# Interactivate heatmaps indirectly generated by pheatmap(), heatmap.2() and heatmap()

#### Zuguang Gu ( z.gu@dkfz.de )

#### 2024-05-01

With InteractiveComplexHeatmap, the following heatmaps can be exported as an interactive Shiny app:

1. heatmaps directly produced from ComplexHeatmap,
2. heatmaps from other functions or packages which are implemented with ComplexHeatmap,
3. heatmaps originally produced by `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.:

    ComplexHeatmap::pheatmap(...)
    htShiny()

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):

    selectMethod("sc3_plot_expression", signature = "SingleCellExperiment")

    ## 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: 0x000002887919f3a8>
    ## <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).

    heatmap_GO

    ## 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: 0x000002887289c960>
    ## <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)`.