Route Seurat objects to downstream metabolic analysis steps.
npx skills add https://github.com/pwwang/immunopipe --skill metabolicinput
Please help me install this Agent Skill. Skill: metabolicinput Source: https://github.com/pwwang/immunopipe/tree/main/skills/metabolicinput Command: npx skills add https://github.com/pwwang/immunopipe --skill metabolicinput
Automates the transition from a prepared Seurat object to downstream metabolic analysis steps, eliminating manual routing and ensuring consistency across analyses.
Invoke the metabolicinput step after Seurat clustering to route the object to downstream metabolic analyses.
High-intent search queries and answers about installing and using this skill.
Routing a Seurat object to metabolic analysis involves a pass-through step that automatically receives upstream scRNA clustering data and forwards it to metabolic landscape pipelines. This eliminates manual routing and ensures analytical consistency.
scRNA metabolic landscape analysis evaluates pathway activity, expression imputation, and heterogeneity from single-cell data. You need it after Seurat clustering to characterize metabolic features across different cell populations.
No direct configuration is required to prepare your Seurat object for metabolic pathway activity analysis. All parameters are defined at the ScrnaMetabolicLandscape group level, allowing the pass-through step to forward data automatically.
Yes, you can use clustered Seurat objects directly for metabolic heterogeneity analysis. A dedicated pass-through step automatically receives the upstream SeuratClustering output and forwards it to the heterogeneity pipeline.
After Seurat clustering, you can analyze metabolic features including expression imputation, pathway activity, and pathway heterogeneity. The pass-through routing step forwards the Seurat object to these specific downstream metabolic pipelines.
A pass-through step is required to automate the transition from a prepared Seurat object to downstream metabolic landscape analysis. It ensures the clustered scRNA data is routed correctly without manual intervention or reconfiguration.
pwwangImpute dropout values in scRNA-seq expression matrices using ALRA, scImpute, or MAGIC.
pwwangCoordinate metabolic pathway analysis workflows for scRNA-seq data with Seurat inputs.
pwwangCalculate metabolic pathway activity scores across cell groups in Seurat objects.
pwwangCompute NES scores for metabolic pathways across scRNA-seq cell groups.
