12 papers · ranked by Valyu relevance
Muhammed Khawatmi, Heba Sailem
Visualization of spatial datasets is essential for understanding biological systems that are composed of several interacting cell types. For example, gene expression data at the molecular level needs to be interpreted based on cell type, spatial context, tissue type, and interactions with the surrounding environment.…
Chitrasen Mohanty, Aman Prasad, Lingxin Cheng, Lisa M. Arkin + 3 more
Spatial transcriptomics (ST) experiments provide spatially localized measurements of genome-wide gene expression allowing for an unprecedented opportunity to investigate cellular heterogeneity and organization within a tissue. Statistical and computational frameworks exist that implement robust methods for…
Simon Warchol, Grace Guo, Johannes Knittel, Dan Freeman + 4 more
Dimensionality reduction techniques help analysts make sense of complex, high-dimensional spatial datasets, such as multiplexed tissue imaging, satellite imagery, and astronomical observations, by projecting data attributes into a two-dimensional space. However, these techniques typically abstract away crucial spatial…
Boyi Guo, Stephanie C. Hicks
The creation of effective visualizations is a fundamental component of data analysis. In biomedical research, new challenges are emerging to visualize multi-dimensional data in a 2D space, but current data visualization tools have limited capabilities. To address this problem, we leverage Gestalt principles to improve…
Wenpin Hou, Zhicheng Ji
In the exploratory data analysis of single-cell or spatial genomic data, single cells or spatial spots are often visualized using a two-dimensional plot where cell clusters or spot clusters are marked with different colors. With tens of clusters, current visualization methods often assigns visually similar colors to…
Christoffer M. Langseth, Bastien Hervé, Hanna P. Piechaczyk, Yuk Kit Lor + 2 more
Spatial omics technologies enable high-resolution mapping of molecular and cellular organization within tissues, yet interactive exploration of these data remains challenging due to computational bottlenecks, and reliance on proprietary software infrastructures. We present KaroSpace, a framework for cell-centric…
Zhenqing Ye, Zhao Lai, Siyuan Zheng, Yidong Chen
Single cell spatial-omics data visualization plays a pivotal role in unraveling the intricate spatial organization and heterogeneity of cellular systems. Although various software tools and packages have been developed for this purpose, challenges persist in terms of user-friendly accessibility, data integration, and…
Nicolas Pielawski, Axel Andersson, Christophe Avenel, Andrea Behanova + 5 more
TissUUmaps is a browser-based tool for GPU-accelerated visualization and interactive exploration of 10^7^+ datapoints overlaying tissue samples. Users can visualize markers and regions, explore spatial statistics and quantitative analyses of tissue morphology, and assess the quality of decoding in situ transcriptomics…
Dee Velazquez, Jean Fan
Displaying proportional data across many spatially resolved coordinates is a challenging but important data visualization task, particularly for spatially resolved transcriptomics data. Scatter pie plots are one type of commonly used data visualization for such data but present perceptual challenges that may lead to…
Annabelle M. Coles, Yuening Liu, Pallav Kosuri
Imaging-based spatial transcriptomics can reveal gene expression in situ by locating and identifying individual RNA molecules at subcellular resolution. These datasets typically contain an abundance of information that when analyzed appropriately can reveal tissue organization across scales from molecules to entire…
Zuguang Gu, Daniel Hübschmann
Spiral layout has two major advantages for data visualization. First, it is able to visualize data with long axes, which greatly improves the resolution of visualization. Second, it is efficient for time series data to reveal periodic patterns. Here we present the R package spiralize that provides a general solution…
Xiao Li
Understanding the spatial dynamics within tissue microenvironments is crucial for deciphering cellular interactions and molecular signaling in living systems. These spatial characteristics govern cell distribution, extracellular matrix components, and signaling molecules, influencing local biochemical and biophysical…