Last updated: 2021-07-02
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Knit directory: MS_lesions/
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Modified: code/ms07_soup.R
Modified: code/ms09_ancombc.R
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source('code/ms00_utils.R')
source('code/ms03_SampleQC.R')
source('code/ms07_soup.R')
source('code/ms09_ancombc.R')
source('code/ms10_muscat_runs.R')
source('code/supp10_muscat_olg_pc.R')
# define inputs
sce_f = 'data/sce_raw/ms_sce.rds'
meta_f = 'data/metadata/metadata_updated_20201127.txt'
labels_f = 'data/byhand_markers/validation_markers_2021-05-31.csv'
labelled_f = 'output/ms13_labelling/conos_labelled_2021-05-31.txt.gz'
# get soup values
soup_dir = 'output/ms07_soup'
pb_soup_f = file.path(soup_dir, 'pb_soup_broad_maximum_2021-06-01.rds')
pb_orig_f = file.path(soup_dir, 'pb_sum_broad_2021-06-01.rds')
# get qc values
qc_dir = 'output/ms03_SampleQC'
qc_f = file.path(qc_dir, 'ms_qc_dt.txt')
# ancombc variables
sample_vars = c('sample_id', 'matter', 'lesion_type',
'neuro_ok', 'neuro_prop', 'source', 'patient_id',
'sex', 'age_norm', 'pmi_cat')
mad_cut = 2
# define variables and celltypes for excluding oligo_D etc
d_vars = c("source", "patient_id", "sex", "age_norm")
d_types = c('Oligo_D', 'COP_A2')
# define pseudobulk files
pb_olg_pc_f = sprintf('%s/pb_olg_pc_sum_broad_%s.rds', soup_dir, '2021-06-30')
# define muscat runs
date_tag = '2021-07-01'
cluster_var = 'type_broad'
method_spec = list(method = 'edgeR', treat = FALSE)
filter = 'both'
padj_cut = 0.05
fc_cut = 0
fgsea_cut = 0.05
min_cells = 10
soup_cut = 0.1
n_cores = 8
subset_d = list(matter = 'WM', neuro_ok = TRUE, oligo_d = FALSE)
# outputs directory
save_dir = 'output/ms10_muscat/run14'
if (!dir.exists(save_dir))
dir.create(save_dir)
# define files to save
formula_str = '~ oligo_pc1 + sex + age_norm + pmi_cat'
run_tag = 'olg_pc1'
this_var = 'oligo_pc1'
olgpc_res_f = sprintf('%s/muscat_res_dt_%s_%s.txt.gz', save_dir, run_tag, date_tag)
fgsea_pat = sprintf('%s/fgsea_dt_%s_%s_%s.txt.gz', save_dir, run_tag, '%s', '%s')
fgsea_fs = sapply(names(paths_list),
function(n) sprintf(fgsea_pat, n, date_tag))
labels_dt = load_names_dt(labels_f) %>%
.[, cluster_id := type_fine]
meta_dt = load_meta_dt(meta_f)
conos_dt = load_labelled_dt(labelled_f, labels_f) %>%
merge(meta_dt, by = 'sample_id') %>%
add_neuro_props(mad_cut = mad_cut)
qc_stats = calc_qc_stats(qc_dir, qc_f,
conos_dt[, .(cell_id, sample_id, type_broad, type_fine, conos)]) %>%
merge(meta_dt[, .(sample_id, lesion_type, patient_id)], by = 'sample_id')
props_wm_all = calc_props_wm_all(conos_dt, sample_vars)
olg_pc_dt = calc_olg_pc_dt(conos_dt, d_types, props_wm_all)
pat_annots = calc_patient_annots(conos_dt)
conos_tweak = calc_conos_tweak(labelled_f, labels_f, meta_dt,
mad_cut, d_types)
meta_tweak = calc_meta_tweak(meta_f, olg_pc_dt, pat_annots)
pb_olg_pc = make_pb_object(pb_olg_pc_f, sce_f, meta_tweak, conos_tweak,
cluster_var = cluster_var, fun = 'sum', n_cores = n_cores)
loading pre-saved object
cols_dt = pb_olg_pc %>% colData %>% as.data.frame %>%
as.data.table(keep.rownames = 'sample_id')
pb_soup = pb_soup_f %>% readRDS
pb_orig = pb_orig_f %>% readRDS %>% .subset_pb(list(matter = c('WM')))
subsetting pb object
restricting to samples that meet subset criteria
updating factors to remove levels no longer observed
contam_dt = .calc_contam_dt(pb_soup, pb_orig, min_cells)
res_dt = calc_patient_muscat(olgpc_res_f, pb_olg_pc, contam_dt,
gtf_f, formula_str, method_spec, filter, min_cells,
padj_cut, fc_cut, soup_cut, n_cores)
calc_patient_level_gsea(olgpc_res_f, fgsea_pat, fgsea_cut, date_tag, n_cores)
already done!
NULL
labels_dt = .load_labels_dt(labels_f, cluster_var)
magma_dt = .load_magma_dt(magma_f, pb_orig)
tfs_dt = .load_tfs_dt(tfs_f, pb_orig)
lof_dt = .load_lof_dt(lof_f, pb_orig)
coloc_dt = .load_coloc_dt(coloc_f, pb_orig)
# get muscat results
res_all = olgpc_res_f %>% fread
res_all[, logCPM := logCPM - min(min(logCPM), 0), by = cluster_id]
res_dt = .load_muscat_results(res_all, labels_dt, params)
# get fgsea results
fgsea_list = .load_fgseas_list(fgsea_fs, labels_dt)
# prep for stacked bars
signif_dt = res_dt[ updown_soup != 'insignif' & !is.na(p_adj.soup) ]
assert_that(all(abs(signif_dt$logFC) >= fc_cut))
[1] TRUE
uniques_dt = .calc_uniques_dt(signif_dt)
stacked_dt = .calc_stacked_dt(uniques_dt)
# restrict to significant genes only
message(' getting top genes')
getting top genes
top_dt = .calc_top_genes(signif_dt, res_dt, uniques_dt,
magma_dt, tfs_dt, lof_dt, coloc_dt, fc_cut, top_n = 30)
top_gwas_dt = .calc_top_gwas_dt(res_dt, uniques_dt, magma_dt,
tfs_dt, lof_dt, coloc_dt, fc_cut = NULL, magma_cut = 0.05)
# uniques_dt[ grepl('Imm', cluster_id) & shared_outside_cl == FALSE ] %>%
# merge(res_dt, by = c('cluster_id', 'gene_id', 'symbol', 'var_type', 'test_var')) %>%
# .[, .(symbol, logFC = round(logFC, 2), p_adj.soup)] %>% .[ order(logFC) ]
# fgsea_dt[ grepl('PLOD1', leadingEdge) & coef == 'oligo_pc1' & padj < 0.5,
# .(cluster_id, pathway = str_match(pathway, gsea_regex)[, 3] %>% tolower,
# main_path, padj, NES) ] %>%
# .[order(cluster_id, padj)] %>% print(200)
Oligo_D
outliersinput_all = conos_dt[(matter == "WM") &
(type_broad %in% c('Oligodendrocytes', 'OPCs / COPs'))]
cat('### All\n')
(plot_patient_clrs(input_all, props_wm_all))
cat('\n\n')
cat('### Oligo_D + COP_A2 excluded\n')
input_sel = input_all[ !(type_fine %in% d_types) ]
(plot_patient_clrs(input_sel, props_wm_all))
cat('\n\n')
cat('### All\n')
(plot_patient_clrs_annots(input_all, props_wm_all, meta_tweak))
cat('\n\n')
cat('### Oligo_D + COP_A2 excluded\n')
input_sel = input_all[ !(type_fine %in% d_types) ]
(plot_patient_clrs_annots(input_sel, props_wm_all, meta_tweak))
cat('\n\n')
tmp_dt = copy(stacked_dt)
print(plot_n_signif_barplot(tmp_dt, facet_by = 'cluster_id'))
this_type = 'test'
how_unique = 'celltype_specific'
cat('# Top ', how_unique, ' genes, ', this_type,
' variables{.tabset}\n', sep='')
for (cl in levels(top_dt$cluster_id)) {
tmp_dt = top_dt[ unique_var %in% unique_ls[[how_unique]] &
cluster_id == cl & test_var == this_var ]
tb = unique(tmp_dt$type_broad)
if ( nrow(tmp_dt) == 0 )
next
cat('## ', cl, '\n')
hm_obj = plot_top_genes_heatmap_fn_across_celltypes(tmp_dt, res_dt, labels_dt,
max_fc = 4, title = paste(tb, cl, sep=': '))
draw(hm_obj, padding = unit(c(0.5, 0.1, 0.1, 0.1), "in"))
.add_fdr_legend('logfc')
cat('\n\n')
}
how_unique = 'non_specific'
cat('# Top ', how_unique, ' genes, ', this_type,
' variables{.tabset}\n', sep='')
for (cl in levels(top_dt$cluster_id)) {
tmp_dt = top_dt[ unique_var %in% unique_ls[[how_unique]] &
cluster_id == cl & test_var == this_var ]
tb = unique(tmp_dt$type_broad)
if ( nrow(tmp_dt) == 0 )
next
cat('## ', cl, '\n')
hm_obj = plot_top_genes_heatmap_fn_across_celltypes(tmp_dt, res_dt, labels_dt,
max_fc = 4, title = paste(tb, cl, sep=': '))
draw(hm_obj, padding = unit(c(0.5, 0.1, 0.1, 0.1), "in"))
.add_fdr_legend('logfc')
cat('\n\n')
}
this_type = 'test'
cat('# GSEA results for ', this_type, ' variables{.tabset}\n', sep = '')
for (p in names(paths_list)) {
# restrict to relevant GO terms
cat('## ', p, '\n', sep='')
dt = fgsea_list[[p]] %>% .[var_type == this_type]
if (nrow(dt[ padj < fgsea_cut ]) == 0)
next
# plot
print(plot_gsea_dotplot(dt, labels_dt, top_n_paths = 60))
cat('\n\n')
}
for (cl in broad_ord) {
# restrict to relevant GO terms
cat('## ', cl, '\n', sep='')
suppressWarnings(print(plot_oligo_pc_heatmap(cl, signif_dt,
cols_dt, pb_olg_pc, this_var, top_n = 60)))
cat('\n\n')
}
plot_dt = res_dt[ coef == 'oligo_pc1',
.(cluster_id, gene_id, symbol, soup_pc = 100 * mean_soup,
abs_logFC = abs(logFC), log_p = log10(p_adj.soup))] %>%
melt.data.table(measure = c('abs_logFC', 'log_p'),
variable.name = 'stat', value.name = 'value')
g = ggplot(plot_dt) +
aes(y = value, x = soup_pc) +
geom_bin2d() +
geom_smooth(method = 'gam', formula = y ~ s(x, bs = "cs"),
se = FALSE, colour = 'grey20') +
scale_x_continuous( breaks = pretty_breaks() ) +
scale_y_continuous( breaks = pretty_breaks() ) +
expand_limits( x = 0 ) +
scale_fill_distiller( palette = 'RdBu', trans = 'log10' ) +
facet_grid( stat ~ cluster_id, scales = 'free' ) +
theme_bw() + labs( x = '% soup', y = 'edgeR statistic' )
print(g)
plot_dt = merge(qc_stats[str_detect(sample_id, 'WM')], olg_pc_dt,
by = 'patient_id')
g = ggplot(plot_dt) + aes(y = qc_val, x = oligo_pc1,
colour = type_broad, group = type_fine) +
geom_point(alpha = 0.1, size = 1) +
geom_smooth(se = FALSE, method = 'gam', formula = y ~ s(x, bs = 'cs')) +
scale_x_continuous( breaks = pretty_breaks() ) +
scale_y_continuous( breaks = pretty_breaks() ) +
scale_colour_manual( values = broad_cols ) +
facet_grid( qc_var ~ type_broad, scales = 'free_y' ) +
theme_bw() + theme(legend.position = 'bottom') +
labs( y = 'QC value', x = 'oligo PC1' )
(g)
devtools::session_info()
- Session info ---------------------------------------------------------------
setting value
version R version 4.0.3 (2020-10-10)
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 2021-07-01
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package * version date lib
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prettyunits 1.1.1 2020-01-24 [2]
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]
purrr * 0.3.4 2020-04-17 [2]
R.methodsS3 1.8.1 2020-08-26 [1]
R.oo 1.24.0 2020-08-26 [1]
R.utils 2.10.1 2020-08-26 [1]
R6 2.5.0 2020-10-28 [2]
rbibutils 2.1.1 2021-04-28 [1]
RColorBrewer * 1.1-2 2014-12-07 [2]
Rcpp 1.0.6 2021-01-15 [2]
RCurl 1.98-1.3 2021-03-16 [1]
Rdpack 2.1.1 2021-02-23 [1]
readr * 1.4.0 2020-10-05 [2]
readxl * 1.3.1 2019-03-13 [2]
registry 0.5-1 2019-03-05 [1]
remotes 2.3.0 2021-04-01 [1]
reprex 2.0.0 2021-04-02 [2]
reshape2 * 1.4.4 2020-04-09 [2]
reticulate * 1.20 2021-05-03 [2]
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 0.4.11 2021-04-30 [2]
rmarkdown * 2.8 2021-05-07 [2]
rprojroot 2.0.2 2020-11-15 [2]
Rsamtools 2.6.0 2020-10-27 [1]
RSQLite 2.2.7 2021-04-22 [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 1.0.0 2021-03-09 [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]
scran * 1.18.7 2021-04-16 [1]
sctransform 0.3.2 2020-12-16 [2]
scuttle 1.0.4 2020-12-17 [1]
seriation * 1.2-9 2020-10-01 [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]
snakecase 0.11.0 2019-05-25 [1]
sparseMatrixStats 1.2.1 2021-02-02 [1]
statmod 1.4.36 2021-05-10 [1]
stringi 1.6.2 2021-05-17 [2]
stringr * 1.4.0 2019-02-10 [2]
SummarizedExperiment * 1.20.0 2020-10-27 [1]
survival 3.2-11 2021-04-26 [2]
testthat 3.0.2 2021-02-14 [2]
tibble * 3.1.2 2021-05-16 [2]
tictoc * 1.0.1 2021-04-19 [1]
tidyr * 1.1.3 2021-03-03 [2]
tidyselect 1.1.1 2021-04-30 [2]
tidyverse * 1.3.1 2021-04-15 [2]
TMB 1.7.20 2021-04-08 [1]
TSP 1.1-10 2020-04-17 [1]
UpSetR * 1.4.0 2019-05-22 [1]
usethis 2.0.1 2021-02-10 [1]
utf8 1.2.1 2021-03-12 [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 * 0.6.1 2021-05-11 [1]
viridisLite * 0.4.0 2021-04-13 [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 0.23 2021-05-15 [1]
XML 3.99-0.6 2021-03-16 [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 4.0.3 (2020-10-10)
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_2.8 writexl_1.4.0
[3] readxl_1.3.1 ComplexHeatmap_2.6.2
[5] fgsea_1.16.0 tictoc_1.0.1
[7] reshape2_1.4.4 scater_1.18.6
[9] Matrix.utils_0.9.8 seriation_1.2-9
[11] UpSetR_1.4.0 dplyr_1.0.6
[13] readr_1.4.0 tidyr_1.1.3
[15] tibble_3.1.2 tidyverse_1.3.1
[17] rtracklayer_1.50.0 ggrepel_0.9.1
[19] reticulate_1.20 MASS_7.3-54
[21] phyloseq_1.34.0 ANCOMBC_1.0.5
[23] purrr_0.3.4 patchwork_1.1.1
[25] nnls_1.4 muscat_1.5.1
[27] DropletUtils_1.10.3 edgeR_3.32.1
[29] limma_3.46.0 BiocParallel_1.24.1
[31] Matrix_1.3-3 scran_1.18.7
[33] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
[35] Biobase_2.50.0 GenomicRanges_1.42.0
[37] GenomeInfoDb_1.26.7 IRanges_2.24.1
[39] S4Vectors_0.28.1 BiocGenerics_0.36.1
[41] MatrixGenerics_1.2.1 matrixStats_0.59.0
[43] googlesheets_0.3.0 forcats_0.5.1
[45] ggplot2_3.3.3 scales_1.1.1
[47] viridis_0.6.1 viridisLite_0.4.0
[49] assertthat_0.2.1 stringr_1.4.0
[51] data.table_1.14.0 magrittr_2.0.1
[53] circlize_0.4.12 RColorBrewer_1.1-2
[55] BiocStyle_2.18.1 colorout_1.2-2
[57] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] R.methodsS3_1.8.1 bit64_4.0.5
[3] knitr_1.33 irlba_2.3.3
[5] DelayedArray_0.16.3 R.utils_2.10.1
[7] RCurl_1.98-1.3 doParallel_1.0.16
[9] generics_0.1.0 callr_3.7.0
[11] microbiome_1.12.0 usethis_2.0.1
[13] RSQLite_2.2.7 future_1.21.0
[15] bit_4.0.4 xml2_1.3.2
[17] lubridate_1.7.10 httpuv_1.6.1
[19] xfun_0.23 hms_1.1.0
[21] jquerylib_0.1.4 TSP_1.1-10
[23] evaluate_0.14 promises_1.2.0.1
[25] fansi_0.4.2 progress_1.2.2
[27] caTools_1.18.2 dbplyr_2.1.1
[29] igraph_1.2.6 DBI_1.1.1
[31] geneplotter_1.68.0 ellipsis_0.3.2
[33] backports_1.2.1 permute_0.9-5
[35] annotate_1.68.0 sparseMatrixStats_1.2.1
[37] vctrs_0.3.8 remotes_2.3.0
[39] Cairo_1.5-12.2 cachem_1.0.5
[41] withr_2.4.2 grr_0.9.5
[43] sctransform_0.3.2 vegan_2.5-7
[45] GenomicAlignments_1.26.0 prettyunits_1.1.1
[47] cluster_2.1.2 ape_5.5
[49] crayon_1.4.1 genefilter_1.72.1
[51] labeling_0.4.2 pkgconfig_2.0.3
[53] pkgload_1.2.1 nlme_3.1-152
[55] vipor_0.4.5 devtools_2.4.1
[57] blme_1.0-5 rlang_0.4.11
[59] globals_0.14.0 lifecycle_1.0.0
[61] registry_0.5-1 modelr_0.1.8
[63] rsvd_1.0.5 cellranger_1.1.0
[65] rprojroot_2.0.2 Rhdf5lib_1.12.1
[67] boot_1.3-28 reprex_2.0.0
[69] beeswarm_0.3.1 processx_3.5.2
[71] GlobalOptions_0.1.2 png_0.1-7
[73] rjson_0.2.20 bitops_1.0-7
[75] R.oo_1.24.0 KernSmooth_2.23-20
[77] rhdf5filters_1.2.1 Biostrings_2.58.0
[79] blob_1.2.1 DelayedMatrixStats_1.12.3
[81] shape_1.4.6 parallelly_1.25.0
[83] beachmat_2.6.4 memoise_2.0.0
[85] plyr_1.8.6 gplots_3.1.1
[87] zlibbioc_1.36.0 compiler_4.0.3
[89] dqrng_0.3.0 clue_0.3-59
[91] lme4_1.1-27 DESeq2_1.30.1
[93] snakecase_0.11.0 cli_2.5.0
[95] Rsamtools_2.6.0 ade4_1.7-16
[97] XVector_0.30.0 lmerTest_3.1-3
[99] listenv_0.8.0 ps_1.6.0
[101] TMB_1.7.20 mgcv_1.8-35
[103] tidyselect_1.1.1 stringi_1.6.2
[105] highr_0.9 yaml_2.2.1
[107] BiocSingular_1.6.0 locfit_1.5-9.4
[109] sass_0.4.0 fastmatch_1.1-0
[111] tools_4.0.3 future.apply_1.7.0
[113] rstudioapi_0.13 bluster_1.0.0
[115] foreach_1.5.1 git2r_0.28.0
[117] janitor_2.1.0 gridExtra_2.3
[119] farver_2.1.0 Rtsne_0.15
[121] digest_0.6.27 BiocManager_1.30.15
[123] Rcpp_1.0.6 broom_0.7.6
[125] scuttle_1.0.4 later_1.2.0
[127] httr_1.4.2 AnnotationDbi_1.52.0
[129] Rdpack_2.1.1 colorspace_2.0-1
[131] rvest_1.0.0 XML_3.99-0.6
[133] fs_1.5.0 splines_4.0.3
[135] statmod_1.4.36 multtest_2.46.0
[137] sessioninfo_1.1.1 xtable_1.8-4
[139] jsonlite_1.7.2 nloptr_1.2.2.2
[141] testthat_3.0.2 R6_2.5.0
[143] pillar_1.6.1 htmltools_0.5.1.1
[145] glue_1.4.2 fastmap_1.1.0
[147] minqa_1.2.4 BiocNeighbors_1.8.2
[149] codetools_0.2-18 pkgbuild_1.2.0
[151] utf8_1.2.1 lattice_0.20-44
[153] bslib_0.2.5 numDeriv_2016.8-1.1
[155] pbkrtest_0.5.1 ggbeeswarm_0.6.0
[157] colorRamps_2.3 gtools_3.8.2
[159] survival_3.2-11 glmmTMB_1.0.2.1
[161] desc_1.3.0 biomformat_1.18.0
[163] munsell_0.5.0 GetoptLong_1.0.5
[165] rhdf5_2.34.0 GenomeInfoDbData_1.2.4
[167] iterators_1.0.13 HDF5Array_1.18.1
[169] variancePartition_1.20.0 haven_2.4.1
[171] gtable_0.3.0 rbibutils_2.1.1