Browse published AI skills maintained by pwwang. Compare skill descriptions and follow each source repository for setup instructions.
Indexed skills
33Repository stars across indexed skills
660Repository forks
132Average repository stars per indexed skill
20Repository stars may repeat across skills from the same repository.
Generate comprehensive clonality statistics and diversity visualizations for TCR/BCR repertoire analysis. Quantifies clonal expansion, measures diversity metrics (Shannon, Simpson, Gini), and creates publicatio...
Performs fast Gene Set Enrichment Analysis (GSEA) on single-cell data using fgsea R package. Identifies enriched biological pathways by ranking genes based on differential expression between cell groups. Genera...
Visualize cell-cell communication inference results from CellCellCommunication process. Creates publication-ready network diagrams, heatmaps, and interaction plots to help interpret ligand-receptor interactions...
Performs enrichment analysis (GSEA-based) for metabolic pathways across different cell groups to identify significantly enriched pathways. Uses fast gene set enrichment analysis (fgsea package) to rank pathways...
Cluster TCR/BCR clones by CDR3 sequences using GIANA or ClusTCR (both Faiss-based). Adds `CDR3_Cluster` column to metadata for clonotype analysis.
Master skill for generating immunopipe pipeline configurations. Determines pipeline architecture based on data type (scRNA-seq with or without scTCR/BCR-seq) and analysis requirements. Routes to individual proc...
Map query single-cell datasets to high-quality reference atlases using Seurat's reference mapping workflow. Performs label transfer, UMAP projection, and integration with reference annotations without modifying...
Configuration skill for immunopipe process
Flexible marker finding process that wraps Seurat's FindMarkers function for custom group comparisons beyond simple cluster-vs-all analysis. Unlike ClusterMarkers (all-vs-all cluster comparisons), MarkersFinder...
The SampleInfo process is the pipeline entry point that reads sample metadata files, performs statistical analyses, and generates visualization reports.
Calculates pathway activity scores for metabolic pathways across different cell groups and subsets. This process quantifies the metabolic activity of each pathway per group, generating visualizations (heatmaps...
Generates comprehensive cluster statistics and visualizations for Seurat objects, including dimension reduction plots, gene expression visualizations, cluster quality metrics, and clustree diagrams. This proces...
Identifies and visualizes the top expressing genes per cluster across ALL cells (before T/B cell selection), followed by pathway enrichment analysis. Provides initial overview of all cell populations by highlig...
Analyzes physicochemical properties of CDR3 amino acid sequences to understand biochemical characteristics of T-cell receptor repertoires. Performs regression analysis between two cell groups at different CDR3...
Pass-through process that prepares Seurat object for metabolic landscape analysis. Routes the processed Seurat object to downstream metabolic analysis processes (MetabolicExprImputation, MetabolicPathwayActivit...
Load pre-existing Seurat objects into the immunopipe pipeline instead of starting from raw count matrices via SampleInfo. This enables analysis on already processed single-cell RNA-seq data stored in Seurat R o...
Combine scTCR/BCR repertoire data with scRNA-seq expression data using `scRepertoire::combineExpression()`. This process integrates immune receptor information (CDR3 sequences, V(D)J genes, clonotypes) into a S...
Performs fine-grained re-clustering on specific subsets of cells (e.g., individual clusters, cell types, or custom subsets). Unlike `Seurat::FindSubCluster` which only finds subclusters within a single cluster,...
Identifies and visualizes the top expressing genes per cluster in T/B cells, followed by pathway enrichment analysis. Provides quick cluster characterization by highlighting the most highly expressed genes and...
TESSA (TCR and Expression Joint Clustering) is a Bayesian model that integrates T-cell receptor (TCR) sequence profiling with transcriptomes of T cells. It maps the functional landscape of the TCR repertoire by...
Load, prepare, and apply quality control (QC) to single-cell RNA-seq data using Seurat. Performs data loading, QC filtering, normalization, and multi-sample integration. This is a core preprocessing process tha...
Load single-cell TCR-seq or scBCR-seq data from various formats into a scRepertoire-compatible object. This process reads VDJ (variable, diversity, joining) receptor contig data from multiple single-cell sequen...
Separates T and non-T cells or B and non-B cells from a mixed cell population. Uses either clonotype percentage from VDJ data, indicator gene expression (CD3 markers for T cells, CD19/CD20 for B cells), custom...
Performs unsupervised clustering on single-cell RNA-seq data using Seurat. This process finds nearest neighbors, computes UMAP for visualization, and applies Louvain/Leiden algorithms to identify cell clusters....
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.