Last updated: 2023-07-28

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Knit directory: SuperCellCyto-analysis/

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Rmd 402358b Givanna Putri 2023-07-28 wflow_publish(c("analysis/*Rmd"))

Introduction

In this analysis, we examine the time required to create the supercells, as well as the run time improvement obtained by analysing supercells vs single cells.

library(here)
library(data.table)
library(stringr)
library(ggplot2)
library(scales)

How long did supercell creation take?

run_time_clust_benchmark <- lapply(c("samusik_all", "levine_32dim"), function(dt_source) {
    dt <- fread(here("output", "explore_supercell_purity_clustering", "20230511", 
                            dt_source, "supercell_runs", "supercell_runtime.txt"),
                       sep = ":", header = FALSE, col.names = c("dataset", "duration_seconds"))
    dt$gamma <- str_split_i(dt$dataset, "_", 2) 
    dt$dataset <- str_to_title(dt_source)
    dt$duration_seconds <- gsub(" sec elapsed", "", dt$duration_seconds)
    
    return(dt)
})
run_time_clust_benchmark <- rbindlist(run_time_clust_benchmark)
run_time_bcells <- fread(here("output", "oetjen_b_cell_panel", "20230511", "supercell_runtime.txt"),
                         sep = ":", header = FALSE, col.names = c("dataset", "duration_seconds"))
run_time_bcells$dataset <- "Oetjen_bcells"
run_time_bcells$duration_seconds <- gsub(" sec elapsed", "", run_time_bcells$duration_seconds)
run_time_bcells$gamma <- "gamma20"
run_time_trussart <- fread(here("output", "trussart_cytofruv", "20230515_supercell_out", "supercell_runtime.txt"),
                         sep = ":", header = FALSE, col.names = c("dataset", "duration_seconds"))
run_time_trussart$dataset <- "Trussart_cytofruv"
run_time_trussart$duration_seconds <- gsub(" sec elapsed", "", run_time_trussart$duration_seconds)
run_time_trussart$gamma <- "gamma20"
run_time <- rbindlist(list(run_time_clust_benchmark, run_time_bcells, run_time_trussart))
run_time$gamma <- gsub("gamma", "", run_time$gamma)
run_time$duration_seconds <- as.numeric(run_time$duration_seconds)
run_time$duration_minutes <- run_time$duration_seconds / 60
run_time$duration_hours <- run_time$duration_seconds / 3600
ggplot(run_time[dataset %in% c("Samusik_all", "Levine_32dim")], 
       aes(x = gamma, y = duration_minutes, colour = dataset)) +
  geom_point(size = 2) +
  scale_color_manual(values = c("Levine_32dim" = "purple", "Samusik_all" = "#FFC000")) +
  theme_classic() +
  scale_y_continuous(breaks = pretty_breaks(n=5), limits = c(2,3)) +
  labs(y = "Duration (minutes)", x = "Gamma", color = "Dataset", title = "Time Taken to Generate Supercells")

ggplot(run_time[dataset %in% c("Oetjen_bcells", "Trussart_cytofruv")], 
       aes(x = dataset, y = duration_minutes, colour = dataset)) +
  geom_point() +
  scale_color_manual(values = c("Oetjen_bcells" = "#FF5733", "Trussart_cytofruv" = "turquoise")) +
  theme_classic() +
  scale_y_continuous(breaks = pretty_breaks(n=5)) +
  labs(y = "Duration (minutes)", x = "Dataset", color = "Dataset", title = "Time Taken to Generate Supercells")

How long did Louvain clustering take?

louvain_runtime_supercell <- lapply(c("samusik_all", "levine_32dim"), function(dt_source) {
    dt <- lapply(seq(5, 50, by=5), function(gam) {
        dt <- fread(here("output", "explore_supercell_purity_clustering", "20230511", 
                         dt_source, "louvain_supercell_runs", paste0("gamma_", gam),
                         "louvain_supercell_runtime.txt"),
                    sep = ":", header = FALSE, col.names = c("dataset", "duration_seconds"))
        dt$duration_seconds <- as.numeric(gsub(" sec elapsed", "", dt$duration_seconds))
        dt$gamma <- gam
        dt$dataset <- str_to_title(dt_source)
        return(dt)
    })
    return(rbindlist(dt))
})
louvain_runtime_supercell <- rbindlist(louvain_runtime_supercell)
louvain_runtime_supercell$type <- "Supercells"

louvain_runtime_allcell <- lapply(c("samusik_all", "levine_32dim"), function(dt_source) {
    dt <- lapply(seq(10, 30, by=5), function(k) {
        dt <- fread(here("output", "explore_supercell_purity_clustering", "20230511", 
                         dt_source, "louvain_allcells", paste0("k", k),
                         "louvain_supercell_runtime.txt"),
                    sep = ":", header = FALSE, col.names = c("dataset", "duration_seconds"))
        dt$duration_seconds <- as.numeric(gsub(" sec elapsed", "", dt$duration_seconds))
        dt$dataset <- str_to_title(dt_source)
        return(dt)
    })
    return(rbindlist(dt))
})
louvain_runtime_allcell <- rbindlist(louvain_runtime_allcell)
louvain_runtime_allcell$type <- "Single cells"

louvain_runtime <- rbind(
  louvain_runtime_allcell, 
  louvain_runtime_supercell[, c("dataset", "duration_seconds", "type")]
)
louvain_runtime[, duration_minutes := duration_seconds / 60]
ggplot(louvain_runtime, aes(x = type, y = duration_minutes, colour = dataset)) +
  geom_boxplot() +
  scale_color_manual(values = c("Levine_32dim" = "purple", "Samusik_all" = "#FFC000")) +
  theme_classic() +
  scale_y_continuous(breaks = pretty_breaks(n=20)) +
  labs(y = "Duration (minutes)", x = "Gamma", color = "Dataset", title = "Louvain clustering run time")

Median run time?

louvain_runtime[, .(median_duration_sec = median(duration_seconds), median_duration_min = median(duration_minutes)), by = c("type", "dataset")]
           type      dataset median_duration_sec median_duration_min
1: Single cells  Samusik_all           5319.1040          88.6517333
2: Single cells Levine_32dim            590.3805           9.8396750
3:   Supercells  Samusik_all              8.2705           0.1378417
4:   Supercells Levine_32dim              1.9170           0.0319500

Any improvement for FlowSOM?

fsom_runtime_supercell <- lapply(c("samusik_all", "levine_32dim"), function(dt_source) {
    dt <- lapply(seq(5, 50, by=5), function(gam) {
        dt <- fread(here("output", "explore_supercell_purity_clustering", "20230511", 
                         dt_source, "flowsom_supercell_runs", paste0("gamma_", gam),
                         "flowsom_supercell_runtime.txt"),
                    sep = ":", header = FALSE, col.names = c("dataset", "duration_seconds"))
        dt$duration_seconds <- as.numeric(gsub(" sec elapsed", "", dt$duration_seconds))
        dt$gamma <- gam
        dt$dataset <- str_to_title(dt_source)
        return(dt)
    })
    return(rbindlist(dt))
})
fsom_runtime_supercell <- rbindlist(fsom_runtime_supercell)
fsom_runtime_supercell$type <- "Supercells"

fsom_runtime_allcell <- lapply(c("samusik_all", "levine_32dim"), function(dt_source) {
    dt <- fread(here("output", "explore_supercell_purity_clustering", "20230509", 
                         dt_source, "flowsom_allcells", "flowsom_allcell_runtime.txt"),
                    sep = ":", header = FALSE, col.names = c("dataset", "duration_seconds"))
    dt$duration_seconds <- as.numeric(gsub(" sec elapsed", "", dt$duration_seconds))
    dt$dataset <- str_to_title(dt_source)
    return(dt)
    return(rbindlist(dt))
})
fsom_runtime_allcell <- rbindlist(fsom_runtime_allcell)
fsom_runtime_allcell$type <- "Single Cells"

fsom_runtime <- rbind(
  fsom_runtime_allcell, 
  fsom_runtime_supercell[, c("dataset", "duration_seconds", "type")]
)
ggplot(fsom_runtime, aes(x = type, y = duration_seconds, colour = dataset)) +
  geom_boxplot() +
  scale_color_manual(values = c("Levine_32dim" = "purple", "Samusik_all" = "#FFC000")) +
  theme_classic() +
  scale_y_continuous(breaks = pretty_breaks(n=20)) +
  labs(y = "Duration (seconds)", x = "Gamma", color = "Dataset", title = "FlowSOM clustering run time")

Median run time?

fsom_runtime[, .(median_duration_sec = median(duration_seconds)), by = c("type", "dataset")]
           type      dataset median_duration_sec
1: Single Cells  Samusik_all             52.7870
2: Single Cells Levine_32dim             15.8945
3:   Supercells  Samusik_all              8.0185
4:   Supercells Levine_32dim              4.1705

sessionInfo()
R version 4.2.3 (2023-03-15)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Monterey 12.6

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] scales_1.2.1      ggplot2_3.4.1     stringr_1.5.0     data.table_1.14.8
[5] here_1.0.1        workflowr_1.7.0  

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.10      highr_0.10       compiler_4.2.3   pillar_1.8.1    
 [5] bslib_0.4.2      later_1.3.0      git2r_0.31.0     jquerylib_0.1.4 
 [9] tools_4.2.3      getPass_0.2-2    digest_0.6.31    gtable_0.3.1    
[13] jsonlite_1.8.4   evaluate_0.20    lifecycle_1.0.3  tibble_3.1.8    
[17] pkgconfig_2.0.3  rlang_1.0.6      cli_3.6.0        rstudioapi_0.14 
[21] yaml_2.3.7       xfun_0.39        fastmap_1.1.0    withr_2.5.0     
[25] dplyr_1.1.0      httr_1.4.4       knitr_1.42       generics_0.1.3  
[29] fs_1.6.1         vctrs_0.5.2      sass_0.4.5       tidyselect_1.2.0
[33] grid_4.2.3       rprojroot_2.0.3  glue_1.6.2       R6_2.5.1        
[37] processx_3.8.0   fansi_1.0.4      rmarkdown_2.20   farver_2.1.1    
[41] callr_3.7.3      magrittr_2.0.3   whisker_0.4.1    ps_1.7.2        
[45] promises_1.2.0.1 htmltools_0.5.4  colorspace_2.1-0 httpuv_1.6.9    
[49] utf8_1.2.3       stringi_1.7.12   munsell_0.5.0    cachem_1.0.6