Predictive performannce

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Last updated: 2024-01-01

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Knit directory: mage_2020_marker-gene-benchmarking/

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File	Version	Author	Date	Message
Rmd	2632193	Jeffrey Pullin	2024-01-01	Implement revisions
Rmd	70dee30	Jeffrey Pullin	2023-12-04	Add new ‘maximum sum’ classifier analysis
Rmd	9487c1e	Jeffrey Pullin	2023-06-17	Add draft of dataset characteristic section analysis
Rmd	686c7b2	Jeffrey Pullin	2023-06-11	Add blood datasets to pred-perf comparison
Rmd	d3539cb	Jeffrey Pullin	2023-06-10	Add ‘blood’ datasets
Rmd	59f00aa	Jeffrey Pullin	2023-05-14	Add cell type difficulty analysis
html	fcecf65	Jeffrey Pullin	2022-09-09	Build site.
Rmd	0c2eafc	Jeffrey Pullin	2022-09-09	Update website
html	af96b34	Jeffrey Pullin	2022-08-30	Build site.
html	a4e328e	Jeffrey Pullin	2022-08-29	Build site.
Rmd	464852e	Jeffrey Pullin	2022-08-29	Add SVM classifier
html	0e47874	Jeffrey Pullin	2022-05-04	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)
Rmd	708cfdd	Jeffrey Pullin	2022-02-18	Add first draft of predictive performance comparison

---

Aim

To compare the predictive performance of the marker gene sets different methods select.

library(SingleCellExperiment)
library(dplyr)
library(ggplot2)
library(tidyr)
library(class)
library(scater)
library(forcats)
library(purrr)

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

plot_classifier_metric <- function(data, metric) {
  
  plot_metric <- metric_lookup[metric]
  plot_data_label <- dataset_lookup[data$data_id[[1]]]
  plot_classifier <- switch(
    data$classifier[[1]], 
    knn = "KNN",
    svm = "SVM", 
    sum_max = "\nMaximum summed expression"
  )
 
  # https://github.com/tidyverse/ggplot2/issues/2799 
  cf <- coord_flip(ylim = c(0.5, 1))
  cf$default <- TRUE
  
  plot_data <- data %>% 
    select(!!sym(metric), pars, method)
  
  rm(data)
  
  # FIXME: Add check of whether `metric` is in data.
  plot_data %>%  
    mutate(
      plot_pars = pars_lookup[pars], 
      plot_method = method_lookup[method]
    ) %>% 
    mutate(plot_pars = fct_reorder(factor(plot_pars), !!sym(metric))) %>% 
    ggplot(aes(x = plot_pars, y = !!sym(metric), colour = plot_method)) + 
    geom_boxplot() + 
    package_colour + 
    cf + 
    labs(
      x = "Method", 
      colour = "Package", 
      y = plot_metric,
      title = paste0("Multiclass prediction ", plot_data_label, ", ", 
                     plot_metric, ", ", plot_classifier)
    ) + 
    theme_bw()
}

plot_confusion_matrix <- function(data, pars) {
  
  plot_data <- dataset_lookup[data$data_id[[1]]]
  plot_pars <- pars_lookup[pars]
  plot_classifier <- switch(
    data$classifier[[1]], 
    knn = "KNN",
    svm = "SVM", 
    sum = "SUM"
  )
  
  data %>% 
    filter(pars == !!pars) %>% 
    pull(confusion_mat) %>% 
    pluck(1) %>% 
    as_tibble() %>% 
    ggplot(aes(x = prediction, y = true)) + 
    geom_tile(aes(fill = n), col = "black") + 
    geom_text(aes(label = n)) +
    scale_fill_gradient(low = "white", high = "forestgreen") +
    labs(
      x = "Predicted cell type",
      y = "True cell type", 
      fill = "Number of cells", 
      title = paste0(plot_data, " data, ", plot_pars, 
                     " method, ", plot_classifier)
    ) + 
    theme_bw() + 
    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)
    )
}

pred_perf_data <- retrieve_real_data_parameters() %>% 
  select(-c(fit_method, covariate, rankby, lambda, test_use, rankby_abs, func, 
         test.type, pval.type, metric, test.use)) %>% 
  expand_grid(classifier = c("svm", "knn", "sum_max")) %>% 
  rowwise() %>% 
  mutate(
    pred_perf_filename = paste0("pred_perf-", data_id, "-", method_name, "-", 
                                classifier, ".rds"), 
    pred_perf_path = here::here("results", "pred_perf", pred_perf_filename), 
    pred_perf = list(readRDS(pred_perf_path))
  ) %>% 
  select(-data_id) %>% 
  unnest(pred_perf)

Results

pbmc3k

pred_perf_data %>% 
  filter(data_id == "pbmc3k", classifier == "knn") %>% 
  plot_classifier_metric("median_f1_score")

pred_perf_data %>% 
  filter(data_id == "pbmc3k", classifier == "svm") %>% 
  plot_classifier_metric("median_f1_score")

pred_perf_data %>% 
  filter(data_id == "pbmc3k", classifier == "sum_max") %>% 
  plot_classifier_metric("median_f1_score") + 
  coord_flip(ylim = c(0.0, 1))

pbmc3k_seurat_wilcox_confmat <- pred_perf_data %>% 
  filter(data_id == "pbmc3k", fold == "1", classifier == "knn") %>% 
  plot_confusion_matrix("seurat_wilcox")

pbmc3k_seurat_wilcox_confmat

saveRDS(
  pbmc3k_seurat_wilcox_confmat,
  here::here("figures", "raw", "pbmc3k-seurat-wilcox-confmat.rds")
)

Endothelial data

pred_perf_data %>% 
  filter(data_id == "endothelial", classifier == "knn") %>% 
  plot_classifier_metric("mean_f1_score") 

pred_perf_data %>% 
  filter(data_id == "endothelial", classifier == "svm") %>% 
  plot_classifier_metric("mean_f1_score") 

Zeisel

pred_perf_data %>% 
  filter(data_id == "zeisel", classifier == "knn") %>% 
  plot_classifier_metric("mean_f1_score") 

pred_perf_data %>% 
  filter(data_id == "zeisel", classifier == "svm") %>% 
  plot_classifier_metric("mean_f1_score") 

Paul

pred_perf_data %>% 
  filter(data_id == "paul", classifier == "knn") %>% 
  plot_classifier_metric("mean_f1_score") 

pred_perf_data %>% 
  filter(data_id == "paul", classifier == "svm") %>% 
  plot_classifier_metric("mean_f1_score") 

SMaRT-seq3

pred_perf_data %>% 
  filter(data_id == "ss3_pbmc", classifier == "knn") %>% 
  plot_classifier_metric("mean_f1_score") 

pred_perf_data %>% 
  filter(data_id == "ss3_pbmc", classifier == "svm") %>% 
  plot_classifier_metric("mean_f1_score") 

Mesenchymal

pred_perf_data %>% 
  filter(data_id == "mesenchymal", classifier == "knn") %>% 
  plot_classifier_metric("mean_f1_score") 

pred_perf_data %>% 
  filter(data_id == "mesenchymal", classifier == "svm") %>% 
  plot_classifier_metric("mean_f1_score") 

Lawlor

pred_perf_data %>% 
  filter(data_id == "lawlor", classifier == "knn") %>% 
  plot_classifier_metric("mean_f1_score") 

pred_perf_data %>% 
  filter(data_id == "lawlor", classifier == "svm") %>% 
  plot_classifier_metric("mean_f1_score") + 
  coord_flip(ylim = c(0.0, 0.6))

Astrocyte

pred_perf_data %>% 
  filter(data_id == "astrocyte", classifier == "knn") %>% 
  plot_classifier_metric("mean_f1_score") 

pred_perf_data %>% 
  filter(data_id == "astrocyte", classifier == "svm") %>% 
  plot_classifier_metric("mean_f1_score") 

Zhao

zhao_pred_perf <- pred_perf_data %>% 
  filter(data_id == "zhao", classifier == "knn") %>% 
  plot_classifier_metric("median_f1_score") + 
  coord_flip(ylim = c(0.5, 0.85))

zhao_pred_perf

saveRDS(
  zhao_pred_perf,
  here::here("figures", "raw", "zhao-pred-perf.rds")
)

zhao_pred_perf_svm <- pred_perf_data %>% 
  filter(data_id == "zhao", classifier == "svm") %>% 
  plot_classifier_metric("median_f1_score") + 
  coord_flip(ylim = c(0.5, 0.95))

zhao_pred_perf_svm 

saveRDS(
  zhao_pred_perf_svm,
  here::here("figures", "raw", "zhao-pred-perf-svm.rds")
)

zhao_pred_perf_sum_max <- pred_perf_data %>% 
  filter(data_id == "zhao", classifier == "sum_max") %>% 
  plot_classifier_metric("median_f1_score") + 
  coord_flip(ylim = c(0, 1))

zhao_pred_perf_sum_max

saveRDS(
  zhao_pred_perf_sum_max,
  here::here("figures", "raw", "zhao-pred-perf-sum_max.rds")
)

Overall

overall_multiclass_pred_rank_knn_plot <- pred_perf_data %>% 
  filter(classifier == "knn") %>% 
  group_by(data_id, pars, method) %>% 
  summarise(median_f1_score = median(median_f1_score), .groups = "drop") %>% 
  group_by(data_id) %>% 
  mutate(rank = rank(median_f1_score)) %>%
  ungroup() %>% 
  mutate(
    plot_method = method_lookup[method], 
    plot_pars = pars_lookup[pars],
    plot_data_id = dataset_lookup[data_id]
  ) %>% 
  # This step encodes the ranking by rank. 
  mutate(plot_pars = fct_reorder(factor(plot_pars), rank, .fun = median)) %>% 
  ggplot(aes(x = plot_data_id, y = plot_pars)) +
  geom_tile(aes(fill = rank), colour = "black") + 
  scale_fill_distiller(palette = "RdYlBu", 
                       breaks = seq(1, 56, by = 5),
                       labels = seq(56, 1, by = -5)) + 
  theme_bw() + 
  labs(
    title = "Median F1-score rank across datasets, KNN",
    x = "Dataset", 
    y = "Method", 
    fill = "Rank",
  ) + 
  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_multiclass_pred_rank_knn_plot

overall_multiclass_pred_rank_svm_plot <- pred_perf_data %>% 
  filter(data_id != "lawlor") %>% 
  filter(classifier == "svm") %>% 
  group_by(data_id, pars, method) %>% 
  summarise(median_f1_score = median(median_f1_score), .groups = "drop") %>% 
  group_by(data_id) %>% 
  mutate(rank = rank(median_f1_score)) %>%
  ungroup() %>% 
  mutate(
    plot_method = method_lookup[method], 
    plot_pars = pars_lookup[pars],
    plot_data_id = dataset_lookup[data_id]
  ) %>% 
  # This step encodes the ranking by rank. 
  mutate(plot_pars = fct_reorder(factor(plot_pars), rank, .fun = median)) %>% 
  ggplot(aes(x = plot_data_id, y = plot_pars)) +
  geom_tile(aes(fill = rank), colour = "black") + 
  scale_fill_distiller(palette = "RdYlBu", 
                       breaks = seq(1, 56, by = 5),
                       labels = seq(56, 1, by = -5)) + 
  theme_bw() + 
  labs(
    title = "Median F1-score rank across datasets, SVM",
    x = "Dataset", 
    y = "Method", 
    fill = "Rank",
  ) + 
  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_multiclass_pred_z_score_knn_plot <- pred_perf_data %>% 
  filter(classifier == "knn") %>% 
  group_by(data_id, pars, method) %>% 
  summarise(median_f1_score = median(median_f1_score), .groups = "drop") %>% 
  group_by(data_id) %>% 
  mutate(score = scale(median_f1_score)[, 1]) %>% 
  ungroup() %>% 
  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), score, .fun = mean)) %>% 
  ggplot(aes(x = plot_data_id, y = plot_pars)) +
  geom_tile(aes(fill = score), colour = "black") + 
  scale_fill_distiller(palette = "RdYlBu") + 
  theme_bw() + 
  labs(
    title = "Mean z-score F1-score across datasets, KNN",
    x = "Dataset", 
    y = "Method", 
    fill = "z-score",
  ) + 
  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)
  )

saveRDS(
  overall_multiclass_pred_z_score_knn_plot,
  here::here("figures", "raw", "overall-mc-pred-plot-z-score-knn.rds")
)

overall_multiclass_pred_z_score_svm_plot <- pred_perf_data %>% 
  filter(data_id != "lawlor") %>% 
  filter(classifier == "svm") %>% 
  group_by(data_id, pars, method) %>% 
  summarise(median_f1_score = median(median_f1_score), .groups = "drop") %>% 
  group_by(data_id) %>% 
  mutate(score = scale(median_f1_score)[, 1]) %>% 
  ungroup() %>% 
  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), score, .fun = mean)) %>% 
  ggplot(aes(x = plot_data_id, y = plot_pars)) +
  geom_tile(aes(fill = score), colour = "black") + 
  scale_fill_distiller(palette = "RdYlBu") + 
  theme_bw() + 
  labs(
    title = "Mean z-score F1-score across datasets, SVM",
    x = "Dataset", 
    y = "Method", 
    fill = "z-score",
  ) + 
  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)
  )

saveRDS(
  overall_multiclass_pred_z_score_svm_plot,
  here::here("figures", "raw", "overall-mc-pred-plot-z-score-svm.rds")
)

overall_multiclass_pred_z_score_sum_max_plot <- pred_perf_data %>% 
  filter(classifier == "sum_max") %>% 
  group_by(data_id, pars, method) %>% 
  summarise(median_f1_score = median(median_f1_score), .groups = "drop") %>% 
  group_by(data_id) %>% 
  mutate(score = scale(median_f1_score)[, 1]) %>% 
  ungroup() %>% 
  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), score, .fun = mean)) %>% 
  ggplot(aes(x = plot_data_id, y = plot_pars)) +
  geom_tile(aes(fill = score), colour = "black") + 
  scale_fill_distiller(palette = "RdYlBu") + 
  theme_bw() + 
  labs(
    title = 
    "Mean z-score F1-score across datasets,\nMaximum summed gene expression",
    x = "Dataset", 
    y = "Method", 
    fill = "z-score",
  ) + 
  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)
  )

saveRDS(
  overall_multiclass_pred_z_score_sum_max_plot,
  here::here("figures", "raw", "overall-mc-pred-plot-z-score-sum_max.rds")
)

Session information

devtools::session_info()

─ Session info  ──────────────────────────────────────────────────────────────
 hash: weary cat, man: curly hair, hammer and wrench

 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)
 beachmat               2.10.0   2021-10-26 [1] Bioconductor
 beeswarm               0.4.0    2021-06-01 [2] CRAN (R 4.1.2)
 Biobase              * 2.54.0   2021-10-26 [1] Bioconductor
 BiocGenerics         * 0.40.0   2021-10-26 [1] Bioconductor
 BiocNeighbors          1.12.0   2021-10-26 [1] Bioconductor
 BiocParallel           1.28.3   2021-12-09 [1] Bioconductor
 BiocSingular           1.10.0   2021-10-26 [1] Bioconductor
 bitops                 1.0-7    2021-04-24 [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)
 class                * 7.3-19   2021-05-03 [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)
 DelayedArray           0.20.0   2021-10-26 [1] Bioconductor
 DelayedMatrixStats     1.16.0   2021-10-26 [1] Bioconductor
 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)
 GenomeInfoDb         * 1.30.0   2021-10-26 [1] Bioconductor
 GenomeInfoDbData       1.2.7    2021-12-03 [1] Bioconductor
 GenomicRanges        * 1.46.1   2021-11-18 [1] Bioconductor
 ggbeeswarm             0.6.0    2017-08-07 [2] CRAN (R 4.1.2)
 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)
 gridExtra              2.3      2017-09-09 [2] CRAN (R 4.1.2)
 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)
 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)
 IRanges              * 2.28.0   2021-10-26 [1] Bioconductor
 irlba                  2.3.5    2021-12-06 [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)
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 magrittr               2.0.3    2022-03-30 [1] CRAN (R 4.1.0)
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 pillar                 1.7.0    2022-02-01 [1] CRAN (R 4.1.0)
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 zlibbioc               1.40.0   2021-10-26 [1] Bioconductor

 [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

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