inst/doc/manhattan_plots.R

In metasnf: Meta Clustering with Similarity Network Fusion

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

## ----echo = FALSE-------------------------------------------------------------
options(crayon.enabled = FALSE, cli.num_colors = 0)

## ----eval = FALSE-------------------------------------------------------------
# library(metasnf)
# 
# # Start by making a data list containing all our data frames to more easily
# # identify observations without missing data
# full_dl <- 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)
# dl <- full_dl[1:3]
# target_dl <- full_dl[4:5]
# 
# # Build space of settings to cluster over
# set.seed(42)
# sc <- snf_config(
#     dl = dl,
#     n_solutions = 20,
#     min_k = 20,
#     max_k = 50
# )
# 
# # Clustering
# sol_df <- batch_snf(dl, sc)
# 
# # Calculate p-values between cluster solutions and features
# ext_sol_df <- extend_solutions(
#     sol_df,
#     dl = dl,
#     target = target_dl,
#     min_pval = 1e-10 # p-values below 1e-10 will be thresholded to 1e-10
# )

## ----eval = FALSE-------------------------------------------------------------
# esm_manhattan <- esm_manhattan_plot(
#     ext_sol_df[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
# )

## ----eval = FALSE, echo = FALSE-----------------------------------------------
# ggplot2::ggsave(
#     "vignettes/esm_manhattan.png",
#     esm_manhattan,
#     height = 5,
#     width = 8,
#     dpi = 100
# )

## ----eval = FALSE-------------------------------------------------------------
# # Calculate pairwise similarities between cluster solutions
# sol_aris <- calc_aris(sol_df)
# 
# # Extract hierarchical clustering order of the cluster solutions
# meta_cluster_order <- get_matrix_order(sol_aris)
# 
# # Create a base heatmap for visual meta clustering
# ari_hm <- meta_cluster_heatmap(
#     sol_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, 16)
# 
# # Create a base heatmap for visual meta clustering
# ari_hm <- meta_cluster_heatmap(
#     sol_aris,
#     order = meta_cluster_order,
#     split_vector = split_vec
# )
# 
# ari_hm
# 
# # Label meta clusters based on the split vector
# mc_sol_df <- label_meta_clusters(
#     sol_df = ext_sol_df,
#     split_vector = split_vec,
#     order = meta_cluster_order
# )
# 
# # Extracting representative solutions from each defined meta cluster
# rep_solutions <- get_representative_solutions(sol_aris, mc_sol_df)
# 
# mc_manhattan <- mc_manhattan_plot(
#     rep_solutions,
#     dl = dl,
#     target_dl = target_dl,
#     point_size = 3,
#     text_size = 12,
#     plot_title = "Feature-Meta Cluster Associations",
#     threshold = 0.05,
#     neg_log_pval_thresh = 5
# )

## ----eval = FALSE, echo = FALSE-----------------------------------------------
# ggplot2::ggsave(
#     "vignettes/mc_manhattan_clean.png",
#     mc_manhattan,
#     height = 10,
#     width = 10,
#     dpi = 100
# )

## ----eval = FALSE-------------------------------------------------------------
# var_manhattan <- var_manhattan_plot(
#     dl,
#     key_var = "household_income",
#     plot_title = "Correlation of Features with Household Income",
#     text_size = 16,
#     neg_log_pval_thresh = 3,
#     threshold = 0.05
# )

## ----eval = FALSE, echo = FALSE-----------------------------------------------
# ggplot2::ggsave(
#     "vignettes/var_manhattan.png",
#     var_manhattan,
#     height = 5,
#     width = 9,
#     dpi = 75
# )