
theme(plot.title = element_text(face = 'bold',
hjust = 0.5),
axis.title = element_text(face = 'bold'),
legend.title = element_text(face = 'bold'),
axis.text.x = element_text(angle = 30,hjust = 1),
panel.grid = element_blank(),
plot.margin = unit(c(0,0,0,0), "pt")
)
} else {
plo <- plo +
theme(plot.title = element_text(face = 'bold',
hjust = 0.5),
axis.title = element_text(face = 'bold'),
legend.title = element_text(face = 'bold'),
axis.text.x = element_text(angle = 30,hjust = 1),
axis.text.y = element_blank(),
panel.grid = element_blank(),
plot.margin = unit(c(0,0,0,0), "pt")
)
}
if (isTRUE(dendrogram)) {
offset <- 1 / length(feat) * 0.5
dend_plot <- ggplot() +
geom_segment(
data = dend$segments,
aes(x = y, y = x, xend = yend, yend = xend)) +
scale_x_reverse() +
scale_y_continuous(breaks = seq_along(dend$labels$label),
labels = dend$labels$label,position = 'right',
expand = c(offset,offset)) +
theme_minimal(base_size = 14) +
theme(axis.text.x = element_blank(),
panel.grid = element_blank(),
plot.margin = unit(c(0,0,0,0), "pt"),
axis.text.y = element_blank()) +
labs(x = NULL,
y = NULL)
plo <- dend_plot + plo + plot_layout(widths = c(1, 2))
}
return(plo)
})
}
#' Heatmap plot of explantory features
#' @rdname plotExplanatoryHeatmap
#' @description Plot a heatmap of explanatory features.
#' @param x object of class `Univariate`, `RandomForest` or
#' `Analysis`
#' @param metric importance metric on which to retrieve explanatory features
#' @param threshold score threshold to use for specifying explanatory features
#' @param title plot title
#' @param distanceMeasure distance measure to use for clustering. See details.
#' @param clusterMethod clustering method to use. See details
#' @param featureNames should feature names be plotted?
#' @param dendrogram TRUE/FALSE. Should the dendrogram be plotted?
#' @param featureLimit The maximum number of features to plot
#' @param ... arguments to pass to the appropriate method
#' @details
#' Distance measures can be one of any that can be used for the `method` argument of [dist()].
#'
#' Cluster methods can be one of any that can be used for the `method` argument of [hclust()].
#' @examples
#' library(metaboData)
#' x <- analysisData(data = abr1$neg[,200:300],info = abr1$fact)
#'
#' ## random forest classification example
#' random_forest <- randomForest(x,cls = 'day')
#'
#' plotExplanatoryHeatmap(random_forest)
#'
#' ## random forest regression example
#' random_forest <- randomForest(x,cls = 'injorder')
#'
#' plotExplanatoryHeatmap(random_forest,metric = '%IncMSE',threshold = 2)
#' @export
setGeneric('plotExplanatoryHeatmap',function(x, ...)
standardGeneric('plotExplanatoryHeatmap'))
#' @rdname plotExplanatoryHeatmap
#' @importFrom stats dist hclust
#' @importFrom ggdendro dendro_data
#' @importFrom ggplot2 geom_tile scale_fill_gradient theme_minimal
#' @importFrom ggplot2 labs element_blank
#' @importFrom stringr str_split_fixed
#' @importFrom tibble deframe
#' @importFrom dplyr group_by_at mutate_at
#' @importFrom magrittr set_rownames
#' @importFrom rlang sym
setMethod('plotExplanatoryHeatmap',
signature = 'Univariate',
function(x,
threshold = 0.05,
title = '',
distanceMeasure = "euclidean",
clusterMethod = 'ward.D2',
featureNames = TRUE,
dendrogram = TRUE,
featureLimit = Inf){
res <- x %>%
explanatoryFeatures(threshold = threshold)
if (nrow(res) < 1){
message('No explanatory features found at this threshold.')
return()
}
pl <- res %>%
base::split(.$Response)
if (x@type == 't-test' | x@type == 'ANOVA') {
pl <- heatmapClasses(
pl,
x,
threshold = threshold,
title = title,
distanceMeasure = distanceMeasure,
clusterMethod = clusterMethod,
featureNames = featureNames,
dendrogram = dendrogram,
featureLimit = featureLimit)
}
if (x@type == 'linear regression') {
pl <- heatmapRegression(
pl,
x,
threshold = threshold,
title = title,
distanceMeasure = distanceMeasure,
clusterMethod = clusterMethod,
featureNames = featureNames,
dendrogram = dendrogram,
featureLimit = featureLimit)
}
feat <- res$Feature %>%
unique()
caption <- str_c(
'Explanatory features had a P value below a threshold of ',
threshold,'.')
if (length(feat) > featureLimit) {
caption <- str_c(
caption,'\n',
str_c('Number of features capped at top ',featureLimit,'.'))
}
pl <- wrap_plots(pl) +
plot_annotation(caption = caption,
theme = theme(plot.caption = element_text(hjust = 0))) +
plot_layout(guides = 'collect')
return(pl)
}
)
#' @rdname plotExplanatoryHeatmap
setMethod('plotExplanatoryHeatmap',
signature = 'RandomForest',
function(x,
metric = 'FalsePositiveRate',
threshold = 0.05,
title = '',
distanceMeasure = "euclidean",
clusterMethod = 'ward.D2',
featureNames = TRUE,
dendrogram = TRUE,
featureLimit = Inf){
if (x@type == 'unsupervised') {
stop('Cannot plot heatmap for unsupervised random forest.')
}
explan <- explanatoryFeatures(x,
metric = metric,
threshold = threshold)
if (nrow(explan) < 1){
message('No explanatory features found at this threshold.')
return()
}
pl <- explan %>%
base::split(.$Response)
if (x@type == 'classification') {
pl <- heatmapClasses(
pl,
x,
threshold = threshold,
title = title,
distanceMeasure = distanceMeasure,
clusterMethod = clusterMethod,
featureNames = featureNames,
dendrogram = dendrogram,
featureLimit = featureLimit)
}
if (x@type == 'regression') {
pl <- heatmapRegression(
pl,
x,
threshold = threshold,
title = title,
distanceMeasure = distanceMeasure,
clusterMethod = clusterMethod,
featureNames = featureNames,
dendrogram = dendrogram,
featureLimit = featureLimit)
}
feat <- explan$Feature %>%
unique()
if (metric == 'FalsePositiveRate') {
direction <- 'below'
} else {
direction <- 'above'
}
caption <- str_c(
'Explanatory features had an importance value ',direction, ' a threshold of ',
threshold,'.')
if (length(feat) > featureLimit) {
caption <- str_c(
caption,'\n',
str_c('Number of features capped at top ',featureLimit,'.'))
}
pl <- wrap_plots(pl) +
plot_annotation(caption = caption,
theme = theme(plot.caption = element_text(hjust = 0))) +
plot_layout(guides = 'collect')
return(pl)
}
)
#' @rdname plotExplanatoryHeatmap
setMethod('plotExplanatoryHeatmap',
signature = 'list',
function(x,
threshold = 0.05,
distanceMeasure = "euclidean",
clusterMethod = 'ward.D2',
featureNames = TRUE,
featureLimit = Inf){
object_classes <- x %>%
map_chr(class)
if (FALSE %in% (object_classes == 'RandomForest' |
object_classes == 'Univariate')) {
stop(
str_c('All objects contained within supplied list ',
'should be of class RandomForest or Univariate'),
call. = FALSE)
}
x %>%
names() %>%
map(~{plotExplanatoryHeatmap(
x[[.x]],
threshold = threshold,
title = .x,
distanceMeasure = distanceMeasure,
clusterMethod = clusterMethod,
featureNames = featureNames,
featureLimit = featureLimit
)
})
}
)
#' @rdname plotExplanatoryHeatmap
setMethod('plotExplanatoryHeatmap',
signature = 'Analysis',
function(x,
threshold = 0.05,
distanceMeasure = "euclidean",
clusterMethod = 'ward.D2',
featureNames = TRUE,
featureLimit = Inf){
pl <- x %>%
analysisResults(element = 'modelling') %>%
plotExplanatoryHeatmap(threshold = threshold,
distanceMeasure = distanceMeasure,
clusterMethod = clusterMethod,
featureNames = featureNames,
featureLimit = featureLimit)
if (length(pl) == 1){
pl <- pl[[1]]
}
return(pl)
}
)