pwwang
@pwwang
Public Skills
seuratpreparing
by pwwang
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 that prepares Seurat objects for downstream clustering and analysis.
scfgsea
by pwwang
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. Generates enrichment scores, significance metrics, and publication-ready visualizations.
markersfinder
by pwwang
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 enables targeted differential expression analysis between specific groups, conditions within cell types, or any custom comparison defined by metadata columns. Automatically performs pathway enrichment analysis on significant markers and generates comprehensive visualizations.
seuratsubclustering
by pwwang
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, this process performs the complete clustering workflow (PCA, UMAP, FindNeighbors, FindClusters) on any subset of cells defined by metadata filters or cell barcode lists.
cdr3clustering
by pwwang
Cluster TCR/BCR clones by CDR3 sequences using GIANA or ClusTCR (both Faiss-based). Adds CDR3_Cluster column to metadata for clonotype analysis.
metabolicpathwayheterogeneity
by pwwang
Analyzes metabolic pathway heterogeneity within cell populations by calculating normalized enrichment scores (NES) for each pathway across different groups. Quantifies metabolic diversity and identifies pathways with variable activity patterns. Uses principal component analysis and GSEA to assess pathway heterogeneity, revealing subpopulation-specific metabolic states and transitions.
scrnametaboliclandscape
by pwwang
Comprehensive metabolic landscape analysis pipeline for scRNA-seq data. This is an all-in-one process group performing complete metabolic pathway analysis including expression imputation, feature selection, pathway activity calculation, and heterogeneity analysis. Based on methodology from Xiao et al.
clustermarkers
by pwwang
Finds differentially expressed genes (markers) for clusters of T/B cells using Seurat's FindMarkers function. Performs statistical testing between clusters, identifies cluster-defining genes, and automatically runs pathway enrichment analysis (via Enrichr) on significant markers. Generates publication-ready visualizations including volcano plots, dot plots, heatmaps, and enrichment plots.
sampleinfo
by pwwang
The SampleInfo process is the pipeline entry point that reads sample metadata files, performs statistical analyses, and generates visualization reports.
cdr3aaphyschem
by pwwang
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 lengths for each physicochemical feature (hydrophobicity, volume, isoelectric point, etc.).
screploading
by pwwang
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 sequencing platforms and prepares it for integration with scRNA-seq data.
metabolicpathwayactivity
by pwwang
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 and violin plots) to compare metabolic states between clusters or conditions. Based on the methodology from Xiao et al.
cellcellcommunicationplots
by pwwang
Visualize cell-cell communication inference results from CellCellCommunication process. Creates publication-ready network diagrams, heatmaps, and interaction plots to help interpret ligand-receptor interactions between cell types.
cellcellcommunication
by pwwang
Infer ligand-receptor interactions and cell-cell communication networks from single-cell RNA-seq data using the LIANA+ framework. Identifies potential signaling events between cell types based on gene expression patterns and curated ligand-receptor interaction databases.
seuratclusteringofallcells
by pwwang
Performs coarse clustering on ALL cells (including T cells, B cells, and non-T/B cells) before cell type selection. This process identifies broad cell populations to enable subsequent T/B cell selection via TOrBCellSelection. Unlike SeuratClustering which works on already-selected T/B cells, this provides initial clustering on heterogeneous cell populations.
metabolicfeatures
by pwwang
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 by their association with specific clusters, conditions, or cell states. Generates summary plots and enrichment visualizations for biological interpretation.
clonalstats
by pwwang
Generate comprehensive clonality statistics and diversity visualizations for TCR/BCR repertoire analysis. Quantifies clonal expansion, measures diversity metrics (Shannon, Simpson, Gini), and creates publication-ready plots.
clustermarkersofallcells
by pwwang
Finds marker genes for clusters of ALL cells before T/B cell selection. This process identifies differentially expressed genes across unsupervised clusters to help identify broad cell types (T cells, B cells, Myeloid cells, NK cells, etc.) in mixed immune cell populations.
screpcombiningexpression
by pwwang
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 Seurat object's metadata, enabling clonotype-aware gene expression analysis.
immunopipe-config
by pwwang
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 process skills for detailed configuration. Use this skill when starting a new immunopipe configuration or modifying pipeline-level options.
celltypeannotation
by pwwang
Annotates cell clusters with biological cell type labels using multiple methods: direct assignment, ScType, scCATCH, hitype, or CellTypist. This process is essential for interpreting clustering results by assigning meaningful biological identities to each cluster.
topexpressinggenesofallcells
by pwwang
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 highlighting the most highly expressed genes and their biological functions.
seuratclusterstats
by pwwang
Generates comprehensive cluster statistics and visualizations for Seurat objects, including dimension reduction plots, gene expression visualizations, cluster quality metrics, and clustree diagrams. This process is essential for exploring and validating clustering results.
topexpressinggenes
by pwwang
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 their biological functions.