Simulation validation analysis
Last updated: 2024-01-01
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mage_2020_marker-gene-benchmarking/
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library(SingleCellExperiment)
library(dplyr)
library(ggplot2)
library(patchwork)
library(scater)
library(purrr)
library(tidyr)
source(here::here("code", "analysis-utils.R"))
source(here::here("code", "run-utils.R"))Load data
pbmc3k_sim <- readRDS(
here::here("data", "sim_data", "standard_sim_1-pbmc3k.rds")
)
lawlor_sim <- readRDS(
here::here("data", "sim_data", "standard_sim_1-lawlor.rds")
)
zeisel_sim <- readRDS(
here::here("data", "sim_data", "standard_sim_1-zeisel.rds")
)
endothelial_sim <- readRDS(
here::here("data", "sim_data", "standard_sim_1-endothelial.rds")
)pbmc3k <- readRDS(
here::here("data", "real_data", "pbmc3k.rds")
)
lawlor <- readRDS(
here::here("data", "real_data", "lawlor.rds")
)
zeisel <- readRDS(
here::here("data", "real_data", "zeisel.rds")
)
endothelial <- readRDS(
here::here("data", "real_data", "endothelial.rds")
)pbmc3k_mg_info <- readRDS(
here::here("data", "sim_mgs", "mg-standard_sim_1-pbmc3k.rds")
)
lawlor_mg_info <- readRDS(
here::here("data", "sim_mgs", "mg-standard_sim_1-lawlor.rds")
)
zeisel_mg_info <- readRDS(
here::here("data", "sim_mgs", "mg-standard_sim_1-zeisel.rds")
)
endothelial_mg_info <- readRDS(
here::here("data", "sim_mgs", "mg-standard_sim_1-endothelial.rds")
)pbmc3k_result <- readRDS(
here::here("results", "real_data", "pbmc3k-seurat_wilcox.rds")
)
lawlor_result <- readRDS(
here::here("results", "real_data", "lawlor-seurat_wilcox.rds")
)
zeisel_result <- readRDS(
here::here("results", "real_data", "zeisel-seurat_wilcox.rds")
)
endothelial_result <- readRDS(
here::here("results", "real_data", "endothelial-seurat_wilcox.rds")
)pbmc3k_sim_result <- readRDS(
here::here("results", "sim_data", "standard_sim_1-pbmc3k-seurat_wilcox.rds")
)
lawlor_sim_result <- readRDS(
here::here("results", "sim_data", "standard_sim_1-lawlor-seurat_wilcox.rds")
)
zeisel_sim_result <- readRDS(
here::here("results", "sim_data", "standard_sim_1-zeisel-seurat_wilcox.rds")
)
endothelial_sim_result <- readRDS(
here::here("results", "sim_data", "standard_sim_1-endothelial-seurat_wilcox.rds")
)pbmc3k_expert_mgs <- readRDS(
here::here("data", "expert_mgs", "pbmc3k_expert_mgs.rds")
)
lawlor_expert_mgs <- readRDS(
here::here("data", "expert_mgs", "lawlor_expert_mgs.rds")
)plot_specifc_mgs <- function(sce, mg_info, index = c(1, 10, 20, 30),
direction = "up", cluster_ind = 1) {
last_index <- index[length(index)]
top_mgs <- get_top_true_mgs(mg_info[[cluster_ind]], direction = "up",
n = last_index + 1)
specific_mgs <- top_mgs$gene[index]
plotExpression(sce, x = "label", features = specific_mgs)
}
plot_logfc_sim_mgs <- function(sce, mg_info, n = "all", direction = "up",
cluster_ind = 1) {
top_mgs <- get_top_true_mgs(mg_info[[cluster_ind]], direction = "up",
n = n)
if (n == "all") {
n <- nrow(top_mgs)
}
clusters <- rep(paste0("Group", cluster_ind), n)
log_fc <- calculate_log_fc(sce, top_mgs$gene, clusters)
plot_data <- tibble(
log_fc,
index = 1:n
)
ggplot(plot_data, aes(x = index, y = log_fc)) +
geom_point() +
geom_smooth(se = FALSE, formula = y ~ x, method = "loess") +
labs(
x = "Index",
y = "Log fold-change"
) +
theme_bw()
}
plot_logfc_real_mgs <- function(sce, result, cluster, n = 200, direction = "up") {
top_mgs <- result %>%
pluck("result") %>%
filter(cluster == !!cluster) %>%
get_top_sel_mgs(direction = "up", n = n)
clusters <- rep(cluster, n)
log_fc <- calculate_log_fc(sce, top_mgs$gene, clusters)
plot_data <- tibble(
log_fc,
index = 1:n
)
ggplot(plot_data, aes(x = index, y = log_fc)) +
geom_point() +
geom_smooth(se = FALSE, formula = y ~ x, method = "loess") +
labs(
x = "Index",
y = "Log fold-change"
) +
theme_bw()
}pbmc3k_top_sim_mgs <- pbmc3k_mg_info %>%
map(~ get_top_true_mgs(.x, n = 3, direction = "up")) %>%
imap(~ mutate(.x, cluster = .y)) %>%
bind_rows() %>%
mutate(cluster = paste0("Group", substr(cluster, 7, 7)))
pbmc3k_top_expert_mgs <- pbmc3k_expert_mgs %>%
unnest(expert_mgs) %>%
dplyr::rename(gene = expert_mgs)
pbmc3k_top_sim_logfcs <- calculate_log_fc(
pbmc3k_sim,
genes = pbmc3k_top_sim_mgs$gene,
clusters = pbmc3k_top_sim_mgs$cluster
)
pbmc3k_top_expert_logfcs <- calculate_log_fc(
pbmc3k,
genes = pbmc3k_top_expert_mgs$gene,
clusters = pbmc3k_top_expert_mgs$cluster
)
tibble(sim = pbmc3k_top_sim_logfcs, expert = pbmc3k_top_expert_logfcs) %>%
pivot_longer(cols = everything(), names_to = "type", values_to = "logfc") %>%
ggplot(aes(x = type, y = logfc)) +
geom_boxplot() +
coord_flip() +
theme_bw() +
labs(
x = "",
y = "One-vs-rest log fold-change"
)
lawlor_top_sim_mgs <- lawlor_mg_info %>%
map(~ get_top_true_mgs(.x, n = 1, direction = "up")) %>%
imap(~ mutate(.x, cluster = .y)) %>%
bind_rows() %>%
mutate(cluster = paste0("Group", substr(cluster, 7, 7)))
lawlor_top_expert_mgs <- lawlor_expert_mgs %>%
unnest(expert_mgs) %>%
rename(gene = expert_mgs)
lawlor_top_sim_logfcs <- calculate_log_fc(
lawlor_sim,
genes = lawlor_top_sim_mgs$gene,
clusters = lawlor_top_sim_mgs$cluster
)
lawlor_top_expert_logfcs <- calculate_log_fc(
lawlor,
genes = lawlor_top_expert_mgs$gene,
clusters = lawlor_top_expert_mgs$cluster
)
bind_rows(
tibble(type = "sim", logfc = lawlor_top_sim_logfcs),
tibble(type = "expert", logfc = lawlor_top_expert_logfcs)
) %>%
ggplot(aes(x = type, y = logfc)) +
geom_boxplot() +
coord_flip() +
theme_bw() +
labs(
x = "",
y = "One-vs-rest log fold-change"
)
expert_vs_simulated_lfc <- bind_rows(
tibble(type = "Simulated", data = "Lawlor",
logfc = lawlor_top_sim_logfcs),
tibble(type = "Expert-annotated", data = "Lawlor",
logfc = lawlor_top_expert_logfcs),
tibble(type = "Simulated", data = "pbmc3k",
logfc = pbmc3k_top_sim_logfcs),
tibble(type = "Expert-annotated", data = "pbmc3k",
logfc = pbmc3k_top_expert_logfcs)
) %>%
ggplot(aes(x = data, y = logfc, fill = data)) +
geom_boxplot() +
coord_flip() +
facet_grid(rows = vars(type)) +
theme_bw() +
labs(
x = "",
y = "One-vs-rest log fold-change"
) +
guides(fill = "none")
ggsave(
here::here("figures", "final", "simulated-vs-expert-lfc.pdf"),
expert_vs_simulated_lfc,
width = 12,
height = 12,
units = "in"
)Simulated log fold-change curves
plot_logfc_sim_mgs(pbmc3k_sim, pbmc3k_mg_info) 
plot_logfc_sim_mgs(zeisel_sim, zeisel_mg_info) 
plot_logfc_sim_mgs(lawlor_sim, lawlor_mg_info) 
plot_logfc_sim_mgs(endothelial_sim, endothelial_mg_info) 
Real log fold-change curves
plot_logfc_real_mgs(pbmc3k, pbmc3k_result, cluster = "B") 
#plot_logfc_real_mgs(zeisel, zeisel_result, cluster = "microglia")
plot_logfc_real_mgs(lawlor, lawlor_result, cluster = "Alpha") 
Plot specific cluster examples
plot_specifc_mgs(pbmc3k_sim, pbmc3k_mg_info, c(12, 32, 52))
plot_specifc_mgs(zeisel_sim, zeisel_mg_info, c(1, 10, 20))
plot_specifc_mgs(lawlor_sim, lawlor_mg_info, c(1, 10, 20))
plot_specifc_mgs(lawlor_sim, lawlor_mg_info, c(1, 10, 20))
pbmc3k_mg_info[[1]] %>%
get_top_true_mgs(n = 20, direction = "up", up_mean_filter = 0.1)# A tibble: 20 × 8
gene gene_mean fc mean_score median_score max_score direction de_value
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
1 Gene45 0.313 3.98 3.98 3.17 6.39 up 23.9
2 Gene778 0.216 3.73 3.73 2.91 6.20 up 18.3
3 Gene1916 0.104 3.64 3.64 2.95 5.72 up 19.1
4 Gene82 0.232 3.57 3.57 2.80 5.86 up 16.5
5 Gene1637 0.132 3.48 3.48 3.48 3.48 up 32.5
6 Gene565 0.235 3.47 3.47 2.66 5.88 up 14.3
7 Gene1228 0.175 3.46 3.46 3.46 3.46 up 31.7
8 Gene1087 0.105 3.30 3.30 3.30 3.30 up 27.0
9 Gene189 0.234 3.29 3.29 3.28 6.39 up 26.5
10 Gene37 0.184 3.27 3.27 3.27 3.27 up 26.4
11 Gene823 0.152 3.21 3.21 3.21 3.21 up 24.8
12 Gene1576 0.242 3.20 3.20 3.20 3.20 up 24.5
13 Gene1227 0.200 3.16 3.16 3.16 3.16 up 23.6
14 Gene1904 0.110 3.15 3.15 3.15 3.15 up 23.4
15 Gene1868 0.254 3.15 3.15 3.15 3.15 up 23.3
16 Gene167 0.647 3.14 3.14 3.14 3.14 up 23.1
17 Gene366 0.121 3.13 3.13 3.13 3.13 up 22.9
18 Gene1604 0.145 3.11 3.11 3.11 3.11 up 22.4
19 Gene1481 0.487 3.10 3.10 3.10 3.10 up 22.2
20 Gene1894 0.227 3.09 3.09 3.09 3.09 up 22.0pbmc3k_sim_result$result %>%
filter(cluster == "Group1") %>%
print(n = 30)# A tibble: 510 × 7
p_value p_value_adj cluster log_fc gene raw_statistic scaled_statistic
<dbl> <dbl> <fct> <dbl> <chr> <dbl> <dbl>
1 4.54e-272 9.05e-269 Group1 3.60 Gene1080 0 0
2 3.28e-271 6.54e-268 Group1 3.67 Gene319 0 0
3 9.54e-271 1.90e-267 Group1 3.41 Gene376 0 0
4 1.17e-265 2.33e-262 Group1 3.47 Gene1904 0 0
5 5.14e-265 1.02e-261 Group1 4.15 Gene1637 0 0
6 4.75e-263 9.47e-260 Group1 4.32 Gene37 0 0
7 1.26e-262 2.50e-259 Group1 3.19 Gene1916 0 0
8 9.40e-261 1.87e-257 Group1 3.96 Gene823 0 0
9 1.22e-260 2.42e-257 Group1 4.41 Gene1228 0 0
10 1.40e-260 2.78e-257 Group1 3.64 Gene1604 0 0
11 1.07e-255 2.13e-252 Group1 4.05 Gene158 0 0
12 6.75e-255 1.34e-251 Group1 3.91 Gene82 0 0
13 3.77e-253 7.50e-250 Group1 3.92 Gene467 0 0
14 4.46e-253 8.88e-250 Group1 3.39 Gene1222 0 0
15 2.27e-252 4.52e-249 Group1 4.00 Gene778 0 0
16 2.50e-252 4.98e-249 Group1 4.21 Gene1227 0 0
17 9.19e-252 1.83e-248 Group1 4.69 Gene45 0 0
18 2.36e-250 4.70e-247 Group1 3.05 Gene1050 0 0
19 1.74e-249 3.47e-246 Group1 3.50 Gene1087 0 0
20 2.51e-246 4.99e-243 Group1 4.12 Gene151 0 0
21 3.07e-246 6.12e-243 Group1 3.66 Gene532 0 0
22 8.55e-242 1.70e-238 Group1 4.28 Gene1576 0 0
23 3.57e-241 7.11e-238 Group1 3.40 Gene1250 0 0
24 5.65e-240 1.12e-236 Group1 4.37 Gene1868 0 0
25 7.04e-240 1.40e-236 Group1 2.94 Gene283 0 0
26 1.36e-238 2.70e-235 Group1 4.12 Gene649 0 0
27 4.35e-238 8.67e-235 Group1 3.29 Gene1900 0 0
28 5.47e-238 1.09e-234 Group1 4.43 Gene860 0 0
29 1.38e-236 2.75e-233 Group1 4.20 Gene1894 0 0
30 7.13e-236 1.42e-232 Group1 3.68 Gene841 0 0
# … with 480 more rowspbmc3k_sim_result$result %>%
filter(cluster == "Group1") %>%
mutate(n = 1:n()) %>%
filter(gene %in% c("Gene1270", "Gene46", "Gene865")) %>%
print(n = 30)# A tibble: 1 × 8
p_value p_value_adj cluster log_fc gene raw_statistic scaled_statistic n
<dbl> <dbl> <fct> <dbl> <chr> <dbl> <dbl> <int>
1 3.70e-50 7.38e-47 Group1 -1.50 Gene… 0 0 110plotExpression(pbmc3k_sim, x = "label",
features = c("Gene90", "Gene1413", "Gene1098"))
plotExpression(pbmc3k_sim, x = "label",
features = c("Gene743", "Gene579"))
plotExpression(pbmc3k_sim, x = "label",
features = c("Gene1270", "Gene46", "Gene865"))
plotExpression(pbmc3k_sim, x = "label",
features = c("Gene46"))
Down-regulated marker genes analysis
lawlor_mg_info %>%
pluck("group_1") %>%
filter(gene_mean > 0.1) %>%
arrange(desc(mean_score))# A tibble: 2,000 × 8
gene gene_mean fc mean_score median_score max_score direction de_value
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
1 Gene1232 151. 3.99 3.99 3.24 6.24 up 25.5
2 Gene1840 35.1 3.94 3.94 3.24 6.05 up 25.6
3 Gene65 146. 3.88 3.88 3.05 6.40 up 21.0
4 Gene890 76.8 3.75 3.75 2.95 6.13 up 19.2
5 Gene5 103. 3.71 3.71 2.95 5.98 up 19.1
6 Gene1619 141. 3.70 3.70 2.95 5.97 up 19.1
7 Gene1324 110. 3.64 3.64 2.92 5.80 up 18.5
8 Gene975 117. 3.61 3.61 2.85 5.89 up 17.3
9 Gene786 108. 3.57 3.57 2.88 5.66 up 17.8
10 Gene926 120. 3.55 3.55 2.84 5.69 up 17.1
# … with 1,990 more rowsplotExpression(
lawlor_sim,
x = "label",
c("Gene1333", "Gene151")
)
zeisel_mg_info %>%
pluck("group_1") %>%
filter(gene_mean > 0.1) %>%
arrange(desc(mean_score))# A tibble: 1,938 × 8
gene gene_mean fc mean_score median_score max_score direction de_value
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
1 Gene184 0.523 4.95 4.95 4.87 6.57 up 32.0
2 Gene1031 0.599 4.50 4.50 4.46 6.15 up 19.0
3 Gene1025 0.957 4.03 4.03 3.21 6.49 up 24.8
4 Gene71 0.191 4.00 4.00 3.14 6.57 up 23.1
5 Gene1298 1.18 3.96 3.96 3.18 6.30 up 24.1
6 Gene537 0.818 3.94 3.94 3.19 6.18 up 24.3
7 Gene761 1.98 3.93 3.93 3.05 6.58 up 21.2
8 Gene999 3.49 3.82 3.82 3.10 6.00 up 22.1
9 Gene1203 6.88 3.81 3.81 3.14 5.79 up 23.2
10 Gene167 2.60 3.63 3.63 2.90 5.80 up 18.2
# … with 1,928 more rowsplotExpression(
zeisel_sim,
x = "label",
c("Gene924", "Gene844")
)
plotExpression(
zeisel_sim,
x = "label",
c("Gene572", "Gene858")
)
pbmc3k_mg_info %>%
pluck("group_1") %>%
filter(gene_mean > 0.1) %>%
arrange(mean_score)# A tibble: 1,275 × 8
gene gene_mean fc mean_score median_score max_score direction de_value
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
1 Gene1638 0.391 -4.13 -4.13 -3.33 -3.33 down 0.0358
2 Gene1011 0.381 -3.91 -3.91 -3.18 -3.18 down 0.0418
3 Gene626 0.126 -3.83 -3.83 -3.05 -3.05 down 0.0473
4 Gene1251 0.155 -3.78 -3.78 -2.96 -2.96 down 0.0516
5 Gene141 0.104 -3.71 -3.71 -2.92 -2.92 down 0.0539
6 Gene548 0.391 -3.60 -3.60 -2.90 -2.90 down 0.0551
7 Gene342 0.206 -3.54 -3.54 -2.79 -2.79 down 0.0615
8 Gene1623 0.936 -3.48 -3.48 -2.66 -2.66 down 0.0696
9 Gene60 0.589 -3.47 -3.47 -3.47 -3.47 down 0.0312
10 Gene1435 0.642 -3.46 -3.46 -3.46 -3.46 down 0.0313
# … with 1,265 more rowsplotExpression(
pbmc3k_sim,
x = "label",
c("Gene931", "Gene1081", "Gene624")
)
Gene mean filtering analysis
pbmc3k_sim$Group <- substr(
pbmc3k_sim$label,
6,
6
)
mgs_no_mean_filter <- pbmc3k_mg_info %>%
pluck("group_1") %>%
arrange(desc(mean_score)) %>%
dplyr::slice(1:4) %>%
pull(gene)
mgs_with_mean_filter <- pbmc3k_mg_info %>%
pluck("group_1") %>%
filter(gene_mean > 0.05) %>%
arrange(desc(mean_score)) %>%
dplyr::slice(1:4) %>%
pull(gene)
no_mean_filter_plot <- plotExpression(
pbmc3k_sim,
x = "Group",
mgs_no_mean_filter
) +
labs(
title = "Without mean filter"
)
with_mean_filter_plot <- plotExpression(
pbmc3k_sim,
x = "Group",
mgs_with_mean_filter
) +
labs(
title = "With mean filter"
)
no_mean_filter_plot + with_mean_filter_plot +
plot_annotation(tag_levels = "a") &
theme(plot.tag = element_text(size = 18))
# standard_sim_1_pbmc3k_seurat_wilcox <- readRDS(
# here::here("results", "sim_data",
# "standard_sim_1-pbmc3k-seurat_wilcox.rds")
# )
#
# standard_sim_1_pbmc3k_seurat_t <- readRDS(
# here::here("results", "sim_data",
# "standard_sim_1-pbmc3k-seurat_t.rds")
# )
#
# n <- 40
#
# seurat_wilcox_log_fc <- standard_sim_1_pbmc3k_seurat_wilcox %>%
# pluck("result") %>%
# filter(cluster == "Group1") %>%
# filter(log_fc > 0) %>%
# dplyr::slice(1:n) %>%
# pull(log_fc)
#
# seurat_t_log_fc <- standard_sim_1_pbmc3k_seurat_t %>%
# pluck("result") %>%
# filter(cluster == "Group1") %>%
# filter(log_fc > 0) %>%
# dplyr::slice(1:n) %>%
# pull(log_fc)
#
# top_mgs_seurat_wilcox <- standard_sim_1_pbmc3k_seurat_wilcox %>%
# pluck("result") %>%
# filter(cluster == "Group1") %>%
# filter(log_fc > 0) %>%
# dplyr::slice(1:n) %>%
# pull(gene)
#
# top_mgs_no_filter <- mg_info_standard_sim_1_pbmc3k %>%
# pluck("group_1") %>%
# arrange(desc(mean_score)) %>%
# dplyr::slice(1:n) %>%
# pull(gene)
#
# top_mgs_no_filter_log_fc <- standard_sim_1_pbmc3k_seurat_wilcox %>%
# pluck("result") %>%
# filter(cluster == "Group1") %>%
# filter(gene %in% top_mgs_no_filter) %>%
# pull(log_fc)
#
#
# top_mgs_no_filter_log_fc[[40]] <- mean(top_mgs_no_filter_log_fc)
#
# top_mgs_with_filter <- mg_info_standard_sim_1_pbmc3k %>%
# pluck("group_1") %>%
# filter(gene_mean > 0.1) %>%
# arrange(desc(mean_score)) %>%
# dplyr::slice(1:n) %>%
# pull(gene)
#
# top_mgs_with_filter_log_fc <- standard_sim_1_pbmc3k_seurat_wilcox %>%
# pluck("result") %>%
# filter(cluster == "Group1") %>%
# filter(gene %in% top_mgs_with_filter) %>%
# pull(log_fc)
#
# tibble(
# filter_logfc = top_mgs_with_filter_log_fc,
# nofilter_logfc = top_mgs_no_filter_log_fc,
# wilcox_logfc = seurat_wilcox_log_fc,
# t_logfc = seurat_t_log_fc,
# ) %>%
# pivot_longer(
# cols = everything(),
# names_pattern = "(.*)_.*",
# values_to = "log_fc",
# names_to = "method"
# ) %>%
# mutate(score = if_else(
# method %in% c("filter", "nofilter"),
# "Scored",
# "Selected"
# )
# ) %>%
# mutate(method = case_when(
# method == "filter" ~ "With mean filter",
# method == "nofilter" ~ "Without mean filter",
# method == "t" ~ "Seurat t-test",
# method == "wilcox" ~ "Seurat Wilcoxon"
# )) %>%
# ggplot(aes(x = factor(method), y = log_fc, fill = score)) +
# geom_boxplot() +
# coord_flip() +
# labs(
# fill = "Method",
# y = "One-vs-rest log fold change",
# x = "Marker gene type"
# ) +
# scale_y_continuous(breaks = c(1, 2, 4, 6)) +
# theme_bw()# top_mgs_with_filter <- mg_info_standard_sim_1_pbmc3k %>%
# pluck("group_1") %>%
# filter(gene_mean > 0.1) %>%
# arrange(desc(mean_score)) %>%
# dplyr::slice(1:80) %>%
# mutate(rank = 1:n())
#
# m_1 <- rowMeans(
# expm1(logcounts(sim_1_pbmc3k[, sim_1_pbmc3k$label == "Group1"]))
# )
# m_2 <- rowMeans(
# expm1(logcounts(sim_1_pbmc3k[, sim_1_pbmc3k$label != "Group1"]))
# )
# log_fcs <- log(m_1 + 1, base = 2) - log(m_2 + 1, base = 2)
# log_fcs_tib <- tibble(gene = rownames(sim_1_pbmc3k), log_fc = log_fcs)
#
# top_mgs_with_filter <- top_mgs_with_filter %>%
# left_join(log_fcs_tib, by = "gene")
#
# top_mgs_with_filter %>%
# ggplot(aes(x = rank, y = log_fc)) +
# geom_point(colour = "seagreen") +
# geom_smooth(method = "loess", formula = y ~ x) +
# labs(
# x = "True marker gene rank",
# y = "Two sample log fold change"
# ) +
# theme_bw()
# ```
#
#
# ```{r mean-histogram}
# mg_info_standard_sim_1_pbmc3k %>%
# pluck("group_1") %>%
# ggplot(aes(x = gene_mean)) +
# geom_histogram(
# bins = 20,
# fill = "seagreen",
# colour = "black"
# ) +
# labs(
# x = "Gene mean",
# y = "Count",
# title = "Histogram of simulated gene means"
# ) +
# theme_bw()
#
# standard_sim_1_pbmc3k %>%
# logcounts() %>%
# rowMeans() %>%
# tibble() %>%
# setNames("mean") %>%
# ggplot(aes(x = mean)) +
# geom_histogram()
devtools::session_info()─ Session info ──────────────────────────────────────────────────────────────
hash: tear-off calendar, railway car, person in bed: dark skin tone
setting value
version R version 4.1.2 (2021-11-01)
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