Prediction Targets

Author

Philipp Sven Lars Schäfer

Published

November 28, 2024

1 Packages

Code

suppressPackageStartupMessages({
  library(tidyverse)
  library(ggdark)
  library(magick)
  source(file.path("..", "src", "read_data.R"))
  source(file.path("..", "src", "colors.R"))
  source(file.path("..", "src", "generate_targets.R"))
})

2 Data

Code

input_dir = file.path("..", "data")

Code

meta_data <- read_harmonized_meta_data(input_dir)
gene_meta <- read_gene_meta(input_dir)

experimental_data <- read_raw_experimental_data(input_dir)
experimental_data <- filter_experimental_data(meta_data, experimental_data, gene_meta)

pbmc_cell_frequency | Removed 550 specimens because missing in meta data

plasma_ab_titer | Removed 6931 specimens because missing in meta data

plasma_cytokine_concentration_by_olink | Removed 495 specimens because missing in meta data

pbmc_cell_frequency | Removed 56 features because not in feature subset

plasma_ab_titer | Removed 48 features because not in feature subset

plasma_cytokine_concentration_by_olink | Removed 234 features because not in feature subset

t_cell_polarization | Removed 3 features because not in feature subset

plasma_cytokine_concentration_by_olink | Removed 300 features because qc warning

plasma_ab_titer | Removed 10540 measurements because wrong unit used

plasma_cytokine_concentration_by_olink | Removed 2400 measurements because wrong unit used

plasma_cytokine_concentration_by_legendplex | Removed 8 because specimen is outlier

plasma_cytokine_concentration_by_olink | Removed specimen 750, 760, 824, 833, 894, 903 because fraction of measurements below LOQ > 50%

plasma_ab_titer | Removed specimen 674, 675, 676 because fraction of measurements below LOD > 50%

Code

wide_experimental_data <- generate_wide_experimental_data(experimental_data=experimental_data, 
                                                 impute="zero")

plasma_cytokine_concentration_by_olink | NA Fraction: 0.00288417166589755 | Imputed with zero imputation

t_cell_activation | NA Fraction: 0.0576923076923077 | Removed samples: 681

t_cell_activation | NA Fraction: 0.0553691275167785 | Imputed with zero imputation

t_cell_polarization | NA Fraction: 0.0134099616858238 | Imputed with zero imputation

Code

celltype_meta <- read_celltype_meta(input_dir)
gene_meta <- read_gene_meta(input_dir)
protein_meta <- read_protein_meta(input_dir)

3 Time Points

Code

specimen_list <- get_specimen_per_day(meta_data)

purrr::imap(specimen_list, ~ .x %>% dplyr::mutate(day=.y)) %>%
  dplyr::bind_rows() %>%
  dplyr::mutate(day = factor(day, levels=paste0("day_", c(0, 1, 3, 14, 30)))) %>%
  ggplot() +
  geom_histogram(aes(x=actual_day_relative_to_boost), binwidth=1) +
  geom_vline(xintercept=c(0,1,3,14, 30), color="forestgreen") +
  facet_wrap(~day, ncol=1) +
  ggdark::dark_mode(verbose=FALSE) +
  scale_x_continuous(breaks=seq(min(unlist(acceptable_differences)),
                                max(unlist(acceptable_differences))))

4 Generate Targets for each Task

4.1 Task 1) Antibody level tasks

1.1) Rank the individuals by IgG antibody levels against pertussis toxin (PT) that we detect in plasma 14 days post booster vaccinations.
1.2) Rank the individuals by fold change of IgG antibody levels against pertussis toxin (PT) that we detect in plasma 14 days post booster vaccinations compared to titer values at day 0.

Code

targets_task_1 <- generate_targets_task_1(meta_data=meta_data, 
                                          experimental_data=experimental_data, 
                                          experimental_data_settings=experimental_data_settings, 
                                          specimen_list=specimen_list)

Code

plot_targets(targets_task_1)

Code

targets_task_1 %>%
  dplyr::left_join((meta_data %>% dplyr::select(subject_id, dataset) %>% dplyr::distinct()),
                   by="subject_id") %>%
  ggplot() +
  geom_histogram(aes(x=task11), bins=50, color="black") +
  facet_wrap(~dataset, ncol=1) + 
  ggdark::dark_mode()

Code

targets_task_1 %>%
  dplyr::left_join((meta_data %>% dplyr::select(subject_id, dataset) %>% dplyr::distinct()),
                   by="subject_id") %>%
  ggplot() +
  geom_histogram(aes(x=task12), bins=50, color="black") +
  facet_wrap(~dataset, ncol=1) + 
  ggdark::dark_mode()

Code

targets_task_1 %>%
  dplyr::left_join((meta_data %>% dplyr::select(subject_id, dataset) %>% dplyr::distinct()),
                   by="subject_id") %>%
  ggplot() +
  geom_density(aes(x=task12)) +
  facet_wrap(~dataset, ncol=1) + 
  ggdark::dark_mode()

4.2 Task 2) Cell frequency tasks

2.1) Rank the individuals by predicted frequency of Monocytes on day 1 post boost after vaccination.
2.2) Rank the individuals by fold change of predicted frequency of Monocytes on day 1 post booster vaccination compared to cell frequency values at day 0.

Code

targets_task_2 <- generate_targets_task_2(meta_data=meta_data, 
                                          experimental_data=experimental_data, 
                                          experimental_data_settings=experimental_data_settings,
                                          specimen_list=specimen_list)
targets_task_2

Code

plot_targets(targets_task_2)

Code

targets_task_2 %>%
  dplyr::left_join((meta_data %>% dplyr::select(subject_id, dataset) %>% dplyr::distinct()),
                   by="subject_id") %>%
  ggplot() +
  geom_histogram(aes(x=task21), bins=50, color="black") +
  facet_wrap(~dataset, ncol=1) + 
  ggdark::dark_mode()

Code

targets_task_2 %>%
  dplyr::left_join((meta_data %>% dplyr::select(subject_id, dataset) %>% dplyr::distinct()),
                   by="subject_id") %>%
  ggplot() +
  geom_histogram(aes(x=task22), bins=50, color="black") +
  facet_wrap(~dataset, ncol=1) + 
  ggdark::dark_mode()

Code

targets_task_2 %>%
  dplyr::left_join((meta_data %>% dplyr::select(subject_id, dataset) %>% dplyr::distinct()),
                   by="subject_id") %>%
  ggplot() +
  geom_density(aes(x=task22)) +
  facet_wrap(~dataset, ncol=1) + 
  ggdark::dark_mode()

4.3 Task 3) Gene expression tasks

3.1) Rank the individuals by predicted gene expression of CCL3 on day 3 post-booster vaccination.
3.2) Rank the individuals by fold change of predicted gene expression of CCL3 on day 3 post booster vaccination compared to gene expression values at day 0.

Code

targets_task_3 <- generate_targets_task_3(meta_data=meta_data, 
                                          experimental_data=experimental_data, 
                                          experimental_data_settings=experimental_data_settings, 
                                          specimen_list=specimen_list,
                                          gene_meta=gene_meta)
targets_task_3

Code

plot_targets(targets_task_3)

Code

targets_task_3 %>%
  dplyr::left_join((meta_data %>% dplyr::select(subject_id, dataset) %>% dplyr::distinct()),
                   by="subject_id") %>%
  ggplot() +
  geom_histogram(aes(x=task31), bins=50, color="black") +
  facet_wrap(~dataset, ncol=1) + 
  ggdark::dark_mode()

Code

targets_task_3 %>%
  dplyr::left_join((meta_data %>% dplyr::select(subject_id, dataset) %>% dplyr::distinct()),
                   by="subject_id") %>%
  ggplot() +
  geom_density(aes(x=log10(task31))) +
  facet_wrap(~dataset, ncol=1) + 
  ggdark::dark_mode()

Code

targets_task_3 %>%
  dplyr::left_join((meta_data %>% dplyr::select(subject_id, dataset) %>% dplyr::distinct()),
                   by="subject_id") %>%
  ggplot() +
  geom_histogram(aes(x=task32), bins=50, color="black") +
  facet_wrap(~dataset, ncol=1) + 
  ggdark::dark_mode()

Code

targets_task_2 %>%
  dplyr::left_join((meta_data %>% dplyr::select(subject_id, dataset) %>% dplyr::distinct()),
                   by="subject_id") %>%
  ggplot() +
  geom_density(aes(x=task22)) +
  facet_wrap(~dataset, ncol=1) + 
  ggdark::dark_mode()

5 Conclusions

Task 1: Substantial differences in the marginal distributions of the target variables between the cohorts / years.
Task 2: Substantial differences in the marginal distributions of the target variables between the cohorts / years.
Task 3: Small differences in the marginal distributions of the target variables between the cohorts / years.