Calculate per-cell module scores for Seurat single-cell data.
npx skills add https://github.com/pwwang/immunopipe --skill modulescorecalculator
Please help me install this Agent Skill. Skill: modulescorecalculator Source: https://github.com/pwwang/immunopipe/tree/main/skills/modulescorecalculator Command: npx skills add https://github.com/pwwang/immunopipe --skill modulescorecalculator
Enables per-cell module scoring in single-cell analyses by configuring and running Seurat's AddModuleScore and CellCycleScoring on a Seurat object to quantify pathway activity and functional states.
Load a Seurat object and run ModuleScoreCalculator with your chosen gene sets to generate per-cell scores.
High-intent search queries and answers about installing and using this skill.
To calculate per-cell module scores for scRNA-seq, configure and run Seurat's AddModuleScore on a Seurat object using user-defined gene sets to quantify pathway activity across cell clusters.
Cell cycle scoring in single-cell data uses Seurat's CellCycleScoring to calculate S and G2M phase scores per cell, requiring a Seurat object and predefined cell cycle gene sets to classify cellular proliferation states.
Yes, you can use custom gene sets for exhaustion and cytotoxicity scoring in scRNA-seq by providing user-defined module definitions to calculate per-cell scores for functional state classification.
Module score calculation requires a valid Seurat object along with module definitions, environment defaults, and optional post-processing transformations defined in a structured configuration to generate ModuleScore columns.
The best way to compare functional states across cell clusters after perturbation is calculating per-cell module scores using AddModuleScore to quantify pathway activity differences across trajectories and clusters.
pwwangLoad, preprocess, and integrate multi-sample scRNA-seq data into a Seurat object.
pwwangCalculate metabolic pathway activity scores across cell groups in Seurat objects.
ya-wayIdentify cluster-specific marker genes and annotate cell types in single-cell RNA-seq data.
pwwangImpute dropout values in scRNA-seq expression matrices using ALRA, scImpute, or MAGIC.
