Here you can find open-source software and data resources developed by the Neuro-SC Lab to facilitate single-cell omics analysis.
A comprehensive R package built on the Tidyverse framework for robust and reproducible integration of single-cell RNA-seq and Spatial Transcriptomics datasets.
Reference: Student A, Your Name, Ph.D. (2023). Genome Biology.
GitHub Repository View PublicationA Python utility using machine learning models (Random Forest/XGBoost) to classify microglial cell states (e.g., homeostatic, DAM, inflammatory) based on scRNA-seq profiles.
Reference: Student B, Your Name, Ph.D. (2024). Cell.
GitHub RepositoryRaw and processed data from our major publications are deposited in public repositories, with links provided below.
A curated list of commonly used public databases and interactive portals for quick exploration and reuse.
A large-scale collection of public single-cell datasets with metadata search and expression visualization.
Visit WebsiteThe official Human Cell Atlas portal for cross-tissue reference atlas search and download.
Visit WebsiteBrowse clustering results, metadata, and downloadable data across multiple biological domains.
Visit WebsiteInteractive embeddings for many published datasets, ideal for quick marker and cluster inspection.
Visit WebsiteCore learning resources across the R and Python ecosystems for onboarding and advanced practice.