escheR - Unified multi-dimensional visualizations with Gestalt principles
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 the design and interpretability of multi-dimensional data in 2D data visualizations, layering aesthetics to display multiple variables. The proposed visualization can be applied to spatially-resolved transcriptomics data, but also broadly to data visualized in 2D space, such as embedding visualizations. We provide this open source R package escheR, which is built off of the state-of-the-art ggplot2 visualization framework and can be seamlessly integrated into genomics toolboxes and workflows.
Last updated 24 days ago
spatialsinglecelltranscriptomicsvisualizationsoftwaremultidimensionalsingle-cellspatial-omics
6.89 score 6 stars 1 packages 109 scripts 198 downloads
tpSVG - Thin plate models to detect spatially variable genes
The goal of `tpSVG` is to detect and visualize spatial variation in the gene expression for spatially resolved transcriptomics data analysis. Specifically, `tpSVG` introduces a family of count-based models, with generalizable parametric assumptions such as Poisson distribution or negative binomial distribution. In addition, comparing to currently available count-based model for spatially resolved data analysis, the `tpSVG` models improves computational time, and hence greatly improves the applicability of count-based models in SRT data analysis.
Last updated 24 days ago
spatialtranscriptomicsgeneexpressionsoftwarestatisticalmethoddimensionreductionregressionpreprocessingspatially-resolvespatially-variable-genes
4.85 score 2 stars 2 scripts 142 downloads