1 Introduction

1.1 Motivation

The spatialHeatmap package provides functionalities for visualizing 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 is enhanced with visualization tools for groups of measured items (e.g. gene modules) sharing related abundance profiles, including matrix heatmaps combined with hierarchical clustering dendrograms and network representations. 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.

Overview of spatialHeatmap. (A) The _saptialHeatmap_ package plots numeric assay data onto spatially annotated images. A wide range of omics technologies is supported including genomic, transcriptomic, proteomic and metabolomic profiling data. The assay data can be provided as numeric vectors, tabular data, or _SummarizedExperiment_ objects. The latter is a widely used data container for organizing both assay data as well as associated annotation and experimental design data. (B) Anatomical and other spatial images need to be provided as annotated SVG (aSVG) files where the spatial features and the corresponding data components of the assay data have matching labels (_e.g._ tissue labels). (C) The assay data are used to color the matching spatial features in one or more aSVG images according to a color key. The result is called a spatial heatmap (SHM) plot. Multiple measurements can be visualized in the same plot, such as several factors (_e.g._ genes, proteins, metabolites), treatment conditions, growth stages and more. (D) Data mining graphics, such as matrix heatmaps and network graphs, are integrated to facilitate the identification of factors with similar assay profiles. The functionalities of _spatialHeatmap_ can be accessed from local computers via the R console or a graphical user interface based on Shiny. In addtion, the latter can be deployed as a web service on custom servers or cloud-based systems.

Figure 1: Overview of spatialHeatmap
(A) The saptialHeatmap package plots numeric assay data onto spatially annotated images. A wide range of omics technologies is supported including genomic, transcriptomic, proteomic and metabolomic profiling data. The assay data can be provided as numeric vectors, tabular data, or SummarizedExperiment objects. The latter is a widely used data container for organizing both assay data as well as associated annotation and experimental design data. (B) Anatomical and other spatial images need to be provided as annotated SVG (aSVG) files where the spatial features and the corresponding data components of the assay data have matching labels (e.g. tissue labels). (C) The assay data are used to color the matching spatial features in one or more aSVG images according to a color key. The result is called a spatial heatmap (SHM) plot. Multiple measurements can be visualized in the same plot, such as several factors (e.g. genes, proteins, metabolites), treatment conditions, growth stages and more. (D) Data mining graphics, such as matrix heatmaps and network graphs, are integrated to facilitate the identification of factors with similar assay profiles. The functionalities of spatialHeatmap can be accessed from local computers via the R console or a graphical user interface based on Shiny. In addtion, the latter can be deployed as a web service on custom servers or cloud-based systems.

As anatomical images the package supports both tissue maps from public repositories and custom images provided by the user. In general any type of image can be used as long as it can be provided in SVG (Scalable Vector Graphics) format, where the corresponding spatial features have been defined (see aSVG below). The numeric values plotted onto an SHM are usually quantitative measurements from a wide range of profiling technologies, such as microarrays, next generation sequencing (e.g. RNA-Seq and scRNA-Seq), proteomics, metabolomics, or many other small- or large-scale experiments. For convenience, several preprocessing and normalization methods for the most common use cases are included that support raw and/or preprocessed data. Currently, the main application domains of the spatialHeatmap package are numeric data sets and spatially mapped images from biological, agricultural and biomedical areas. Moreover, the package has been designed to also work with many other spatial data types, such a population data plotted onto geographic maps. This high level of flexibility is one of the unique features of spatialHeatmap. Related software tools for biological applications in this field are largely based on pure web applications (Lekschas et al. 2015; Papatheodorou et al. 2018; Winter et al. 2007; Waese et al. 2017) or local tools (Maag 2018; Muschelli, Sweeney, and Crainiceanu 2014) that typically lack customization functionalities. These restrictions limit users to utilizing pre-existing expression data and/or fixed sets of anatomical image collections. To close this gap for biological use cases, we have developed spatialHeatmap as a generic R/Bioconductor package for plotting quantitative values onto any type of spatially mapped images in a programmable environment and/or in an intuitive to use GUI application.

1.2 Design

The core feature of spatialHeatmap is to map assay values (e.g. gene expression data) of one or many items (e.g. genes) measured under different conditions in form of numerically graded colors onto the corresponding cell types or tissues represented in a chosen SVG image. In the gene profiling field, this feature supports comparisons of the expression values among multiple genes by plotting their SHMs next to each other. Similarly, one can display the expression values of a single or multiple genes across multiple conditions in the same plot (Figure 3). This level of flexibility is very efficient for visualizing complicated expression patterns across genes, cell types and conditions. In case of more complex anatomical images with overlapping multiple layer tissues, it is important to visually expose the tissue layer of interest in the plots. To address this, several default and customizable layer viewing options are provided. They allow to hide features in the top layers by making them transparent in order to expose features below them. This transparency viewing feature is highlighted below in the mouse example (Figure 4). Moreover, one can plot multiple distinct aSVGs in a single SHM plot as shown in Figure 7. This is particularly useful for displaying abundance trends across multiple development stages, where each is represented by its own aSVG image. In addition to static SHM representations, one can visualize them in form of dynamic animations embedded in interactive HTML files or generate videos for them.

To maximize reusability and extensibility, the package organizes large-scale omics assay data along with the associated experimental design information in a SummarizedExperiment object (Figure 1A). The latter is one of the core S4 classes within the Bioconductor ecosystem that has been widely adapted by many other software packages dealing with gene-, protein- and metabolite-level profiling data (Morgan et al. 2018). In case of gene expression data, the assays slot of the SummarizedExperiment container is populated with a gene expression matrix, where the rows and columns represent the genes and tissue/conditions, respectively, while the colData slot contains sample data including replicate information. The tissues and/or cell type information in the object maps via colData to the corresponding features in the SVG images using unique identifiers for the spatial features (e.g. tissues or cell types). This allows to color the features of interest in an SVG image according to the numeric data stored in a SummarizedExperiment object. For simplicity the numeric data can also be provided as numeric vectors or data.frames. This can be useful for testing purposes and/or the usage of simple data sets that may not require the more advanced features of the SummarizedExperiment class, such as measurements with only one or a few data points. The details about how to access the SVG images and properly format the associated expression data are provided in the Supplementary Section of this vignette.

1.3 Image Format: SVG

SHMs are images where colors encode numeric values in features of any shape. For plotting SHMs, Scalable Vector Graphics (SVG) has been chosen as image format since it is a flexible and widely adapted vector graphics format that provides many advantages for computationally embedding numerical and other information in images. SVG is based on XML formatted text describing all components present in images, including lines, shapes and colors. In case of biological images suitable for SHMs, the shapes often represent anatomical or cell structures. To assign colors to specific features in SHMs, annotated SVG (aSVG) files are used where the shapes of interest are labeled according to certain conventions so that they can be addressed and colored programmatically. SVGs and aSVGs of anatomical structures can be downloaded from many sources including the repositories described below. Alternatively, users can generate them themselves with vector graphics software such as Inkscape. Typically, in aSVGs one or more shapes of a feature of interest, such as the cell shapes of an organ, are grouped together by a common feature identifier. Via these group identifiers one or many feature types can be colored simultaneously in an aSVG according to biological experiments assaying the corresponding feature types with the required spatial resolution. Correct assignment of image features and assay results is assured by using for both the same feature identifiers. The color gradient used to visually represent the numeric assay values is controlled by a color gradient parameter. To visually interpret the meaning of the colors, the corresponding color key is included in the SHM plots. Additional details for properly formatting and annotating both aSVG images and assay data are provided in the Supplementary Section section of this vignette.

1.4 Data Repositories

If not generated by the user, SHMs can be generated with data downloaded from various public repositories. This includes gene, protein and metabolic profiling data from databases, such as GEO, BAR and Expression Atlas from EMBL-EBI (Papatheodorou et al. 2018). A particularly useful resource, when working with spatialHeatmap, is the EBI Expression Atlas. This online service contains both assay and anatomical images. Its assay data include mRNA and protein profiling experiments for different species, tissues and conditions. The corresponding anatomical image collections are also provided for a wide range of species including animals and plants. In spatialHeatmap several import functions are provided to work with the expression and aSVG repository from the Expression Atlas directly. The aSVG images developed by the spatialHeatmap project are available in its own repository called spatialHeatmap aSVG Repository, where users can contribute their aSVG images that are formatted according to our guidlines.

1.5 Tutorial Overview

The following sections of this vignette showcase the most important functionalities of the spatialHeatmap package using as initial example a simple to understand toy data set, and then more complex mRNA profiling data from the Expression Atlas and GEO databases. First, SHM plots are generated for both the toy and mRNA expression data. The latter include gene expression data sets from RNA-Seq and microarray experiments of Human Brain, Mouse Organs, Chicken Organs, and Arabidopsis Shoots. The first three are RNA-Seq data from the Expression Atlas, while the last one is a microarray data set from GEO. Second, gene context analysis tools are introduced, which facilitate the visualization of gene modules sharing similar expression patterns. This includes the visualization of hierarchical clustering results with traditional matrix heatmaps (Matrix Heatmap) as well co-expression network plots (Network). Third, an overview of the corresponding Shiny App is presented that provides access to the same functionalities as the R functions, but executes them in an interactive GUI environment (Chang et al., n.d.; Chang and Borges Ribeiro 2018). Fourth, more advanced features for plotting customized SHMs are covered using the Human Brain data set as an example.

2 Getting Started

2.1 Installation

The spatialHeatmap package should be installed from an R (version \(\ge\) 3.6) session with the BiocManager::install command.

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("spatialHeatmap")

2.2 Packages and Documentation

Next, the packages required for running the sample code in this vignette need to be loaded.

library(spatialHeatmap); library(SummarizedExperiment); library(ExpressionAtlas); library(GEOquery)

The following lists the vignette(s) of this package in an HTML browser. Clicking the corresponding name will open this vignette.

browseVignettes('spatialHeatmap')

3 Spatial Heatmaps (SHMs)

3.1 Toy Example

SHMs are plotted with the spatial_hm function. To provide a quick and intuitive overview how these plots are generated, the following uses a generalized toy example where a small vector of random numeric values is generated that are used to color features in an aSVG image. The image chosen for this example is an aSVG depicting the human brain. The corresponding image file ‘homo_sapiens.brain.svg’ is included in this package for testing purposes. The path to this image on a user's system, where spatialHeatmap is installed, can be obtained with the system.file function.

3.1.1 aSVG Image

The following commands obtain the directory of the aSVG collection and the full path to the chosen target aSVG image on a user’s system, respectively.

svg.dir <- system.file("extdata/shinyApp/example", package="spatialHeatmap")
svg.hum <- system.file("extdata/shinyApp/example", 'homo_sapiens.brain.svg', package="spatialHeatmap")

To identify feature labels of interest in annotated aSVG images, the return_feature function can be used. The following searches the aSVG images stored in dir for the query terms ‘lobe’ and ‘homo sapiens’ under the feature and species fields, respectively. The identified matches are returned as a data.frame.

feature.df <- return_feature(feature=c('lobe'), species=c('homo sapiens'), remote=FALSE, dir=svg.dir)
## Accessing features... 
## arabidopsis_thaliana.organ_shm.svg, arabidopsis_thaliana.organ_shm1.svg, arabidopsis_thaliana.organ_shm2.svg, arabidopsis_thaliana.root.cross_shm.svg, arabidopsis_thaliana.root.ebi_shm.svg, arabidopsis_thaliana.root.roottip_shm.svg, arabidopsis_thaliana.shoot.root_shm.svg, arabidopsis_thaliana.shoot_shm.svg, gallus_gallus.svg, homo_sapiens.brain.svg, mus_musculus.male.svg, us_map_shm.svg,
feature.df
##          feature             id                    SVG    parent index index1
## 1 occipital lobe UBERON_0002021 homo_sapiens.brain.svg LAYER_EFO     9      7
## 2  parietal lobe UBERON_0001872 homo_sapiens.brain.svg LAYER_EFO    10      8
## 3  temporal lobe UBERON_0001871 homo_sapiens.brain.svg LAYER_EFO    26     24
fnames <- feature.df[, 1]

3.1.2 Numeric Data

The following example generates a small numeric toy vector, where the data slot contains four numbers and its name slot is populated with the three feature names obtained from the above aSVG image. In addition, a non-matching entry (here ‘notMapped’) is included for demonstration purposes. Note, the numbers are mapped to features via matching names among the numeric vector and the aSVG, respectively. Accordingly, only numbers and features with matching name counterparts can be colored in the aSVG image. Entries without name matches are indicated by a message printed to the R console, here “notMapped”. This behavior can be turned off with verbose=FALSE in the corresponding function call. In addition, a summary of the numeric assay to feature mappings is stored in the result data.frame returned by the spatial_hm function (see below).

my_vec <- sample(1:100, length(unique(fnames))+1)
names(my_vec) <- c(unique(fnames), 'notMapped')
my_vec
## occipital lobe  parietal lobe  temporal lobe      notMapped 
##             69             83             88             89

3.1.3 Plot SHM

Next, the SHM is plotted with the spatial_hm function (Figure 2). Internally, the numbers in my_vec are translated into colors based on the color key assigned to the col.com argument, and then painted onto the corresponding features in the aSVG, where the path to the image file is defined by svg.path=svg.hum. The remaining arguments used here include: ID for defining the title of the plot; ncol for setting the column-wise layout of the plot excluding the feature legend plot on the right; and height for defining the height of the SHM relative to its width. In the given example (Figure 2) only three features in my_vec (‘occipital lobe’, ‘parietal lobe’, and ‘temporal lobe’) have matching entries in the corresponding aSVG.

shm.df <- spatial_hm(svg.path=svg.hum, data=my_vec, ID='toy', ncol=1, height=0.9, width=0.8, sub.title.size=20, legend.nrow=2)
## Syntactically valid column names are made! 
## Coordinates: homo_sapiens.brain.svg ... 
## Enrties not mapped: notMapped 
## Grobs: homo_sapiens.brain.svg ... 
## SHMs and legend...