scatterbar - Scattered Stacked Bar Chart Plots

Provides a powerful and flexible tool for visualizing proportional data across spatially resolved contexts. By combining the concepts of scatter plots and stacked bar charts, `scatterbar` allows users to create scattered bar chart plots, which effectively display the proportions of different categories at each (x, y) location. This visualization is particularly useful for applications where understanding the distribution of categories across spatial coordinates is essential. This package features automatic determination of optimal scaling factors based on data, customizable scaling and padding options for both x and y axes, flexibility to specify custom colors for each category, options to customize the legend title, and integration with `ggplot2` for robust and high-quality visualizations. For more details, see Velazquez et al. (2024) <doi:10.1101/2024.08.14.606810>.

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data-visualizationspatial-analysisspatial-data-analysisspatial-transcriptomics

6.01 score 11 stars 31 scripts 193 downloads

SEraster - Rasterization Preprocessing Framework for Scalable Spatial Omics Data Analysis

SEraster is a rasterization preprocessing framework that aggregates cellular information into spatial pixels to reduce resource requirements for spatial omics data analysis. SEraster reduces the number of spatial points in spatial omics datasets for downstream analysis through a process of rasterization where single cells’ gene expression or cell-type labels are aggregated into equally sized pixels based on a user-defined resolution. SEraster is built on an R/Bioconductor S4 class called SpatialExperiment. SEraster can be incorporated with other packages to conduct downstream analyses for spatial omics datasets, such as detecting spatially variable genes.

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softwarespatialgeneexpressiontranscriptomicssinglecellpreprocessingspatial-analysisspatial-data-analysisspatial-omicsspatial-transcriptomics

5.84 score 19 stars 18 scripts 246 downloads