Author: Chen Yang
Date: 2026-07-20

# Advanced Features & Case Studies

#### Chen Yang

#### 2026-07-20

This article explores advanced features of mLLMCelltype and presents practical examples demonstrating its application in various research contexts.

## Hierarchical Cell Type Annotation

### Understanding Hierarchical Annotation

Cell types often exist in hierarchical relationships. For example, T cells can be further classified into CD4+ T cells, CD8+ T cells, regulatory T cells, etc. mLLMCelltype can be used in a multi-step workflow to capture these hierarchical relationships.

### Implementing Hierarchical Annotation

Here’s a practical approach to perform hierarchical annotation:

    library(mLLMCelltype)
    library(Seurat)
    library(dplyr)

    # Step 1: Perform initial high-level annotation
    high_level_results <- annotate_cell_types(
      input = marker_data,
      tissue_name = "human PBMC",
      model = "claude-sonnet-4-6",
      api_key = Sys.getenv("ANTHROPIC_API_KEY"),
      top_gene_count = 10
    )

    # Step 2: Add high-level annotations to Seurat object
    seurat_obj$high_level_celltype <- plyr::mapvalues(
      x = as.character(Idents(seurat_obj)),
      from = names(high_level_results),
      to = high_level_results
    )

    # Step 3: Subset T cells for further annotation
    t_cells <- subset(seurat_obj, high_level_celltype == "T cells")

    # Step 4: Find markers within T cells
    t_cell_markers <- FindAllMarkers(t_cells, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)

    # Step 5: Perform T cell subtype annotation
    t_cell_subtypes <- annotate_cell_types(
      input = t_cell_markers,
      tissue_name = "human PBMC T cells",
      model = "claude-sonnet-4-6",
      api_key = Sys.getenv("ANTHROPIC_API_KEY"),
      top_gene_count = 10
    )

    # Step 6: Add T cell subtypes back to original object
    t_cell_barcodes <- WhichCells(t_cells)
    seurat_obj$detailed_celltype <- seurat_obj$high_level_celltype
    seurat_obj$detailed_celltype[t_cell_barcodes] <- plyr::mapvalues(
      x = as.character(Idents(t_cells)),
      from = names(t_cell_subtypes),
      to = paste0("T cells: ", t_cell_subtypes)
    )

### Validating Hierarchical Annotations

After creating hierarchical annotations, it’s important to validate the consistency between levels:

    # Create a simple function to check parent-child consistency
    validate_hierarchy <- function(high_level, detailed_level) {
      # Extract parent type from detailed annotation (before the colon)
      parent_from_detailed <- sapply(strsplit(detailed_level, ": "), function(x) x[1])

      # Check if parent matches high-level annotation
      consistent <- parent_from_detailed == high_level

      # Return consistency check results
      data.frame(
        high_level = high_level,
        detailed_level = detailed_level,
        consistent = consistent
      )
    }

    # Apply validation
    hierarchy_validation <- validate_hierarchy(
      seurat_obj$high_level_celltype,
      seurat_obj$detailed_celltype
    )

    # Identify inconsistencies
    inconsistencies <- hierarchy_validation[!hierarchy_validation$consistent, ]
    print(inconsistencies)

## Handling Noisy Input Data

### Strategies for Noisy Marker Genes

Real-world scRNA-seq data often contains noise. Here are practical strategies for handling noisy input:

#### 1. Adjust the top_gene_count parameter

For noisy datasets, using fewer top genes can help focus on the strongest signals:

    # For noisy data, use fewer top genes
    results_fewer_genes <- annotate_cell_types(
      input = marker_data,  # Your marker gene data
      tissue_name = "human PBMC",
      model = "claude-sonnet-4-6",
      api_key = Sys.getenv("ANTHROPIC_API_KEY"),
      top_gene_count = 5  # Use fewer genes to focus on strongest signals
    )

#### 2. Apply stricter filtering for marker genes

Pre-filtering marker genes with stricter thresholds can improve annotation quality:

    # Apply stricter filtering to marker genes
    filtered_markers <- marker_data %>%
      filter(p_val_adj < 0.01, avg_log2FC > 1.0)  # Stricter thresholds

    # Annotate with filtered markers
    results_filtered <- annotate_cell_types(
      input = filtered_markers,
      tissue_name = "human PBMC",
      model = "claude-sonnet-4-6",
      api_key = Sys.getenv("ANTHROPIC_API_KEY")
    )

#### 3. Use multi-model consensus

The consensus approach can help overcome noise by combining predictions from multiple models:

    # Set up API keys
    api_keys <- list(
      anthropic = Sys.getenv("ANTHROPIC_API_KEY"),
      openai = Sys.getenv("OPENAI_API_KEY"),
      gemini = Sys.getenv("GEMINI_API_KEY")
    )

    # Define multiple models to use
    models <- c(
      "claude-sonnet-4-6",
      "gpt-5.5",
      "gemini-3.1-pro-preview"
    )

    # Create consensus using interactive_consensus_annotation
    consensus_results <- interactive_consensus_annotation(
      input = marker_data,  # Your marker gene data
      tissue_name = "human PBMC",
      models = models,
      api_keys = api_keys,
      controversy_threshold = 0.7,
      entropy_threshold = 1.0,
      consensus_check_model = "claude-sonnet-4-6"
    )

### Handling Data with Batch Effects

When working with data affected by batch effects, you can:

#### 1. Use the consensus approach with a lower controversy threshold

    # For data with batch effects, use consensus with lower threshold
    batch_consensus <- interactive_consensus_annotation(
      input = marker_data,  # Your marker gene data with batch effects
      tissue_name = "mouse brain",
      models = c("claude-sonnet-4-6", "gpt-5.5", "gemini-3.1-pro-preview"),
      api_keys = api_keys,
      controversy_threshold = 0.4,  # Lower threshold to discuss more clusters
      entropy_threshold = 0.8  # Lower entropy threshold
    )

#### 2. Include batch information in the tissue context

    # Include batch information in the tissue context
    batch_aware_results <- annotate_cell_types(
      input = marker_data,  # Your marker gene data with batch effects
      tissue_name = "mouse brain with technical batch effects",  # Include batch context
      model = "claude-sonnet-4-6",
      api_key = Sys.getenv("ANTHROPIC_API_KEY")
    )

## Incorporating Domain Knowledge

### Using Tissue Context

One of the key features of mLLMCelltype is the ability to incorporate domain knowledge through the `tissue_name` parameter. This provides important context to the LLM:

    # Basic annotation without specific tissue context
    basic_results <- annotate_cell_types(
      input = marker_data,
      tissue_name = "human sample",  # Generic context
      model = "claude-sonnet-4-6",
      api_key = Sys.getenv("ANTHROPIC_API_KEY")
    )

    # Annotation with specific tissue context
    specific_results <- annotate_cell_types(
      input = marker_data,
      tissue_name = "human fetal liver at 20 weeks gestation",  # Detailed context
      model = "claude-sonnet-4-6",
      api_key = Sys.getenv("ANTHROPIC_API_KEY")
    )

### Creating Custom Prompts

For advanced use cases, you can create and modify the annotation prompt directly:

    # Create a custom annotation prompt
    custom_prompt <- create_annotation_prompt(
      input = marker_data,
      tissue_name = "human PBMC",
      top_gene_count = 10
    )

    # Modify the prompt to include additional context
    modified_prompt <- paste0(
      custom_prompt$prompt,
      "\n\nAdditional context: This sample is from a patient with rheumatoid arthritis. ",
      "Previous studies have identified activated T cells, B cells, and CXCR4-high monocytes in this condition."
    )

    # Use the modified prompt directly
    custom_results <- get_model_response(
      prompt = modified_prompt,
      model = "claude-sonnet-4-6",
      api_key = Sys.getenv("ANTHROPIC_API_KEY")
    )

### Combining with External Resources

You can enhance your annotation workflow by combining mLLMCelltype with other R packages and resources:

    library(Seurat)
    library(dplyr)

    # Example: Using CellMarker database information to validate annotations
    # This is a conceptual example - implementation would depend on your specific needs

    # 1. Get annotations with mLLMCelltype
    annotations <- annotate_cell_types(
      input = marker_data,
      tissue_name = "human PBMC",
      model = "claude-sonnet-4-6",
      api_key = Sys.getenv("ANTHROPIC_API_KEY")
    )

    # 2. Compare with known marker genes (conceptual)
    # In a real workflow, you would query a database or use a reference dataset
    known_markers <- list(
      "T cells" = c("CD3D", "CD3E", "CD3G"),
      "B cells" = c("CD19", "MS4A1", "CD79A"),
      "Monocytes" = c("CD14", "LYZ", "CSF1R")
    )

    # 3. Validate annotations against known markers
    # This is a simplified example of how you might validate annotations
    validate_annotations <- function(annotations, marker_data, known_markers) {
      validation_results <- list()

      for (i in 1:length(annotations)) {
        cluster_id <- i
        predicted_type <- annotations[i]

        # Get markers for this cluster
        cluster_markers <- marker_data %>%
          filter(cluster == cluster_id) %>%
          arrange(desc(avg_log2FC)) %>%
          pull(gene) %>%
          head(20)

        # Check overlap with known markers for this cell type
        if (predicted_type %in% names(known_markers)) {
          expected_markers <- known_markers[[predicted_type]]
          overlap <- intersect(cluster_markers, expected_markers)

          validation_results[[i]] <- list(
            cluster = cluster_id,
            predicted_type = predicted_type,
            overlap_count = length(overlap),
            overlap_genes = paste(overlap, collapse = ", "),
            confidence = length(overlap) / length(expected_markers)
          )
        } else {
          validation_results[[i]] <- list(
            cluster = cluster_id,
            predicted_type = predicted_type,
            overlap_count = 0,
            overlap_genes = "",
            confidence = 0
          )
        }
      }

      return(validation_results)
    }

    # This is a conceptual example of how you might validate annotations
    # validation_results <- validate_annotations(annotations, marker_data, known_markers)

## Practical Case Studies

### Case Study 1: PBMC Dataset Analysis

This example demonstrates a complete workflow for analyzing a PBMC dataset:

    library(Seurat)
    library(mLLMCelltype)
    library(ggplot2)
    library(dplyr)

    # Load example PBMC data
    # In a real workflow, you would use your own data
    data("pbmc_small")  # Example dataset from Seurat

    # Find marker genes
    pbmc_markers <- FindAllMarkers(pbmc_small,
                                  only.pos = TRUE,
                                  min.pct = 0.25,
                                  logfc.threshold = 0.25)

    # Set up API keys
    api_keys <- list(
      anthropic = Sys.getenv("ANTHROPIC_API_KEY"),
      openai = Sys.getenv("OPENAI_API_KEY"),
      gemini = Sys.getenv("GEMINI_API_KEY")
    )

    # Use consensus annotation
    consensus_results <- interactive_consensus_annotation(
      input = pbmc_markers,
      tissue_name = "human PBMC",
      models = c("claude-sonnet-4-6", "gpt-5.5", "gemini-3.1-pro-preview"),
      api_keys = api_keys,
      controversy_threshold = 0.7,
      entropy_threshold = 1.0,
      consensus_check_model = "claude-sonnet-4-6"
    )

    # Add results to Seurat object
    pbmc_small$cell_type <- plyr::mapvalues(
      x = as.character(Idents(pbmc_small)),
      from = names(consensus_results$final_annotations),
      to = consensus_results$final_annotations
    )

    # Visualize results
    # In a real workflow, you would create a UMAP or t-SNE plot
    # DimPlot(pbmc_small, group.by = "cell_type", label = TRUE) +
    #   ggtitle("PBMC Cell Types")

### Case Study 2: Identifying Rare Cell Types

When working with datasets containing rare cell populations, you can adjust parameters to improve detection:

    # For rare cell types, use these strategies:

    # 1. Increase the number of marker genes considered
    rare_cell_annotation <- annotate_cell_types(
      input = marker_data,  # Your marker gene data
      tissue_name = "human bone marrow",
      model = "claude-sonnet-4-6",
      api_key = Sys.getenv("ANTHROPIC_API_KEY"),
      top_gene_count = 20  # Use more genes for rare cell types
    )

    # 2. Use consensus with lower thresholds to discuss more clusters
    rare_cell_consensus <- interactive_consensus_annotation(
      input = marker_data,  # Your marker gene data
      tissue_name = "human bone marrow",
      models = c("claude-sonnet-4-6", "gpt-5.5", "gemini-3.1-pro-preview"),
      api_keys = api_keys,
      controversy_threshold = 0.4,  # Lower threshold to discuss more clusters
      entropy_threshold = 0.8,  # Lower entropy threshold
      consensus_check_model = "claude-sonnet-4-6"
    )

    # 3. Provide more specific tissue context
    specific_annotation <- annotate_cell_types(
      input = marker_data,  # Your marker gene data
      tissue_name = "human bone marrow with expected rare plasma cells and basophils",
      model = "claude-sonnet-4-6",
      api_key = Sys.getenv("ANTHROPIC_API_KEY")
    )

### Case Study 3: Cross-Species Comparison

mLLMCelltype can be used to compare cell types across different species:

    # Example workflow for cross-species comparison

    # 1. Annotate human and mouse datasets separately
    # (Assuming you have marker data for both species)
    human_annotations <- annotate_cell_types(
      input = human_marker_data,  # Your human marker data
      tissue_name = "human brain cortex",
      model = "claude-sonnet-4-6",
      api_key = Sys.getenv("ANTHROPIC_API_KEY")
    )

    mouse_annotations <- annotate_cell_types(
      input = mouse_marker_data,  # Your mouse marker data
      tissue_name = "mouse brain cortex",
      model = "claude-sonnet-4-6",
      api_key = Sys.getenv("ANTHROPIC_API_KEY")
    )

    # 2. Compare annotations
    # This is a conceptual example - in a real workflow, you would:
    # - Map annotations to Seurat objects
    # - Calculate proportions
    # - Create comparison visualizations
    # - Identify conserved and species-specific cell types

    # Example comparison function (conceptual)
    compare_species_annotations <- function(human_annotations, mouse_annotations) {
      # Get unique cell types from both species
      human_types <- unique(human_annotations)
      mouse_types <- unique(mouse_annotations)

      # Find common cell types
      common_types <- intersect(human_types, mouse_types)

      # Find species-specific cell types
      human_specific <- setdiff(human_types, mouse_types)
      mouse_specific <- setdiff(mouse_types, human_types)

      # Return comparison results
      list(
        common_types = common_types,
        human_specific = human_specific,
        mouse_specific = mouse_specific
      )
    }

    # This is a conceptual example
    # comparison <- compare_species_annotations(human_annotations, mouse_annotations)

## Performance Considerations

### API Cost Management

When using mLLMCelltype, it’s important to consider the costs associated with API calls to different LLM providers:

    # Example of cost-efficient model selection
    # Choose models based on your specific needs and budget

    # For initial exploration or smaller datasets
    # Use more affordable models
    affordable_annotation <- annotate_cell_types(
      input = marker_data,  # Your marker gene data
      tissue_name = "human PBMC",
      model = "claude-haiku-4-5-20251001",  # More affordable model
      api_key = Sys.getenv("ANTHROPIC_API_KEY")
    )

    # For final analysis or challenging datasets
    # Use larger models
    premium_annotation <- annotate_cell_types(
      input = marker_data,  # Your marker gene data
      tissue_name = "human PBMC",
      model = "claude-sonnet-4-6",  # Larger model
      api_key = Sys.getenv("ANTHROPIC_API_KEY")
    )

    # Use OpenRouter for access to free models
    openrouter_annotation <- annotate_cell_types(
      input = marker_data,  # Your marker gene data
      tissue_name = "human PBMC",
      model = "meta-llama/llama-3.3-70b-instruct:free",  # Free model via OpenRouter
      api_key = Sys.getenv("OPENROUTER_API_KEY")
    )

### Optimizing Runtime

To optimize runtime when working with large datasets:

    # 1. Use caching with interactive_consensus_annotation
    consensus_with_cache <- interactive_consensus_annotation(
      input = marker_data,  # Your marker gene data
      tissue_name = "human PBMC",
      models = c("claude-sonnet-4-6", "gpt-5.5"),
      api_keys = api_keys,
      use_cache = TRUE,  # Enable caching
      cache_dir = NULL  # Uses default system cache directory
    )

    # 2. Process clusters in batches
    # This is a conceptual example - implementation would depend on your workflow
    process_in_batches <- function(marker_data, batch_size = 5) {
      # Get unique clusters
      clusters <- unique(marker_data$cluster)

      # Process in batches
      results <- list()
      for (i in seq(1, length(clusters), by = batch_size)) {
        # Get current batch of clusters
        batch_clusters <- clusters[i:min(i + batch_size - 1, length(clusters))]

        # Filter marker data for current batch
        batch_data <- marker_data %>% filter(cluster %in% batch_clusters)

        # Process batch
        batch_results <- annotate_cell_types(
          input = batch_data,
          tissue_name = "human PBMC",
          model = "claude-sonnet-4-6",
          api_key = Sys.getenv("ANTHROPIC_API_KEY")
        )

        # Store results
        results <- c(results, batch_results)
      }

      return(results)
    }

    # 3. Use faster models for initial exploration
    fast_annotation <- annotate_cell_types(
      input = marker_data,  # Your marker gene data
      tissue_name = "human PBMC",
      model = "claude-haiku-4-5-20251001",  # Faster model
      api_key = Sys.getenv("ANTHROPIC_API_KEY")
    )

## Advanced Customization

### Custom Processing Functions

For advanced users, mLLMCelltype allows you to register custom providers and models:

    # Define a custom processing function
    # This function must accept prompt, model, and api_key parameters
    custom_process_fn <- function(prompt, model, api_key) {
      # Custom implementation to process prompts and get responses
      # This is a simplified example
      cat("Processing prompt with custom provider\n")
      cat("Model:", model, "\n")

      # In a real implementation, you would make API calls here
      # For example:
      # response <- httr::POST(
      #   url = "https://api.custom-provider.com/v1/chat/completions",
      #   body = list(prompt = prompt, model = model),
      #   httr::add_headers(Authorization = paste("Bearer", api_key)),
      #   encode = "json"
      # )
      # result <- httr::content(response)$choices[[1]]$text

      # For this example, just return a fixed response
      result <- "T cells"
      return(result)
    }

    # Register the custom provider
    register_custom_provider(
      provider_name = "custom_provider",
      process_fn = custom_process_fn,
      description = "My custom LLM provider"
    )

    # Register a custom model
    register_custom_model(
      model_name = "custom-model",
      provider_name = "custom_provider",
      model_config = list(
        temperature = 0.7,
        max_tokens = 2000
      )
    )

    # Use the custom model
    # custom_results <- annotate_cell_types(
    #   input = marker_data,
    #   tissue_name = "human PBMC",
    #   model = "custom-model",
    #   api_key = "your-custom-api-key"
    # )

### Using the Unified Logging System

mLLMCelltype provides a comprehensive unified logging system with structured output, performance monitoring, and multi-level logging:

    # Configure the global logger (recommended approach)
    configure_logger(level = "INFO", console_output = TRUE, json_format = TRUE)

    # Use simple logging functions
    log_info("Starting analysis of cluster 0", list(
      cluster_id = "0",
      tissue_name = "human PBMC",
      marker_genes = c("CD3D", "CD3E", "CD2", "IL7R", "LTB")
    ))

    # Log API calls with performance tracking
    log_info("API call completed", list(
      provider = "anthropic",
      model = "claude-sonnet-4-6",
      duration_seconds = 2.34,
      success = TRUE
    ))

    # Log warnings and errors
    log_warn("Model response had unusual format", list(
      model = "gpt-5.5",
      response_length = 50
    ))

    log_error("API call failed", list(
      provider = "openai",
      error = "Rate limit exceeded"
    ))

    # Alternatively, create a custom logger instance
    custom_logger <- UnifiedLogger$new(
      base_dir = "custom_logs",
      level = "DEBUG",
      console_output = TRUE,
      json_format = TRUE
    )

    # Use the custom logger
    custom_logger$info("Custom log message", list(analysis_step = "preprocessing"))
    custom_logger$debug("Detailed debugging info", list(variable_state = "initialized"))

    # Get performance summary
    performance <- get_logger()$get_performance_summary()
    print(performance)

### Using the CacheManager

The `CacheManager` class helps optimize performance by caching consensus results:

    # Create a cache manager
    cache_manager <- CacheManager$new(cache_dir = NULL)

    # Generate a cache key
    cache_key <- cache_manager$generate_key(
      input = marker_data,
      models = c("claude-sonnet-4-6", "gpt-5.5"),
      cluster_id = "0"
    )

    # Check if results exist in cache
    if (cache_manager$has_cache(cache_key)) {
      # Load from cache
      cached_results <- cache_manager$load_from_cache(cache_key)
    } else {
      # Process and save to cache
      # results <- process_cluster(...)
      # cache_manager$save_to_cache(cache_key, results)
    }

    # Get cache statistics
    cache_stats <- cache_manager$get_cache_stats()

    # Clear cache (with confirmation)
    # cache_manager$clear_cache(confirm = TRUE)

### Cache Management

mLLMCelltype provides convenient functions for managing cache directories:

    # Check cache location
    mllmcelltype_cache_dir()

    # Use local cache
    mllmcelltype_cache_dir("local")

    # Clear cache
    mllmcelltype_clear_cache()

## Next Steps

Now that you’ve explored the advanced features of mLLMCelltype, you can:

- Contribute to the project: Learn how to contribute to mLLMCelltype
- Review the version history: Explore the development history of the package
- Return to the introduction: Review the basic concepts