Last updated: 2021-04-13
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Knit directory: mage_2020_marker-gene-benchmarking/
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library(tibble)
library(dplyr)
library(ggplot2)
library(tidyr)
library(purrr)
library(pals)
source(here::here("code", "analysis-utils.R"))
Measure the time the different methods take to run.
<- here::here("results")
results_folder <- list.files(results_folder, full.names = TRUE)
file_names
<- numeric(length(file_names))
times for (i in seq_along(file_names)) {
if (!isTRUE(getOption("knitr.in.progress"))) {
print(i)
}<- readRDS(file_names[[i]])
res <- res$time
times[[i]] rm(res)
}
<- tibble(time = times, file_name = basename(file_names))
times <- retrive_simulation_parameters()
pars
<- left_join(pars, times, by = "file_name")
time_data
<- time_data %>%
time_data rowwise() %>%
mutate(num_clusters = if_else(is.na(n_clus), length(group.prob), n_clus)) %>%
ungroup()
Linear time scale
%>%
time_data mutate(pars = method_name) %>%
filter(sim_label == "num_cells") %>%
ggplot(aes(x = batchCells, y = time, colour = pars)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, formula = y ~ x) +
scale_x_continuous(breaks = c(100, 500, 1000, 2000, 3000)) +
# FIXME: Improve colours.
scale_colour_manual(values = unname(polychrome(20))) +
labs(
title = "Runtime vs total number of cells - all methods",
x = "Number of cells",
y = "Time (s)",
colour = "Method"
+
) theme_bw()
log time scale
%>%
time_data mutate(pars = method_name) %>%
filter(sim_label == "num_cells") %>%
ggplot(aes(x = batchCells, y = time, colour = pars)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, formula = y ~ x) +
scale_x_continuous(breaks = c(100, 500, 1000, 2000, 3000)) +
scale_y_log10() +
scale_colour_manual(values = unname(polychrome(20))) +
labs(
title = "log(runtime) vs total number of cells - all methods",
x = "Number of cells",
y = "Time (s)",
colour = "Method"
+
) theme_bw()
Linear time scale
%>%
time_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_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()
%>%
time_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()
%>%
time_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()
%>%
time_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))
%>%
time_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()
For slurm optimization
%>%
time_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()
::session_info() devtools
─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.0.3 (2020-10-10)
os CentOS Linux 7 (Core)
system x86_64, linux-gnu
ui X11
language (EN)
collate en_AU.UTF-8
ctype en_AU.UTF-8
tz UTC
date 2021-04-13
─ Packages ───────────────────────────────────────────────────────────────────
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