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

## ----eval = FALSE-------------------------------------------------------------
#  library(metasnf)
#  
#  # Start by making a data list containing all our dataframes to more easily
#  # identify subjects without missing data
#  full_data_list <- generate_data_list(
#      list(subc_v, "subcortical_volume", "neuroimaging", "continuous"),
#      list(income, "household_income", "demographics", "continuous"),
#      list(pubertal, "pubertal_status", "demographics", "continuous"),
#      list(anxiety, "anxiety", "behaviour", "ordinal"),
#      list(depress, "depressed", "behaviour", "ordinal"),
#      uid = "unique_id"
#  )
#  
#  # Partition into a data and target list (optional)
#  data_list <- full_data_list[1:3]
#  target_list <- full_data_list[4:5]
#  
#  # Build space of settings to cluster over
#  set.seed(42)
#  settings_matrix <- generate_settings_matrix(
#      data_list,
#      nrow = 20,
#      min_k = 20,
#      max_k = 50
#  )
#  
#  # Clustering
#  solutions_matrix <- batch_snf(data_list, settings_matrix)
#  
#  # Calculate p-values between cluster solutions and features
#  extended_solutions_matrix <- extend_solutions(
#      solutions_matrix,
#      data_list = data_list,
#      target_list = target_list,
#      min_pval = 1e-10 # p-values below 1e-10 will be thresholded to 1e-10
#  )

## ----eval = FALSE-------------------------------------------------------------
#  esm_manhattan <- esm_manhattan_plot(
#      extended_solutions_matrix[1:5, ],
#      neg_log_pval_thresh = 5,
#      threshold = 0.05,
#      point_size = 3,
#      jitter_width = 0.1,
#      jitter_height = 0.1,
#      plot_title = "Feature-Solution Associations",
#      text_size = 14,
#      bonferroni_line = TRUE
#  )
#  
#  ggplot2::ggsave(
#      "esm_manhattan.png",
#      esm_manhattan,
#      height = 5,
#      width = 8,
#      dpi = 100
#  )

## ----eval = FALSE-------------------------------------------------------------
#  # Calculate pairwise similarities between cluster solutions
#  solutions_matrix_aris <- calc_aris(solutions_matrix)
#  
#  # Extract hierarchical clustering order of the cluster solutions
#  meta_cluster_order <- get_matrix_order(solutions_matrix_aris)
#  
#  # Create a base heatmap for visual meta clustering
#  ari_hm <- adjusted_rand_index_heatmap(
#      solutions_matrix_aris,
#      order = meta_cluster_order
#  )
#  
#  # Identify meta cluster boundaries
#  # This can also be by trial & error if you do not wish to use the shiny app.
#  shiny_annotator(ari_hm)
#  
#  # Result of meta cluster examination
#  split_vec <- c(2, 5, 12, 17)
#  
#  # Create a base heatmap for visual meta clustering
#  ari_hm <- adjusted_rand_index_heatmap(
#      solutions_matrix_aris,
#      order = meta_cluster_order,
#      split_vector = split_vec
#  )
#  
#  # Extracting representative solutions from each defined meta cluster
#  rep_solutions <- get_representative_solutions(
#      solutions_matrix_aris,
#      split_vector = split_vec,
#      order = meta_cluster_order,
#      extended_solutions_matrix
#  )
#  
#  mc_manhattan <- mc_manhattan_plot(
#      rep_solutions,
#      data_list = data_list,
#      target_list = target_list,
#      point_size = 3,
#      text_size = 12,
#      plot_title = "Feature-Meta Cluster Associations",
#      threshold = 0.05,
#      neg_log_pval_thresh = 5
#  )
#  
#  ggplot2::ggsave(
#      "mc_manhattan_clean.png",
#      mc_manhattan,
#      height = 10,
#      width = 10,
#      dpi = 100
#  )

## ----eval = FALSE-------------------------------------------------------------
#  var_manhattan <- var_manhattan_plot(
#      data_list,
#      key_var = "household_income",
#      plot_title = "Correlation of Features with Household Income",
#      text_size = 16,
#      neg_log_pval_thresh = 3,
#      threshold = 0.05
#  )
#  ggplot2::ggsave(
#      "var_manhattan.png",
#      var_manhattan,
#      height = 7,
#      width = 12,
#      dpi = 100
#  )