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    Ivy Biosciences Docs/Workflows/Workflows/Omics and target discovery/Discover Targets From Omics

    Discover Targets From Omics

    Discover Targets From Omics: an analysis recipe chaining scanpy.

    recipesanalysis

    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: discover targets from omics — an analysis goal.

    The prompt

    Slice a COVID-19 PBMC severe-vs-healthy cohort out of Arc Institute SE-167M-Human single-cell RNA atlas (167M human cells across tissues) (Hugging Face arcinstitute/SE-167M-Human; datasets.load_dataset('arcinstitute/SE-167M-Human', streaming=True) — slice by tissue/disease metadata before materialising.) (HF streaming loader, filter on disease + tissue=PBMC), analyse with Scanpy: cluster to identify cell types, run DE between COVID-severe vs healthy for each cluster, annotate top markers with MyGene.info, check OpenTargets for druggability, and query DGIdb for existing drugs targeting DE genes - which cell-type-specific markers are druggable, and do the hyperinflammatory monocyte markers suggest repurposing opportunities for approved JAK inhibitors or anti-IL-6 antibodies?

    What each tool contributes

    • scanpy — Single-cell clustering, DE analysis, marker gene identification
    • mygene query — Gene annotations, pathways, and cross-references
    • opentargets associations — Disease-target association scores

    Why these tools pair

    Scanpy identifies marker genes, MyGene annotates them, OpenTargets assesses disease relevance — data-driven target discovery pipeline

    Data flow

    Every tool operates on the same input: gene_list.

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