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GLUE (Graph-Linked Unified Embedding) — deep learning framework for integrating unpaired single-cell multi-omics data (scRNA-seq, scATAC-seq, snmC-seq). Uses a guidance graph of prior regulatory inter
Use with AI
Install the MCP server or CLI to instantly fetch GLUE documentation:
Install command
claude mcp add biocontext7 -- npx @biocontext7/mcpOr share this page: biocontext7.com/tools/glue
scGen — deep learning framework for predicting single-cell perturbation responses using variational autoencoders. Predicts how cells respond to unseen conditions (drug treatment, genetic knockout, dis
2 shared topics • 1 shared operation
Tangram — deep learning framework for mapping single-cell and single-nucleus gene expression data onto spatial transcriptomics data. Built on PyTorch and scanpy, Tangram optimizes a probabilistic mapp
2 shared topics • 1 shared operation
Cellranger-arc — 10x Genomics pipeline for processing Multiome ATAC + Gene Expression data from the Chromium Multiome kit. Aligns FASTQ reads, calls peaks, quantifies gene expression and chromatin acc
1 shared topic • 2 shared operations
Harmony -- fast and scalable single-cell data integration and batch correction algorithm. Operates on PCA embeddings using iterative soft k-means clustering to remove batch effects while preserving bi
1 shared topic • 2 shared operations
STARmap (Spatially-resolved Transcript Amplicon Readout mapping) — in situ spatial transcriptomics method that combines hydrogel-tissue chemistry with sequencing-by-hybridization (SEDAL) for multiplex
1 shared topic • 2 shared operations