With InteractiveComplexHeatmap, the following heatmaps can be exported as an interactive Shiny app:
stats::heatmap(),
gplots::heatmap.2() and pheatmap::pheatmap(),
but can be reproduced by the “translation functions”:
ComplexHeatmap:::heatmap(),
ComplexHeatmap:::heatmap.2() and
ComplexHeatmap::pheatmap().All these types of heatmaps can be turned into interactive just by
calling htShiny() after the heatmaps are drawn. E.g.:
which means you don’t need to touch your heatmap code. After you see
the heatmap in your R terminal or generated in a file, directly calling
htShiny() with no argument will produce an interactive
heatmap, like magic. :P
Now there is a fourth scenario where the heatmap is produced by
third-party functions which internally use
stats::heatmap(), gplots::heatmap.2() or
pheatmap::pheatmap(). Since now we cannot directly interact
with heatmap(), heatmap.2() or
pheatmap(), how can we turn these heatmaps into
interactive? The solution is fairly simple. We just need to go to
e.g. pheatmap namespace and replace
pheatmap with ComplexHeatmap::pheatmap.
The following example is from the SC3 package where
function sc3_plot_expression() internally uses
pheatmap().
library(SingleCellExperiment)
library(SC3)
library(scater)
sce <- SingleCellExperiment(
assays = list(
counts = as.matrix(yan),
logcounts = log2(as.matrix(yan) + 1)
),
colData = ann
)
rowData(sce)$feature_symbol <- rownames(sce)
sce <- sce[!duplicated(rowData(sce)$feature_symbol), ]
sce <- runPCA(sce)
sce <- sc3(sce, ks = 2:4, biology = TRUE)
sc3_plot_expression(sce, k = 3)To replace the internally use of pheatmap::pheatmap with
ComplexHeatmap::pheatmap, we can use
assignInNamespace() to directly change the value of
pheatmap in pheatmap namespace. After
that, recalling sc3_plot_expression() will directly use
ComplexHeatmap::pheatmap() and now you can use
htShiny() to export it as an interactive app. Of course,
you need to regenerate the heatmap with the same code.
assignInNamespace("pheatmap", ComplexHeatmap::pheatmap, ns = "pheatmap")
library(InteractiveComplexHeatmap)
sc3_plot_expression(sce, k = 3)
htShiny()If you check the source code of sc3_plot_expression(),
pheatmap() is used by explicitely adding its namespace
(check the last few lines of the function definition):
## Method Definition:
##
## function (object, k, show_pdata = NULL)
## {
## if (is.null(metadata(object)$sc3$consensus)) {
## warning(paste0("Please run sc3_consensus() first!"))
## return(object)
## }
## hc <- metadata(object)$sc3$consensus[[as.character(k)]]$hc
## dataset <- get_processed_dataset(object)
## if (!is.null(metadata(object)$sc3$svm_train_inds)) {
## dataset <- dataset[, metadata(object)$sc3$svm_train_inds]
## }
## add_ann_col <- FALSE
## ann <- NULL
## if (!is.null(show_pdata)) {
## ann <- make_col_ann_for_heatmaps(object, show_pdata)
## if (!is.null(ann)) {
## add_ann_col <- TRUE
## rownames(ann) <- colnames(dataset)
## }
## }
## if (nrow(dataset) > 100) {
## do.call(pheatmap::pheatmap, c(list(dataset, cluster_cols = hc,
## kmeans_k = 100, cutree_cols = k, show_rownames = FALSE,
## show_colnames = FALSE), list(annotation_col = ann)[add_ann_col]))
## }
## else {
## do.call(pheatmap::pheatmap, c(list(dataset, cluster_cols = hc,
## cutree_cols = k, show_rownames = FALSE, show_colnames = FALSE),
## list(annotation_col = ann)[add_ann_col]))
## }
## }
## <bytecode: 0x5648b0b679b8>
## <environment: namespace:SC3>
##
## Signatures:
## object
## target "SingleCellExperiment"
## defined "SingleCellExperiment"In this case, changing pheatmap in
pheatmap namespace directly affects
sc3_plot_expression().
However, if the heatmap function is called without adding the
namespace (e.g., in previous example, the
pheatmap:: prefix), you need to first unload the package,
modify the heatmap function in the heatmap namespace and later load the
package back.
Let’s look at the next example from GOexpress
package where the function heatmap_GO() internally use
heatmap.2().
library(GOexpress)
data(AlvMac)
set.seed(4543)
AlvMac_results <- GO_analyse(
eSet = AlvMac, f = "Treatment",
GO_genes=AlvMac_GOgenes, all_GO=AlvMac_allGO, all_genes=AlvMac_allgenes)
BP.5 <- subset_scores(
result = AlvMac_results.pVal,
namespace = "biological_process",
total = 5,
p.val=0.05)
heatmap_GO(
go_id = "GO:0034142", result = BP.5, eSet=AlvMac, cexRow=0.4,
cexCol=1, cex.main=1, main.Lsplit=30)Now note in heatmap_GO() function,
heatmap.2() is used without gplots
namespace (go to the end of the function definition listed below).
## function (go_id, result, eSet, f = result$factor, subset = NULL,
## gene_names = TRUE, NA.names = FALSE, margins = c(7, 5), scale = "none",
## cexCol = 1.2, cexRow = 0.5, labRow = NULL, cex.main = 1,
## trace = "none", expr.col = bluered(75), row.col.palette = "Accent",
## row.col = c(), main = paste(go_id, result$GO[result$GO$go_id ==
## go_id, "name_1006"]), main.Lsplit = NULL, ...)
## {
## if (!all(c("factor", "GO", "genes") %in% names(result))) {
## stop("'result=' argument misses required slots.\n Is it a GO_analyse() output?")
## }
## if (!go_id %in% result$GO$go_id) {
## stop("go_id: ", go_id, " was not found in result$mapping$go_id.")
## }
## if (!is.null(subset)) {
## eSet <- subEset(eSet = eSet, subset = subset)
## }
## if (length(row.col) != ncol(eSet)) {
## row.col <- brewer.pal(n = length(unique(pData(eSet)[,
## f])), name = row.col.palette)
## }
## gene_ids <- list_genes(go_id = go_id, result = result, data.only = TRUE)
## genes_expr <- t(exprs(eSet)[gene_ids, ])
## if (is.null(labRow)) {
## labRow <- pData(eSet)[, f]
## }
## else {
## if (length(labRow) == 1) {
## labRow = pData(eSet)[, labRow]
## }
## else if (length(labRow) != ncol(eSet)) {
## stop("The number of custom row labels provided (",
## length(labRow), ") does not match the number of samples (",
## ncol(eSet), ".")
## }
## }
## if (gene_names) {
## gene_labels <- result$genes[gene_ids, "external_gene_name"]
## if (any(gene_labels == "") & !NA.names) {
## gene_labels[gene_labels == ""] <- gene_ids[gene_labels ==
## ""]
## }
## }
## else {
## gene_labels <- gene_ids
## }
## if (!is.null(main.Lsplit)) {
## if (is.numeric(main.Lsplit)) {
## main <- string_Lsplit(string = main, line.length = main.Lsplit)
## }
## else {
## stop("main.Lsplit should be a numeric value or NULL.")
## }
## }
## samples.col <- row.col[as.factor(pData(eSet)[, f])]
## op <- par(no.readonly = TRUE)
## on.exit(par(op))
## par(cex.main = cex.main)
## heatmap.2(genes_expr, labRow = labRow, labCol = gene_labels,
## scale = scale, cexCol = cexCol, cexRow = cexRow, main = main,
## trace = trace, RowSideColors = samples.col, col = expr.col,
## margins = margins, ...)
## }
## <bytecode: 0x5648ae0f8a50>
## <environment: namespace:GOexpress>In this case, since we have already loaded the
GOexpress namespace, the GOexpress
namespace should firstly be removed by detach(), or else
heatmap_GO() will still use
gplots::heatmap.2().
detach("package:GOexpress", unload = TRUE)
assignInNamespace("heatmap.2", ComplexHeatmap:::heatmap.2, ns = "gplots")
library(GOexpress)
library(InteractiveComplexHeatmap)
heatmap_GO(
go_id = "GO:0034142", result = BP.5, eSet=AlvMac, cexRow=0.4,
cexCol=1, cex.main=1, main.Lsplit=30)
htShiny()In the end, to safely change all stats::heatmap(),
gplots::heatmap.2() and pheatmap::pheatmap()
to ComplexHeatmap:::heatmap(),
ComplexHeatmap:::heatmap.2() and
ComplexHeatmap::pheatmap(), you can add following lines to
the start of your R session:
library(pheatmap)
library(gplots)
assignInNamespace("heatmap", ComplexHeatmap:::heatmap, ns = "stats")
assignInNamespace("heatmap.2", ComplexHeatmap:::heatmap.2, ns = "gplots")
assignInNamespace("pheatmap", ComplexHeatmap::pheatmap, ns = "pheatmap")You can find runnable examples in htShinyExample(8.1),
htShinyExample(8.2) and
htShinyExample(8.3).