Loading homepage
scGPT — generative pretrained transformer foundation model for single-cell multi-omics analysis. Provides cell type annotation, gene perturbation prediction, multi-batch integration, multi-omic integr
Use with AI
Install the MCP server or CLI to instantly fetch scGPT documentation:
Install command
claude mcp add biocontext7 -- npx @biocontext7/mcpOr share this page: biocontext7.com/tools/scgpt
FUMA (Functional Mapping and Annotation of Genome-Wide Association Studies) maps GWAS summary statistics to genes and biological functions via three complementary strategies: positional mapping, eQTL
2 shared topics • 1 shared operation
GNNExplainer explains predictions made by graph neural networks by learning sparse masks over subgraph structure and node features. Use for post-hoc GNN interpretability, identifying important edges a
2 shared topics • 1 shared operation
scArches (Single-cell Architecture Surgery) — Python toolkit for reference-based single-cell data integration via transfer learning. Maps query datasets into pre-trained reference atlases using archit
2 shared topics • 1 shared operation
scJoint integrates atlas-scale single-cell RNA-seq and scATAC-seq data using transfer learning. Transfers cell type labels from annotated RNA datasets to unannotated ATAC datasets via joint neural net
2 shared topics • 1 shared operation
Use when working with AUCell — an R/Python tool for scoring gene set activity in single cells using the Area Under the Curve (AUC) metric. Computes per-cell activity scores for gene sets (regulons, pa
2 shared topics