mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills- - Page 5

sc-cytotrace

Predict cell differentiation potency from scRNA-seq data using gene expression complexity as a proxy for stemness.

sc-de

Differential expression for single-cell RNA-seq using exploratory Scanpy ranking, R-backed MAST, or replicate-aware pseudobulk DESeq2. The wrapper separates cluster/group marker ranking from sample-aware condition DE.

sc-differential-abundance

Sample-aware differential abundance and compositional analysis for scRNA-seq using Milo, scCODA, or an exploratory proportion-based fallback.

sc-doublet-detection

Annotate putative doublets in single-cell RNA-seq data using Scrublet, DoubletDetection, DoubletFinder, scDblFinder, or scds. The wrapper preserves the current AnnData matrix semantics, standardizes output columns in `obs`, and exports a reusable figure/table…

sc-enrichment

Statistical enrichment analysis for single-cell RNA-seq using ORA or preranked GSEA on marker or differential-expression rankings. This skill is for GO/KEGG/Reactome/Hallmark term significance, not per-cell pathway activity scoring.

sc-fastq-qc

Start here if you have raw single-cell FASTQ files. Checks read quality before counting with FastQC and MultiQC when available, plus a stable local fallback summary.

sc-filter

Filter cells and genes from single-cell RNA-seq AnnData objects using QC-derived thresholds or tissue presets. This wrapper removes low-quality cells/genes but does not normalize, cluster, or annotate the dataset.

sc-gene-programs

Discover de novo gene programs and per-cell usage scores from scRNA-seq data using cNMF-compatible or NMF workflows.

sc-grn

Infer gene regulatory networks from scRNA-seq using the pySCENIC workflow: GRNBoost2 for adjacency inference, cisTarget-style motif pruning, and AUCell regulon scoring.

sc-in-silico-perturbation

In-silico perturbation analysis for scRNA-seq. Simulates the effect of knocking out a target gene and identifies differentially regulated genes.

sc-markers

Rank cluster marker genes from normalized single-cell AnnData using Scanpy-backed Wilcoxon, t-test, or logistic-regression methods. The wrapper standardizes outputs for downstream annotation and review.

sc-metacell

Compress scRNA-seq data into metacell-level summaries using SEACells or a lightweight k-means aggregation fallback.

sc-multi-count

Merge multiple single-sample scRNA-seq count matrices (from sc-count) into one downstream-ready AnnData with sample labels.

sc-pathway-scoring

Single-cell pathway and gene-set activity scoring for preprocessed scRNA-seq data using AUCell or a lightweight normalized-expression module-score path.

sc-perturb-prep

Prepare perturbation-ready scRNA AnnData objects by merging barcode-to-guide assignments into expression data and exporting a downstream-safe h5ad for `sc-perturb`.

sc-perturb

Single-cell perturbation analysis for scRNA-seq perturbation screens using the official pertpy Mixscape workflow.

sc-preprocessing

Base scRNA preprocessing after QC: QC-aware filtering, normalization, highly variable gene selection, and PCA.

sc-pseudotime

Single-cell pseudotime and lineage inference after clustering, with DPT, Palantir, VIA, CellRank, or Slingshot plus post-hoc trajectory gene ranking.

sc-qc

Review cell quality before filtering. Computes counts, detected genes, mitochondrial percentage, and ribosomal percentage, but does not remove cells.

sc-standardize-input

Start here if you already have an external single-cell h5ad. Fixes the AnnData contract so downstream OmicsClaw scRNA skills can use it safely.

sc-velocity-prep

Start here for RNA velocity when you have Cell Ranger BAM, loom, or STARsolo Velocyto output. Creates the spliced and unspliced layers needed by scVelo.

sc-velocity

Run scVelo on a velocity-ready h5ad using stochastic, dynamical, or steady-state modes. Use `sc-velocity-prep` first if spliced/unspliced layers are missing.

spatial-annotate

Cell type annotation for spatial transcriptomics data using Scanpy marker-gene overlap scoring, Tangram mapping, scANVI transfer, or CellAssign probabilistic models.

spatial-cnv

Infer copy number variation programs from spatial transcriptomics data using inferCNVpy or Numbat, with method-aware matrix selection, reference controls, and spatially mappable CNV summaries.

spatial-communication

Cell-cell communication analysis for spatial transcriptomics using LIANA, CellPhoneDB, FastCCC, or CellChat, with method-specific parameter hints and standardized ligand-receptor outputs.

spatial-condition

Compare experimental conditions in spatial transcriptomics data using pseudobulk differential expression with method-aware PyDESeq2 or Wilcoxon testing and explicit replicate handling.

spatial-de

Differential expression and marker discovery for spatial transcriptomics using Scanpy Wilcoxon / t-test or sample-aware pseudobulk PyDESeq2.

spatial-deconv

Cell type deconvolution for spatial transcriptomics using FlashDeconv, Cell2location, RCTD, DestVI, Stereoscope, Tangram, SPOTlight, or CARD, with method-specific parameter hints and standardized proportion outputs.

spatial-domains

Identify tissue regions and spatial niches from preprocessed spatial transcriptomics data using Leiden, Louvain, SpaGCN, STAGATE, GraphST, BANKSY, or CellCharter.

spatial-enrichment

Pathway and gene-set enrichment analysis for spatial transcriptomics using ORA-style enrichr, preranked GSEA, or ssGSEA with local-first gene-set resolution and method-aware parameter controls.

spatial-genes

Find genes with spatially variable expression patterns using Moran's I, SpatialDE, SPARK-X, or FlashS. Identifies genes whose expression is non-randomly distributed across tissue coordinates.

spatial-integrate

Multi-sample integration and batch correction for spatial transcriptomics data using Harmony, BBKNN, or Scanorama.

spatial-microenvironment-subset

Extract a local spatial microenvironment by selecting cells or spots within a physical radius of a center population, preserving coordinates and labels in a downstream-ready h5ad subset for tumor microenvironment, neighborhood, spatial communication, and…

spatial-preprocess

Load matrix-level spatial transcriptomics data (including raw_counts.h5ad from spatial-raw-processing), run the current OmicsClaw scanpy-standard preprocessing workflow, and export a downstream-ready AnnData with explicit effective QC parameters plus a…

spatial-raw-processing

Process barcoded spatial transcriptomics FASTQ pairs with st_pipeline, preserve upstream artifacts, convert the counts matrix into a standardized raw_counts.h5ad, and hand off cleanly to spatial-preprocess.

spatial-register

Spatial registration and multi-slice alignment for spatial transcriptomics data using PASTE or STalign, with method-specific parameter hints, standardized aligned-coordinate outputs, and a unified registration visualization contract.

spatial-statistics

Spatial statistics for spatial transcriptomics using neighborhood enrichment, Ripley's statistics, co-occurrence, Moran/Geary autocorrelation, local Moran, Getis-Ord Gi*, bivariate Moran, and spatial graph centrality summaries.

spatial-trajectory

Trajectory inference and pseudotime analysis for spatial transcriptomics using DPT, CellRank, or Palantir, with method-specific parameter hints and standardized trajectory outputs.

spatial-velocity

RNA velocity and cellular dynamics analysis for spatial transcriptomics using scVelo stochastic / deterministic / dynamical models or VELOVI, with method-aware preprocessing, graph, training controls, and a standardized OmicsClaw gallery + figure_data output…

claude-code-operations-2026

Build and operate Claude Code workflows in the terminal, IDE, and GitHub Actions with hooks, MCP, and approval-aware repo automation. Use when evaluating or deploying Anthropic's coding-agent surface rather than generic Claude API usage.