metabolicexpimputation
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.
npx skills add majiayu000/claude-skill-registry --skill metabolicexpimputation --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Imputes missing/dropout values in scRNA-seq expression data to improve metabolic pathway analysis. This process fills zero values using one of three imputation methods (ALRA, scImpute, or MAGIC) to produce an imputed Seurat object intended for downstream metabolic pathway activity calculations and feature selection.
How it works
- Receives input Seurat object from MetabolicInput and outputs an imputed Seurat object with a .imputed.qs suffix.
- The environment variable tool selects one imputation method: "alra" (ALRA), "scimpute" (scImpute), or "rmagic" (MAGIC).
- Each method has configuration options: alra_args (empty by default), scimpute_args (drop_thre, kcluster, ncores, refgene), rmagic_args (python, threshold).
- The process is integrated with the ScrnaMetabolicLandscape group; it can be enabled by default or skipped via noimpute.
- Output validation includes creating a new imputed assay and renaming the original assay to RAW in certain tools, with non-negative imputed values and preserved dimensions.
When to use it
- Second step in the metabolic analysis workflow, after MetabolicInput and before MetabolicPathwayActivity and MetabolicFeatures.
- Data with high sparsity (>50% zeros) where imputation can improve pathway activity calculations.
- When downstream MetabolicFeatures analysis requires imputed expression.
- When imputation is believed to improve results; skip if data is already imputed or dataset is very large or unimputed analysis is preferred.
What it can touch
- Input: Seurat object from MetabolicInput.
- Output: Imputed Seurat object with .imputed.qs suffix.
- No explicit file edits or external data are described beyond the Seurat object transformations; tool names and configuration keys must be quoted exactly as shown in the skill.
Caveats
- Imputation method choices have different trade-offs in speed, accuracy, and memory (ALRA fastest, scImpute most accurate but slower, MAGIC diffusion-based with trajectory suitability).
- Validation rules specify possible constraints per method (e.g., drop_thre bounds for scImpute, Python path for MAGIC).
- Common issues include potential memory errors, negative values from some methods, and Python availability for MAGIC; the skill provides guidance to switch methods or adjust parameters.
- Output assumes imputed data is stored in a dedicated assay and the RAW assay is preserved/renamed according to method-specific behavior.
# MetabolicExprImputation Process Configuration ## Purpose 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. **Note**: This process is part of the `ScrnaMetabolicLandscape` process group. When using the full group, MetabolicExprImputation is automatically enabled (or skipped via `noimpute` flag). Use this skill when configuring the imputation step individually or when customizing imputation parameters within the ScrnaMetabolicLandscape group. ## When to Use - **Second step in metabolic analysis workflow**: After MetabolicInput, before MetabolicPathwayActivity and MetabolicFeatures - **High sparsity data**: When your scRNA-seq data has many zero/dropout values (>50% zeros) - **Metabolic pathway sensitivity**: When pathway activity calculations require complete expression matrices - **Before feature selection**: When downstream MetabolicFeatures analysis
- Purpose
- When to Use
- Configuration Structure
- Process Enablement
- Input Specification
- Environment Variables
- Imputation Methods Comparison
- Method Selection Guide
- Configuration Examples
- Minimal Configuration (Default ALRA)
- Skip Imputation (Use Raw Data)
- scImpute with Custom Parameters
- MAGIC for Trajectory Data
- High-Performance Imputation (Large Dataset)
pip install magic-impute magic-impute (Python)
What does the metabolicexpimputation skill do?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill metabolicexpimputation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.