Differential expression parameter simulation analysis
Last updated: 2024-01-01
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Knit directory:
mage_2020_marker-gene-benchmarking/
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library(tibble)
library(dplyr)
library(ggplot2)
library(tidyr)
library(SingleCellExperiment)
library(scater)
library(forcats)
library(purrr)
library(patchwork)
source(here::here("code", "top-genes.R"))
source(here::here("code", "analysis-utils.R"))
source(here::here("code", "plot-utils.R"))plot_metric_overall <- function(data,
n_true = 20,
n_sel = 20,
direction = "up",
metric = "recall") {
plot_metric <- metric_lookup[metric]
plot_data <- data %>%
mutate(n_true = n_true, n_sel = n_sel, direction = direction) %>%
rowwise() %>%
dplyr::filter(!is.null(sel_mgs)) %>%
mutate(
true_mgs = list(get_top_true_mgs(
true_mgs,
n = n_true,
direction = direction,
sort_by_score = "mean_score")
),
sel_mgs = list(get_top_sel_mgs(
sel_mgs,
n = n_sel,
direction = direction)
),
recall = calculate_recall(sel_mgs$gene, true_mgs$gene),
precision = calculate_precision(sel_mgs$gene, true_mgs$gene),
f1_score = (2 * recall * precision) / (recall + precision)
) %>%
mutate(f1_score = if_else(recall == 0 & precision == 0, 0, f1_score)) %>%
ungroup() %>%
group_by(pars, data_id, method) %>%
summarise(!!sym(metric) := median(!!sym(metric)), .groups = "drop") %>%
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), !!sym(metric)))
rm(data)
ggplot(plot_data, aes(x = plot_data_id, y = plot_pars)) +
geom_tile(aes(fill = !!sym(metric)), colour = "black") +
scale_fill_distiller(palette = "RdYlBu", limits = c(0, 1)) +
theme_bw() +
labs(
title = paste0(plot_metric, " across simulations"),
x = "Dataset",
y = "Method",
fill = plot_metric,
) +
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)
)
}de_pars_metrics_data <- retrive_simulation_parameters() %>%
filter(sim_label == "de_facloc") %>%
rowwise() %>%
mutate(
mgs_raw = list(readRDS(full_filename)$result),
mgs = list(split(mgs_raw, mgs_raw$cluster))
) %>%
ungroup() %>%
unnest_longer(
col = mgs,
values_to = "mgs",
indices_to = "cluster"
) %>%
select(-mgs_raw) %>%
mutate(umg_path = here::here(
"data", "sim_mgs", paste0("mg-", sim_name, "-", data_id, ".rds"))
) %>%
rowwise() %>%
mutate(true_mgs = list(readRDS(umg_path))) %>%
mutate(cluster_2 = paste0("group_", substr(cluster, 6, 6))) %>%
mutate(true_mgs = list(true_mgs[[cluster_2]])) %>%
dplyr::rename(sel_mgs = mgs) %>%
ungroup()de_facloc_1_results <- de_pars_metrics_data %>%
filter(de.facLoc == 1) %>%
plot_metric_overall(
n_true = 20,
n_sel = 20,
direction = "up",
metric = "f1_score"
) +
ggtitle("de.facLoc = 1")
de_facloc_2_results <- de_pars_metrics_data %>%
filter(de.facLoc == 2) %>%
plot_metric_overall(
n_true = 20,
n_sel = 20,
direction = "up",
metric = "f1_score"
) +
ggtitle("de.facLoc = 2") +
theme(axis.title.y = element_blank())
de_facloc_3_results <- de_pars_metrics_data %>%
filter(de.facLoc == 3) %>%
plot_metric_overall(
n_true = 20,
n_sel = 20,
direction = "up",
metric = "f1_score"
) +
ggtitle("de.facLoc = 3") +
theme(axis.title.y = element_blank())
de_pars_results <- de_facloc_1_results + de_facloc_2_results + de_facloc_3_results +
plot_annotation(tag_levels = "a") +
plot_layout(guides = "collect") &
theme(plot.tag = element_text(size = 18))
de_pars_results
ggsave(
here::here("figures", "final", "de_pars_results.pdf"),
de_pars_results,
width = 14,
height = 8,
units = "in"
)sim_1 <- readRDS(here::here("data", "sim_data", "de_facloc_1_sim_1-pbmc3k.rds"))
sim_2 <- readRDS(here::here("data", "sim_data", "de_facloc_2_sim_1-pbmc3k.rds"))
sim_3 <- readRDS(here::here("data", "sim_data", "de_facloc_3_sim_1-pbmc3k.rds"))
top_1_mgs <- de_pars_metrics_data %>%
filter(de.facLoc == 1, rep == 1, data_id == "pbmc3k") %>%
pull(true_mgs) %>%
pluck(1) %>%
get_top_true_mgs(n = "all", direction = "up") %>%
dplyr::slice(1:2) %>%
pull(gene)
top_2_mgs <- de_pars_metrics_data %>%
filter(de.facLoc == 2, rep == 1, data_id == "pbmc3k") %>%
pull(true_mgs) %>%
pluck(1) %>%
get_top_true_mgs(n = "all", direction = "up") %>%
dplyr::slice(1:2) %>%
pull(gene)
top_3_mgs <- de_pars_metrics_data %>%
filter(de.facLoc == 3, rep == 1, data_id == "pbmc3k") %>%
pull(true_mgs) %>%
pluck(1) %>%
get_top_true_mgs(n = "all", direction = "up") %>%
dplyr::slice(1:2) %>%
pull(gene)
de_facloc_1_mgs <- plotExpression(sim_1, x = "label", features = top_1_mgs) +
ggtitle("defacLoc = 1")
de_facloc_2_mgs <- plotExpression(sim_2, x = "label", features = top_2_mgs) +
ggtitle("defacLoc = 2")
de_facloc_3_mgs <- plotExpression(sim_3, x = "label", features = top_3_mgs) +
ggtitle("defacLoc = 3")
de_pars_mgs <- de_facloc_1_mgs / de_facloc_2_mgs / de_facloc_3_mgs +
plot_annotation(tag_levels = "a") &
theme(plot.tag = element_text(size = 18))
de_pars_mgs
ggsave(
here::here("figures", "final", "de_pars_mgs.pdf"),
de_pars_mgs,
width = 8,
height = 8,
units = "in"
)
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