1 Introduction

1.1 Motivation

The primary functionality of spatialHeatmap package is to visualize cell-, tissue- and organ-specific data of biological assays by coloring the corresponding spatial features defined in anatomical images according to a numeric color key. The color scheme used to represent the assay values can be customized by the user. This core functionality of the package is called a spatial heatmap (SHM) plot. It also has extended functionalities of spatial enrichment (SE) and clustering. SE is specialized in detecting genes that are specifically expressed in a particular spatial feature, while clustering is designed to detect biological molecule groups sharing related abundance profiles (e.g. gene modules) and visualize them in matrix heatmaps combined with hierarchical clustering dendrograms and network representations. Moreover, an advanced functionality of integrated co-visualization of bulk and single-cell data (co-visualization) is also developed. Sinlge cells in embedding plots (PCA, UMAP, TSNE) are matched with corresponding bulk tissues in SHMs manually or automatically. These functionalities form an integrated methodology for spatial biological assay data visualization and analysis.

The functionalities of spatialHeatmap can be used either in a command-driven mode from within R or a graphical user interface (GUI) provided by a Shiny App that is also part of this package. While the R-based mode provides flexibility to customize and automate analysis routines, the Shiny App includes a variety of convenience features that will appeal to experimentalists and other users less familiar with R. Moreover, the Shiny App can be used on both local computers as well as centralized server-based deployments (e.g. cloud-based or custom servers) that can be accessed remotely as a public web service for using spatialHeatmap’s functionalities with community and/or private data. The functionalities of the spatialHeatmap package are illustrated in Figure 1.