Last updated: 2021-02-10

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Knit directory: mage_2020_marker-gene-benchmarking/

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Rmd 1ad9d6d Jeffrey Pullin 2021-02-10 Add workflowr website
Rmd ffe660c Jeffrey Pullin 2021-02-09 Update 9/2/2021

library(tibble)
library(dplyr)
library(ggplot2)
library(tidyr)
library(SingleCellExperiment)
source(here::here("code", "top-genes.R"))


# Averages over groups.
calculate_fdr_tpr <- function(found, true, clusters = NULL) {
  stopifnot(is.list(found) && is.list(true))
  stopifnot(length(found) == length(true))
  
  # If groups is not specified, calculate for and average over all groups. 
  if (is.null(clusters)) {
    clusters <- 1:length(found)
  }
  
  # TODO: These defintions are not quite right because we are restricting the 
  # outcome. 
  
  fdr <- numeric(length(clusters)) 
  tpr <- numeric(length(clusters))
  
  for (i in clusters) {
    n <- length(found[[i]])
    n_tpr <- length(intersect(found[[i]], true[[i]])) 
    tpr[[i]] <- n_tpr / n
    fdr[[i]] <- (n - n_tpr) / n
  }
  
  list(tpr = mean(tpr), fdr = mean(fdr))
}

Aim

To investigate the FDR and TPR performance of the different methods

Data loading

results_folder <- here::here("results")
sim_data_folder <- here::here("data", "sim_data")
file_names <- list.files(results_folder, full.names = TRUE)

# For now, all simulations are based on the same set of true DE genes, so we 
# can just get it once and then use throughout. 
sim_1_data <- readRDS(here::here("data", "sim_data", "sim_1.rds"))
de_gene_inds <- sim_1_data@metadata$de_inds
# Convert the names to be compatible with those returned by the methods. 
de_gene_inds <- lapply(de_gene_inds, function(x) paste0("Gene", x))

metrics_data <- tibble(path = file_names) %>% 
  expand_grid(n_genes = c(20, 100, 1000)) %>% 
  rowwise() %>% 
  mutate(
    output = list(readRDS(path)), 
    top_genes = list(get_top_genes(output, n_genes)),
    metrics = list(calculate_fdr_tpr(top_genes, de_gene_inds))
  ) %>% 
  mutate(
    fdr = metrics$fdr, 
    tpr = metrics$tpr,
  )

# TODO: Yuck: Refactor! Maybe don't pass from file.
plot_data <- metrics_data %>% 
  select(path, n_genes, fdr, tpr) %>% 
  mutate(file_name = basename(path), .keep = "unused") %>% 
  mutate(file_name = substr(file_name, 1,  nchar(file_name) - 4)) %>% 
  separate(file_name, sep = "-", into = c("sim", "method")) %>% 
  separate(method, sep = "_", into = c("method_name", "pars"), extra = "merge") %>% 
  separate(sim, sep = "_", into = c("sim", "sim_id")) %>% 
  select(-c(sim))
plot_data %>% 
  ggplot(aes(x = pars, y = fdr, colour = factor(n_genes))) + 
  geom_point() + 
  labs(x = "Parameters", y = "FDR") + 
  theme_bw() + 
  theme(axis.text.x = element_text(angle = 45, hjust=1))

At the moment, this is the inverse of the above plot…

plot_data %>% 
  ggplot(aes(x = pars, y = tpr, colour = factor(n_genes))) + 
  geom_point() + 
  labs(x = "Parameters", y = "TPR") + 
  theme_bw() + 
  theme(axis.text.x = element_text(angle = 45, hjust=1))


devtools::session_info()
─ Session info ───────────────────────────────────────────────────────────────
 setting  value                       
 version  R version 4.0.3 (2020-10-10)
 os       Red Hat Enterprise Linux    
 system   x86_64, linux-gnu           
 ui       X11                         
 language (EN)                        
 collate  en_AU.UTF-8                 
 ctype    en_AU.UTF-8                 
 tz       Australia/Melbourne         
 date     2021-02-10                  

─ Packages ───────────────────────────────────────────────────────────────────
 package              * version  date       lib source        
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[1] /mnt/mcfiles/jpullin/R/x86_64-pc-linux-gnu-library/4.0
[2] /opt/R/4.0.3/lib/R/library