Compare Cell Types (Optional)

This function allows you to determine which cell type is most likely to be the true cell type of a cluster, utilizing multiple LLMs. Under default settings, 3 state-of-the-art LLMs are used to score the candidate cell types based on the marker genes.

Function Parameters

compareCelltypes(
    tissue,        # The tissue type being analyzed (e.g., "large intestine")
    celltypes,     # Vector of cell types to compare (e.g., c("Plasma Cells", "IgA-secreting Plasma Cells"))
    marker,        # String of marker genes separated by commas
    species,       # Species of origin ("human" or "mouse")
    output_file,   # Name for the output file (e.g., "plasma_cell_subtype")
    model_list     # Optional: List of LLM models to use (has default values)
)
R

Parameter Details

• tissue: Specifies the tissue source of your data (e.g., "large intestine", "small intestine", "brain")

• celltypes: List of cell types you want to compare (maximum recommended: 4-5). Example: c("Plasma Cells", "IgA-secreting Plasma Cells", "IgG-secreting Plasma Cells")

• marker: Comma-separated list of marker genes (e.g., "IGLL5, IGLV6-57, JCHAIN, FAM92B, IGLC3")

• species: Specifies the species origin of your data

• output_file: Name for the output file (without extension)

• model_list: Optional parameter. Default models (if none provided) are state-of-the-art LLMs:

  model_list = c(
      "anthropic/claude-3.5-sonnet",  # Anthropic's  model
      "openai/o1-mini",              # OpenAI's model
      "google/gemini-pro-1.5"        # Google's model
  )
  R
  

Output Format

1. Console Output:

   • Similarity scores from each LLM for each cell type
   • Consensus results (if reached)
   • Warning messages (if any)

2. Output File (saved as "[output_file].txt"):

   • Detailed comparison results from each LLM
   • Marker gene analysis
   • Final consensus (if reached)

Interpretation Guide

High Confidence Result

• A high confidence result is obtained when ALL LLMs give a score above 80% for the same cell type
• This indicates a clear, unambiguous cell type identification

No Consensus Reached

If no clear consensus is reached, consider these possible scenarios:

1. Low Quality Cluster

   • Symptom: Inconsistent or low scores across LLMs
   • Solution: Increase the number of marker genes in your analysis

2. Mixed Cluster

   • Symptom: Different LLMs strongly favor different cell types
   • Solution: Perform subclustering to separate potential distinct populations

3. Last Resort

   • If issues persist after trying the above solutions
   • Consult domain experts for manual review