p-value analysis

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Last updated: 2024-01-01

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Knit directory: mage_2020_marker-gene-benchmarking/

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File	Version	Author	Date	Message
Rmd	6eb1355	Jeffrey Pullin	2023-12-29	More changes for revisions
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Rmd	0c2eafc	Jeffrey Pullin	2022-09-09	Update website
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Rmd	048156f	Jeffrey Pullin	2022-05-02	Tweak analysis outputs
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html	8b989e1	Jeffrey Pullin	2022-05-02	Build site.
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Rmd	d1aca16	Jeffrey Pullin	2022-02-09	Refresh website
Rmd	aca9ad2	Jeffrey Pullin	2021-11-29	Various changes made in the last days before thesis submission
Rmd	d1445fc	Jeffrey Pullin	2021-09-21	Add p-value analysis

---

library(SingleCellExperiment)
library(dplyr)
library(ggplot2)
library(tibble)
library(purrr)
library(tidyr)
library(patchwork)
library(forcats)
library(ggrepel)

source(here::here("code", "analysis-utils.R"))
source(here::here("code", "plot-utils.R"))

Load data

real_data_cluster_data <- load_real_data_results("all")
no_pvalue_methods <- c("logfc", "rankcorr", "random", "lm", "cepo", "nsforest", "cosg")

p-values boxplot, all methods, Endothelial dataset

real_data_cluster_data %>% 
  filter(data_id == "endothelial") %>% 
  filter(!(method %in% no_pvalue_methods)) %>% 
  # Remove scoreMarkers.
  filter(is.na(metric)) %>% 
  rowwise() %>% 
  mutate(mgs = list(get_top_sel_mgs(mgs, n = 40))) %>% 
  mutate(p_value_adj = list(mgs$p_value_adj)) %>% 
  ungroup() %>% 
  unnest(cols = p_value_adj) %>% 
  filter(p_value_adj > 0) %>%
  mutate(neg_log10_p_value_adj = -log(p_value_adj, base = 10)) %>% 
  mutate(
    plot_pars = pars_lookup[pars],
    plot_method = method_lookup[method]
  ) %>% 
  mutate(plot_pars = fct_reorder(factor(plot_pars), neg_log10_p_value_adj)) %>% 
  ggplot(aes(x = plot_pars, y = neg_log10_p_value_adj, fill = plot_method)) + 
  geom_boxplot() + 
  coord_flip() + 
  de_package_fill + 
  scale_y_continuous(
     breaks = c(10, 50, 100, 150, 200, 250, 300),
     labels = c(10^-10, 10^-50, 10^-100, 10^-150, 10^-200, 10^-250, 10^-300),
  ) + 
  labs(
    x = "Method", 
    y = "Adjusted p-value",
    title = "Endothelial dataset"
  ) + 
  theme_bw() 

p-values boxplot, all methods, Zhao dataset

zhao_pvalue_boxplots <- real_data_cluster_data %>% 
  filter(data_id == "zhao") %>% 
  filter(pars != "seurat_roc") %>% 
  filter(!(method %in% no_pvalue_methods)) %>% 
  # Remove scoreMarkers.
  filter(is.na(metric)) %>% 
  rowwise() %>% 
  mutate(mgs = list(get_top_sel_mgs(mgs, n = 40))) %>% 
  mutate(p_value_adj = list(mgs$p_value_adj)) %>% 
  ungroup() %>% 
  unnest(cols = p_value_adj) %>% 
  filter(p_value_adj > 0) %>%
  mutate(neg_log10_p_value_adj = -log(p_value_adj, base = 10)) %>% 
  mutate(
    plot_pars = pars_lookup[pars],
    plot_method = method_lookup[method]
  ) %>% 
  mutate(plot_pars = fct_reorder(factor(plot_pars), neg_log10_p_value_adj)) %>% 
  ggplot(aes(x = plot_pars, y = neg_log10_p_value_adj, fill = plot_method)) + 
  geom_boxplot() + 
  coord_flip() + 
  de_package_fill + 
  scale_y_continuous(
     breaks = c(10, 50, 100, 150, 200, 250, 300),
     labels = c(10^-10, 10^-50, 10^-100, 10^-150, 10^-200, 10^-250, 10^-300),
  ) + 
  labs(
    x = "Method", 
    y = "Adjusted p-value",
    title = "Zhao dataset",
    colour = "Method"
  ) + 
  theme_bw() 
zhao_pvalue_boxplots

p-values boxplot, all methods, pbmc3k dataset

pbmc3k_pvalue_boxplots <- real_data_cluster_data %>% 
  filter(data_id == "pbmc3k") %>% 
  filter(!(method %in% no_pvalue_methods)) %>% 
  # Remove scoreMarkers.
  filter(is.na(metric)) %>% 
  rowwise() %>% 
  mutate(mgs = list(get_top_sel_mgs(mgs, n = 40))) %>% 
  mutate(p_value_adj = list(mgs$p_value_adj)) %>% 
  ungroup() %>% 
  unnest(cols = p_value_adj) %>% 
  filter(p_value_adj > 0) %>%
  mutate(neg_log10_p_value_adj = -log(p_value_adj, base = 10)) %>% 
  mutate(
    plot_pars = pars_lookup[pars],
    plot_method = method_lookup[method]
  ) %>%  
  mutate(plot_pars = fct_reorder(factor(plot_pars), neg_log10_p_value_adj)) %>% 
  ggplot(aes(x = plot_pars, y = neg_log10_p_value_adj, fill = plot_method)) + 
  geom_boxplot() + 
  de_package_fill + 
  coord_flip() + 
  scale_y_continuous(
     breaks = c(10, 50, 100, 150, 200, 250, 300),
     labels = c(10^-10, 10^-50, 10^-100, 10^-150, 10^-200, 10^-250, 10^-300),
  ) + 
  labs(
    x = "Method", 
    y = "Adjusted p-value",
    title = "pbmc3k dataset", 
    fill = "Package"
  ) + 
  theme_bw() 
pbmc3k_pvalue_boxplots

combined_pvalue_boxplot <- pbmc3k_pvalue_boxplots + zhao_pvalue_boxplots + 
  plot_layout(guides = "collect") + 
  plot_annotation(tag_levels = "a") &
  theme(plot.tag = element_text(size = 18))

combined_pvalue_boxplot

ggsave(
  here::here("figures", "final", "combined-pvalue-boxplot.pdf"),
  combined_pvalue_boxplot,
  width = 12,
  height = 8,
  units = "in"
)

Number of zero p-values, all methods, Endothelial dataset

real_data_cluster_data %>% 
  filter(!(method %in% no_pvalue_methods)) %>% 
  filter(pars != "seurat_roc") %>% 
  # Remove scoreMarkers.
  filter(is.na(metric)) %>% 
  rowwise() %>% 
  mutate(n_zero = sum(mgs$p_value == 0)) %>% 
  ungroup() %>% 
  filter(data_id == "endothelial") %>% 
  select(pars, cluster, n_zero) %>% 
  mutate(plot_pars = pars_lookup[pars]) %>% 
  mutate(plot_pars = fct_reorder(factor(plot_pars), n_zero)) %>% 
  ggplot(aes(x = cluster, y = plot_pars)) +
  geom_tile(aes(fill = n_zero), colour = "black") + 
  scale_fill_distiller(palette = "Blues", direction = 1) + 
  theme_bw() + 
  labs(
    x = "Cell type", 
    y = "", 
    fill = "Number of\n0 p-values",
    title = "Endothelial dataset"
  ) + 
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.border = element_blank(),
    axis.text.x = element_text(angle = 45, vjust = 0.9, hjust = 0.9)
  ) 

Number of zero p-values, all methods, Zhao dataset

real_data_cluster_data %>% 
  filter(!(method %in% no_pvalue_methods)) %>% 
  filter(pars != "seurat_roc") %>% 
  # Remove scoreMarkers.
  filter(is.na(metric)) %>% 
  rowwise() %>% 
  mutate(n_zero = sum(mgs$p_value == 0)) %>% 
  ungroup() %>% 
  filter(data_id == "zhao") %>% 
  select(pars, cluster, n_zero) %>% 
  mutate(plot_pars = pars_lookup[pars]) %>% 
  mutate(plot_pars = fct_reorder(factor(plot_pars), n_zero)) %>% 
  ggplot(aes(x = cluster, y = plot_pars)) +
  geom_tile(aes(fill = n_zero), colour = "black") + 
  scale_fill_distiller(palette = "RdYlBu") + 
  theme_bw() + 
  labs(
    x = "Cell type", 
    y = "", 
    fill = "Number of\n0 p-values",
    title = "Zhao dataset"
  ) + 
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.border = element_blank(),
    axis.text.x = element_text(angle = 45, vjust = 0.9, hjust = 0.9)
  ) 

Number of zero p-values, Seurat Wilcoxon rank sum test, all datasets

real_data_cluster_data %>% 
  filter(pars == "seurat_wilcox") %>% 
  rowwise() %>% 
  mutate(n_zero = sum(mgs$p_value == 0)) %>% 
  ungroup() %>% 
  mutate(plot_data_id = dataset_lookup[data_id]) %>% 
  mutate(plot_data_id = fct_reorder(factor(plot_data_id), n_zero)) %>% 
  ggplot(aes(x = plot_data_id, y = n_zero)) + 
  geom_boxplot() + 
  geom_jitter(size = 2, width = 0.1) + 
  coord_flip() + 
  labs(
    x = "Dataset", 
    y = "Number of zero p-values"
  ) + 
  theme_bw()

Number of zero p-values, Scanpy t test, all datasets

real_data_cluster_data %>% 
  filter(pars == "scanpy_t_rankby_raw") %>% 
  rowwise() %>% 
  mutate(n_zero = sum(mgs$p_value == 0)) %>% 
  ungroup() %>% 
  mutate(plot_data_id = dataset_lookup[data_id]) %>% 
  mutate(plot_data_id = fct_reorder(factor(plot_data_id), n_zero)) %>% 
  ggplot(aes(x = plot_data_id, y = n_zero)) + 
  geom_boxplot() + 
  geom_jitter(size = 2, width = 0.1) + 
  coord_flip() + 
  labs(
    x = "Dataset", 
    y = "Number of zero p-values"
  ) + 
  theme_bw()

Number of zero p-values, all p-value methods, all datasets

all_n_zero_pvalues <- real_data_cluster_data %>% 
  filter(!(method %in% no_pvalue_methods)) %>% 
  filter(pars != "seurat_roc") %>% 
  filter(is.na(metric)) %>% 
  rowwise() %>% 
  mutate(n_zero = sum(mgs$p_value == 0)) %>% 
  ungroup() %>% 
  group_by(data_id, pars) %>% 
  summarise(n_zero = median(n_zero), .groups = "drop") %>% 
  mutate(plot_data_id = dataset_lookup[data_id]) %>% 
  mutate(plot_data_id = fct_reorder(factor(plot_data_id), n_zero)) %>% 
  ggplot(aes(x = plot_data_id, y = n_zero)) + 
  geom_boxplot(outlier.shape = NA) + 
  geom_jitter(size = 3, width = 0.1, alpha = 0.5, colour = "forestgreen") + 
  geom_text_repel(
    aes(label = if_else(n_zero > 300, pars_lookup[pars], "")),
    hjust = -0.1, colour = "black", size = 4
  ) + 
  coord_flip() + 
  labs(
    x = "Dataset", 
    y = "Number of zero p-values"
  ) + 
  theme_bw()

all_n_zero_pvalues

p_value_plot <- ((pbmc3k_pvalue_boxplots + zhao_pvalue_boxplots) / all_n_zero_pvalues) + 
  plot_layout(guides = "collect") +
  plot_annotation(tag_levels = "a") &
  theme(plot.tag = element_text(size = 18))

p_value_plot

ggsave(
  here::here("figures", "final", "p-value-plot.pdf"),
  p_value_plot,
  width = 12,
  height = 10,
  units = "in"
)

Session information

devtools::session_info()

─ Session info  ──────────────────────────────────────────────────────────────
 hash: flag: Libya, alien monster, kick scooter

 setting  value
 version  R version 4.1.2 (2021-11-01)
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 language (EN)
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 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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