SMART-seq3 expert-annotated marker gene analysis
Last updated: 2024-01-01
Checks: 7 0
Knit directory:
mage_2020_marker-gene-benchmarking/
This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20190102) was run prior to running
the code in the R Markdown file. Setting a seed ensures that any results
that rely on randomness, e.g. subsampling or permutations, are
reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version 2632193. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for
the analysis have been committed to Git prior to generating the results
(you can use wflow_publish or
wflow_git_commit). workflowr only checks the R Markdown
file, but you know if there are other scripts or data files that it
depends on. Below is the status of the Git repository when the results
were generated:
Ignored files:
Ignored: .Renviron
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: .snakemake/
Ignored: NSForest/.Rhistory
Ignored: NSForest/NS-Forest_v3_Extended_Binary_Markers_Supplmental.csv
Ignored: NSForest/NS-Forest_v3_Full_Results.csv
Ignored: NSForest/NSForest3_medianValues.csv
Ignored: NSForest/NSForest_v3_Final_Result.csv
Ignored: NSForest/__pycache__/
Ignored: NSForest/data/
Ignored: RankCorr/picturedRocks/__pycache__/
Ignored: benchmarks/
Ignored: config/
Ignored: data/cellmarker/
Ignored: data/downloaded_data/
Ignored: data/expert_annotations/
Ignored: data/expert_mgs/
Ignored: data/raw_data/
Ignored: data/real_data/
Ignored: data/sim_data/
Ignored: data/sim_mgs/
Ignored: data/special_real_data/
Ignored: figures/
Ignored: logs/
Ignored: results/
Ignored: weights/
Unstaged changes:
Deleted: analysis/expert-mgs-direction.Rmd
Modified: smash-fork
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were
made to the R Markdown
(analysis/ss3-pbmc-expert-analysis.Rmd) and HTML
(public/ss3-pbmc-expert-analysis.html) files. If you’ve
configured a remote Git repository (see ?wflow_git_remote),
click on the hyperlinks in the table below to view the files as they
were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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. |
| Rmd | 9dccf15 | Jeffrey Pullin | 2022-08-30 | Update all analyses |
| html | 4ced4aa | Jeffrey Pullin | 2022-05-20 | Build site. |
| Rmd | f61c73a | Jeffrey Pullin | 2022-05-20 | Add number of cluster annotated analysis |
| html | f61c73a | Jeffrey Pullin | 2022-05-20 | Add number of cluster annotated analysis |
| html | 0e47874 | Jeffrey Pullin | 2022-05-04 | Build site. |
| html | 048156f | Jeffrey Pullin | 2022-05-02 | Tweak analysis outputs |
| html | 8b989e1 | Jeffrey Pullin | 2022-05-02 | Build site. |
| Rmd | 2e627e5 | Jeffrey Pullin | 2022-05-02 | Refresh all analyses |
library(dplyr)
library(ggplot2)
library(SingleCellExperiment)
library(scater)
library(tidyr)
library(forcats)
source(here::here("code", "analysis-utils.R"))
source(here::here("code", "plot-utils.R"))
source(here::here("code", "expert-annotation-utils.R"))ss3_pbmc_results <- load_real_data_results("ss3_pbmc")
ss3_pbmc <- readRDS(here::here("data", "real_data", "ss3_pbmc.rds"))ss3_pbmc_marker_genes <- tibble(
cluster = c(
"NK cells",
"Naive/Memory CD8 T",
"Cytotoxic T",
"Naive B",
"Dendritic cells",
"CD14 monocytes",
"plasmacytoid DC",
"Plasma cells"
),
expert_mgs = list(
c("NCAM1", "KLRB1"),
c("PECAM1"),
c("GZMB", "GZMA"),
c("CD27", "IGHM", "IGHD", "IL4R"),
c("KLF4", "CD1C"),
c("CD14"),
c("IL3RA", "TLR7"),
c("PRDM1", "IRF4")
)
) Results
expert_ss3_pbmc3_heatmap <- ss3_pbmc_results %>%
filter(cluster %in% ss3_pbmc_marker_genes$cluster) %>%
plot_expert_anno_heatmap(
"ss3_pbmc",
ss3_pbmc_marker_genes,
n_top = 5,
metric = "recall"
) +
ggtitle("Smart-seq3 data (At most 5 genes selected)")
expert_ss3_pbmc3_heatmap
saveRDS(
expert_ss3_pbmc3_heatmap,
here::here("figures", "raw", "expert-ss3_pbmc-heatmap.rds")
)expert_ss3_pbmc_n_annotated <- ss3_pbmc_results %>%
filter(cluster %in% ss3_pbmc_marker_genes$cluster) %>%
plot_expert_anno_n_cluster(
"ss3_pbmc",
ss3_pbmc_marker_genes,
n_top = 5
) +
ggtitle("Smart-seq3 data (At most 5 genes selected)")
expert_ss3_pbmc_n_annotated
saveRDS(
expert_ss3_pbmc_n_annotated,
here::here("figures", "raw", "expert-ss3_pbmc-n-annotated.rds")
)Marker genes
NK cells
plotExpression(ss3_pbmc, x = "label", features = c("NCAM1", "KLRB1")) +
theme(axis.text.x = element_text(angle = 45, hjust = 0.9))
Naive/Memory CD8 T
pecam1_expression <- plotExpression(ss3_pbmc, x = "label", features = "PECAM1") +
theme(axis.text.x = element_text(angle = 45, hjust = 0.9))
pecam1_expression
ggsave(
here::here("figures", "final", "pecam1-expression.pdf"),
pecam1_expression,
width = 8,
height = 8,
units = "in"
)Cytotoxic T
cytotoxic_t_expert_markers <- plotExpression(ss3_pbmc, x = "label",
features = c("GZMB", "GZMA")) +
theme(axis.text.x = element_text(angle = 45, hjust = 0.9))
cytotoxic_t_expert_markers
Naive B cells
plotExpression(ss3_pbmc, x = "label",
features = c("CD27", "IGHM", "IGHD", "IL4R")) +
theme(axis.text.x = element_text(angle = 45, hjust = 0.9))
Dendritic cells
plotExpression(ss3_pbmc, x = "label",
features = c("KLF4", "CD1C")) +
theme(axis.text.x = element_text(angle = 45, hjust = 0.9))
CD14 monocytes
plotExpression(ss3_pbmc, x = "label", features = c("CD14")) +
theme(axis.text.x = element_text(angle = 45, hjust = 0.9))
plasmacytoid DC
plotExpression(ss3_pbmc, x = "label", features = c("IL3RA", "TLR7")) +
theme(axis.text.x = element_text(angle = 45, hjust = 0.9))
Plasma cells
plotExpression(ss3_pbmc, x = "label", features = c("PRDM1", "IRF4")) +
theme(axis.text.x = element_text(angle = 45, hjust = 0.9))
scran_wilcox_any <- readRDS(
here::here("results", "real_data", "ss3_pbmc-scran_findMarkers_t_all.rds")
)
scran_wilcox_any$result %>%
filter(cluster == "Cytotoxic T")# A tibble: 2,008 × 7
p_value raw_statistic p_value_adj gene scaled_statistic cluster log_fc
<dbl> <dbl> <dbl> <chr> <dbl> <chr> <dbl>
1 0.000156 -0.500 0.314 CCL5 0 Cytotoxic… 1.87
2 0.00531 -0.519 1 IL7R 0 Cytotoxic… -0.318
3 0.00647 0.642 1 GZMA 0 Cytotoxic… 0.448
4 0.00714 -0.476 1 RPL9 0 Cytotoxic… -1.72
5 0.00733 -0.285 1 CD8B 0 Cytotoxic… -0.241
6 0.00962 -0.222 1 MALAT1 0 Cytotoxic… -4.79
7 0.00971 0.170 1 UBASH3A 0 Cytotoxic… -0.258
8 0.0207 0.478 1 ATP5E 0 Cytotoxic… -0.482
9 0.0210 0.112 1 TRAT1 0 Cytotoxic… -1.49
10 0.0216 0.580 1 MYL6B 0 Cytotoxic… -0.701
# … with 1,998 more rowsplotExpression(ss3_pbmc, x = "label", features = "CCL5")
devtools::session_info()─ Session info ──────────────────────────────────────────────────────────────
hash: face with rolling eyes, crossed swords, woman singer: medium-light skin tone
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)
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)
cowplot 1.1.1 2020-12-30 [2] CRAN (R 4.1.2)
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)
knitr 1.36 2021-09-29 [1] CRAN (R 4.1.0)
labeling 0.4.2 2020-10-20 [2] CRAN (R 4.1.2)
later 1.3.0 2021-08-18 [1] CRAN (R 4.1.0)
lattice 0.20-45 2021-09-22 [2] CRAN (R 4.1.2)
lifecycle 1.0.1 2021-09-24 [1] CRAN (R 4.1.0)
magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.1.0)
Matrix 1.3-4 2021-06-01 [2] CRAN (R 4.1.2)
MatrixGenerics * 1.6.0 2021-10-26 [1] Bioconductor
matrixStats * 0.62.0 2022-04-19 [1] CRAN (R 4.1.0)
memoise 2.0.1 2021-11-26 [1] CRAN (R 4.1.0)
munsell 0.5.0 2018-06-12 [2] CRAN (R 4.1.2)
pillar 1.7.0 2022-02-01 [1] CRAN (R 4.1.0)
pkgbuild 1.2.0 2020-12-15 [2] CRAN (R 4.1.2)
pkgconfig 2.0.3 2019-09-22 [2] CRAN (R 4.1.2)
pkgload 1.2.3 2021-10-13 [2] CRAN (R 4.1.2)
prettyunits 1.1.1 2020-01-24 [2] CRAN (R 4.1.2)
processx 3.5.2 2021-04-30 [2] CRAN (R 4.1.2)
promises 1.2.0.1 2021-02-11 [2] CRAN (R 4.1.2)
ps 1.7.1 2022-06-18 [1] CRAN (R 4.1.0)
purrr 0.3.4 2020-04-17 [2] CRAN (R 4.1.2)
R6 2.5.1 2021-08-19 [1] CRAN (R 4.1.0)
Rcpp 1.0.8.3 2022-03-17 [1] CRAN (R 4.1.0)
RCurl 1.98-1.5 2021-09-17 [1] CRAN (R 4.1.0)
remotes 2.4.2 2021-11-30 [1] CRAN (R 4.1.0)
rlang 1.0.3 2022-06-27 [1] CRAN (R 4.1.0)
rmarkdown 2.14 2022-04-25 [1] CRAN (R 4.1.0)
rprojroot 2.0.3 2022-04-02 [1] CRAN (R 4.1.0)
rstudioapi 0.14 2022-08-22 [1] CRAN (R 4.1.0)
rsvd 1.0.5 2021-04-16 [1] CRAN (R 4.1.0)
S4Vectors * 0.32.3 2021-11-21 [1] Bioconductor
sass 0.4.1 2022-03-23 [1] CRAN (R 4.1.0)
ScaledMatrix 1.2.0 2021-10-26 [1] Bioconductor
scales 1.2.1 2022-08-20 [1] CRAN (R 4.1.0)
scater * 1.22.0 2021-10-26 [1] Bioconductor
scuttle * 1.4.0 2021-10-26 [1] Bioconductor
sessioninfo 1.2.0 2021-10-31 [2] CRAN (R 4.1.2)
SingleCellExperiment * 1.16.0 2021-10-26 [1] Bioconductor
sparseMatrixStats 1.6.0 2021-10-26 [1] Bioconductor
stringi 1.7.6 2021-11-29 [1] CRAN (R 4.1.0)
stringr 1.4.0 2019-02-10 [2] CRAN (R 4.1.2)
SummarizedExperiment * 1.24.0 2021-10-26 [1] Bioconductor
testthat 3.1.0 2021-10-04 [2] CRAN (R 4.1.2)
tibble 3.1.7 2022-05-03 [1] CRAN (R 4.1.0)
tidyr * 1.2.0 2022-02-01 [1] CRAN (R 4.1.0)
tidyselect 1.1.2 2022-02-21 [1] CRAN (R 4.1.0)
usethis 2.1.3 2021-10-27 [2] CRAN (R 4.1.2)
utf8 1.2.3 2023-01-31 [1] CRAN (R 4.1.0)
vctrs 0.4.1 2022-04-13 [1] CRAN (R 4.1.0)
vipor 0.4.5 2017-03-22 [2] CRAN (R 4.1.2)
viridis 0.6.2 2021-10-13 [1] CRAN (R 4.1.0)
viridisLite 0.4.2 2023-05-02 [1] CRAN (R 4.1.0)
whisker 0.4 2019-08-28 [2] CRAN (R 4.1.2)
withr 2.5.0 2022-03-03 [1] CRAN (R 4.1.0)
workflowr 1.7.0 2021-12-21 [1] CRAN (R 4.1.0)
xfun 0.31 2022-05-10 [1] CRAN (R 4.1.0)
XVector 0.34.0 2021-10-26 [1] Bioconductor
yaml 2.3.5 2022-02-21 [1] CRAN (R 4.1.0)
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
──────────────────────────────────────────────────────────────────────────────