Memory usage analysis

workflowr

• Summary
• Checks
• Past versions

Last updated: 2024-01-01

Checks: 7 0

Knit directory: mage_2020_marker-gene-benchmarking/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

---

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20190102)

The command set.seed(20190102) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 2632193

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 2632193. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

Ignored files:
    Ignored:    .Renviron
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    .snakemake/
    Ignored:    NSForest/.Rhistory
    Ignored:    NSForest/NS-Forest_v3_Extended_Binary_Markers_Supplmental.csv
    Ignored:    NSForest/NS-Forest_v3_Full_Results.csv
    Ignored:    NSForest/NSForest3_medianValues.csv
    Ignored:    NSForest/NSForest_v3_Final_Result.csv
    Ignored:    NSForest/__pycache__/
    Ignored:    NSForest/data/
    Ignored:    RankCorr/picturedRocks/__pycache__/
    Ignored:    benchmarks/
    Ignored:    config/
    Ignored:    data/cellmarker/
    Ignored:    data/downloaded_data/
    Ignored:    data/expert_annotations/
    Ignored:    data/expert_mgs/
    Ignored:    data/raw_data/
    Ignored:    data/real_data/
    Ignored:    data/sim_data/
    Ignored:    data/sim_mgs/
    Ignored:    data/special_real_data/
    Ignored:    figures/
    Ignored:    logs/
    Ignored:    results/
    Ignored:    weights/

Unstaged changes:
    Deleted:    analysis/expert-mgs-direction.Rmd
    Modified:   smash-fork

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/memory.Rmd) and HTML (public/memory.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	bc98f8b	Jeffrey Pullin	2023-11-20	GB resubmission July 2023
html	fcecf65	Jeffrey Pullin	2022-09-09	Build site.
html	af96b34	Jeffrey Pullin	2022-08-30	Build site.
html	0e47874	Jeffrey Pullin	2022-05-04	Build site.
html	781c4f8	Jeffrey Pullin	2022-05-02	Build site.
html	8b989e1	Jeffrey Pullin	2022-05-02	Build site.
html	0548273	Jeffrey Pullin	2022-05-02	Build site.
Rmd	50bca7c	Jeffrey Pullin	2022-05-02	workflowr::wflow_publish(all = TRUE, republish = TRUE)
html	50bca7c	Jeffrey Pullin	2022-05-02	workflowr::wflow_publish(all = TRUE, republish = TRUE)
html	5cc008f	Jeffrey Pullin	2022-02-09	Build site.
Rmd	d1aca16	Jeffrey Pullin	2022-02-09	Refresh website
Rmd	afbca9a	Jeffrey Pullin	2022-01-27	Add memory measurement and analysis of memory usage

---

Aim

Compare the memory usage of the the different methods.

library(tibble)
library(dplyr)
library(ggplot2)
library(tidyr)
library(purrr)
library(forcats)
library(khroma)
library(stringr)
library(readr)
library(ggrepel)

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

Simulated data

sim_pars <- retrive_simulation_parameters()

memory_sim_data <- sim_pars %>%
  filter(sim_label == "num_cells") %>% 
  mutate(
    memory_file_name = full_filename %>% 
      basename() %>% 
      str_sub(end = -5) %>%
      paste0("_benchmark.txt")
  ) %>%
  mutate(memory_path = here::here("benchmarks", memory_file_name)) %>%
  rowwise() %>%
  mutate(memory = read.csv(memory_path, sep = "\t")$max_vms)

memory_sim_num_clusters_data <- sim_pars %>%
  filter(sim_label == "num_clusters") %>% 
  mutate(
    memory_file_name = full_filename %>% 
      basename() %>% 
      str_sub(end = -5) %>%
      paste0("_benchmark.txt")
  ) %>%
  mutate(memory_path = here::here("benchmarks", memory_file_name)) %>%
  rowwise() %>%
  mutate(memory = read.csv(memory_path, sep = "\t")$max_vms)

label_pars <- c(
  "edger_ql",
  "scran_t_any", 
  "nsforest", 
  "cepo", 
  "rankcorr_2", 
  "seurat_negbinom", 
  "limma_trend", 
  "limma_voom",
  "glmgampoi", 
  "seurat_MAST", 
  "scanpy_wilcoxon_rankby_abs",
  "edger_ql_none",
  "presto", 
  "smash"
)

memory_by_num_cells_plot <- memory_sim_data %>% 
  filter(sim_label == "num_cells") %>% 
   # Remove methods we implemented.
  filter(!(pars %in% c("lm_two_sample", "logfc_abs", "logfc_raw", "random"))) %>% 
  group_by(pars, method, batchCells) %>%  
  summarise(memory = mean(memory), .groups = "drop") %>% 
  mutate(method = method_lookup[method]) %>%  
  mutate(gb = memory / 1000) %>% 
  ggplot(aes(x = batchCells, y = gb, colour = method)) +
  geom_point() + 
  geom_smooth(se = FALSE, formula = y ~ x, method = "loess", span = 1) +
  geom_text_repel(
    aes(label = if_else(
      pars %in% label_pars & batchCells == 100000, 
      pars_lookup[pars], 
      "")
    ),
    hjust = -0.1, colour = "black", size = 3, xlim = c(101000, NA)
  ) + 
  external_package_colour + 
  coord_cartesian(xlim = c(1000, 130000)) + 
  scale_x_continuous(
    breaks = c(1000, 25000, 50000, 75000, 100000)
  ) + 
  scale_y_continuous(
    breaks = c(1, 5, 10, 20, 30, 40)
  ) + 
  labs(
    title = "Memory usage vs total number of cells",
    x = "Number of cells", 
    y = "Memory usage (GB)", 
    colour = "Method"
  ) + 
  guides(color = "none") + 
  theme_bw()

memory_by_num_cells_plot

Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider
increasing max.overlaps

ggsave(
  here::here("figures", "final", "memory-num-cells.pdf"),
  memory_by_num_cells_plot,
  width = 8,
  height = 8,
  units = "in"
)

label_pars <- c(
  "edger_ql",
  "scran_t_any", 
  "nsforest", 
  "cepo", 
  "rankcorr_2", 
  "seurat_negbinom", 
  "limma_trend", 
  "limma_voom",
  "glmgampoi", 
  "seurat_MAST", 
  "scanpy_wilcoxon_rankby_abs",
  "edger_ql_none",
  "presto", 
  "smash"
)

memory_by_num_clusters_plot <- memory_sim_num_clusters_data %>% 
  # Remove methods we implemented.
  filter(!(pars %in% c("lm_two_sample", "logfc_abs", "logfc_raw", "random"))) %>% 
  group_by(pars, method, n_clus) %>%  
  summarise(memory = mean(memory), .groups = "drop") %>% 
  mutate(method = method_lookup[method]) %>%  
  mutate(gb = memory / 1000) %>% 
  ggplot(aes(x = n_clus, y = gb, colour = method)) +
  geom_point() + 
  geom_smooth(se = FALSE, formula = y ~ x, method = "loess", span = 1) +
  geom_text_repel(
    aes(label = if_else(
      pars %in% label_pars & n_clus == 20, 
      pars_lookup[pars], 
      "")
    ),
    hjust = -0.1, colour = "black", size = 3, xlim = c(21, NA)
  ) + 
  external_package_colour + 
  coord_cartesian(xlim = c(5, 28)) + 
  scale_x_continuous(
    breaks = c(5, 10, 15, 20)
  ) + 
  scale_y_continuous(
    breaks = c(2.5, 5, 7.5, 10)
  ) + 
  labs(
    title = "Memory usage vs. numbers of clusters",
    x = "Number of clusters", 
    y = "Memory usage (GB)", 
    colour = "Method"
  ) + 
  theme_bw()

memory_by_num_clusters_plot

Warning: ggrepel: 3 unlabeled data points (too many overlaps). Consider
increasing max.overlaps

saveRDS(memory_by_num_clusters_plot, here::here("figures", "raw", "memory-num-clusters.rds"))

Real data

real_data_memory <- retrieve_real_data_parameters() %>% 
  mutate(
    memory_file_name = full_filename %>% 
      basename() %>% 
      str_sub(string = , end = -5) %>% 
      paste0("_benchmark.txt")
  ) %>% 
  mutate(memory_path = here::here("benchmarks", memory_file_name)) %>% 
  rowwise() %>%  
  mutate(memory = read.csv(memory_path, sep = "\t")$max_vms) %>% 
  ungroup()

Endothelial

endothelial_memory <- real_data_memory %>% 
  filter(data_id == "endothelial") %>% 
  filter(!(method %in% c("lm", "difference", "random"))) %>% 
  mutate(method = method_lookup[method]) %>%  
  mutate(pars = pars_lookup[pars]) %>% 
  mutate(pars = fct_reorder(factor(pars), memory)) %>% 
  mutate(gb = memory / 1000) %>% 
  ggplot(aes(x = pars, y = gb, colour = method)) + 
  geom_point(alpha = 0.8, size = 3) + 
  external_package_colour + 
  coord_flip() + 
  labs(
    y = "Memory usage (GB)", 
    x = "Method",
    title = "Endothelial data",
    colour = "Package"
  ) + 
  theme_bw()

endothelial_memory

saveRDS(endothelial_memory, here::here("figures", "raw", "endothelial-memory.rds"))

Zhao

zhao_memory <- real_data_memory %>% 
  filter(data_id == "zhao") %>% 
  filter(!(method %in% c("lm", "logfc", "random"))) %>% 
  mutate(method = method_lookup[method]) %>%  
  mutate(pars = pars_lookup[pars]) %>% 
  mutate(pars = fct_reorder(factor(pars), memory)) %>% 
  mutate(gb = memory / 1000) %>% 
  ggplot(aes(x = pars, y = gb, colour = method)) + 
  scale_y_continuous(breaks = seq(0, 30, by = 5)) + 
  geom_point(alpha = 0.8, size = 3) + 
  external_package_colour + 
  coord_flip(ylim = c(0, 25)) + 
  labs(
    y = "Memory usage (GB)", 
    x = "Method",
    title = "Zhao data",
    colour = "Package"
  ) + 
  theme_bw()

zhao_memory

saveRDS(zhao_memory, here::here("figures", "raw", "zhao-memory.rds"))

Overall

overall_memory <- real_data_memory %>% 
  filter(!(pars %in% c("lm_two_sample", "logfc_abs", "logfc_raw", "random"))) %>%
  mutate(log_memory = log(memory, base = 10)) %>% 
  mutate(
    plot_method = method_lookup[method], 
    plot_pars = pars_lookup[pars],
    plot_data_id = dataset_lookup[data_id]
  ) %>% 
  mutate(plot_pars = fct_reorder(factor(plot_pars), memory)) %>% 
  ggplot(aes(x = plot_data_id, y = plot_pars)) +
  geom_tile(aes(fill = log_memory), colour = "black") + 
  scale_fill_distiller(
    breaks = log(c(1200, 2000, 5000, 10000, 20000), base = 10), 
    labels = c("1.2GB", "2GB", "5GB", "10GB", "20GB"),
    palette = "RdYlBu") + 
  theme_bw() + 
  labs(
    title = "Memory across datasets",
    x = "Dataset", 
    y = "Method", 
    fill = "Memory",
  ) + 
  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)
  )
overall_memory

saveRDS(overall_memory, here::here("figures", "raw", "overall-memory.rds"))

Session information

devtools::session_info()

─ Session info  ──────────────────────────────────────────────────────────────
 hash: woman firefighter: medium-light skin tone, flag: Åland Islands, flag: Denmark

 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)

─ Packages ───────────────────────────────────────────────────────────────────
 package      * version date (UTC) lib source
 assertthat     0.2.1   2019-03-21 [2] CRAN (R 4.1.2)
 bslib          0.3.1   2021-10-06 [1] CRAN (R 4.1.0)
 cachem         1.0.6   2021-08-19 [1] CRAN (R 4.1.0)
 callr          3.7.0   2021-04-20 [2] CRAN (R 4.1.2)
 cli            3.6.1   2023-03-23 [1] CRAN (R 4.1.0)
 colorspace     2.1-0   2023-01-23 [1] CRAN (R 4.1.0)
 crayon         1.5.1   2022-03-26 [1] CRAN (R 4.1.0)
 DBI            1.1.2   2021-12-20 [1] CRAN (R 4.1.0)
 desc           1.4.0   2021-09-28 [2] CRAN (R 4.1.2)
 devtools       2.4.2   2021-06-07 [2] CRAN (R 4.1.2)
 digest         0.6.29  2021-12-01 [1] CRAN (R 4.1.0)
 dplyr        * 1.0.9   2022-04-28 [1] CRAN (R 4.1.0)
 ellipsis       0.3.2   2021-04-29 [2] CRAN (R 4.1.2)
 evaluate       0.14    2019-05-28 [2] CRAN (R 4.1.2)
 fansi          1.0.4   2023-01-22 [1] CRAN (R 4.1.0)
 farver         2.1.1   2022-07-06 [1] CRAN (R 4.1.0)
 fastmap        1.1.0   2021-01-25 [2] CRAN (R 4.1.2)
 forcats      * 0.5.1   2021-01-27 [2] CRAN (R 4.1.2)
 fs             1.5.2   2021-12-08 [1] CRAN (R 4.1.0)
 generics       0.1.3   2022-07-05 [1] CRAN (R 4.1.0)
 ggplot2      * 3.3.6   2022-05-03 [1] CRAN (R 4.1.0)
 ggrepel      * 0.9.1   2021-01-15 [2] CRAN (R 4.1.2)
 git2r          0.28.0  2021-01-10 [2] CRAN (R 4.1.2)
 glue           1.6.0   2021-12-17 [1] CRAN (R 4.1.0)
 gtable         0.3.0   2019-03-25 [2] CRAN (R 4.1.2)
 here           1.0.1   2020-12-13 [1] CRAN (R 4.1.0)
 highr          0.9     2021-04-16 [2] CRAN (R 4.1.2)
 hms            1.1.1   2021-09-26 [2] CRAN (R 4.1.2)
 htmltools      0.5.2   2021-08-25 [1] CRAN (R 4.1.0)
 httpuv         1.6.5   2022-01-05 [1] CRAN (R 4.1.0)
 jquerylib      0.1.4   2021-04-26 [2] CRAN (R 4.1.2)
 jsonlite       1.8.0   2022-02-22 [1] CRAN (R 4.1.0)
 khroma       * 1.7.0   2021-09-02 [1] CRAN (R 4.1.0)
 knitr          1.36    2021-09-29 [1] CRAN (R 4.1.0)
 labeling       0.4.2   2020-10-20 [2] CRAN (R 4.1.2)
 later          1.3.0   2021-08-18 [1] CRAN (R 4.1.0)
 lattice        0.20-45 2021-09-22 [2] CRAN (R 4.1.2)
 lifecycle      1.0.1   2021-09-24 [1] CRAN (R 4.1.0)
 magrittr       2.0.3   2022-03-30 [1] CRAN (R 4.1.0)
 Matrix         1.3-4   2021-06-01 [2] CRAN (R 4.1.2)
 memoise        2.0.1   2021-11-26 [1] CRAN (R 4.1.0)
 mgcv           1.8-38  2021-10-06 [2] CRAN (R 4.1.2)
 munsell        0.5.0   2018-06-12 [2] CRAN (R 4.1.2)
 nlme           3.1-153 2021-09-07 [2] CRAN (R 4.1.2)
 pillar         1.7.0   2022-02-01 [1] CRAN (R 4.1.0)
 pkgbuild       1.2.0   2020-12-15 [2] CRAN (R 4.1.2)
 pkgconfig      2.0.3   2019-09-22 [2] CRAN (R 4.1.2)
 pkgload        1.2.3   2021-10-13 [2] CRAN (R 4.1.2)
 prettyunits    1.1.1   2020-01-24 [2] CRAN (R 4.1.2)
 processx       3.5.2   2021-04-30 [2] CRAN (R 4.1.2)
 promises       1.2.0.1 2021-02-11 [2] CRAN (R 4.1.2)
 ps             1.7.1   2022-06-18 [1] CRAN (R 4.1.0)
 purrr        * 0.3.4   2020-04-17 [2] CRAN (R 4.1.2)
 R6             2.5.1   2021-08-19 [1] CRAN (R 4.1.0)
 RColorBrewer   1.1-3   2022-04-03 [1] CRAN (R 4.1.0)
 Rcpp           1.0.8.3 2022-03-17 [1] CRAN (R 4.1.0)
 readr        * 2.1.1   2021-11-30 [1] CRAN (R 4.1.0)
 remotes        2.4.2   2021-11-30 [1] CRAN (R 4.1.0)
 rlang          1.0.3   2022-06-27 [1] CRAN (R 4.1.0)
 rmarkdown      2.14    2022-04-25 [1] CRAN (R 4.1.0)
 rprojroot      2.0.3   2022-04-02 [1] CRAN (R 4.1.0)
 rstudioapi     0.14    2022-08-22 [1] CRAN (R 4.1.0)
 sass           0.4.1   2022-03-23 [1] CRAN (R 4.1.0)
 scales         1.2.1   2022-08-20 [1] CRAN (R 4.1.0)
 sessioninfo    1.2.0   2021-10-31 [2] CRAN (R 4.1.2)
 stringi        1.7.6   2021-11-29 [1] CRAN (R 4.1.0)
 stringr      * 1.4.0   2019-02-10 [2] CRAN (R 4.1.2)
 testthat       3.1.0   2021-10-04 [2] CRAN (R 4.1.2)
 tibble       * 3.1.7   2022-05-03 [1] CRAN (R 4.1.0)
 tidyr        * 1.2.0   2022-02-01 [1] CRAN (R 4.1.0)
 tidyselect     1.1.2   2022-02-21 [1] CRAN (R 4.1.0)
 tzdb           0.2.0   2021-10-27 [1] CRAN (R 4.1.0)
 usethis        2.1.3   2021-10-27 [2] CRAN (R 4.1.2)
 utf8           1.2.3   2023-01-31 [1] CRAN (R 4.1.0)
 vctrs          0.4.1   2022-04-13 [1] CRAN (R 4.1.0)
 whisker        0.4     2019-08-28 [2] CRAN (R 4.1.2)
 withr          2.5.0   2022-03-03 [1] CRAN (R 4.1.0)
 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)
 yaml           2.3.5   2022-02-21 [1] CRAN (R 4.1.0)

 [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

──────────────────────────────────────────────────────────────────────────────