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    Ivy Biosciences Docs/Workflows/Workflows/Omics and target discovery/Analyze Single-Cell Data

    Analyze Single-Cell Data

    Analyze Single-Cell Data: a genomics recipe chaining scanpy.

    recipesgenomics

    Generated recipe

    Seeded from a curated quick-action prompt. The prompt below runs the real routing — copy it, change the target, and run.

    Best fit: analyze single-cell data — a genomics goal.

    The prompt

    Run Scanpy clustering on the Allen Brain Atlas mouse cortex single-cell data (GEO: GSE71585) to identify cell types, then run CellRank to map differentiation trajectories between neuronal subtypes, run pathway enrichment on marker genes with Enrichr, and fold the top marker protein for each cluster with ESMFold to assess druggability with P2Rank - load the analysis into the sandbox and use scanpy's plotting to generate a UMAP with CellRank velocity arrows and a side-panel of P2Rank druggability scores for each cluster's marker, recommending the top druggable neuron-subtype marker for a selective CNS drug?

    What each tool contributes

    • scanpy — Differential expression, marker gene ranking per cluster
    • mygene query — Gene annotation with GO terms, pathways, cross-references
    • enrichr enrichment — KEGG, Reactome, GO pathway enrichment analysis

    Why these tools pair

    Scanpy DE identifies marker genes, MyGene annotates them, Enrichr runs pathway enrichment — full functional characterization pipeline

    Data flow

    Every tool operates on the same input: gene_list.

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