Use this skill for CellRank 2 fate mapping workflows in single-cell data. CellRank combines transition kernels (for example RNA velocity, pseudotime, CytoTRACE, connectivity, or experimental time) wit
Use with AI
Install the MCP server or CLI to instantly fetch Cellrank documentation:
Install command
claude mcp add biocontext7 -- npx @biocontext7/mcpOr share this page: biocontext7.com/tools/cellrank
SEACells — single-cell metacell identification using kernel archetypal analysis. Aggregates highly similar single cells into metacells that preserve transcriptional and epigenomic heterogeneity while
2 shared topics • 2 shared operations
Seurat — comprehensive R toolkit for single-cell genomics enabling QC, normalization (LogNormalize, SCTransform), feature selection, dimensionality reduction (PCA, UMAP, t-SNE), graph-based clustering
2 shared topics • 2 shared operations
IsoSeq — PacBio official full-length isoform sequencing pipeline for generating high-quality, full-length cDNA transcripts from PacBio SMRT/HiFi reads. Converts raw CCS reads through primer removal (l
2 shared topics • 1 shared operation
STARmap (Spatially-resolved Transcript Amplicon Readout mapping) — in situ spatial transcriptomics method that combines hydrogel-tissue chemistry with sequencing-by-hybridization (SEDAL) for multiplex
2 shared topics • 1 shared operation
stLearn — spatial transcriptomics analysis in Python integrating gene expression with tissue morphology. Provides SME (spatial morphological gene expression) normalization, spatial clustering, spatial
2 shared topics • 1 shared operation