Figures for DEG analysis - GM
Will Macnair
Institute for Molecular Life Sciences, University of Zurich, SwitzerlandMarch 24, 2022
Last updated: 2022-03-24
Checks: 4 3
Knit directory: MS_lesions/
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- plot_de_barplot_fine
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Modified: code/ms99_deg_figures.R
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | b2b9746 | Macnair | 2022-03-18 | Tweak DEG manuscript figures |
| html | b2b9746 | Macnair | 2022-03-18 | Tweak DEG manuscript figures |
| Rmd | 1f90a86 | wmacnair | 2022-03-06 | Tweak DE figures |
| html | 1f90a86 | wmacnair | 2022-03-06 | Tweak DE figures |
| Rmd | 05aa09c | wmacnair | 2022-02-23 | Tweak ms99_deg figures |
| html | 05aa09c | wmacnair | 2022-02-23 | Tweak ms99_deg figures |
| Rmd | 8e47b9f | wmacnair | 2022-02-18 | Add logFC heatmaps for selected genes |
| html | 8e47b9f | wmacnair | 2022-02-18 | Add logFC heatmaps for selected genes |
| Rmd | 2c2025a | wmacnair | 2022-02-18 | Add GSEA figures to manuscript figures |
| html | 2c2025a | wmacnair | 2022-02-18 | Add GSEA figures to manuscript figures |
| Rmd | 2b68ff2 | wmacnair | 2022-02-16 | Add fine type DEG barplots |
| html | 2b68ff2 | wmacnair | 2022-02-16 | Add fine type DEG barplots |
| Rmd | 1daed8b | wmacnair | 2022-02-02 | Update DEG figures |
| html | 1daed8b | wmacnair | 2022-02-02 | Update DEG figures |
| Rmd | d83826d | wmacnair | 2022-01-27 | Add pages summarizing DE results |
| html | d83826d | wmacnair | 2022-01-27 | Add pages summarizing DE results |
| Rmd | 79b10e2 | wmacnair | 2022-01-24 | Plot MS vs donor effect barplots |
| html | 79b10e2 | wmacnair | 2022-01-24 | Plot MS vs donor effect barplots |
Setup / definitions
Libraries
source('code/ms00_utils.R')
source('code/ms09_ancombc.R')
source('code/ms15_mofa.R')
source('code/ms99_deg_figures.R')
setDTthreads(4)Inputs
# specify what goes into muscat run
labels_f = 'data/byhand_markers/validation_markers_2021-05-31.csv'
pb_f = file.path(soup_dir, 'pb_sum_broad_2021-10-11.rds')Outputs
# which run?
broad_spec = list(
run_tag = 'run23',
time_stamp = '2021-11-15',
sel_cl = c("OPCs / COPs", "Oligodendrocytes", "Astrocytes",
"Microglia", "Excitatory neurons", "Inhibitory neurons",
"Endothelial cells", "Pericytes")
)
fine_spec = list(
run_tag = 'run24',
time_stamp = '2021-11-19',
sel_cl = c("OPCs / COPs", "Oligodendrocytes", "Astrocytes",
"Microglia", "Excitatory neurons", "Inhibitory neurons",
"Endothelial cells", "Pericytes")
)
# which GO terms to show?
sel_sets = c('hallmark', 'go_bp', 'go_cc')
# sel genes
gm_hm_cl = "Excitatory neurons"
sel_gs = list(
`glutamate\nsignalling` = c('GRIA1', 'GRIA2', 'GRIA4', 'GRIN2B', 'GRM1', 'GRM5'),
`glucose\nhomeostasis` = c('SLC2A12', 'SLC22A10'),
`ion\nchannels` = c('SCN1A', 'SCN1B', 'SCN2B', 'SCN4B', 'KCNA1', 'KCNA2', 'KCNC1'),
`oxidative\nphosphorylation` = c('OXPHOS', 'ATP1A1', 'ATP1B1', 'NDUFB10', 'NDUFS3', 'UQCRH')
)
# parameters for gene selection
min_sd = log(2)
min_fc = log(2)
max_p = 0.05
log_p_mad = 2
# param for clustering logFC profiles
logfc_cut = log(4)Load inputs
# unpack
run_tag = broad_spec$run_tag
time_stamp = broad_spec$time_stamp
sel_cl = broad_spec$sel_cl
# define files
model_dir = file.path('output/ms10_muscat', run_tag)
muscat_f = '%s/muscat_res_dt_%s_%s.txt.gz' %>%
sprintf(model_dir, run_tag, time_stamp)
anova_f = '%s/muscat_goodness_dt_%s_%s.txt.gz' %>%
sprintf(model_dir, run_tag, time_stamp)
params_f = '%s/muscat_params_%s_%s.rds' %>%
sprintf(model_dir, run_tag, time_stamp)
ranef_dt_f = sprintf('%s/muscat_ranef_dt_%s_%s.txt.gz',
model_dir, run_tag, time_stamp)
# which sets to show?
fgsea_pat = sprintf('%s/fgsea_dt_%s_%s_%s.txt.gz',
model_dir, run_tag, '%s', time_stamp)# load parameters
params = params_f %>% readRDS
# load pseudobulk object
pb = readRDS(params$pb_f) %>%
.subset_pb(params$subset_spec) %>%
subset_pb_celltypes(sel_cl) subsetting pb object restricting to samples that meet subset criteria updating factors to remove levels no longer observed# check for any massive outliers
outliers_dt = calc_log_prop_outliers(pb, mad_cut = log_p_mad)the following samples have half or more of celltypes with very extreme
(2 > MADs) log proportions:
EU005, EU044ok_samples = outliers_dt[ props_ok == TRUE ]$sample_id
pb = pb[ , ok_samples ]
# get random effects
labels_dt = .load_labels_dt(labels_f, params$cluster_var)Warning in FUN(X[[i]], ...): unable to translate '<U+00C4>' to native encodingWarning in FUN(X[[i]], ...): unable to translate '<U+00D6>' to native encodingWarning in FUN(X[[i]], ...): unable to translate '<U+00DC>' to native encodingWarning in FUN(X[[i]], ...): unable to translate '<U+00E4>' to native encodingWarning in FUN(X[[i]], ...): unable to translate '<U+00F6>' to native encodingWarning in FUN(X[[i]], ...): unable to translate '<U+00FC>' to native encodingWarning in FUN(X[[i]], ...): unable to translate '<U+00DF>' to native encodingWarning in FUN(X[[i]], ...): unable to translate '<U+00C6>' to native encodingWarning in FUN(X[[i]], ...): unable to translate '<U+00E6>' to native encodingWarning in FUN(X[[i]], ...): unable to translate '<U+00D8>' to native encodingWarning in FUN(X[[i]], ...): unable to translate '<U+00F8>' to native encodingWarning in FUN(X[[i]], ...): unable to translate '<U+00C5>' to native encodingWarning in FUN(X[[i]], ...): unable to translate '<U+00E5>' to native encodingranef_dt = .load_ranef_dt(ranef_dt_f, labels_dt, pb)
# get results
res_all = muscat_f %>% fread %>%
.load_muscat_results(labels_dt, params) %>%
.[, padj := p_adj.soup ] %>%
.[ !is.na(padj) ]
# prep for stacked bars
signif_dt = res_all[ updown_soup != 'insignif' & !is.na(p_adj.soup) ]
assert_that(all(abs(signif_dt$logFC) >= params$fc_cut))[1] TRUE# add specificity and tfs to results
magma_dt = .load_magma_dt(magma_f, pb)
tfs_dt = .load_tfs_dt(tfs_f, pb)
uniques_dt = .calc_uniques_dt(signif_dt, params) %>%
.[, .(cluster_id, gene_id, unique_var)] %>% unique
res_dt = copy(res_all) %>%
merge(uniques_dt, by = c('cluster_id', 'gene_id'), all.x = TRUE) %>%
.[ is.na(unique_var), unique_var := 'not_signif'] %>%
merge(magma_dt, by = 'gene_id', all.x = TRUE) %>%
merge(tfs_dt, by = 'gene_id', all.x = TRUE) %>%
.[ is.na(is_tf), is_tf := FALSE ] %>%
.[, p_coloc := 1 ]
# get anova results
anova_dt = .load_anova_dt(anova_f, res_dt) %>%
.[ is.na(full), full := 1 ]# get GSEA results
fgsea_dt = lapply(sel_sets, function(s)
s %>% sprintf(fgsea_pat, .) %>% fread) %>% rbindlist %>%
.[ cluster_id %in% sel_cl ] %>% .[ var_type == 'test' ]
# edit cluster names
labs_short = copy(labels_dt) %>%
.[, cluster_id := unlist(broad_short)[ cluster_id ] %>%
factor(levels = broad_short) ]
fgsea_dt[, cluster_id := unlist(broad_short)[ cluster_id ] %>%
factor(levels = broad_short) ]# get random effects
sd_dt = ranef_dt %>% calc_ranef_melt %>% calc_sd_dt
filter_dt = calc_filter_dt(res_dt, sd_dt, pb, anova_dt,
max_p = max_p, min_sd = min_sd, min_fc = min_fc)
causes_dt = filter_dt[ cluster_id %in% sel_cl ] %>% calc_gene_causes_dtProcessing / calculations
fc_cl_dt = res_dt[ (var_type == 'test') & (cluster_id %in% sel_cl) ] %>%
calc_fc_clusters( max_fdr = max_p, logfc_cut = logfc_cut )removing genes with best FDR > 5%row_split = lapply(seq_along(sel_gs), function(i)
rep(names(sel_gs)[[i]], length(sel_gs[[i]])) %>% setNames(sel_gs[[i]])) %>%
do.call(c, .)
sel_dt = res_dt[ (var_type == 'test') & (cluster_id == gm_hm_cl) ] %>%
.[ symbol %in% names(row_split) ]Analysis
DE darplot, broad
(plot_de_barplot_sel(broad_spec, padj_cut = max_p, facet_by = 'cluster_id')) subsetting pb object restricting to samples that meet subset criteria updating factors to remove levels no longer observedWarning in FUN(X[[i]], ...): unable to translate '<U+00C4>' to native encodingWarning in FUN(X[[i]], ...): unable to translate '<U+00D6>' to native encodingWarning in FUN(X[[i]], ...): unable to translate '<U+00DC>' to native encodingWarning in FUN(X[[i]], ...): unable to translate '<U+00E4>' to native encodingWarning in FUN(X[[i]], ...): unable to translate '<U+00F6>' to native encodingWarning in FUN(X[[i]], ...): unable to translate '<U+00FC>' to native encodingWarning in FUN(X[[i]], ...): unable to translate '<U+00DF>' to native encodingWarning in FUN(X[[i]], ...): unable to translate '<U+00C6>' to native encodingWarning in FUN(X[[i]], ...): unable to translate '<U+00E6>' to native encodingWarning in FUN(X[[i]], ...): unable to translate '<U+00D8>' to native encodingWarning in FUN(X[[i]], ...): unable to translate '<U+00F8>' to native encodingWarning in FUN(X[[i]], ...): unable to translate '<U+00C5>' to native encodingWarning in FUN(X[[i]], ...): unable to translate '<U+00E5>' to native encoding
DE darplot, fine
(plot_de_barplot_sel(fine_spec, padj_cut = max_p, facet_by = 'test_var')) subsetting pb object restricting to samples that meet subset criteria updating factors to remove levels no longer observed
Split of genes
(plot_causes_of_variability(causes_dt))
Top genes per celltype
rank_names = c(rank_fc = "logFC", rank_p = "FDR")
for (ud in c('up', 'down')) {
for (rank_var in c('rank_fc', 'rank_p')) {
cat('### ', rank_names[[rank_var]], ', ', ud, '\n', sep = '')
suppressMessages({
hm_obj = plot_top_genes_across_celltypes(broad_spec,
updown = ud, rank_var = rank_var, padj_cut = max_p)
})
draw(hm_obj, merge_legend = TRUE)
cat('\n\n')
}
}logFC, up
FDR, up
logFC, down
Clusters of logFC patterns
(plot_fc_cluster_profiles(fc_cl_dt))
GSEA dotplots
for (s in sel_sets) {
cat('### ', s, '\n')
print(plot_gsea_dotplot(fgsea_dt[ path_set == s ], labs_short,
n_top_per_celltype = 5, fgsea_cut = 0.1))
cat('\n\n')
}hallmark
go_bp
Outputs
devtools::session_info()- Session info ---------------------------------------------------------------
setting value
version R version 4.0.5 (2021-03-31)
os CentOS Linux 7 (Core)
system x86_64, linux-gnu
ui X11
language (EN)
collate en_US.UTF-8
ctype C
tz Europe/Zurich
date 2022-02-18
- Packages -------------------------------------------------------------------
package * version date lib
ade4 1.7-18 2021-09-16 [1]
ANCOMBC * 1.0.5 2021-03-09 [1]
annotate 1.68.0 2020-10-27 [1]
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ape 5.5 2021-04-25 [1]
assertthat * 0.2.1 2019-03-21 [2]
backports 1.2.1 2020-12-09 [2]
basilisk 1.2.1 2020-12-16 [1]
basilisk.utils 1.2.2 2021-01-27 [1]
beachmat 2.6.4 2020-12-20 [1]
beeswarm 0.4.0 2021-06-01 [1]
Biobase * 2.50.0 2020-10-27 [1]
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data.table * 1.14.2 2021-09-27 [2]
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DelayedArray 0.16.3 2021-03-24 [1]
DelayedMatrixStats 1.12.3 2021-02-03 [1]
desc 1.4.0 2021-09-28 [1]
DESeq2 1.30.1 2021-02-19 [1]
devtools 2.4.2 2021-06-07 [1]
digest 0.6.28 2021-09-23 [2]
doParallel 1.0.16 2020-10-16 [1]
dplyr * 1.0.7 2021-06-18 [2]
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nlme 3.1-153 2021-09-07 [2]
nloptr 1.2.2.2 2020-07-02 [1]
numDeriv 2016.8-1.1 2019-06-06 [2]
parallelly 1.28.1 2021-09-09 [2]
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performance * 0.8.0 2021-10-01 [1]
permute 0.9-5 2019-03-12 [1]
pheatmap 1.0.12 2019-01-04 [1]
phyloseq * 1.34.0 2020-10-27 [1]
pillar 1.6.4 2021-10-18 [1]
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pkgconfig 2.0.3 2019-09-22 [2]
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processx 3.5.2 2021-04-30 [2]
progress 1.2.2 2019-05-16 [2]
promises 1.2.0.1 2021-02-11 [2]
ps 1.6.0 2021-02-28 [2]
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R6 2.5.1 2021-08-19 [2]
rappdirs 0.3.3 2021-01-31 [2]
rbibutils 2.2.4 2021-10-11 [1]
RColorBrewer * 1.1-2 2014-12-07 [2]
Rcpp 1.0.7 2021-07-07 [1]
RCurl 1.98-1.5 2021-09-17 [1]
Rdpack 2.1.2 2021-06-01 [1]
readr * 2.0.2 2021-09-27 [2]
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registry 0.5-1 2019-03-05 [1]
remotes 2.4.1 2021-09-29 [1]
reprex 2.0.1 2021-08-05 [2]
reshape2 * 1.4.4 2020-04-09 [2]
reticulate * 1.22 2021-09-17 [2]
rgl * 0.107.14 2021-08-21 [1]
rhdf5 2.34.0 2020-10-27 [1]
rhdf5filters 1.2.1 2021-05-03 [1]
Rhdf5lib 1.12.1 2021-01-26 [1]
rjson 0.2.20 2018-06-08 [1]
rlang 0.4.12 2021-10-18 [2]
rmarkdown * 2.11 2021-09-14 [1]
rprojroot 2.0.2 2020-11-15 [2]
Rsamtools 2.6.0 2020-10-27 [1]
RSQLite 2.2.8 2021-08-21 [1]
rstudioapi 0.13 2020-11-12 [2]
rsvd 1.0.5 2021-04-16 [1]
rtracklayer * 1.50.0 2020-10-27 [1]
Rtsne 0.15 2018-11-10 [2]
rvest 1.0.2 2021-10-16 [2]
S4Vectors * 0.28.1 2020-12-09 [1]
sass 0.4.0 2021-05-12 [2]
scales * 1.1.1 2020-05-11 [2]
scater * 1.18.6 2021-02-26 [1]
sctransform 0.3.2 2020-12-16 [2]
scuttle 1.0.4 2020-12-17 [1]
seriation * 1.3.1 2021-10-16 [1]
sessioninfo 1.1.1 2018-11-05 [1]
shape 1.4.6 2021-05-19 [1]
SingleCellExperiment * 1.12.0 2020-10-27 [1]
sparseMatrixStats 1.2.1 2021-02-02 [1]
stringi 1.7.4 2021-08-25 [1]
stringr * 1.4.0 2019-02-10 [2]
SummarizedExperiment * 1.20.0 2020-10-27 [1]
survival 3.2-13 2021-08-24 [2]
testthat 3.1.0 2021-10-04 [2]
tibble * 3.1.5 2021-09-30 [1]
tictoc * 1.0.1 2021-04-19 [1]
tidyr * 1.1.4 2021-09-27 [2]
tidyselect 1.1.1 2021-04-30 [2]
tidyverse * 1.3.1 2021-04-15 [2]
TMB 1.7.22 2021-09-28 [1]
TSP 1.1-11 2021-10-06 [1]
tzdb 0.1.2 2021-07-20 [2]
UpSetR * 1.4.0 2019-05-22 [1]
usethis 2.1.2 2021-10-25 [1]
utf8 1.2.2 2021-07-24 [1]
uwot 0.1.10 2020-12-15 [2]
variancePartition 1.20.0 2020-10-27 [1]
vctrs 0.3.8 2021-04-29 [2]
vegan 2.5-7 2020-11-28 [1]
vipor 0.4.5 2017-03-22 [1]
viridis * 0.6.2 2021-10-13 [1]
viridisLite * 0.4.0 2021-04-13 [1]
whisker 0.4 2019-08-28 [1]
withr 2.4.2 2021-04-18 [2]
workflowr * 1.6.2 2020-04-30 [1]
writexl * 1.4.0 2021-04-20 [1]
xfun 0.27 2021-10-18 [1]
XML 3.99-0.8 2021-09-17 [1]
xml2 1.3.2 2020-04-23 [2]
xtable 1.8-4 2019-04-21 [2]
XVector 0.30.0 2020-10-27 [1]
yaml 2.2.1 2020-02-01 [2]
zlibbioc 1.36.0 2020-10-27 [1]
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[1] /pstore/home/macnairw/lib/conda_r3.12
[2] /pstore/home/macnairw/.conda/envs/r_4.0.3/lib/R/library
sessionInfo()R version 4.0.5 (2021-03-31)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /pstore/home/macnairw/.conda/envs/r_4.0.3/lib/libopenblasp-r0.3.12.so
locale:
[1] LC_CTYPE=C LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] grid parallel stats4 stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] rmarkdown_2.11 writexl_1.4.0
[3] ComplexHeatmap_2.6.2 fgsea_1.16.0
[5] tictoc_1.0.1 performance_0.8.0
[7] edgeR_3.32.1 limma_3.46.0
[9] reshape2_1.4.4 scater_1.18.6
[11] Matrix.utils_0.9.8 Matrix_1.3-4
[13] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
[15] Biobase_2.50.0 MatrixGenerics_1.2.1
[17] matrixStats_0.61.0 UpSetR_1.4.0
[19] BiocParallel_1.24.1 muscat_1.5.1
[21] dplyr_1.0.7 readr_2.0.2
[23] tidyr_1.1.4 tibble_3.1.5
[25] tidyverse_1.3.1 rtracklayer_1.50.0
[27] GenomicRanges_1.42.0 GenomeInfoDb_1.26.7
[29] IRanges_2.24.1 S4Vectors_0.28.1
[31] BiocGenerics_0.36.1 fastcluster_1.2.3
[33] rgl_0.107.14 seriation_1.3.1
[35] MOFA2_1.0.1 ggbeeswarm_0.6.0
[37] ggrepel_0.9.1 reticulate_1.22
[39] MASS_7.3-54 phyloseq_1.34.0
[41] ANCOMBC_1.0.5 purrr_0.3.4
[43] patchwork_1.1.1 readxl_1.3.1
[45] forcats_0.5.1 ggplot2_3.3.5
[47] scales_1.1.1 viridis_0.6.2
[49] viridisLite_0.4.0 assertthat_0.2.1
[51] stringr_1.4.0 data.table_1.14.2
[53] magrittr_2.0.1 circlize_0.4.13
[55] RColorBrewer_1.1-2 BiocStyle_2.18.1
[57] colorout_1.2-2 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] rappdirs_0.3.3 bit64_4.0.5
[3] knitr_1.36 irlba_2.3.3
[5] DelayedArray_0.16.3 doParallel_1.0.16
[7] RCurl_1.98-1.5 generics_0.1.1
[9] callr_3.7.0 cowplot_1.1.1
[11] microbiome_1.12.0 usethis_2.1.2
[13] RSQLite_2.2.8 future_1.22.1
[15] bit_4.0.4 tzdb_0.1.2
[17] xml2_1.3.2 lubridate_1.8.0
[19] httpuv_1.6.3 xfun_0.27
[21] hms_1.1.1 jquerylib_0.1.4
[23] evaluate_0.14 promises_1.2.0.1
[25] TSP_1.1-11 fansi_0.5.0
[27] progress_1.2.2 caTools_1.18.2
[29] dbplyr_2.1.1 igraph_1.2.7
[31] DBI_1.1.1 geneplotter_1.68.0
[33] htmlwidgets_1.5.4 ellipsis_0.3.2
[35] corrplot_0.90 backports_1.2.1
[37] insight_0.14.5 permute_0.9-5
[39] annotate_1.68.0 sparseMatrixStats_1.2.1
[41] vctrs_0.3.8 remotes_2.4.1
[43] Cairo_1.5-12.2 cachem_1.0.6
[45] withr_2.4.2 grr_0.9.5
[47] sctransform_0.3.2 vegan_2.5-7
[49] GenomicAlignments_1.26.0 prettyunits_1.1.1
[51] cluster_2.1.2 ape_5.5
[53] crayon_1.4.1 basilisk.utils_1.2.2
[55] genefilter_1.72.1 labeling_0.4.2
[57] pkgconfig_2.0.3 pkgload_1.2.3
[59] nlme_3.1-153 vipor_0.4.5
[61] devtools_2.4.2 blme_1.0-5
[63] rlang_0.4.12 globals_0.14.0
[65] lifecycle_1.0.1 registry_0.5-1
[67] filelock_1.0.2 modelr_0.1.8
[69] rsvd_1.0.5 cellranger_1.1.0
[71] rprojroot_2.0.2 Rhdf5lib_1.12.1
[73] boot_1.3-28 reprex_2.0.1
[75] beeswarm_0.4.0 processx_3.5.2
[77] whisker_0.4 GlobalOptions_0.1.2
[79] pheatmap_1.0.12 png_0.1-7
[81] rjson_0.2.20 bitops_1.0-7
[83] KernSmooth_2.23-20 rhdf5filters_1.2.1
[85] Biostrings_2.58.0 blob_1.2.2
[87] DelayedMatrixStats_1.12.3 shape_1.4.6
[89] parallelly_1.28.1 beachmat_2.6.4
[91] memoise_2.0.0 plyr_1.8.6
[93] gplots_3.1.1 zlibbioc_1.36.0
[95] compiler_4.0.5 clue_0.3-60
[97] lme4_1.1-27.1 DESeq2_1.30.1
[99] Rsamtools_2.6.0 cli_3.0.1
[101] ade4_1.7-18 XVector_0.30.0
[103] listenv_0.8.0 lmerTest_3.1-3
[105] ps_1.6.0 TMB_1.7.22
[107] mgcv_1.8-38 tidyselect_1.1.1
[109] stringi_1.7.4 highr_0.9
[111] yaml_2.2.1 BiocSingular_1.6.0
[113] locfit_1.5-9.4 sass_0.4.0
[115] fastmatch_1.1-3 tools_4.0.5
[117] future.apply_1.8.1 rstudioapi_0.13
[119] foreach_1.5.1 git2r_0.28.0
[121] gridExtra_2.3 farver_2.1.0
[123] Rtsne_0.15 digest_0.6.28
[125] BiocManager_1.30.16 Rcpp_1.0.7
[127] broom_0.7.9 scuttle_1.0.4
[129] later_1.3.0 httr_1.4.2
[131] AnnotationDbi_1.52.0 Rdpack_2.1.2
[133] colorspace_2.0-2 rvest_1.0.2
[135] XML_3.99-0.8 fs_1.5.0
[137] splines_4.0.5 uwot_0.1.10
[139] basilisk_1.2.1 multtest_2.46.0
[141] sessioninfo_1.1.1 xtable_1.8-4
[143] jsonlite_1.7.2 nloptr_1.2.2.2
[145] testthat_3.1.0 R6_2.5.1
[147] pillar_1.6.4 htmltools_0.5.2
[149] glue_1.4.2 fastmap_1.1.0
[151] minqa_1.2.4 BiocNeighbors_1.8.2
[153] codetools_0.2-18 pkgbuild_1.2.0
[155] utf8_1.2.2 lattice_0.20-45
[157] bslib_0.3.1 pbkrtest_0.5.1
[159] numDeriv_2016.8-1.1 colorRamps_2.3
[161] gtools_3.9.2 magick_2.7.3
[163] survival_3.2-13 glmmTMB_1.1.2.3
[165] desc_1.4.0 biomformat_1.18.0
[167] munsell_0.5.0 GetoptLong_1.0.5
[169] rhdf5_2.34.0 GenomeInfoDbData_1.2.4
[171] iterators_1.0.13 variancePartition_1.20.0
[173] HDF5Array_1.18.1 haven_2.4.3
[175] gtable_0.3.0 rbibutils_2.2.4