inst/doc/consensus-principles.R

In mLLMCelltype: Cell Type Annotation Using Large Language Models

## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(
  echo = TRUE,
  message = FALSE,
  warning = FALSE,
  eval = FALSE
)

## -----------------------------------------------------------------------------
# # Conceptual representation of the initial annotation process
# initial_results <- list()
# for (model in models) {
#   initial_results[[model]] <- annotate_cell_types(
#     input = marker_data,
#     tissue_name = tissue_name,
#     model = model,
#     api_key = api_keys[[get_provider(model)]]
#   )
# }

## -----------------------------------------------------------------------------
# # Conceptual representation of controversial cluster identification
# controversial_clusters <- identify_controversial_clusters(
#   initial_results,
#   threshold = discussion_threshold
# )

## -----------------------------------------------------------------------------
# # Conceptual representation of the discussion process
# discussion_results <- facilitate_cluster_discussion(
#   controversial_clusters,
#   initial_results,
#   marker_data,
#   tissue_name,
#   discussion_model,
#   api_key
# )

## -----------------------------------------------------------------------------
# # Conceptual representation of consensus formation
# final_annotations <- combine_results(
#   initial_results,
#   discussion_results,
#   controversial_clusters
# )

## -----------------------------------------------------------------------------
# # Conceptual calculation of consensus proportion
# consensus_proportion <- sapply(clusters, function(cluster) {
#   annotations <- sapply(models, function(model) initial_results[[model]][cluster])
#   most_common <- names(which.max(table(annotations)))
#   sum(annotations == most_common) / length(annotations)
# })

## -----------------------------------------------------------------------------
# # Conceptual calculation of Shannon entropy
# shannon_entropy <- sapply(clusters, function(cluster) {
#   annotations <- sapply(models, function(model) initial_results[[model]][cluster])
#   p <- table(annotations) / length(annotations)
#   -sum(p * log2(p))
# })