scGNN — single-cell Graph Neural Network framework for scRNA-seq analysis. Uses graph autoencoders and graph convolutional networks for cell clustering, expression imputation, cell-type inference, and
Use with AI
Install the MCP server or CLI to instantly fetch scGNN documentation:
Install command
claude mcp add biocontext7 -- npx @biocontext7/mcpOr share this page: biocontext7.com/tools/scgnn
SCENIC (pySCENIC) — gene regulatory network inference and transcription factor regulon analysis for single-cell RNA-seq data. Infers TF-target gene networks using GRNBoost2/GENIE3, refines regulons vi
3 shared topics • 1 shared operation
CellChat — R package for inference, analysis, and visualization of cell-cell communication networks from single-cell RNA-seq data. Uses a curated ligand-receptor database (CellChatDB) to quantify sign
3 shared topics
Decoupler -- Python framework for inferring biological activities from omics data. Estimates transcription factor (TF) activities, pathway activities, and ligand-receptor interactions from gene expres
3 shared topics
PROGENy (Pathway RespOnsive GENes) is an R/Bioconductor package and Python decoupler-py model for inferring the activity of 14 cancer-relevant signaling pathways (EGFR, MAPK, PI3K, JAK-STAT, TGFb, TNF
3 shared topics
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
3 shared topics