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Claude-skill-registry-data metabolicinput

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.

install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry-data
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry-data "$T" && mkdir -p ~/.claude/skills && cp -r "$T/data/metabolicinput" ~/.claude/skills/majiayu000-claude-skill-registry-data-metabolicinput && rm -rf "$T"
manifest: data/metabolicinput/SKILL.md
source content

MetabolicInput Process Configuration

Purpose

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. All metabolic analysis parameters are configured at the ScrnaMetabolicLandscape group level.

When to Use

  • First step in modular metabolic analysis workflow
  • When you want to perform metabolic pathway analysis on single-cell RNA-seq data
  • Alternative to ScrnaMetabolicLandscape (same group, modular approach)
  • After clustering is complete (SeuratClustering or related processes)
  • When investigating metabolic heterogeneity across cell types or conditions

Configuration Structure

Process Enablement

[ScrnaMetabolicLandscape]
# This enables the entire metabolic analysis group
# MetabolicInput is automatically included as part of this group

[ScrnaMetabolicLandscape.envs]
# Configure metabolic analysis parameters here

Input Specification

MetabolicInput automatically receives input from upstream processes:

  • Requires: Seurat object from CombinedInput (includes RNA + optional VDJ data)
  • Typically follows: 
    SeuratClustering
    , 
    TESSA
    , or other clustering/annotation processes

Environment Variables (Group Level)

All metabolic analysis configuration is done at the ScrnaMetabolicLandscape group level:

[ScrnaMetabolicLandscape.envs]
# Metabolic pathway database file
gmtfile = "KEGG_2021_Human"

# Skip imputation (if data already complete)
noimpute = false

# Number of cores for parallelization
ncores = 4

# Optional: Subset data by metadata column
# subset_by = "Response"  # Remove NA values in this column

# Optional: Group data by metadata column
# group_by = "cluster"

# Optional: Add metadata columns for grouping/subsetting
# mutaters = {timepoint = "if_else(treatment == 'control', 'pre', 'post')"}

Metabolic Pathway Databases

Available Databases (via enrichit)

The 

gmtfile
parameter accepts either:

  1. Built-in database names (auto-downloaded):

    • "KEGG_2021_Human"
      - KEGG pathways (human, default)
    • "KEGG"
      - KEGG pathways (latest)
    • "Reactome_Pathways_2024"
      - Reactome pathways
    • "Reactome"
      - Reactome pathways (latest)
    • "BioCarta_2016"
      - BioCarta pathways
    • "MSigDB_Hallmark_2020"
      - MSigDB Hallmark gene sets
    • See full list: https://pwwang.github.io/enrichit/reference/FetchGMT.html

  2. Custom GMT files (local paths or URLs):

    • Local file: 
      /path/to/custom.gmt
    • URL: 
      https://example.com/pathways.gmt

Database Descriptions

  • KEGG: Kyoto Encyclopedia of Genes and Genomes - manually curated metabolic pathways. Comprehensive coverage of metabolism, including carbohydrate, energy, lipid, nucleotide, amino acid, xenobiotics, and other pathways. Species-specific versions available.

  • Reactome: Curated pathway database covering cellular processes, signal transduction, metabolic pathways, and more. More comprehensive than KEGG for signaling and regulatory pathways. Good for human/mouse.

  • BioCarta: Curated pathways focusing on cell signaling, metabolic, and disease pathways. Older database but still useful for classic pathways.

  • Custom GMT: Your own gene sets in GMT format (Gene Set Enrichment Format). Format: 

    name\tdescription\tgene1,gene2,gene3
    (tab-separated).

Species-Specific Considerations

  • Human data: Use 
    "KEGG_2021_Human"
    , 
    "Reactome_Pathways_2024"
    , or species-specific GMT files
  • Mouse data: Use KEGG with mouse gene IDs or download mouse-specific GMT from MSigDB
  • Other species: Provide custom GMT file with appropriate gene identifiers matching your Seurat object
  • Gene name matching: Ensure gene names in Seurat object match GMT file (case-sensitive, human: UPPERCASE, mouse: TitleCase)

Configuration Examples

Minimal Configuration (Default KEGG)

[ScrnaMetabolicLandscape]

KEGG Human Pathways (Explicit)

[ScrnaMetabolicLandscape]
[ScrnaMetabolicLandscape.envs]
gmtfile = "KEGG_2021_Human"
ncores = 4
noimpute = false

Reactome Pathways

[ScrnaMetabolicLandscape]
[ScrnaMetabolicLandscape.envs]
gmtfile = "Reactome_Pathways_2024"
ncores = 8

Custom Metabolic Pathway GMT File

[ScrnaMetabolicLandscape]
[ScrnaMetabolicLandscape.envs]
gmtfile = "/data/pathways/custom_metabolism.gmt"
ncores = 4

Subset Analysis by Response Group

[ScrnaMetabolicLandscape]
[ScrnaMetabolicLandscape.envs]
gmtfile = "KEGG_2021_Human"
subset_by = "Response"  # Analyze responders vs non-responders
group_by = "cluster"
ncores = 4

Multiple Pathway Databases (Via Cases)

[ScrnaMetabolicLandscape]
[ScrnaMetabolicLandscape.envs]
ncores = 4

# Analyze with KEGG
[ScrnaMetabolicLandscape.envs.cases.KEGG]
gmtfile = "KEGG_2021_Human"
group_by = "cluster"

# Analyze with Reactome
[ScrnaMetabolicLandscape.envs.cases.Reactome]
gmtfile = "Reactome_Pathways_2024"
group_by = "cluster"

Adding Custom Metadata for Grouping

[ScrnaMetabolicLandscape]
[ScrnaMetabolicLandscape.envs]
gmtfile = "KEGG_2021_Human"
ncores = 4
# Create timepoint column based on treatment
mutaters = {timepoint = "if_else(treatment == 'control', 'pre', 'post')"}
subset_by = "timepoint"
group_by = "cluster"

Common Patterns

Pattern 1: Standard Metabolic Analysis

# Basic setup with KEGG pathways
[ScrnaMetabolicLandscape]
[ScrnaMetabolicLandscape.envs]
gmtfile = "KEGG_2021_Human"
ncores = 4

Pattern 2: Skip Imputation (Clean Data)

# If data is already complete, skip imputation step
[ScrnaMetabolicLandscape]
[ScrnaMetabolicLandscape.envs]
gmtfile = "KEGG_2021_Human"
noimpute = true
ncores = 4

Pattern 3: Disease vs Control Comparison

# Compare metabolic pathways between conditions
[ScrnaMetabolicLandscape]
[ScrnaMetabolicLandscape.envs]
gmtfile = "KEGG_2021_Human"
subset_by = "diagnosis"  # e.g., "disease", "control"
group_by = "cluster"
ncores = 4

Pattern 4: Time Series Analysis

# Analyze metabolic changes across timepoints
[ScrnaMetabolicLandscape]
[ScrnaMetabolicLandscape.envs]
gmtfile = "Reactome_Pathways_2024"
subset_by = "timepoint"  # e.g., "day0", "day7", "day14"
group_by = "cluster"
ncores = 8

Pattern 5: Species-Specific Analysis

# Non-human data with custom pathways
[ScrnaMetabolicLandscape]
[ScrnaMetabolicLandscape.envs]
gmtfile = "/data/pathways/mouse_metabolism.gmt"
ncores = 4

Dependencies

Upstream Processes

  • Required: Seurat object from 
    CombinedInput
    • CombinedInput can be: 
      ScRepCombiningExpression
      (RNA + VDJ) or 
      RNAInput
      (RNA only)
    • RNAInput typically: 
      SeuratClustering
      , 
      SeuratMap2Ref
      , 
      CellTypeAnnotation
      , or 
      TESSA
  • Preceding: Clustering must be complete before metabolic analysis

Downstream Processes (In ScrnaMetabolicLandscape Group)

  • MetabolicExprImputation (optional): Impute missing expression values (ALRA, scImpute, or MAGIC)
  • MetabolicPathwayActivity: Calculate pathway activity scores per group
  • MetabolicFeatures: Enrichment analysis of metabolic pathways per group
  • MetabolicPathwayHeterogeneity: Calculate metabolic heterogeneity across groups

Validation Rules

Database Validation

  • gmtfile
    must be a valid enrichit database name OR accessible GMT file path/URL
  • For custom GMT files:
    • File must exist (absolute path or relative to config file)
    • Format must be GMT: 
      name\tdescription\tgene1,gene2,gene3
    • Gene identifiers must match Seurat object (case-sensitive)

Species Validation

  • Gene names in Seurat object must match GMT file:
    • Human: UPPERCASE (e.g., 
      CD3D
      , 
      IFNG
      )
    • Mouse: TitleCase (e.g., 
      Cd3d
      , 
      Ifng
      )
    • Verify with: 
      sobj@assays$RNA@features
      (Seurat R command)

Metadata Validation

  • If 
    subset_by
    specified: column must exist in Seurat object metadata
  • If 
    group_by
    specified: column must exist in Seurat object metadata
  • NA values in 
    subset_by
    column are automatically removed

Troubleshooting

Common Pathway Loading Issues

Issue: "GMT file not found"

Cause: Invalid path to custom GMT file Solution:

# Use absolute path
gmtfile = "/full/path/to/pathways.gmt"

# Or path relative to config file location
gmtfile = "./data/pathways.gmt"

Issue: "Gene names not found in Seurat object"

Cause: Gene identifier mismatch between GMT and Seurat object Solution:

  • Check gene format in Seurat: 
    sobj@assays$RNA@features[1:10,]
  • Ensure case matches: Human (UPPERCASE) vs Mouse (TitleCase)
  • Consider using gene symbol conversion tools if needed

Issue: "Empty pathway results"

Cause: Too few genes matching between pathways and data Solution:

  • Verify species compatibility (human GMT with mouse data won't work)
  • Try different database: Switch from KEGG to Reactome or vice versa
  • Use custom GMT with species-specific pathways

Issue: "No enriched pathways found"

Cause: Statistical thresholds too strict or no biological differences Solution:

  • Relax p-value cutoff in downstream processes (e.g., 
    pathway_pval_cutoff
    )
  • Check grouping: Ensure groups have distinct biological differences
  • Use more comprehensive database (Reactome often has more pathways than KEGG)

Performance Issues

Issue: Metabolic analysis too slow

Cause: Insufficient cores for parallelization Solution:

# Increase cores for metabolic analysis
[ScrnaMetabolicLandscape.envs]
ncores = 8  # Increase based on available CPU

Issue: Memory errors during imputation

Cause: Large dataset with imputation enabled Solution:

# Skip imputation if data is complete
[ScrnaMetabolicLandscape.envs]
noimpute = true

Integration Issues

Issue: Process not running

Cause: ScrnaMetabolicLandscape not enabled in config Solution:

# Ensure the group is enabled
[ScrnaMetabolicLandscape]

Issue: Wrong input data

Cause: Clustering not complete or incorrect upstream process Solution:

  • Ensure 
    SeuratClustering
    or similar process runs before metabolic analysis
  • Check that Seurat object has cluster assignments: 
    sobj@meta.data$seurat_clusters
  • Verify no missing values in metadata columns used for grouping

Reference