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# Make Interactive Complex Heatmaps [![R-CMD-check](]( [![bioc](]( [![bioc](]( <img width="1150" alt="Screenshot 2021-07-19 at 21 31 14" src=""> **InteractiveComplexHeatmap** is an R package that converts static heatmaps produced from [**ComplexHeatmap**]( package into an interactive Shiny app only with one extra line of code. The first example is the default layout of the interactive complex heatmap widget. <img src="" width='100%' border="black" /> The second example demonstrates a DESeq2 result with integrating the package **shinydashboard**. <img src="" width="100%" border="black" /> ### Citation Zuguang Gu, et al., Make Interactive Complex Heatmaps in R, 2021, Bioinformatics, ## Install **InteractiveComplexHeatmap** is available on [Bioconductor](, you can install it by: ```r if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("InteractiveComplexHeatmap") ``` If you want the latest version, install it directly from GitHub: ```r library(devtools) install_github("jokergoo/InteractiveComplexHeatmap") ``` ## Documentation There are the following vignettes along with the package: 1. [How to visualize heatmaps interactively]( 2. [How interactive complex heatmap is implemented]( 3. [Functions for Shiny app development]( 4. [Decorations on heatmaps]( 5. [Interactivate heatmaps indirectly generated by pheatmap(), heatmap.2() and heatmap()]( 6. [A Shiny app for visualizing DESeq2 results]( 7. [Implement interactive heatmap from scratch]( 8. [Share interactive heatmaps to collaborators]( A printer-friendly version of the documentation is available at [bioRxiv]( ## Usage ### Directly turn heatmaps interactive With any `Heatmap`/`HeatmapList` object, directly send to `htShiny()` to create a Shiny app for your heatmap(s): ```r htShiny(ht_list) ``` If the heatmaps are already drawn, `ht_list` can be omitted and the last heatmap object is retrieved automatically: ```r Heatmap(...) + other_heatmaps_or_annotations # or other functions that internally use Heatmap() htShiny() ``` ### Shiny app development There are also two functions for Shiny app development: - `InteractiveComplexHeatmapOutput()`: for the UI on the client side. - `makeInteractiveComplexHeatmap()`: for processing on the sever side. ```r library(InteractiveComplexHeatmap) library(ComplexHeatmap) ht = Heatmap(m) ht = draw(ht) ui = fluidPage( InteractiveComplexHeatmapOutput() ) server = function(input, output, session) { makeInteractiveComplexHeatmap(input, output, session, ht) } shiny::shinyApp(ui, server) ``` You can also put multiple interactive heatmaps widgets in the same Shiny app: ```r ht1 = Heatmap(m, col = c("white", "blue")) ht1 = draw(ht1) ht2 = Heatmap(m, col = c("white", "red")) ht2 = draw(ht2) ui = fluidPage( h3("The first heatmap"), InteractiveComplexHeatmapOutput("ht1"), hr(), h3("The second heatmap"), InteractiveComplexHeatmapOutput("ht2") ) server = function(input, output, session) { makeInteractiveComplexHeatmap(input, output, session, ht1, "ht1") makeInteractiveComplexHeatmap(input, output, session, ht2, "ht2") } shiny::shinyApp(ui, server) ``` Two additional functions to let you dynamically load interactive heatmap widgets: - `InteractiveComplexHeatmapModal()`: The interactive heatmap widget is inserted as a "modal". - `InteractiveComplexHeatmapWidget()`: The interactive heatmap widget is inserted into a place defined by users. ```r m = matrix(rnorm(100), 10) ht = Heatmap(m) ui = fluidPage( actionButton("show_heatmap", "Generate_heatmap"), ) server = function(input, output, session) { observeEvent(input$show_heatmap, { InteractiveComplexHeatmapModal(input, output, session, ht) }) } shiny::shinyApp(ui, server) # or use InteractiveComplexHeatmapWidget() ui = fluidPage( actionButton("show_heatmap", "Generate_heatmap"), htmlOutput("heatmap_output") ) server = function(input, output, session) { observeEvent(input$show_heatmap, { InteractiveComplexHeatmapWidget(input, output, session, ht, output_id = "heatmap_output") }) } shiny::shinyApp(ui, server) ``` ## Interactivate pheatmap(), heatmap.2() and heatmap() If you directly use these three funtions, simply replace them with `ComplexHeatmap::pheatmap()`, `ComplexHeatmap:::heatmap.2()` and `ComplexHeatmap:::heatmap()`. If the three functions are used indirectly, e.g. a function `foo()` (maybe from another packages or other people's functions) which internally uses these three heatmap functions, check the vignette ["Interactivate indirect use of pheatmap(), heatmap.2() and heatmap()"]( to find out how. ## Live examples Following lists several live examples of interactive heatmaps. Details can be found in the package vignette. - - - - - - - - - - There are also many other examples provided in the package. ```r htShinyExample() ``` ``` There are following examples. Individual example can be run by e.g. htShinyExample(1.1). ──────── 1. Simple examples ───────────────────────────────────────────────────────── 1.1 A single heatmap with minimal arguments. 1.2 A single heatmap from a character matrix. 1.3 A single heatmap with annotations on both rows and columns. 1.4 A single heatmap where rows and columns are split. 1.5 A list of two heatmaps. 1.6 A list of two vertically concatenated heatmaps 1.7 Use last generated heatmap, an example from cola package. 1.8 Use last generated heatmap, an app with three interactive heatmaps 1.9 Demonstrate hover, click and dblclick actions to select cells. 1.10 Only response to one of click/hover/dblclick/hover events. Please use htShinyExample('1.10') to get this example (quote the index, or else htShinyExample(1.10) will be treated as the same as htShinyExample(1.1)). 1.11 Interactive heatmap under compact mode. ──────── 2. On other plots and packages ───────────────────────────────────────────── 2.1 A density heatmap. 2.2 An oncoPrint. 2.3 A UpSet plot. 2.4 An interactive heatmap from pheatmap(). 2.5 An interactive heatmap from heatmap(). 2.6 An interactive heatmap from heatmap.2(). 2.7 A heatmap produced from tidyHeatmap package. 2.8 Genome-scale heatmap. 2.9 A package-dependency heatmap. You can try to control "Fill figure region" and "Remove empty rows and columns" in the tools under the sub-heatmap. ──────── 3. Enriched heatmaps ─────────────────────────────────────────────────────── 3.1 A single enriched heatmap. 3.2 A list of enriched heatmaps. 3.3 An enriched heatmap with discrete signals. ──────── 4. On public datasets ────────────────────────────────────────────────────── 4.1 An example from Lewis et al 2019. 4.2 Visualize cell heterogeneity from single cell RNASeq. 4.3 Correlations between methylation, expression and other genomic features. ──────── 5. Shiny app development ─────────────────────────────────────────────────── 5.1 A single Shiny app with two interactive heatmap widgets. 5.2 Self-define the output. The selected sub-matrix is shown as a text table. 5.3 Self-define the output. Additional annotations for the selected genes are shown. 5.4 Visualize Gene Ontology similarities. A list of selected GO IDs as well as their descriptions are shown in the output. 5.5 Interactive correlation heatmap. Clicking on the cell generates a scatterplot of the two corresponding variables. 5.6 A heatmap on Jaccard coefficients for a list of genomic regions. Clicking on the cell generates a Hilbert curve of how the two sets of genomic regions overlap. 5.7 Implement interactivity from scratch. Instead of generating the whole interactive heatmap widget, it only returns the information of rows and columns that user have selected on heatmap and users can use this information to build their own interactive heatmap widgets. 5.8 Implement interactivity from scratch. A visualization of 2D density distribution. Brushing on heatmap triggers a new 2D density estimation only on the subset of data. ──────── 6. Dynamically generate heatmap widget in Shiny app ──────────────────────── 6.1 The matrix with different dimensions is dynamically generated. 6.2 Reorder by a column that is specified by user. 6.3 Dynamically generate the widget with InteractiveComplexHeatmapModal(). The modal is triggered by an action button. 6.4 Dynamically select interactive heatmaps. The modal is triggered by radio buttons. 6.5 Dynamically generate the widget. A customized Javascript code is inserted after the UI to change the default behavior of the action button. 6.6 The widget is generated by InteractiveComplexHeatmapWidget() where the UI is directly put in the place defined by htmlOutput(). 6.7 The widget is generated by InteractiveComplexHeatmapWidget() and a customized Javascript code is inserted after the UI. ──────── 7. Interactive R markdown document ───────────────────────────────────────── 7.1 Integrate in an interactive R Markdown document. 7.2 Integrate in an interactive R Markdown document where the heatmap widgets are dynamically generated. ──────── 8. Interactivate heatmaps indirectly generated by heatmap()/heatmap.2()/pheatmap() 8.1 Indirect use of pheatmap(). 8.2 Indirect use of heatmap.2(). 8.3 Two interactive heatmap widgets from indirect use of pheatmap(). ──────── 9. Float output UI along with mouse positions ────────────────────────────── 9.1 A simple example that demonstrates output UI floating with the three actions: hover, click and dblclick. 9.2 Floating self-defined outputs. 9.3 Floating output only from one event on heatmap, i.e. hover/click/dblclick/brush-output. ──────── 10. Work with shinydashboard ──────────────────────────────────────────────── 10.1 Separate the three UI components into three boxes. 10.2 The three UI components are draggable. 10.3 A Shiny dashboard with two tabs. 10.4 Only contain the original heatmap where output floats. 10.5 A complex dashboard that visualizes a DESeq2 results. ``` ## License MIT @ Zuguang Gu