Question 1

How do I install the latest version of heatmaply from GitHub?

Accepted Answer

First, ensure you have devtools and dependencies like Rcpp and plotly installed from GitHub, then run devtools::install_github('talgalili/heatmaply'). The README provides a detailed step-by-step list, but it requires Rtools on Windows.

Question 2

heatmaply vs d3heatmap: which is better for my project?

Accepted Answer

Use heatmaply as it is actively maintained and replaces the deprecated d3heatmap, offering better performance with large data, more features like sidebar annotations, and seamless Shiny integration. d3heatmap is no longer updated.

Question 3

How can I add sidebar annotations to my heatmap in heatmaply?

Accepted Answer

heatmaply supports sidebar annotations through its function parameters; refer to the online vignette on CRAN for specific examples on labeling rows and columns with contextual data, which is useful for bioinformatics and other fields.

Question 4

Can heatmaply handle large gene expression datasets from RNA-seq?

Accepted Answer

Yes, heatmaply is well-suited for bioinformatics; the heatmaplyExamples package includes dedicated examples for visualizing gene expression data, such as raw and transformed matrices, with interactive clustering.

Question 5

How to save a heatmaply plot as a static PDF for publication?

Accepted Answer

Use the 'file' parameter in the heatmaply function with the '.pdf' extension, e.g., heatmaply(mtcars, file = 'plot.pdf'). You might need to install phantomjs via webshot for static image exports, as noted in the README.

Question 6

Is heatmaply easy to integrate into a Shiny app for a dashboard?

Accepted Answer

Yes, heatmaply outputs are plotly objects, so you can use plotlyOutput and renderPlotly in your Shiny UI and server logic, making it straightforward to add interactive heatmaps, as detailed in the 'Replacing d3heatmap' section.

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