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File Version Author Date Message
Rmd bc98f8b Jeffrey Pullin 2023-11-20 GB resubmission July 2023
Rmd 9487c1e Jeffrey Pullin 2023-06-17 Add draft of dataset characteristic section analysis

library(SingleCellExperiment)
library(dplyr)
library(ggplot2)
library(tidyr)
library(class)
library(scater)
library(forcats)
library(purrr)

source(here::here("code", "analysis-utils.R"))
source(here::here("code", "plot-utils.R"))
pred_perf_data <- retrieve_real_data_parameters() %>% 
  select(-c(fit_method, covariate, rankby, lambda, test_use, rankby_abs, func, 
         test.type, pval.type, metric, test.use)) %>% 
  expand_grid(classifier = c("svm", "knn")) %>% 
  rowwise() %>% 
  mutate(
    pred_perf_filename = paste0("pred_perf-", data_id, "-", method_name, "-", 
                                classifier, ".rds"), 
    pred_perf_path = here::here("results", "pred_perf", pred_perf_filename), 
    pred_perf = list(readRDS(pred_perf_path))
  ) %>% 
  select(-data_id) %>% 
  unnest(pred_perf)

Cell-type predictive difficulty

cell_type_pred_difficulty_plot <- pred_perf_data %>% 
  filter(classifier == "knn") %>% 
  select(method, data_id, f1_score) %>% 
  unnest_wider(col = f1_score, strict = TRUE) %>% 
  pivot_longer(
    cols = -c(method, data_id), 
    names_to = "cell_type", 
    values_to = "f1_score"
  ) %>% 
  mutate(plot_data_id = dataset_lookup[data_id]) %>% 
  mutate(
    cell_type = case_when(
      data_id == "endothelial" ~ endothelial_clusters_lookup[cell_type],
      data_id == "astrocyte" ~ astrocyte_clusters_lookup[cell_type],
      TRUE ~ cell_type
    )
  ) %>% 
  filter(!is.na(f1_score)) %>% 
  ggplot(aes(x = cell_type, y = f1_score)) +
  geom_boxplot() + 
  facet_wrap(~ plot_data_id, scales = "free_y") + 
  coord_flip() + 
  theme_bw() + 
  labs(
    x = "Cell type", 
    y = "F1 score"
  )
cell_type_pred_difficulty_plot

ggsave(
  here::here("figures", "final", "cell-type-pred-perf.pdf"),
  cell_type_pred_difficulty_plot,
  width = 12,
  height = 12,
  units = "in"
)
n_cells_data <- lapply(
  list.files(here::here("data", "real_data"), full.names = TRUE), 
  function(x) {
   data <- readRDS(x)
   out <- tibble(
     data_id = tools::file_path_sans_ext(basename(x)),
     cell_type = unique(data$label),
     n = as.vector(table(data$label))
   )
   rm(data)
   out
  }
) %>% 
  bind_rows(!!!.)
n_cells_pred_perf_plot <- pred_perf_data %>% 
  filter(classifier == "knn") %>% 
  select(pars, data_id, f1_score) %>% 
  unnest_wider(col = f1_score, strict = TRUE) %>% 
  pivot_longer(
    cols = -c(pars, data_id), 
    names_to = "cell_type", 
    values_to = "f1_score"
  ) %>% 
  filter(!is.na(f1_score)) %>% 
  left_join(select(n_cells_data, c(cell_type, n)), by = "cell_type") %>% 
  mutate(log_n = log10(n)) %>% 
  mutate(log_n_binned = cut(log_n, breaks = c(0.4, 2, 3, 4.5), 
                            labels = c("10-100", "100-1,000", "1,000-10,000"))) %>% 
  group_by(log_n_binned, pars) %>% 
  summarise(f1_score = mean(f1_score), .groups = "drop") %>% 
  mutate(plot_pars = pars_lookup[pars]) %>% 
  mutate(plot_pars = fct_reorder(factor(plot_pars), f1_score, .fun = mean)) %>% 
  ggplot(aes(x = log_n_binned, y = plot_pars)) +
  geom_tile(aes(fill = f1_score), colour = "black") + 
  scale_fill_distiller(palette = "RdYlBu") + 
  theme_bw() + 
  labs(
    x = "Number of cells", 
    y = "Method", 
    fill = "Median\nF1 score"
  ) + 
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)
  )
n_cells_pred_perf_plot

ggsave(
  here::here("figures", "final", "n-cells-pred-perf.pdf"),
  n_cells_pred_perf_plot,
  width = 12,
  height = 12,
  units = "in"
)
n_clusters_pred_perf_plot <- pred_perf_data %>% 
  filter(classifier == "knn") %>% 
  select(pars, data_id, f1_score) %>% 
  unnest_wider(col = f1_score, strict = TRUE) %>% 
  pivot_longer(
    cols = -c(pars, data_id), 
    names_to = "cell_type", 
    values_to = "f1_score"
  ) %>% 
  filter(!is.na(f1_score)) %>% 
  group_by(data_id) %>% 
  mutate(n_clus = n_distinct(cell_type)) %>% 
  ungroup() %>% 
  group_by(pars, n_clus) %>% 
  summarise(f1_score = mean(f1_score), .groups = "drop") %>% 
  mutate(plot_pars = pars_lookup[pars]) %>% 
  mutate(plot_pars = fct_reorder(factor(plot_pars), f1_score, .fun = mean)) %>% 
  ggplot(aes(x = n_clus, y = plot_pars)) +
  geom_tile(aes(fill = f1_score), colour = "black") + 
  scale_fill_distiller(palette = "RdYlBu") + 
  theme_bw() + 
  labs(
    x = "Number of clusters", 
    y = "Method", 
    fill = "Mean\nF1 score"
  ) + 
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)
  )
n_clusters_pred_perf_plot

ggsave(
  here::here("figures", "final", "n-clusters-pred-perf.pdf"),
  n_clusters_pred_perf_plot,
  width = 12,
  height = 12,
  units = "in"
)
metrics_data_num_clusters <- retrive_simulation_parameters() %>% 
  filter(sim_label == "num_clusters") %>% 
  rowwise() %>% 
  mutate(
    mgs_raw = list(readRDS(full_filename)$result), 
    mgs = list(split(mgs_raw, mgs_raw$cluster))
  ) %>% 
  ungroup() %>% 
  unnest_longer(
    col = mgs, 
    values_to = "mgs", 
    indices_to = "cluster"
  ) %>% 
  select(-mgs_raw) %>% 
  mutate(umg_path = here::here(
    "data", "sim_mgs", paste0("mg-", sim_name, "-", data_id, ".rds")) 
  ) %>% 
  rowwise() %>% 
  mutate(true_mgs = list(readRDS(umg_path))) %>% 
  mutate(cluster_2 = paste0("group_", substr(cluster, 6, 7))) %>% 
  mutate(true_mgs = list(true_mgs[[cluster_2]])) %>% 
  dplyr::rename(sel_mgs = mgs) %>% 
  ungroup()
plot_data <- metrics_data_num_clusters %>% 
  mutate(n_true = 20, n_sel = 20, direction = "up") %>% 
  rowwise() %>% 
  filter(!is.null(sel_mgs)) %>% 
  mutate(
    true_mgs = list(get_top_true_mgs(
      true_mgs, 
      n = n_true, 
      direction = direction,
      sort_by_score = "mean_score")
    ),
    sel_mgs = list(get_top_sel_mgs(
      sel_mgs, 
      n = n_sel, 
      direction = direction)
    ), 
    recall = calculate_recall(sel_mgs$gene, true_mgs$gene),
    precision = calculate_precision(sel_mgs$gene, true_mgs$gene),
    f1_score = (2 * recall * precision) / (recall + precision) 
  ) %>% 
  mutate(f1_score = if_else(recall == 0 & precision == 0, 0, f1_score)) %>%  
  ungroup() %>% 
  group_by(pars, n_clus, method) %>% 
  summarise(f1_score = median(f1_score), .groups = "drop") %>% 
  mutate(
    plot_method = method_lookup[method], 
    plot_pars = pars_lookup[pars],
  ) %>% 
  mutate(plot_pars = fct_reorder(factor(plot_pars), f1_score))
  
num_clusters_sim_f1_score <- ggplot(plot_data, aes(x = n_clus, y = plot_pars)) +
  geom_tile(aes(fill = f1_score), colour = "black") + 
  scale_fill_distiller(palette = "RdYlBu", limits = c(0, 1)) + 
  theme_bw() + 
  labs(
    title = "F1 score by number of clusters",
    x = "Number of clusters", 
    y = "Method", 
    fill = "F1 score",
  ) + 
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)
  )

num_clusters_sim_f1_score

ggsave(
  here::here("figures", "final", "n-clusters-sim-f1-score.pdf"),
  num_clusters_sim_f1_score,
  width = 12,
  height = 12,
  units = "in"
)
blood_results <- retrieve_real_data_parameters() %>% 
  select(-c(fit_method, covariate, rankby, lambda, test_use, rankby_abs, func, 
         test.type, pval.type, metric, test.use)) %>% 
  filter(data_id %in% c("ren", "dominguez_conde", "stephenson", "yoshida")) %>% 
  rowwise() %>% 
  mutate(
    mgs_raw = list(readRDS(full_filename)$result), 
    mgs = list(split(mgs_raw, mgs_raw$cluster))
  ) %>% 
  ungroup() %>% 
  unnest_longer(
    col = mgs, 
    values_to = "mgs", 
    indices_to = "cluster"
  ) %>% 
  select(-mgs_raw)
concordance_across_datasets_plot <- blood_results %>% 
  add_count(pars, cluster) %>% 
  filter(n == 4) %>% 
  rowwise() %>% 
  mutate(sel_mgs = 
      list(get_top_sel_mgs(
        mgs, 
        n = 10
      )$gene)
  ) %>% 
  ungroup() %>% 
  filter(pars != "cepo") %>% 
  group_by(cluster, pars) %>% 
  summarise(
    all_genes = list(unlist(sel_mgs)), 
    in_all = list(base::Reduce(intersect, sel_mgs)), 
    .groups = "drop"
  ) %>% 
  rowwise() %>% 
  mutate(prop = length(in_all) / length(unique(all_genes))) %>% 
  ungroup() %>% 
  mutate(plot_pars = pars_lookup[pars]) %>% 
  mutate(plot_pars = fct_reorder(factor(plot_pars), prop, .fun = median)) %>% 
  ggplot(aes(x = cluster, y = plot_pars)) +
  geom_tile(aes(fill = prop), colour = "black") + 
  scale_fill_distiller(palette = "RdYlBu") + 
  theme_bw() + 
  labs(
    x = "Cluster", 
    y = "", 
    fill = "Proportion\nsame\nacross\ndatasets",
  ) + 
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)
  )
concordance_across_datasets_plot

ggsave(
  here::here("figures", "final", "blood-dataset-concordance.pdf"),
  concordance_across_datasets_plot,
  width = 12,
  height = 12,
  units = "in"
)

devtools::session_info()
─ Session info  ──────────────────────────────────────────────────────────────
 hash: weary cat, man: curly hair, hammer and wrench

 setting  value
 version  R version 4.1.2 (2021-11-01)
 os       Red Hat Enterprise Linux 9.2 (Plow)
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  en_AU.UTF-8
 ctype    en_AU.UTF-8
 tz       Australia/Melbourne
 date     2024-01-01
 pandoc   2.18 @ /apps/easybuild-2022/easybuild/software/MPI/GCC/11.3.0/OpenMPI/4.1.4/RStudio-Server/2022.07.2+576-Java-11-R-4.1.2/bin/pandoc/ (via rmarkdown)

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 workflowr              1.7.0    2021-12-21 [1] CRAN (R 4.1.0)
 xfun                   0.31     2022-05-10 [1] CRAN (R 4.1.0)
 XVector                0.34.0   2021-10-26 [1] Bioconductor
 yaml                   2.3.5    2022-02-21 [1] CRAN (R 4.1.0)
 zlibbioc               1.40.0   2021-10-26 [1] Bioconductor

 [1] /home/jpullin/R/x86_64-pc-linux-gnu-library/4.1
 [2] /apps/easybuild-2022/easybuild/software/MPI/GCC/11.3.0/OpenMPI/4.1.4/R/4.1.2/lib64/R/library

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