Last updated: 2022-02-09

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Rmd aca9ad2 Jeffrey Pullin 2021-11-29 Various changes made in the last days before thesis submission
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Rmd 16c471e Jeffrey Pullin 2021-09-22 Polish time plots
Rmd e552e95 Jeffrey Pullin 2021-09-21 Extend time analysis
Rmd 17f2a0f Jeffrey Pullin 2021-08-07 Add new plots and analysis for lab meeting 5/8/2021
Rmd ef9f1c7 Jeffrey Pullin 2021-08-02 Add code to plot time of methods on real data
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Rmd 3a4cb2e Jeffrey Pullin 2021-03-16 Update 16/3/2021
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Rmd ffe660c Jeffrey Pullin 2021-02-09 Update 9/2/2021

library(tibble)
library(dplyr)
library(ggplot2)
library(tidyr)
library(purrr)
library(forcats)
library(khroma)
library(pals)
library(patchwork)

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

Aim

Measure the time the different methods take to run.

Data loading

time_sim_data <- retrive_simulation_parameters() %>% 
  rowwise() %>% 
  mutate(time = readRDS(full_filename)$time) %>%
  ungroup()
time_sim_data %>% 
  filter(sim_label == "num_cells") %>%
  # Remove methods we implemented.
  filter(!(method %in% c("lm", "difference", "random"))) %>% 
  group_by(pars, method, batchCells) %>% 
  summarise(time = mean(time), .groups = "drop") %>% 
  mutate(method = method_lookup[method]) %>% 
  ggplot(aes(x = batchCells, y = time, colour = method)) +
  geom_point() + 
  scale_x_continuous(
    breaks = c(1000, 5000, 10000, 30000, 50000)
  ) + 
  scale_y_log10(
    breaks = c(1, 10, 60, 600, 3600, 10800),
    labels = c("1s", "10s", "1m", "10min", "1hr", "3hr")
  )  + 
  labs(
    title = "Runtime vs total number of cells",
    x = "Number of cells", 
    y = "Time (s)", 
    colour = "Method"
  ) + 
  theme_bw()

time_sim_data %>% 
  filter(sim_label == "num_cells") %>%
  filter(method == "seurat") %>% 
  group_by(pars, test.use, batchCells) %>% 
  summarise(time = mean(time), .groups = "drop") %>% 
  mutate(test.use = test.use_lookup[test.use]) %>% 
  ggplot(aes(x = batchCells, y = time, colour = test.use)) +
  geom_jitter(width = 200) + 
  scale_x_continuous(
    breaks = c(1000, 5000, 10000, 30000, 50000)
  ) + 
  scale_y_log10(
    breaks = c(1, 10, 60, 600, 3600, 10800),
    labels = c("1s", "10s", "1m", "10min", "1hr", "3hr")
  )  + 
  labs(
    title = "Runtime vs total number of cells",
    x = "Number of cells", 
    y = "Time (s)", 
    colour = "Method"
  ) + 
  theme_bw()

Linear time scale

# time_sim_data %>% 
#   rowwise() %>% 
#   mutate(num_clusters = if_else(is.na(n_clus), length(group.prob), n_clus)) %>% 
#   filter(sim_label == "num_clusters") %>%
#   ggplot(aes(x = num_clusters, y = time, col = pars)) +
#   geom_point() + 
#   geom_smooth(method = "lm", se = FALSE, formula = y ~ x) +
#   scale_colour_manual(values = unname(polychrome(20))) + 
#   labs(
#     title = "Runtime vs number of clusters - all methods",
#     x = "Number of clusters", 
#     y = "Time (s)", 
#     colour = "Method") + 
#   theme_bw()
# time_sim_data %>% 
#   rowwise() %>% 
#   mutate(num_clusters = if_else(is.na(n_clus), length(group.prob), n_clus)) %>% 
#   filter(sim_label == "num_clusters") %>%
#   ggplot(aes(x = num_clusters, y = time, col = pars)) +
#   geom_point() + 
#   geom_smooth(method = "lm", se = FALSE, formula = y ~ x) + 
#   scale_y_log10() + 
#   scale_colour_manual(values = unname(polychrome(20))) + 
#   labs(
#     title = "Runtime vs number of clusters - all methods",
#     x = "Number of clusters", 
#     y = "Time (s)", 
#     colour = "Method") + 
#   theme_bw()

scran

Total number of cells

time_sim_data %>% 
  filter(sim_label == "num_cells") %>% 
  filter(method == "scran") %>%
  mutate(scran_pars = paste0(test.type, "_", pval.type)) %>% 
  ggplot(aes(x = batchCells, y = time, colour = scran_pars)) +
  geom_point() + 
  geom_smooth(method = "lm", se = FALSE, formula = y ~ x) + 
  scale_x_continuous(breaks = c(100, 500, 1000, 2000, 3000)) + 
  labs(
    title = "Runtime vs total number of cells - scran", 
    x = "Total number of cells", 
    y = "Time (s)", 
    colour = "scran options") + 
  theme_bw()

Number of clusters

# time_sim_data %>% 
#   rowwise() %>% 
#   mutate(num_clusters = if_else(is.na(n_clus), length(group.prob), n_clus)) %>% 
#   filter(sim_label == "num_clusters") %>%
#   filter(method == "scran") %>%
#   ggplot(aes(x = num_clusters, y = time, col = pars)) +
#   geom_point() + 
#   geom_smooth(method = "lm", se = FALSE, formula = y ~ x) + 
#   labs(
#     title = "Runtime vs number of clusters - scran", 
#     x = "Number of clusters", 
#     y = "Time (s)",
#     colour = "scran options"
#   ) + 
#   theme_bw()

Seurat

Total number of cells

time_sim_data %>% 
  filter(sim_label == "num_cells") %>% 
  filter(method == "seurat") %>%
  ggplot(aes(x = batchCells, y = time, col = test.use)) +
  geom_point() + 
  geom_smooth(method = "lm", se = FALSE, formula = y ~ x) + 
  labs(
    y = "Time (s)",
    x = "Test method", 
    col = "Test used"
  ) + 
  theme_bw() + 
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Number of clusters

# time_sim_data %>% 
#   filter(sim_label == "num_clusters") %>%
#   filter(method == "seurat") %>%
#   ggplot(aes(x = num_clusters, y = time, col = test.use)) +
#   geom_point() + 
#   geom_smooth(method = "lm", se = FALSE, formula = y ~ x) + 
#   labs(
#     y = "Time (s)", 
#     x = "Number of clusters",
#     col = "Test used"
#   ) + 
#   theme_bw()

Real data

real_data_data <- retrieve_real_data_parameters() %>% 
  rowwise() %>% 
  mutate(time = readRDS(full_filename)$time) %>%
  ungroup()
pbmc3k_time <- real_data_data %>% 
  filter(data_id == "pbmc3k") %>% 
  filter(!(pars %in% c("lm_two_sample", "random"))) %>% 
  mutate(pars = pars_lookup[pars]) %>% 
  mutate(pars = fct_reorder(factor(pars), time)) %>%
  mutate(ms = 1000 * time) %>% 
  ggplot(aes(x = pars, y = ms)) + 
  geom_point(colour = "seagreen", alpha = 0.8, size = 3) + 
  scale_y_log10(
    breaks = c(500, 1000, 10000, 60000, 600000),
    labels = c("0.5s", "1s", "10s", "1m", "10min")
  ) + 
  coord_flip() + 
  labs(
    y = "Time (log scale)", 
    x = "Method",
    title = "pbmc3k data"
  ) + 
  theme_bw()
pbmc3k_time

real_data_data %>% 
  filter(data_id == "lawlor") %>% 
  mutate(pars = pars_lookup[pars]) %>% 
  mutate(pars = fct_reorder(factor(pars), time)) %>% 
  ggplot(aes(x = pars, y = time)) + 
  geom_col(fill = "seagreen", alpha = 0.8) + 
  coord_flip() + 
  labs(
    y = "Time (s)", 
    x = "Method"
  ) + 
  theme_bw()

real_data_data %>% 
  filter(data_id == "zeisel") %>% 
  mutate(pars = pars_lookup[pars]) %>% 
  mutate(pars = fct_reorder(factor(pars), time)) %>%
  mutate(ms = 1000 * time) %>% 
  ggplot(aes(x = pars, y = ms)) + 
  geom_point(colour = "seagreen", alpha = 0.8) + 
  scale_y_log10(
    breaks = c(500, 1000, 10000, 60000, 600000),
    labels = c("0.5s", "1s", "10s", "1m", "10min")
  ) + 
  coord_flip() + 
  labs(
    y = "Time", 
    x = "Method",
    title = "Method time (Zeisel data)"
  ) + 
  theme_bw()

endothelial_time <- real_data_data %>% 
  filter(data_id == "endothelial") %>% 
  mutate(pars = pars_lookup[pars]) %>% 
  mutate(pars = fct_reorder(factor(pars), time)) %>%
  mutate(ms = 1000 * time) %>% 
  ggplot(aes(x = pars, y = ms)) + 
  geom_point(colour = "seagreen", size = 3) + 
  scale_y_log10(
    breaks = c(1000, 10000, 60000, 600000, 3600000, 18000000),
    labels = c("1s", "10s", "1m", "10min", "1hr", "5hr")
  ) + 
  coord_flip() + 
  labs(
    y = "Time (log scale)", 
    x = "Method",
    title = "Endothelial data"
  ) + 
  theme_bw()
endothelial_time

endothelial_time_talk <- real_data_data %>% 
  filter(data_id == "endothelial") %>% 
  filter(method != "rankcorr") %>% 
  filter(pars != "lm_two_sample") %>% 
  filter(method != "random") %>% 
  filter(pars != "difference_log_fc") %>% 
  mutate(method = method_lookup[method]) %>% 
  mutate(pars = pars_lookup[pars]) %>% 
  mutate(pars = fct_reorder(factor(pars), time)) %>%
  mutate(ms = 1000 * time) %>% 
  ggplot(aes(x = pars, y = ms, colour = method)) + 
  geom_point(size = 3) + 
  scale_y_log10(
    breaks = c(1000, 10000, 60000, 600000, 3600000, 18000000),
    labels = c("1s", "10s", "1m", "10m", "1hr", "5hr")
  ) + 
  coord_flip() + 
  labs(
    y = "Time (log scale)", 
    x = "Method",
    title = "Endothelial data",
    colour = "Method"
  ) + 
  theme_bw()
endothelial_time_talk

pbmc3k_time + endothelial_time +
  plot_annotation(tag_levels = "A")

real_data_data %>% 
  filter(data_id == "endothelial") %>% 
  mutate(plot_pars = pars_lookup[pars]) %>% 
  mutate(plot_pars = fct_reorder(factor(plot_pars), time)) %>%
  mutate(highlight_pars = pars %in% 
           c("scran_t_any", "seurat_wilcox", "scanpy_t")) %>% 
  mutate(ms = 1000 * time) %>% 
  ggplot(aes(x = plot_pars, y = ms, colour = highlight_pars)) + 
  geom_segment(aes(x = plot_pars, xend = plot_pars, y = 400, yend = ms), 
               lineend = "round", colour = "darkgrey") + 
  geom_point(size = 3) + 
  scale_colour_manual(values = c("#228833", "#EE6677")) + 
  scale_y_log10(
    breaks = c(1000, 10000, 60000, 600000, 3600000, 18000000),
    labels = c("1s", "10s", "1m", "10min", "1hr", "5hr")
  ) + 
  coord_flip() + 
  labs(
    y = "Time (log scale)", 
    x = "Method",
    title = "Method time (Endothelial data)"
  ) + 
  theme_bw() + 
  theme(legend.position = "none")

real_data_data %>% 
  filter(data_id == "endothelial") %>% 
  filter(pars %in% c("scran_t_any", "seurat_wilcox", "scanpy_t")) %>% 
  mutate(min = time / 60) %>%
  select(pars, sec = time, min)
# A tibble: 2 × 3
  pars             sec     min
  <chr>          <dbl>   <dbl>
1 scran_t_any     2.54  0.0424
2 seurat_wilcox 729.   12.2   
real_data_data %>% 
  filter(data_id == "endothelial") %>% 
  mutate(pars = pars_lookup[pars]) %>% 
  mutate(pars = fct_reorder(factor(pars), time)) %>%
  mutate(min = time / 60) %>% 
  ggplot(aes(x = pars, y = min)) + 
  geom_segment(aes(x = pars, xend = pars, y = 0, yend = min), 
               lineend = "round", colour = "darkgrey") + 
  geom_point(colour = "seagreen") + 
  coord_flip() + 
  labs(
    y = "Time (min)", 
    x = "Method",
    title = "Method time (Endothelial data)"
  ) + 
  theme_bw()

All times

For slurm optimization

time_sim_data %>% 
  ggplot(aes(x = time)) + 
  geom_histogram(binwidth = 200) +
  geom_vline(xintercept = 60 * 20, col = "blue") + 
  geom_vline(xintercept = 60 * 30, col = "red") + 
  geom_vline(xintercept = 60 * 60, col = "green") + 
  geom_vline(xintercept = 120 * 60, col = "yellow") + 
  labs(x = "Time (s)", y = "Count") + 
  theme_bw()


devtools::session_info()
─ Session info ───────────────────────────────────────────────────────────────
 setting  value                       
 version  R version 4.1.0 (2021-05-18)
 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     2022-02-09                  

─ Packages ───────────────────────────────────────────────────────────────────
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 vctrs         0.3.8   2021-04-29 [2] CRAN (R 4.1.0)
 whisker       0.4     2019-08-28 [2] CRAN (R 4.1.0)
 withr         2.4.3   2021-11-30 [1] CRAN (R 4.1.0)
 workflowr     1.6.2   2020-04-30 [1] CRAN (R 4.1.0)
 xfun          0.23    2021-05-15 [2] CRAN (R 4.1.0)
 yaml          2.2.1   2020-02-01 [2] CRAN (R 4.1.0)

[1] /home/jpullin/R/x86_64-pc-linux-gnu-library/4.1
[2] /usr/local/easybuild-2019/easybuild/software/mpi/gcc/10.2.0/openmpi/4.0.5/r/4.1.0/lib64/R/library