pbmc3k experted-annotated marker gene analysis
Last updated: 2024-01-01
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mage_2020_marker-gene-benchmarking/
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| Rmd | aab8bbb | Jeffrey Pullin | 2021-09-22 | Polish pbmc3k marker gene analysis |
| Rmd | 1ad466a | Jeffrey Pullin | 2021-09-21 | Extend analysis of expert marker genes recovery |
| Rmd | 17f2a0f | Jeffrey Pullin | 2021-08-07 | Add new plots and analysis for lab meeting 5/8/2021 |
library(ggplot2)
library(patchwork)
library(dplyr)
library(purrr)
library(tidyr)
library(forcats)
library(scater)
source(here::here("code", "analysis-utils.R"))
source(here::here("code", "plot-utils.R"))
source(here::here("code", "expert-annotation-utils.R"))Load data
pbmc3k_results <- load_real_data_results("pbmc3k")
pbmc3k <- readRDS(here::here("data", "special_real_data", "pbmc3k.rds"))
# These are taken from the Seurat tutorial.
pbmc3k_expert_mgs <- tibble(
cluster = c("Naive CD4 T", "CD14+ Mono", "Memory CD4 T", "B", "CD8 T",
"FCGR3A+ Mono", "NK", "DC", "Platelet"),
expert_mgs = list(c("IL7R", "CCR7"), c("CD14", "LYZ"), c("IL7R", "S100A4"),
"MS4A1", "CD8A", c("FCGR3A", "MS4A7"), c("GNLY", "NKG7"),
c("FCER1A", "CST3"), "PPBP")
)Seurat tutorial annotation, pbmc3k data
5 genes selected
plot_expert_anno_heatmap(pbmc3k_results, "pbmc3k", pbmc3k_expert_mgs, n_top = 5, metric = "recall")
plot_expert_anno_cols(pbmc3k_results, "pbmc3k", pbmc3k_expert_mgs, n_top = 5)
10 genes selected
pbmc3k_ea_hm <- plot_expert_anno_heatmap(
pbmc3k_results,
"pbmc3k",
pbmc3k_expert_mgs,
n_top = 10,
metric = "recall"
) +
ggtitle("pbmc3k data (At most 10 genes selected)")
pbmc3k_ea_hm
saveRDS(pbmc3k_ea_hm, here::here("figures", "raw",
"expert-pbmc3k-heatmap.rds"))
pbmc3k_n_annotated <- plot_expert_anno_n_cluster(
pbmc3k_results,
"pbmc3k",
pbmc3k_expert_mgs,
n_top = 10
) +
ggtitle("pbmc3k data (At most 10 genes selected)")
pbmc3k_n_annotated
saveRDS(
pbmc3k_n_annotated,
here::here("figures", "raw", "expert-pbmc3k-n-annotated.rds")
)20 genes selected
plot_expert_anno_heatmap(pbmc3k_results, "pbmc3k", pbmc3k_expert_mgs,
n_top = 20, metric = "recall")
plot_expert_anno_cols(pbmc3k_results, "pbmc3k", pbmc3k_expert_mgs, n_top = 20)
CD8+ T analysis
plotExpression(pbmc3k, c("CD8A", "CCR7","S100A4", "IL7R"), x = "label") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Naive and Memory CD4 T cells analysis
cd4_t_cell_markers <- plotExpression(
pbmc3k,
x = "label",
features = c("IL7R", "CCR7", "S100A4")
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
cd4_t_cell_markers
ggsave(
here::here("figures", "final", "cd4-t-cell-markers.pdf"),
cd4_t_cell_markers,
width = 8,
height = 8,
units = "in"
)
devtools::session_info()─ Session info ──────────────────────────────────────────────────────────────
hash: flag: Guinea-Bissau, crossed fingers: medium-dark skin tone, flag: Saudi Arabia
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
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