Multi-Omics and Gene Perturbation Insights into Zebrafish Embryonic Cell Development
This study integrates ATAC-seq and RNA-seq data from zebrafish at various developmental stages, leveraging the CellOracle library to explore gene perturbation effects. Additionally, it examines cell-to-cell communication using a ligand-receptor interaction approach to understand signaling pathways across development.
Exploring Spatial Transcriptomics and Cell Signaling with RNA-seq Integration
Integrating RNA-seq and spatial RNA-seq datasets to examine spatial transcriptomics provides insights into gene expression patterns across tissue architecture. Additionally, it investigates cell-to-cell communication through ligand-receptor interactions, uncovering signaling dynamics within the spatial context of cellular organization.
Simulating Gene Perturbation Effects In Silico: An AI Model for Single-Cell CRISPR Screens
This project is an AI model that utilizes single-cell RNA-seq CRISPR screen datasets, including CRISPRi, CRISPRa, and CRISPR KO, to simulate the effects of single and multi-gene perturbations on cellular gene expression in mice and humans.
Simulating Cellular Responses: AI-Powered Image Generation for Gene and Compound Perturbations
Leveraging microscopic pooled screen data, this AI model generates simulated cell images that illustrate the outcomes of various gene and compound perturbations, capturing complex cellular responses in visual form.