single-cell

Plugin single-cell from marketplace aiscientist-skills in lijinbio/aiscientist-skills. Commands read from the repo's manifest — 3 days ago.

Skills (19)

• compare-perturbations-to-control

  Per-perturbation differential expression against one shared non-targeting control in a pooled CRISPR screen with single-cell readout (Perturb-seq, CROP-seq, CRISPRi or CRISPRa) with scanpy rank_genes_groups (Wilcoxon, explicit reference), from an AnnData with a guide or perturbation column to one DE

• filter-escapers-with-mixscape

  Label and remove escaping cells in a pooled CRISPR screen with single-cell readout (Perturb-seq, CROP-seq) with Mixscape from pertpy, from a log-normalized AnnData with a guide or perturbation column and a non-targeting control to adata_mixscape.h5ad without the non-perturbed (NP) cells, plus the NP

• rank-perturbations-by-edistance

  Rank the perturbations of a pooled CRISPR screen with single-cell readout (Perturb-seq, CROP-seq, CRISPRi or CRISPRa) by E-distance (energy distance) to the non-targeting control in PCA space, with a label-permutation test and Benjamini-Hochberg correction, from an AnnData with a guide or perturbati

• answer-eye-questions-with-visionkb-kg

  Answer chat-style knowledge questions about eye genes, cell types, diseases and datasets from the VisionKB knowledge graph through its read-only HTTP API or the equivalent visionkb-kg MCP tools, citing the KG release and dataset versions. Resolves a gene or cell type to a CURIE, reads per-class and

• read-whole-corpus-with-claude

  Answer a question from a chunked full-text literature corpus completely, by having Claude Haiku read every passage (not the top-k of a search) and return the numbers of the sentences that bear on it, so every quote is verbatim. Writes evidence.csv (each bearing passage with its sentences, PMID, page

• recover-paper-pdfs

  Recover and assemble a corpus of scientific paper PDFs from a list of PMIDs or DOIs through a ladder of legal sources (open-access APIs, PMC and Europe PMC, publisher open access, then the user's institutional access through the campus network, VPN or library discovery search), verifying every PDF a

• annotate-clusters-by-markers

  Reference-free cell-type annotation of clusters from a curated marker panel with scanpy. Signature scores (score_genes) give a z-scored first pass, and the final label is decided on raw log-normalised expression of a few lineage-specific discriminator genes, so lineages that share markers are not mi

• compare-conditions-by-celltype

  Condition-vs-control differential expression within each cell type (for example KO vs WT, or disease vs control, per cell type) with scanpy rank_genes_groups (Wilcoxon, explicit reference group). Takes an AnnData with a condition column and an existing cell-type column and writes per-cell-type DE ta

• compare-two-groups-with-scanpy

  Differential expression between two named groups of cells (for example disease vs control, or cluster 3 vs cluster 5) with scanpy rank_genes_groups (Wilcoxon) and an explicit reference group, from an AnnData file to a ranked CSV table and a JSON summary. Use when a question compares exactly two grou

• crossvalidate-scgpt-vs-leiden

  Cross-validate scGPT per-cell cell-type predictions against the data's own structure. Joins the scGPT predictions CSV to a clustered AnnData by barcode, never by row position (scGPT labels every raw cell, QC keeps a subset), and for the Leiden clusters and any existing majorclass or celltype labels

• explore-atlas-expression-with-aiscientist-mcp

  Answer a gene expression query such as "show LRAT across retinal cell types" on a large single-cell or single-nucleus atlas .h5ad with raw counts, by calling the atlas-expression-exploration MCP server from the aiscientist-mcp repository. Its tools resolve gene names (case, HGNC alias, previous symb

• prioritize-disease-genes-with-atlas

  Prioritize disease-associated genes by combining an existing condition-vs-control differential expression result (per cell type) with a precalculated cell-type atlas, offline. The atlas (long-form CSV, TSV or Parquet profiles, or an h5ad of profiles or of cells) adds cell-type expression specificity

• qc-snrna-with-cellqc-standalone

  Run the CellQC pipeline (ambient correction, count/feature/mitochondrial filtering, doublet calling, nuclear fraction) over a cohort of 10x Cell Ranger libraries by hand, with bash plus conda or a container, from installing cellqc to validating result/metrics.csv. Use when setting up a cellqc enviro

• score-gene-signatures-with-scanpy

  Score every cell for one or more gene signatures with scanpy score_genes (the signature's mean expression minus that of a matched random control gene set) and summarise each score per group, for example cell type by condition, as n, mean, median and IQR, with per-cell scores, box plots and a JSON co

• build-variant-db-with-tiledbvcf

  Build and query a TileDB-VCF variant database for a cohort with tiledbvcf-py - ingest many indexed single-sample VCFs into one compressed sparse-array dataset (a re-run adds only new samples, with no re-merging), query genomic regions across all or selected samples, and export the matching records (

• normalize-vcf-with-bcftools

  Normalize a VCF with bcftools norm before annotation or ClinVar/gnomAD matching - split multiallelic sites into biallelic records, atomize MNPs into SNPs, then left-align and trim indels against the reference FASTA of the same build - writing an indexed normalized.vcf.gz and a JSON of record counts

• prioritize-clinical-variants

  Rank annotated variants into research-triage tiers (ClinVar pathogenic, likely pathogenic, VUS favouring pathogenic, VUS, likely benign, benign) from ClinVar significance used verbatim, gnomAD allele frequency against a dominant or recessive disease-model cutoff and the ACMG BA1 5 percent rule, and

• qc-vcf-callset

  Judge whether a germline VCF callset is trustworthy before interpreting any variant - Ti/Tv, Het/Hom, SNP, indel and multiallelic counts, sample count and genotype call rate from bcftools stats (falling back to cyvcf2, then to a plain-Python Ti/Tv scan) - and flag values outside the expected human W

• write-variant-tables

  Write the five standard variant-annotation result tables - consequence, impact and ClinVar-significance distributions, a ClinVar pathogenic and likely-pathogenic list, and a shortlist of rare, damaging variants not in ClinVar - plus a JSON summary, from an annotated per-variant TSV (VEP consequence

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