pwwang
@pwwang
Public Skills
seuratclustering
by pwwang
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. Clusters can be explored at multiple resolutions to balance granularity and biological relevance.
pseudobulkdeg
by pwwang
Performs pseudo-bulk differential gene expression analysis using DESeq2 or edgeR. Aggregates single-cell counts to sample-level pseudo-bulk data, then identifies differentially expressed genes between conditions while accounting for biological replicates. Supports complex experimental designs including batch effects, paired samples, and interaction terms.
seuratmap2ref
by pwwang
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 query expression data. Enables transfer learning from curated atlases like Azimuth PBMC or custom tissue-specific references.
modulescorecalculator
by pwwang
Configuration skill for immunopipe process
metabolicexpimputation
by pwwang
Imputes missing/dropout values in scRNA-seq expression data to improve metabolic pathway analysis. This process handles sparsity common in single-cell RNA sequencing data by filling in zero values using advanced imputation methods (ALRA, scImpute, or MAGIC). The imputed data provides more accurate metabolic pathway activity calculations and feature selection in downstream analysis.
metabolicinput
by pwwang
Pass-through process that prepares Seurat object for metabolic landscape analysis. Routes the processed Seurat object to downstream metabolic analysis processes (MetabolicExprImputation, MetabolicPathwayActivity, MetabolicFeatures, MetabolicPathwayHeterogeneity). Note: This process requires no direct configuration.
loadingrnafromseurat
by pwwang
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 objects.
tessa
by pwwang
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 learning unified representations across modalities. The process employs BriseisEncoder to capture TCR sequence features, creating numerical embeddings that reconstruct Atchley Factor matrices and CDR3 sequences.