UC Riverside

Visualizing spatial assay data in anatomical images is vital for understanding biological processes in cell, tissue, and organ organizations. Technologies requiring this functionality include traditional one-at-a-time assays, and bulk and single-cell omics experiments, including transcript, protein and metabolite profiling. To address this need, this Ph.D. project developed the \textit{spatialHeatmap} software environment. As an integrated multipurpose visualization and analysis toolbox, \textit{spatialHeatmap} provides flexible solutions for these needs by allowing users to visualize spatial biological data in adequately formatted anatomical images from public collections or custom images, and extend them with a series of large scale data mining graphics. \textit{spatialHeatmap} colors the spatial features (\textit{e.g.} tissues) annotated in the images according to the quantitative abundance levels of measured biomolecules (\textit{e.g.} mRNAs) using a color key. This core functionality of the package is called a spatial heatmap (SHM) plot. Single-cell data can be co-visualized in composite plots that combine SHMs with embedding plots of high-dimensional data. The resulting spatial context information is essential for gaining insights into the tissue-level organization of single-cell data, or vice versa. Functionalities for integrated visualization of cellular compositions of bulk data with deconvolution algorithms are also included. Alternatively, a new co-clustering method automates the association of unlabeled single-cell data with the corresponding source tissues. Spatially resolved single-cell data from more recently developed spatial sequencing technologies can also be co-visualized with the corresponding anatomical SHMs. Additional core functionalities include large-scale identification of biomolecules with spatially selective abundance patterns, and clusters of biomolecules sharing similar abundance profiles.

\textit{spatialHeatmap} is a new software for visualizing spatial assay data in anatomical images with extensions for large-scale data mining and co-visualization of single-cell data. It integrates many novel functionalities into a generic spatial visualization toolbox that is flexible, extensible, and object-oriented by design. A graphical and a command-line interface are provided to support the needs of both non-expert and computational users, respectively. It is distributed as a free, open-source Bioconductor package (\href{https://bioconductor.org/packages/release/bioc/html/spatialHeatmap.html}{https://bioconductor.org/packages/ spatialHeatmap}) that users can install on personal computers, shared servers, or cloud systems. Because of its rich functionalities and broad applicability, \textit{spatialHeatmap} is expected to be widely adopted by the spatial biology community.