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Claude Skills by mdbabumiamssm
github.com/mdbabumiamssm1,578 skills3 installs3,530 views
- Sc Cell CommunicationCell-cell communication analysis for annotated scRNA-seq data using a built-in ligand-receptor scorer, LIANA, CellPhoneDB, CellChat, or a NicheNet R path.Votes: 0GitHub stars: 32
- Sc ClusteringBuild the neighbor graph, run a low-dimensional embedding, and cluster single-cell data from a normalized scRNA AnnData object.Votes: 0GitHub stars: 32
- Sc CountDefault scRNA counting route. Turn FASTQ or existing Cell Ranger, STARsolo, SimpleAF / Alevin-fry, or kb-python outputs into a downstream-ready standardized AnnData.Votes: 0GitHub stars: 32
- Sc CytotracePredict cell differentiation potency from scRNA-seq data using gene expression complexity as a proxy for stemness.Votes: 0GitHub stars: 32
- Sc DeDifferential 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.Votes: 0GitHub stars: 32
- Sc Differential AbundanceSample-aware differential abundance and compositional analysis for scRNA-seq using Milo, scCODA, or an exploratory proportion-based fallback.Votes: 0GitHub stars: 32
- Sc Doublet DetectionAnnotate 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 gallery.Votes: 0GitHub stars: 32
- Sc Drug Response**Single-cell drug response prediction** — predict drug sensitivity from single-cell transcriptomes using pharmacogenomic models or gene expression correlation with known drug targets.Votes: 0GitHub stars: 32
- Sc EnrichmentStatistical 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.Votes: 0GitHub stars: 32
- Sc Fastq QcStart 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.Votes: 0GitHub stars: 32
- Sc FilterFilter 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.Votes: 0GitHub stars: 32
- Sc Gene ProgramsDiscover de novo gene programs and per-cell usage scores from scRNA-seq data using cNMF-compatible or NMF workflows.Votes: 0GitHub stars: 32
- Sc GrnInfer gene regulatory networks from scRNA-seq using the pySCENIC workflow: GRNBoost2 for adjacency inference, cisTarget-style motif pruning, and AUCell regulon scoring.Votes: 0GitHub stars: 32
- Sc In Silico PerturbationIn-silico perturbation analysis for scRNA-seq. Simulates the effect of knocking out a target gene and identifies differentially regulated genes.Votes: 0GitHub stars: 32
- Sc MarkersRank 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.Votes: 0GitHub stars: 32
- Sc MetacellCompress scRNA-seq data into metacell-level summaries using SEACells or a lightweight k-means aggregation fallback.Votes: 0GitHub stars: 32
- Sc Multi CountMerge multiple single-sample scRNA-seq count matrices (from sc-count) into one downstream-ready AnnData with sample labels.Votes: 0GitHub stars: 32
- Sc Pathway ScoringSingle-cell pathway and gene-set activity scoring for preprocessed scRNA-seq data using AUCell or a lightweight normalized-expression module-score path.Votes: 0GitHub stars: 32
- Sc Perturb PrepPrepare perturbation-ready scRNA AnnData objects by merging barcode-to-guide assignments into expression data and exporting a downstream-safe h5ad for `sc-perturb`.Votes: 0GitHub stars: 32
- Sc PerturbSingle-cell perturbation analysis for scRNA-seq perturbation screens using the official pertpy Mixscape workflow.Votes: 0GitHub stars: 32
- Sc PreprocessingBase scRNA preprocessing after QC: QC-aware filtering, normalization, highly variable gene selection, and PCA.Votes: 0GitHub stars: 32
- Sc PseudotimeSingle-cell pseudotime and lineage inference after clustering, with DPT, Palantir, VIA, CellRank, or Slingshot plus post-hoc trajectory gene ranking.Votes: 0GitHub stars: 32
- Sc QcReview cell quality before filtering. Computes counts, detected genes, mitochondrial percentage, and ribosomal percentage, but does not remove cells.Votes: 0GitHub stars: 32
- Sc Standardize InputStart here if you already have an external single-cell h5ad. Fixes the AnnData contract so downstream OmicsClaw scRNA skills can use it safely.Votes: 0GitHub stars: 32
- Sc Velocity PrepStart here for RNA velocity when you have Cell Ranger BAM, loom, or STARsolo Velocyto output. Creates the spliced and unspliced layers needed by scVelo.Votes: 0GitHub stars: 32
- Sc VelocityRun scVelo on a velocity-ready h5ad using stochastic, dynamical, or steady-state modes. Use `sc-velocity-prep` first if spliced/unspliced layers are missing.Votes: 0GitHub stars: 32
- Spatial AnnotateCell type annotation for spatial transcriptomics data using Scanpy marker-gene overlap scoring, Tangram mapping, scANVI transfer, or CellAssign probabilistic models.Votes: 0GitHub stars: 32
- Spatial CnvInfer copy number variation programs from spatial transcriptomics data using inferCNVpy or Numbat, with method-aware matrix selection, reference controls, and spatially mappable CNV summaries.Votes: 0GitHub stars: 32
- Spatial CommunicationCell-cell communication analysis for spatial transcriptomics using LIANA, CellPhoneDB, FastCCC, or CellChat, with method-specific parameter hints and standardized ligand-receptor outputs.Votes: 0GitHub stars: 32
- Spatial ConditionCompare experimental conditions in spatial transcriptomics data using pseudobulk differential expression with method-aware PyDESeq2 or Wilcoxon testing and explicit replicate handling.Votes: 0GitHub stars: 32
- Spatial DeDifferential expression and marker discovery for spatial transcriptomics using Scanpy Wilcoxon / t-test or sample-aware pseudobulk PyDESeq2.Votes: 0GitHub stars: 32
- Spatial DeconvCell type deconvolution for spatial transcriptomics using FlashDeconv, Cell2location, RCTD, DestVI, Stereoscope, Tangram, SPOTlight, or CARD, with method-specific parameter hints and standardized proportion outputs.Votes: 0GitHub stars: 32
- Spatial DomainsIdentify tissue regions and spatial niches from preprocessed spatial transcriptomics data using Leiden, Louvain, SpaGCN, STAGATE, GraphST, BANKSY, or CellCharter.Votes: 0GitHub stars: 32
- Spatial EnrichmentPathway 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.Votes: 0GitHub stars: 32
- Spatial GenesFind 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.Votes: 0GitHub stars: 32
- Spatial IntegrateMulti-sample integration and batch correction for spatial transcriptomics data using Harmony, BBKNN, or Scanorama.Votes: 0GitHub stars: 32
- Spatial Microenvironment SubsetExtract 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 related downstream analyses.Votes: 0GitHub stars: 32
- Spatial PreprocessLoad 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 standardized preprocessing visualization contract.Votes: 0GitHub stars: 32
- Spatial Raw ProcessingProcess 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.Votes: 0GitHub stars: 32
- Spatial RegisterSpatial 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.Votes: 0GitHub stars: 32
- Spatial StatisticsSpatial 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.Votes: 0GitHub stars: 32
- Spatial TrajectoryTrajectory inference and pseudotime analysis for spatial transcriptomics using DPT, CellRank, or Palantir, with method-specific parameter hints and standardized trajectory outputs.Votes: 0GitHub stars: 32
- Spatial VelocityRNA 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 contract.Votes: 0GitHub stars: 32
- ClaudeThis skill performs comprehensive quality control on single-cell RNA-seq data using MAD-based filtering. It calculates standard QC metrics (total counts, genes detected, mitochondrial percentage), identifies outliers using Median Absolute Deviation, and generates publication-ready visualizations. Follows scverse consortium best practices.Votes: 0GitHub stars: 32
- Compartment Analysis--> --- name: bio-hi-c-analysis-compartment-analysis description: Detect A/B compartments from Hi-C data using cooltools and eigenvector decomposition. Identify active (A) and inactive (B) chromatin compartments from contact matrices. Use when identifying A/B compartments from Hi-C data. tool_type: python primary_tool: cooltools measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Detect A/B compartment...Votes: 0GitHub stars: 6
- Contact Pairs--> --- name: bio-hi-c-analysis-contact-pairs description: Process Hi-C read pairs using pairtools. Parse alignments, filter duplicates, classify pairs, and generate contact statistics from Hi-C sequencing data. Use when processing raw Hi-C read pairs. tool_type: cli primary_tool: pairtools measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Process Hi-C read pairs with pairtools.Votes: 0GitHub stars: 6
- Hic Data Io--> --- name: bio-hi-c-analysis-hic-data-io description: Load, convert, and manipulate Hi-C contact matrices using cooler format. Read .cool/.mcool files, convert from .hic format, access matrix data, and export to different formats. Use when loading or converting Hi-C contact matrices. tool_type: mixed primary_tool: cooler measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Load and manipulate Hi-C co...Votes: 0GitHub stars: 6
- Hic Differential--> --- name: bio-hi-c-analysis-hic-differential description: Compare Hi-C contact matrices between conditions to identify differential chromatin interactions. Compute log2 fold changes, statistical significance, and visualize differential contact maps. Use when comparing Hi-C contacts between conditions. tool_type: python primary_tool: cooltools measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Comp...Votes: 0GitHub stars: 6
- Hic Visualization--> --- name: bio-hi-c-analysis-hic-visualization description: Visualize Hi-C contact matrices, TADs, loops, and genomic features using matplotlib, cooltools, and HiCExplorer. Create triangle plots, virtual 4C, and multi-track figures. Use when visualizing contact matrices or genomic features. tool_type: python primary_tool: cooltools measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Visualize Hi-C c...Votes: 0GitHub stars: 6
- Loop Calling--> --- name: bio-hi-c-analysis-loop-calling description: Detect chromatin loops and point interactions from Hi-C data using cooltools, chromosight, and HiCCUPS-like methods. Identify CTCF-mediated loops and enhancer-promoter contacts. Use when detecting chromatin loops from Hi-C data. tool_type: mixed primary_tool: cooltools measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Detect chromatin loops an...Votes: 0GitHub stars: 6