Contributions

We have developed a suite of software tools to handle tree-structured biological data. These tools (e.g., treeio, ggtree) address challenges in data parsing, integration and visualization, providing a programmable environment for evolutionary data analysis. These methods facilitate the processing of phylogenetic trees and associated data across various species.

We are developing AI-native infrastructure for scientific computing in R. aisdk provides a unified runtime for model providers, tools, agents, skills, channels, and image generation, while ggai builds on this foundation to support grammar-guided AI visualization, editable ggplot2 sessions, and data-grounded scientific figure polish.

We have developed methods and tools for biological knowledge mining. Our work—led by clusterProfiler—supports functional analysis of multi-omics data through semantic similarity measurement, enrichment analysis, and comparative analysis, extending to single-cell and spatial omics data. These tools assist researchers in interpreting large-scale biological datasets.

We have developed a set of visualization tools to support scientific discovery. These tools apply the grammar of graphics to various data types, enabling researchers to visualize relationships across genomic, molecular, and cellular scales. Our aim is to provide effective visualization solutions for biological data analysis.