Last updated: 2022-03-24
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Knit directory: MS_lesions/
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Rmd | 7c17d96 | Macnair | 2022-03-18 | Add various MOFA run outputs |
html | 7c17d96 | Macnair | 2022-03-18 | Add various MOFA run outputs |
source('code/ms00_utils.R')
source('code/ms08_modules.R')
source('code/ms09_ancombc.R')
source('code/ms10_muscat_runs.R')
source('code/ms15_mofa.R')
n_cores = 8
setDTthreads(n_cores)
# specify what goes into muscat run
meta_f = "data/metadata/metadata_checked_assumptions_2021-10-08.xlsx"
olg_grps_f = 'data/metadata/oligo_groupings.txt'
labels_f = 'data/byhand_markers/validation_markers_2021-05-31.csv'
labelled_f = 'output/ms13_labelling/conos_labelled_2021-05-31.txt.gz'
pb_f = file.path(soup_dir, 'pb_gm_w_pcs_sum_broad_2021-11-12.rds')
pb_fine_f = file.path(soup_dir, 'pb_gm_w_pcs_sum_fine_2021-11-12.rds')
soup_f = 'data/ambient/ambient.100UMI.txt'
# get summary QC metrics for each sample
qc_dir = "output/ms03_SampleQC"
qc_f = file.path(qc_dir, "ms_qc_dt.txt")
# define run to load
run_tag = 'run23'
time_stamp = '2021-11-15'
# 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)
mds_sep_f = sprintf('%s/mds_sep_dt_%s_%s.txt.gz',
model_dir, run_tag, time_stamp)
# where to save
save_dir = 'output/ms15_mofa'
date_tag = '2021-03-04_edger'
if (!dir.exists(save_dir))
dir.create(save_dir)
# file for summary of QC metrics
qc_stats_f = sprintf('%s/qc_stats_by_sample_%s.txt', save_dir, date_tag)
# parameters for gene selection
min_sd = log(1.5)
min_fc = log(1.5)
max_p = 0.01
n_factors = 5
sel_cl = c("OPCs / COPs", "Oligodendrocytes", "Astrocytes",
"Microglia", "Excitatory neurons", "Inhibitory neurons",
"Endothelial cells", "Pericytes")
fgsea_cut = 0.1
sel_ps = c('go_bp', 'go_cc', 'go_mf', 'hallmark', 'kegg')
log_p_mad = 2
n_paths = 50
# parameters for plotting
min_var = 5
w_cut = 0.2
# checking if metadata can explain factors
formula_str = '~ lesion_type + sex + age_scale + pmi_cat2'
random_var = 'subject_orig'
# output files
mofa_f = sprintf('%s/mofa_%s_%s.hdf5', save_dir, run_tag, date_tag)
fgsea_pat = sprintf('%s/mofa_fgsea_%s_%s_%s.txt',
save_dir, run_tag, '%s', date_tag)
interesting_f = sprintf('%s/mofa_interesting_genes_%s_%s.xlsx',
save_dir, run_tag, date_tag)
# what to use to illustrate random effects concept?
example_cl = "Astrocytes"
example_gs = c("HGF_ENSG00000019991", "OXTR_ENSG00000180914")
# 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, EU044
ok_samples = outliers_dt[ props_ok == TRUE ]$sample_id
pb = pb[ , ok_samples ]
# load other useful things
labels_dt = .load_labels_dt(labels_f, params$cluster_var)
Warning in FUN(X[[i]], ...): unable to translate '<U+00C4>' to native encoding
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magma_dt = .load_magma_dt(magma_f, pb)
tfs_dt = .load_tfs_dt(tfs_f, pb)
lof_dt = .load_lof_dt(lof_f, pb)
# load annotations
annots_dt = .get_cols_dt(pb) %>%
.[, sample := sample_id ] %>% .[, group := 'single_group'] %>%
.[, .(sample, sample_id, diagnosis, lesion_type, subject_id, subject_orig,
sample_source, age = age_at_death, age_at_death, age_scale,
years_w_ms, sex, pmi_cat, pmi_cat2, smoker )]
# annots_dt = add_oligo_groups(annots_dt, olg_grps_f)
# get random effects
ranef_dt = .load_ranef_dt(ranef_dt_f, labels_dt, pb)
# get results
res_dt = muscat_f %>% fread %>%
.load_muscat_results(labels_dt, params) %>%
.[, .(cluster_id, gene_id, symbol, var_type, coef, test_var,
logCPM, mean_soup, padj = p_adj.soup, logFC)] %>%
.[ !is.na(padj) ]
# get anova results
anova_dt = .load_anova_dt(anova_f, res_dt) %>%
.[ is.na(full), full := 1 ]
# get MDS outputs
mds_sep_dt = mds_sep_f %>% fread
if (params$cluster_var == 'type_broad')
mds_sep_dt[, cluster_id := factor(cluster_id, levels = broad_ord)]
# 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)
filtered_dt = filter_dt[ ( (ms_signif == 'signif') & (ms_effect == 'big') ) |
( (pt_signif == 'signif') & (pt_variab == 'variable')) ] %>%
.[ cluster_id %in% sel_cl ] %>%
.[, is_ms := ifelse(ms_effect == "big" & ms_signif == "signif", "ms", "not") ] %>%
.[, is_pt := ifelse(pt_signif == "signif" & pt_variab == "variable", "pt", "not") ]
# check what we've got
filtered_dt[, .N, by = .(cluster_id, is_ms, is_pt)] %>%
.[, total := sum( N ), by = cluster_id ] %>%
dcast.data.table(cluster_id + total ~ is_ms + is_pt, fill = 0, value.var = "N")
cluster_id total ms_not ms_pt not_pt
1: OPCs / COPs 187 34 4 149
2: Oligodendrocytes 531 254 22 255
3: Astrocytes 721 176 18 527
4: Microglia 385 71 21 293
5: Excitatory neurons 952 530 19 403
6: Inhibitory neurons 597 400 4 193
7: Endothelial cells 582 117 62 403
8: Pericytes 298 135 47 116
n_cells_dt = calc_n_cells_dt(pb_fine_f, annots_dt, sel_cl)
soup_dt = get_soup_logcpms(soup_f, pb)
qc_stats = calc_qc_stats_by_sample(qc_stats_f, qc_dir, qc_f,
meta_f, labels_f, labelled_f)
message("which genes have strong layer associations? (FDR < 1%)")
layer_fits = calc_layer_fits(pb, filtered_dt, sel_cl, params,
lib_size_method = 'edger')
layer_fits[ fdr < 0.01 ] %>% .[ order(fdr) ] %>%
.[, .(celltype = view, pc = coef, symbol, coef = estimate %>% round(2),
log10_p = fdr %>% log10 %>% round(2)) ] %>% print
mofa_obj = make_mofa_obj_samples_regress_layers(pb, filtered_dt,
sel_cl, params, lib_size_method = 'edger')
Removing 6210 rows with all zero counts
Removing 5516 rows with all zero counts
Removing 5102 rows with all zero counts
Removing 7360 rows with all zero counts
Removing 3123 rows with all zero counts
Removing 4072 rows with all zero counts
Removing 6842 rows with all zero counts
Removing 9754 rows with all zero counts
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Creating MOFA object from a data.frame...
if (file.exists(mofa_f)) {
model = load_model(mofa_f)
} else {
# set up
data_opts = get_default_data_options(mofa_obj)
model_opts = get_default_model_options(mofa_obj)
train_opts = get_default_training_options(mofa_obj)
# specify how many factors
model_opts$num_factors = n_factors
# train mofa
mofa_obj = prepare_mofa(
object = mofa_obj,
data_options = data_opts,
model_options = model_opts,
training_options = train_opts
)
model = run_mofa(mofa_obj, mofa_f)
}
# update metadata
model = add_metadata(model, annots_dt)
# put weights and scores in MS order
model = put_model_in_ms_order(model)
var_exp_dt = get_variance_explained(model, as.data.frame = TRUE) %>%
as.data.table %>%
.[, .(
view = r2_per_factor.view %>% factor(levels = broad_short),
factor = r2_per_factor.factor,
var_exp = r2_per_factor.value
)]
to_plot_dt = var_exp_dt[ var_exp > min_var ] %>% .[order(factor, -var_exp)]
# get weights, define expected files
ws_dt = extract_weights(model, sd_dt)
fgsea_fs = sapply(sel_ps, function(p) sprintf(fgsea_pat, p))
# if necessary, run FGSEA
if (all(file.exists(fgsea_fs))) {
gsea_list = lapply(fgsea_fs, fread)
} else {
# do fgsea for these
bpparam = MulticoreParam(workers = n_cores,
progressbar = TRUE, tasks = 50)
bpstart()
gsea_list = calc_mofa_fgsea(paths_list[ sel_ps ], ws_dt, fgsea_pat, fgsea_cut, bpparam)
bpstop()
}
# restrict to interesting ones
gsea_main = gsea_list %>% map( ~.x[ main_path == TRUE ]) %>% rbindlist
# show what we found
gsea_main[, .(factor, view, path_set, pathway = pathway %>% tolower %>%
str_extract("(?<=(hallmark|gobp|gocc|gomf|kegg)_).+"),
log10_p = log10(padj) %>% round(2), NES = round(NES, 2))] %>%
.[ order(factor, NES) ] %>% print
factor view path_set
1: Factor1 inhib go_cc
2: Factor1 endo go_bp
3: Factor1 endo hallmark
4: Factor1 excit go_bp
5: Factor1 excit go_bp
---
412: Factor5 excit go_bp
413: Factor5 excit hallmark
414: Factor5 excit go_bp
415: Factor5 endo hallmark
416: Factor5 peri hallmark
pathway
1: mitochondrion
2: response_to_type_i_interferon
3: interferon_alpha_response
4: atp_metabolic_process
5: oxidative_phosphorylation
---
412: response_to_oxygen_containing_compound
413: tnfa_signaling_via_nfkb
414: negative_regulation_of_nucleobase_containing_compound_metabolic_process
415: tnfa_signaling_via_nfkb
416: tnfa_signaling_via_nfkb
log10_p NES
1: -4.04 -2.56
2: -1.90 -2.49
3: -3.43 -2.38
4: -1.79 -2.29
5: -1.69 -2.28
---
412: -4.45 2.08
413: -4.24 2.14
414: -4.45 2.22
415: -6.33 2.22
416: -5.01 2.25
r2_dt = calc_r2_for_factors(model, annots_dt, formula_str, random_var)
anova_dt = calc_lrts(model, annots_dt, formula_str, random_var)
muscat
results vs SDfor (what in c('log10_padj', 'log2FC')) {
cat('### ', what, '\n', sep = '')
print(plot_muscat_vs_sd(res_dt, sd_dt, NULL, what = what))
cat('\n\n')
}
cyto_gs = unique(res_dt$gene_id) %>% str_subset('(^IL[0-9]+|^CCL|^CXCL|^IFN|^TGF|^TNF|^CSF)')
(plot_muscat_vs_sd_min(res_dt[ gene_id %in% cyto_gs ], sd_dt[ gene_id %in% cyto_gs ],
sel_cl, min_sd, max_p, do_labels = TRUE))
(plot_age_duration(annots_dt))
Version | Author | Date |
---|---|---|
7c17d96 | Macnair | 2022-03-18 |
cat('### All genes\n')
print(plot_gene_overlap(model))
Version | Author | Date |
---|---|---|
7c17d96 | Macnair | 2022-03-18 |
cat('\n\n')
for (sel_f in factors_names(model)) {
cat('### Genes in ', sel_f, '\n', sep = '')
suppressWarnings(print(plot_gene_overlap(model, sel_f = sel_f, w_cut = w_cut)))
cat('\n\n')
}
source('code/ms15_mofa.R')
cat('### All genes\n')
suppressWarnings(print(plot_gene_overlap(model, what = 'prop')))
Version | Author | Date |
---|---|---|
7c17d96 | Macnair | 2022-03-18 |
cat('\n\n')
for (sel_f in factors_names(model)) {
cat('### Genes in ', sel_f, '\n', sep = '')
suppressWarnings(print(plot_gene_overlap(model, what = 'prop',
sel_f = sel_f, w_cut = w_cut)))
cat('\n\n')
}
for (annot in c('lesion_type', 'diagnosis', 'sex', 'sample_source', 'smoker')) {
cat('### by ', annot, '\n', sep = '')
print(plot_factors_univariate(model, annots_dt, pb, by = annot))
cat('\n\n')
}
for (annot in c('subject_id', 'lesion_type', 'diagnosis', 'sex', 'sample_source', 'smoker')) {
cat('### by ', annot, '\n', sep = '')
print(plot_factors_pairwise(model, annots_dt, pb, by = annot))
cat('\n\n')
}
for (cl in broad_ord) {
if (!(broad_short[[cl]] %in% views_names(model)))
next
cat('### ', cl, '\n', sep = '')
print(plot_factors_over_mds_samples(model, mds_sep_dt, cl = cl))
cat('\n\n')
}
for (v in c('score', 'score_scaled')) {
cat('### ', v, '\n', sep = '')
draw(plot_factors_heatmap(model, annots_dt, pb, what = 'few', plot_var = v))
cat('\n\n')
}
for (v in c('score', 'score_scaled')) {
cat('### ', v, '\n', sep = '')
draw(plot_factors_heatmap(model, annots_dt, pb, what = 'all', plot_var = v))
cat('\n\n')
}
Warning in brewer.pal(length(batch_lvls), "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
draw(plot_factors_heatmap_w_qc(model, annots_dt, pb, qc_stats))
Version | Author | Date |
---|---|---|
7c17d96 | Macnair | 2022-03-18 |
for (f in factors_names(model)) {
cat('### ', f, '\n', sep = '')
draw(plot_top_weights_heatmap_by_factor(model, var_exp_dt, sel_f = f))
cat('\n\n')
}
# iterate plots
for (i in seq.int(nrow(to_plot_dt))) {
sel_v = as.character(to_plot_dt[i]$view)
sel_f = to_plot_dt[i]$factor
this_r2 = to_plot_dt[i]$var_exp
cat('### ', sel_v, '-F', as.integer(sel_f),
' (', round(this_r2, 0), '%)', '\n', sep = '')
draw(plot_top_weights_expression_sample(model, pb, annots_dt, filter_dt,
tfs_dt, sel_v = sel_v, sel_f = sel_f, n_top = 40, is_regressed = TRUE),
merge_legend = TRUE )
cat('\n\n')
}
# iterate plots
for (sel_v in broad_short[sel_cl]) {
cat('### ', sel_v, '\n', sep = '')
draw(plot_top_genes_expression_all_factors(model, pb, annots_dt, filter_dt,
tfs_dt, var_exp_dt, sel_v = sel_v, n_top = 10, min_var, is_regressed = TRUE),
merge_legend = TRUE )
cat('\n\n')
}
for (f in factors_names(model) ) {
cat('### ', f, '\n', sep = '')
print(plot_mofa_vs_n_cells(model, n_cells_dt, sel_f = f))
cat('\n\n')
}
for (f in factors_names(model)) {
cat('### ', f, '\n', sep = '')
print(plot_mofa_vs_logcpm(model, annots_dt, sel_f = f))
cat('\n\n')
}
for (f in factors_names(model) ) {
cat('### ', f, '\n', sep = '')
print(plot_mofa_vs_soup_logcpm(model, annots_dt, soup_dt,
sel_f = f, trans = 'linear'))
cat('\n\n')
}
(plot_mofa_weights(model))
Version | Author | Date |
---|---|---|
7c17d96 | Macnair | 2022-03-18 |
muscat
resultsfor (what in c('log10_padj', 'log2FC')) {
cat('### ', what, '\n', sep = '')
print(plot_muscat_vs_mofa(model, filter_dt, what = what))
cat('\n\n')
}
for (v in broad_short[sel_cl]) {
cat('### ', v, '\n', sep = '')
print(plot_factor_weight_corrs(model, v, by = 'type', how = 'bin'))
cat('\n\n')
}
for (f in factors_names(model) ) {
cat('### ', f, '\n', sep = '')
print(plot_factor_weight_corrs(model, f, by = 'factor', how = 'point'))
cat('\n\n')
}
for (p in names(gsea_list)) {
# restrict to relevant GO terms
cat('### ', p, '\n', sep='')
dt = gsea_list[[p]]
if (nrow(dt[ main_path == TRUE ]) == 0)
next
# plot
print(plot_mofa_gsea_dotplot(dt, labels_dt,
fgsea_cut = fgsea_cut, n_total = 60))
cat('\n\n')
}
# merge filtered and weights
xls_dt = calc_xls_dt(model, filtered_dt)
# save outputs
write_xlsx(list(mofa_weights = xls_dt), path = interesting_f)
for (g in example_gs) {
cat('### ', str_extract(g, '^[^_]+'), '\n', sep = '')
suppressWarnings(print(plot_ranef_example(pb, example_cl, g)))
cat('\n\n')
}
for (what in c('fc_vs_sd_all', 'fc_vs_sd_signif', 'ms_p_vs_lrt_p')) {
cat("### ", what, "\n")
print(plot_ms_vs_random(filter_dt, sel_cl, max_p, min_fc, min_sd, what = what))
cat("\n\n")
}
muscat
results vs SD, MAGMA
hits onlymagma_hits = magma_dt[ p_magma_adj < 0.1 ]$gene_id
(plot_muscat_vs_sd_min(res_dt[ gene_id %in% magma_hits ], sd_dt,
sel_cl, min_sd, max_p))
muscat
results vs LoFs(plot_muscat_and_sd_vs_lof(res_dt, sd_dt, lof_dt, sel_cl))
Version | Author | Date |
---|---|---|
7c17d96 | Macnair | 2022-03-18 |
Some notes:
for (o in c("pca", "clustered", "three_per_patient", "by_lesion", factors_names(model))) {
cat("### ", o, "\n")
draw(plot_expression_heatmap_samples(pb, filtered_dt, annots_dt, sel_cl,
model, ordering = o)
, merge_legend = TRUE)
cat("\n\n")
}
for (o in c("by_lesion", "clustered")) {
cat("### ", o, "\n")
draw(plot_expression_heatmap_samples(pb, filtered_dt, annots_dt, sel_cl,
model, ordering = o) , merge_legend = TRUE)
cat("\n\n")
}
Warning: Unknown levels in `f`: WM
(plot_factors_univariate(model, annots_dt, pb, by = 'diagnosis'))
Version | Author | Date |
---|---|---|
7c17d96 | Macnair | 2022-03-18 |
(plot_factors_univariate(model, annots_dt, pb, by = 'lesion_type'))
Version | Author | Date |
---|---|---|
7c17d96 | Macnair | 2022-03-18 |
for (what in c("diagnosis", "lesion_type", "subject_id")) {
cat('### ', what, '\n', sep = '')
print(plot_factors_pair(model, annots_dt, pb,
f_pair = c("Factor2", "Factor1"), by = what))
cat('\n\n')
}
(plot_factor_r2s(r2_dt, var_exp_dt))
Version | Author | Date |
---|---|---|
7c17d96 | Macnair | 2022-03-18 |
(plot_factor_anovas(anova_dt))
Version | Author | Date |
---|---|---|
7c17d96 | Macnair | 2022-03-18 |
print(plot_mofa_gsea_dotplot(gsea_list[['go_bp']], labels_dt,
fgsea_cut = fgsea_cut, n_total = 50))
Version | Author | Date |
---|---|---|
7c17d96 | Macnair | 2022-03-18 |
draw( plot_top_genes_expression(model, pb, annots_dt,
filter_dt, tfs_dt, var_exp_dt, sel_f = 'Factor1',
min_var = 10, min_w = 0.2, n_top = 10, is_regressed = TRUE) )
Version | Author | Date |
---|---|---|
7c17d96 | Macnair | 2022-03-18 |
draw( plot_top_genes_expression(model, pb, annots_dt,
filter_dt, tfs_dt, var_exp_dt, sel_f = 'Factor2',
min_var = 10, min_w = 0.2, n_top = 20, is_regressed = TRUE) )
Version | Author | Date |
---|---|---|
7c17d96 | Macnair | 2022-03-18 |
draw( plot_top_genes_expression(model, pb, annots_dt,
filter_dt, tfs_dt, var_exp_dt, sel_f = 'Factor3',
min_var = 10, min_w = 0.2, n_top = 20, is_regressed = TRUE) )
Version | Author | Date |
---|---|---|
7c17d96 | Macnair | 2022-03-18 |
draw( plot_top_genes_expression(model, pb, annots_dt,
filter_dt, tfs_dt, var_exp_dt, sel_f = 'Factor4',
min_var = 5, min_w = 0.2, n_top = 10, is_regressed = TRUE) )
Version | Author | Date |
---|---|---|
7c17d96 | Macnair | 2022-03-18 |
draw( plot_top_genes_expression(model, pb, annots_dt,
filter_dt, tfs_dt, var_exp_dt, sel_f = 'Factor5',
min_var = 5, min_w = 0.2, n_top = 20, is_regressed = TRUE) )
Version | Author | Date |
---|---|---|
7c17d96 | Macnair | 2022-03-18 |
devtools::session_info()
- Session info ---------------------------------------------------------------
setting value
version R version 4.1.2 (2021-11-01)
os Red Hat Enterprise Linux 8.2 (Ootpa)
system x86_64, linux-gnu
ui X11
language (EN)
collate en_US.UTF-8
ctype C
tz Europe/Amsterdam
date 2022-03-24
pandoc 2.5 @ /apps/rocs/pRED/2020.08/cascadelake/software/Pandoc/2.5/bin/ (via rmarkdown)
- Packages -------------------------------------------------------------------
package * version date (UTC) lib source
abind 1.4-5 2016-07-21 [5] CRAN (R 4.1.2)
ade4 1.7-18 2021-09-16 [5] CRAN (R 4.1.2)
ANCOMBC * 1.4.0 2021-10-26 [3] Bioconductor
annotate 1.72.0 2021-10-26 [3] Bioconductor
AnnotationDbi 1.56.2 2021-11-09 [3] Bioconductor
ape 5.5 2021-04-25 [5] CRAN (R 4.1.2)
assertthat * 0.2.1 2019-03-21 [5] CRAN (R 4.1.2)
backports 1.4.0 2021-11-23 [5] CRAN (R 4.1.2)
basilisk 1.6.0 2021-10-26 [1] Bioconductor
basilisk.utils 1.6.0 2021-10-26 [1] Bioconductor
beachmat 2.10.0 2021-10-26 [3] Bioconductor
beeswarm 0.4.0 2021-06-01 [3] CRAN (R 4.1.2)
Biobase * 2.54.0 2021-10-26 [3] Bioconductor
BiocGenerics * 0.40.0 2021-10-26 [3] Bioconductor
BiocIO 1.4.0 2021-10-26 [3] Bioconductor
BiocManager 1.30.16 2021-06-15 [3] CRAN (R 4.1.2)
BiocNeighbors 1.12.0 2021-10-26 [3] Bioconductor
BiocParallel * 1.28.3 2021-12-09 [1] Bioconductor
BiocSingular 1.10.0 2021-10-26 [3] Bioconductor
BiocStyle * 2.22.0 2021-10-26 [3] Bioconductor
biomformat 1.22.0 2021-10-26 [3] Bioconductor
Biostrings 2.62.0 2021-10-26 [3] Bioconductor
bit 4.0.4 2020-08-04 [5] CRAN (R 4.1.2)
bit64 4.0.5 2020-08-30 [5] CRAN (R 4.1.2)
bitops 1.0-7 2021-04-24 [5] CRAN (R 4.1.2)
blme 1.0-5 2021-01-05 [3] CRAN (R 4.1.2)
blob 1.2.2 2021-07-23 [5] CRAN (R 4.1.2)
boot 1.3-28 2021-05-03 [5] CRAN (R 4.1.2)
brew 1.0-6 2011-04-13 [5] CRAN (R 4.1.2)
broom 0.7.10 2021-10-31 [5] CRAN (R 4.1.2)
bslib 0.3.1 2021-10-06 [5] CRAN (R 4.1.2)
cachem 1.0.6 2021-08-19 [5] CRAN (R 4.1.2)
callr 3.7.0 2021-04-20 [5] CRAN (R 4.1.2)
caTools 1.18.2 2021-03-28 [5] CRAN (R 4.1.2)
cellranger 1.1.0 2016-07-27 [5] CRAN (R 4.1.2)
circlize * 0.4.13 2021-06-09 [3] CRAN (R 4.1.2)
cli 3.2.0 2022-02-14 [1] CRAN (R 4.1.2)
clue 0.3-60 2021-10-11 [5] CRAN (R 4.1.2)
cluster 2.1.2 2021-04-17 [5] CRAN (R 4.1.2)
coda 0.19-4 2020-09-30 [5] CRAN (R 4.1.2)
codetools 0.2-18 2020-11-04 [5] CRAN (R 4.1.2)
colorspace 2.0-3 2022-02-21 [1] CRAN (R 4.1.2)
ComplexHeatmap * 2.10.0 2021-10-26 [3] Bioconductor
corrplot 0.92 2021-11-18 [3] CRAN (R 4.1.2)
cowplot 1.1.1 2020-12-30 [5] CRAN (R 4.1.2)
crayon 1.5.0 2022-02-14 [1] CRAN (R 4.1.2)
data.table * 1.14.2 2021-09-27 [5] CRAN (R 4.1.2)
DBI 1.1.1 2021-01-15 [5] CRAN (R 4.1.2)
dbplyr 2.1.1 2021-04-06 [5] CRAN (R 4.1.2)
DelayedArray 0.20.0 2021-10-26 [3] Bioconductor
DelayedMatrixStats 1.16.0 2021-10-26 [3] Bioconductor
deldir 1.0-6 2021-10-23 [5] CRAN (R 4.1.2)
dendsort 0.3.4 2021-04-20 [1] CRAN (R 4.1.2)
desc 1.4.0 2021-09-28 [5] CRAN (R 4.1.2)
DESeq2 1.34.0 2021-10-26 [3] Bioconductor
devtools 2.4.3 2021-11-30 [5] CRAN (R 4.1.2)
digest 0.6.29 2021-12-01 [5] CRAN (R 4.1.2)
dir.expiry 1.2.0 2021-10-26 [1] Bioconductor
doParallel 1.0.16 2020-10-16 [5] CRAN (R 4.1.2)
dplyr * 1.0.7 2021-06-18 [5] CRAN (R 4.1.2)
drat 0.2.2 2021-12-02 [1] CRAN (R 4.1.2)
edgeR * 3.36.0 2021-10-26 [3] Bioconductor
ellipsis 0.3.2 2021-04-29 [5] CRAN (R 4.1.2)
emmeans 1.7.1-1 2021-11-29 [3] CRAN (R 4.1.2)
estimability 1.3 2018-02-11 [3] CRAN (R 4.1.2)
evaluate 0.15 2022-02-18 [1] CRAN (R 4.1.2)
fansi 1.0.2 2022-01-14 [1] CRAN (R 4.1.2)
farver 2.1.0 2021-02-28 [5] CRAN (R 4.1.2)
fastmap 1.1.0 2021-01-25 [5] CRAN (R 4.1.2)
fastmatch 1.1-3 2021-07-23 [3] CRAN (R 4.1.2)
fgsea * 1.20.0 2021-10-26 [3] Bioconductor
filelock 1.0.2 2018-10-05 [3] CRAN (R 4.1.2)
fitdistrplus 1.1-6 2021-09-28 [5] CRAN (R 4.1.2)
forcats * 0.5.1 2021-01-27 [5] CRAN (R 4.1.2)
foreach 1.5.1 2020-10-15 [5] CRAN (R 4.1.2)
fs 1.5.1 2021-11-30 [5] CRAN (R 4.1.2)
future * 1.23.0 2021-10-31 [5] CRAN (R 4.1.2)
future.apply 1.8.1 2021-08-10 [5] CRAN (R 4.1.2)
genefilter 1.76.0 2021-10-26 [3] Bioconductor
geneplotter 1.72.0 2021-10-26 [3] Bioconductor
generics 0.1.1 2021-10-25 [5] CRAN (R 4.1.2)
GenomeInfoDb * 1.30.1 2022-01-30 [1] Bioconductor
GenomeInfoDbData 1.2.7 2022-03-15 [3] Bioconductor
GenomicAlignments 1.30.0 2021-10-26 [3] Bioconductor
GenomicRanges * 1.46.1 2021-11-18 [3] Bioconductor
GetoptLong 1.0.5 2020-12-15 [3] CRAN (R 4.1.2)
ggbeeswarm * 0.6.0 2017-08-07 [3] CRAN (R 4.1.2)
ggplot.multistats * 1.0.0 2019-10-28 [1] CRAN (R 4.1.2)
ggplot2 * 3.3.5 2021-06-25 [5] CRAN (R 4.1.2)
ggrepel * 0.9.1 2021-01-15 [5] CRAN (R 4.1.2)
ggridges 0.5.3 2021-01-08 [5] CRAN (R 4.1.2)
git2r 0.29.0 2021-11-22 [5] CRAN (R 4.1.2)
glmmTMB 1.1.2.3 2021-09-20 [3] CRAN (R 4.1.2)
GlobalOptions 0.1.2 2020-06-10 [3] CRAN (R 4.1.2)
globals 0.14.0 2020-11-22 [5] CRAN (R 4.1.2)
glue 1.6.2 2022-02-24 [1] CRAN (R 4.1.2)
goftest 1.2-3 2021-10-07 [5] CRAN (R 4.1.2)
gplots 3.1.1 2020-11-28 [5] CRAN (R 4.1.2)
gridExtra 2.3 2017-09-09 [5] CRAN (R 4.1.2)
grr 0.9.5 2016-08-26 [1] CRAN (R 4.1.2)
gtable 0.3.0 2019-03-25 [5] CRAN (R 4.1.2)
gtools 3.9.2 2021-06-06 [5] CRAN (R 4.1.2)
haven 2.4.3 2021-08-04 [5] CRAN (R 4.1.2)
HDF5Array 1.22.1 2021-11-14 [3] Bioconductor
hexbin 1.28.2 2021-01-08 [5] CRAN (R 4.1.2)
highr 0.9 2021-04-16 [5] CRAN (R 4.1.2)
hms 1.1.1 2021-09-26 [5] CRAN (R 4.1.2)
htmltools 0.5.2 2021-08-25 [5] CRAN (R 4.1.2)
htmlwidgets 1.5.4 2021-09-08 [5] CRAN (R 4.1.2)
httpuv 1.6.3 2021-09-09 [5] CRAN (R 4.1.2)
httr 1.4.2 2020-07-20 [5] CRAN (R 4.1.2)
ica 1.0-2 2018-05-24 [5] CRAN (R 4.1.2)
igraph * 1.2.11 2022-01-04 [1] CRAN (R 4.1.2)
insight 0.16.0 2022-02-17 [1] CRAN (R 4.1.2)
IRanges * 2.28.0 2021-10-26 [3] Bioconductor
irlba 2.3.5 2021-12-06 [5] CRAN (R 4.1.2)
iterators 1.0.13 2020-10-15 [5] CRAN (R 4.1.2)
janitor 2.1.0 2021-01-05 [2] CRAN (R 4.1.2)
jquerylib 0.1.4 2021-04-26 [5] CRAN (R 4.1.2)
jsonlite 1.8.0 2022-02-22 [1] CRAN (R 4.1.2)
KEGGREST 1.34.0 2021-10-26 [3] Bioconductor
KernSmooth 2.23-20 2021-05-03 [5] CRAN (R 4.1.2)
knitr 1.37 2021-12-16 [1] CRAN (R 4.1.2)
labeling 0.4.2 2020-10-20 [5] CRAN (R 4.1.2)
later 1.3.0 2021-08-18 [5] CRAN (R 4.1.2)
lattice 0.20-45 2021-09-22 [5] CRAN (R 4.1.2)
lazyeval 0.2.2 2019-03-15 [5] CRAN (R 4.1.2)
leiden 0.3.9 2021-07-27 [5] CRAN (R 4.1.2)
lifecycle 1.0.1 2021-09-24 [5] CRAN (R 4.1.2)
limma * 3.50.0 2021-10-26 [3] Bioconductor
listenv 0.8.0 2019-12-05 [5] CRAN (R 4.1.2)
lme4 1.1-27.1 2021-06-22 [5] CRAN (R 4.1.2)
lmerTest 3.1-3 2020-10-23 [3] CRAN (R 4.1.2)
lmtest 0.9-39 2021-11-07 [5] CRAN (R 4.1.2)
locfit 1.5-9.4 2020-03-25 [5] CRAN (R 4.1.2)
lubridate 1.8.0 2021-10-07 [5] CRAN (R 4.1.2)
magick 2.7.3 2021-08-18 [2] CRAN (R 4.1.2)
magrittr * 2.0.2 2022-01-26 [1] CRAN (R 4.1.2)
MASS * 7.3-54 2021-05-03 [5] CRAN (R 4.1.2)
Matrix * 1.3-4 2021-06-01 [5] CRAN (R 4.1.2)
Matrix.utils * 0.9.8 2020-02-26 [1] CRAN (R 4.1.2)
MatrixGenerics * 1.6.0 2021-10-26 [3] Bioconductor
matrixStats * 0.61.0 2021-09-17 [5] CRAN (R 4.1.2)
memoise 2.0.1 2021-11-26 [5] CRAN (R 4.1.2)
mgcv 1.8-38 2021-10-06 [5] CRAN (R 4.1.2)
microbiome 1.16.0 2021-10-26 [3] Bioconductor
mime 0.12 2021-09-28 [5] CRAN (R 4.1.2)
miniUI 0.1.1.1 2018-05-18 [5] CRAN (R 4.1.2)
minqa 1.2.4 2014-10-09 [5] CRAN (R 4.1.2)
modelr 0.1.8 2020-05-19 [5] CRAN (R 4.1.2)
MOFA2 * 1.4.0 2021-10-26 [1] Bioconductor
multcomp 1.4-17 2021-04-29 [5] CRAN (R 4.1.2)
multtest 2.50.0 2021-10-26 [3] Bioconductor
munsell 0.5.0 2018-06-12 [5] CRAN (R 4.1.2)
muscat * 1.8.0 2021-10-26 [3] Bioconductor
mvtnorm 1.1-3 2021-10-08 [5] CRAN (R 4.1.2)
N2R 1.0.1 2022-01-18 [1] CRAN (R 4.1.2)
nlme 3.1-153 2021-09-07 [5] CRAN (R 4.1.2)
nloptr 1.2.2.3 2021-11-02 [5] CRAN (R 4.1.2)
numDeriv 2016.8-1.1 2019-06-06 [5] CRAN (R 4.1.2)
pagoda2 * 1.0.9 2022-03-02 [1] CRAN (R 4.1.2)
parallelly 1.29.0 2021-11-21 [5] CRAN (R 4.1.2)
patchwork * 1.1.0.9000 2022-03-23 [1] Github (thomasp85/patchwork@79223d3)
pbapply 1.5-0 2021-09-16 [5] CRAN (R 4.1.2)
pbkrtest 0.5.1 2021-03-09 [5] CRAN (R 4.1.2)
performance * 0.8.0 2021-10-01 [1] CRAN (R 4.1.2)
permute 0.9-5 2019-03-12 [3] CRAN (R 4.1.2)
pheatmap 1.0.12 2019-01-04 [3] CRAN (R 4.1.2)
phyloseq * 1.38.0 2021-10-26 [3] Bioconductor
pillar 1.7.0 2022-02-01 [1] CRAN (R 4.1.2)
pkgbuild 1.2.1 2021-11-30 [5] CRAN (R 4.1.2)
pkgconfig 2.0.3 2019-09-22 [5] CRAN (R 4.1.2)
pkgload 1.2.4 2021-11-30 [5] CRAN (R 4.1.2)
plotly 4.10.0 2021-10-09 [5] CRAN (R 4.1.2)
plyr 1.8.6 2020-03-03 [5] CRAN (R 4.1.2)
png 0.1-7 2013-12-03 [5] CRAN (R 4.1.2)
polyclip 1.10-0 2019-03-14 [5] CRAN (R 4.1.2)
prettyunits 1.1.1 2020-01-24 [5] CRAN (R 4.1.2)
processx 3.5.2 2021-04-30 [5] CRAN (R 4.1.2)
progress 1.2.2 2019-05-16 [5] CRAN (R 4.1.2)
promises 1.2.0.1 2021-02-11 [5] CRAN (R 4.1.2)
ps 1.6.0 2021-02-28 [5] CRAN (R 4.1.2)
purrr * 0.3.4 2020-04-17 [5] CRAN (R 4.1.2)
R.methodsS3 1.8.1 2020-08-26 [5] CRAN (R 4.1.2)
R.oo 1.24.0 2020-08-26 [5] CRAN (R 4.1.2)
R.utils 2.11.0 2021-09-26 [5] CRAN (R 4.1.2)
R6 2.5.1 2021-08-19 [5] CRAN (R 4.1.2)
RANN 2.6.1 2019-01-08 [5] CRAN (R 4.1.2)
rappdirs 0.3.3 2021-01-31 [5] CRAN (R 4.1.2)
rbibutils 2.2.7 2021-12-07 [5] CRAN (R 4.1.2)
RColorBrewer * 1.1-2 2014-12-07 [5] CRAN (R 4.1.2)
Rcpp 1.0.8.3 2022-03-17 [1] CRAN (R 4.1.2)
RcppAnnoy 0.0.19 2021-07-30 [5] CRAN (R 4.1.2)
RCurl 1.98-1.6 2022-02-08 [1] CRAN (R 4.1.2)
Rdpack 2.1.3 2021-12-08 [5] CRAN (R 4.1.2)
readr * 2.1.1 2021-11-30 [5] CRAN (R 4.1.2)
readxl * 1.3.1 2019-03-13 [5] CRAN (R 4.1.2)
registry 0.5-1 2019-03-05 [5] CRAN (R 4.1.2)
remotes 2.4.2 2021-11-30 [5] CRAN (R 4.1.2)
reprex 2.0.1 2021-08-05 [5] CRAN (R 4.1.2)
reshape2 * 1.4.4 2020-04-09 [5] CRAN (R 4.1.2)
restfulr 0.0.13 2017-08-06 [3] CRAN (R 4.1.2)
reticulate * 1.22 2021-09-17 [5] CRAN (R 4.1.2)
rhdf5 2.38.0 2021-10-26 [3] Bioconductor
rhdf5filters 1.6.0 2021-10-26 [3] Bioconductor
Rhdf5lib 1.16.0 2021-10-26 [3] Bioconductor
rjson 0.2.20 2018-06-08 [5] CRAN (R 4.1.2)
rlang 1.0.2 2022-03-04 [1] CRAN (R 4.1.2)
rmarkdown * 2.13 2022-03-10 [1] CRAN (R 4.1.2)
RMTstat 0.3 2014-11-01 [1] CRAN (R 4.1.2)
ROCR 1.0-11 2020-05-02 [5] CRAN (R 4.1.2)
Rook 1.1-1 2014-10-20 [1] CRAN (R 4.1.2)
rpart 4.1-15 2019-04-12 [5] CRAN (R 4.1.2)
rprojroot 2.0.2 2020-11-15 [5] CRAN (R 4.1.2)
Rsamtools 2.10.0 2021-10-26 [3] Bioconductor
RSQLite 2.2.9 2021-12-06 [5] CRAN (R 4.1.2)
rstudioapi 0.13 2020-11-12 [5] CRAN (R 4.1.2)
rsvd 1.0.5 2021-04-16 [5] CRAN (R 4.1.2)
rtracklayer * 1.54.0 2021-10-26 [3] Bioconductor
Rtsne 0.15 2018-11-10 [5] CRAN (R 4.1.2)
rvest 1.0.2 2021-10-16 [5] CRAN (R 4.1.2)
S4Vectors * 0.32.3 2021-11-21 [3] Bioconductor
sandwich 3.0-1 2021-05-18 [5] CRAN (R 4.1.2)
sass 0.4.0 2021-05-12 [5] CRAN (R 4.1.2)
ScaledMatrix 1.2.0 2021-10-26 [3] Bioconductor
scales * 1.1.1 2020-05-11 [5] CRAN (R 4.1.2)
scater * 1.22.0 2021-10-26 [3] Bioconductor
scattermore 0.7 2020-11-24 [5] CRAN (R 4.1.2)
sccore 1.0.1 2021-12-12 [1] CRAN (R 4.1.2)
sctransform 0.3.2 2020-12-16 [5] CRAN (R 4.1.2)
scuttle * 1.4.0 2021-10-26 [3] Bioconductor
seriation * 1.3.1 2021-10-16 [3] CRAN (R 4.1.2)
sessioninfo 1.2.2 2021-12-06 [5] CRAN (R 4.1.2)
Seurat * 4.0.5 2021-10-17 [5] CRAN (R 4.1.2)
SeuratObject * 4.0.4 2021-11-23 [5] CRAN (R 4.1.2)
shape 1.4.6 2021-05-19 [3] CRAN (R 4.1.2)
shiny 1.7.1 2021-10-02 [5] CRAN (R 4.1.2)
SingleCellExperiment * 1.16.0 2021-10-26 [3] Bioconductor
snakecase 0.11.0 2019-05-25 [2] CRAN (R 4.1.2)
sparseMatrixStats 1.6.0 2021-10-26 [3] Bioconductor
spatstat.core 2.3-2 2021-11-26 [5] CRAN (R 4.1.2)
spatstat.data 2.1-0 2021-03-21 [5] CRAN (R 4.1.2)
spatstat.geom 2.3-0 2021-10-09 [5] CRAN (R 4.1.2)
spatstat.sparse 2.0-0 2021-03-16 [5] CRAN (R 4.1.2)
spatstat.utils 2.2-0 2021-06-14 [5] CRAN (R 4.1.2)
stringi 1.7.6 2021-11-29 [5] CRAN (R 4.1.2)
stringr * 1.4.0 2019-02-10 [5] CRAN (R 4.1.2)
SummarizedExperiment * 1.24.0 2021-10-26 [3] Bioconductor
survival 3.2-13 2021-08-24 [5] CRAN (R 4.1.2)
tensor 1.5 2012-05-05 [5] CRAN (R 4.1.2)
testthat 3.1.1 2021-12-03 [5] CRAN (R 4.1.2)
TH.data 1.1-0 2021-09-27 [5] CRAN (R 4.1.2)
tibble * 3.1.6 2021-11-07 [5] CRAN (R 4.1.2)
tictoc * 1.0.1 2021-04-19 [1] CRAN (R 4.1.2)
tidyr * 1.1.4 2021-09-27 [5] CRAN (R 4.1.2)
tidyselect 1.1.1 2021-04-30 [5] CRAN (R 4.1.2)
tidyverse * 1.3.1 2021-04-15 [5] CRAN (R 4.1.2)
TMB 1.7.22 2021-09-28 [3] CRAN (R 4.1.2)
triebeard 0.3.0 2016-08-04 [2] CRAN (R 4.1.2)
TSP 1.1-11 2021-10-06 [3] CRAN (R 4.1.2)
tzdb 0.2.0 2021-10-27 [5] CRAN (R 4.1.2)
UpSetR * 1.4.0 2019-05-22 [1] CRAN (R 4.1.2)
urltools 1.7.3 2019-04-14 [2] CRAN (R 4.1.2)
usethis 2.1.3 2021-10-27 [5] CRAN (R 4.1.2)
utf8 1.2.2 2021-07-24 [5] CRAN (R 4.1.2)
uwot 0.1.11 2021-12-02 [5] CRAN (R 4.1.2)
variancePartition 1.24.0 2021-10-26 [3] Bioconductor
vctrs 0.3.8 2021-04-29 [5] CRAN (R 4.1.2)
vegan 2.5-7 2020-11-28 [3] CRAN (R 4.1.2)
vipor 0.4.5 2017-03-22 [3] CRAN (R 4.1.2)
viridis * 0.6.2 2021-10-13 [5] CRAN (R 4.1.2)
viridisLite * 0.4.0 2021-04-13 [5] CRAN (R 4.1.2)
whisker 0.4 2019-08-28 [5] CRAN (R 4.1.2)
withr 2.5.0 2022-03-03 [1] CRAN (R 4.1.2)
workflowr 1.7.0 2021-12-21 [1] CRAN (R 4.1.2)
writexl * 1.4.0 2021-04-20 [1] CRAN (R 4.1.2)
xfun 0.30 2022-03-02 [1] CRAN (R 4.1.2)
XML 3.99-0.8 2021-09-17 [5] CRAN (R 4.1.2)
xml2 1.3.3 2021-11-30 [5] CRAN (R 4.1.2)
xtable 1.8-4 2019-04-21 [5] CRAN (R 4.1.2)
XVector 0.34.0 2021-10-26 [3] Bioconductor
yaml 2.3.5 2022-02-21 [1] CRAN (R 4.1.2)
zlibbioc 1.40.0 2021-10-26 [3] Bioconductor
zoo 1.8-9 2021-03-09 [5] CRAN (R 4.1.2)
[1] /gpfs/homefs/global/home/macnairw/R/x86_64-pc-linux-gnu-library/4.1.2-foss
[2] /apps/rocs/2020.08/cascadelake/software/R-Roche-bundle/2021.12-foss-2020a-R-4.1.2
[3] /apps/rocs/2020.08/cascadelake/software/R-bundle-Bioconductor/3.14-foss-2020a-R-4.1.2
[4] /apps/rocs/2020.08/cascadelake/software/ncdf4/1.18-foss-2020a-R-4.1.2
[5] /apps/rocs/2020.08/cascadelake/software/R/4.1.2-foss-2020a/lib64/R/library
------------------------------------------------------------------------------
sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux 8.2 (Ootpa)
Matrix products: default
BLAS/LAPACK: /apps/rocs/2020.08/cascadelake/software/OpenBLAS/0.3.9-GCC-9.3.0/lib/libopenblas_skylakexp-r0.3.9.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 stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] MOFA2_1.4.0 rmarkdown_2.13
[3] tictoc_1.0.1 performance_0.8.0
[5] edgeR_3.36.0 limma_3.50.0
[7] reshape2_1.4.4 scater_1.22.0
[9] scuttle_1.4.0 Matrix.utils_0.9.8
[11] UpSetR_1.4.0 muscat_1.8.0
[13] dplyr_1.0.7 readr_2.1.1
[15] tidyr_1.1.4 tibble_3.1.6
[17] tidyverse_1.3.1 rtracklayer_1.54.0
[19] ggbeeswarm_0.6.0 ggrepel_0.9.1
[21] MASS_7.3-54 phyloseq_1.38.0
[23] ANCOMBC_1.4.0 patchwork_1.1.0.9000
[25] writexl_1.4.0 reticulate_1.22
[27] fgsea_1.20.0 BiocParallel_1.28.3
[29] ggplot.multistats_1.0.0 seriation_1.3.1
[31] ComplexHeatmap_2.10.0 pagoda2_1.0.9
[33] igraph_1.2.11 SeuratObject_4.0.4
[35] Seurat_4.0.5 future_1.23.0
[37] Matrix_1.3-4 SingleCellExperiment_1.16.0
[39] SummarizedExperiment_1.24.0 Biobase_2.54.0
[41] GenomicRanges_1.46.1 GenomeInfoDb_1.30.1
[43] IRanges_2.28.0 S4Vectors_0.32.3
[45] BiocGenerics_0.40.0 MatrixGenerics_1.6.0
[47] matrixStats_0.61.0 purrr_0.3.4
[49] readxl_1.3.1 forcats_0.5.1
[51] ggplot2_3.3.5 scales_1.1.1
[53] viridis_0.6.2 viridisLite_0.4.0
[55] assertthat_0.2.1 stringr_1.4.0
[57] data.table_1.14.2 magrittr_2.0.2
[59] circlize_0.4.13 RColorBrewer_1.1-2
[61] BiocStyle_2.22.0
loaded via a namespace (and not attached):
[1] rsvd_1.0.5 ica_1.0-2
[3] ps_1.6.0 Rsamtools_2.10.0
[5] foreach_1.5.1 lmtest_0.9-39
[7] rprojroot_2.0.2 crayon_1.5.0
[9] spatstat.core_2.3-2 rbibutils_2.2.7
[11] rhdf5filters_1.6.0 nlme_3.1-153
[13] backports_1.4.0 reprex_2.0.1
[15] basilisk_1.6.0 rlang_1.0.2
[17] XVector_0.34.0 ROCR_1.0-11
[19] microbiome_1.16.0 irlba_2.3.5
[21] callr_3.7.0 nloptr_1.2.2.3
[23] filelock_1.0.2 rjson_0.2.20
[25] bit64_4.0.5 glue_1.6.2
[27] pheatmap_1.0.12 sctransform_0.3.2
[29] processx_3.5.2 pbkrtest_0.5.1
[31] parallel_4.1.2 vipor_0.4.5
[33] spatstat.sparse_2.0-0 AnnotationDbi_1.56.2
[35] spatstat.geom_2.3-0 haven_2.4.3
[37] tidyselect_1.1.1 usethis_2.1.3
[39] fitdistrplus_1.1-6 variancePartition_1.24.0
[41] XML_3.99-0.8 zoo_1.8-9
[43] GenomicAlignments_1.30.0 xtable_1.8-4
[45] evaluate_0.15 Rdpack_2.1.3
[47] cli_3.2.0 zlibbioc_1.40.0
[49] rstudioapi_0.13 miniUI_0.1.1.1
[51] whisker_0.4 bslib_0.3.1
[53] rpart_4.1-15 fastmatch_1.1-3
[55] shiny_1.7.1 BiocSingular_1.10.0
[57] xfun_0.30 clue_0.3-60
[59] pkgbuild_1.2.1 multtest_2.50.0
[61] cluster_2.1.2 caTools_1.18.2
[63] TSP_1.1-11 biomformat_1.22.0
[65] KEGGREST_1.34.0 ape_5.5
[67] listenv_0.8.0 Biostrings_2.62.0
[69] png_0.1-7 permute_0.9-5
[71] withr_2.5.0 bitops_1.0-7
[73] plyr_1.8.6 cellranger_1.1.0
[75] coda_0.19-4 pillar_1.7.0
[77] gplots_3.1.1 GlobalOptions_0.1.2
[79] cachem_1.0.6 multcomp_1.4-17
[81] fs_1.5.1 GetoptLong_1.0.5
[83] DelayedMatrixStats_1.16.0 vctrs_0.3.8
[85] ellipsis_0.3.2 generics_0.1.1
[87] devtools_2.4.3 urltools_1.7.3
[89] tools_4.1.2 beeswarm_0.4.0
[91] munsell_0.5.0 emmeans_1.7.1-1
[93] DelayedArray_0.20.0 pkgload_1.2.4
[95] fastmap_1.1.0 compiler_4.1.2
[97] abind_1.4-5 httpuv_1.6.3
[99] sessioninfo_1.2.2 plotly_4.10.0
[101] GenomeInfoDbData_1.2.7 gridExtra_2.3
[103] glmmTMB_1.1.2.3 workflowr_1.7.0
[105] dir.expiry_1.2.0 lattice_0.20-45
[107] deldir_1.0-6 utf8_1.2.2
[109] later_1.3.0 jsonlite_1.8.0
[111] ScaledMatrix_1.2.0 dendsort_0.3.4
[113] sparseMatrixStats_1.6.0 pbapply_1.5-0
[115] estimability_1.3 genefilter_1.76.0
[117] lazyeval_0.2.2 promises_1.2.0.1
[119] doParallel_1.0.16 R.utils_2.11.0
[121] goftest_1.2-3 spatstat.utils_2.2-0
[123] brew_1.0-6 sandwich_3.0-1
[125] cowplot_1.1.1 blme_1.0-5
[127] Rtsne_0.15 uwot_0.1.11
[129] HDF5Array_1.22.1 Rook_1.1-1
[131] survival_3.2-13 numDeriv_2016.8-1.1
[133] yaml_2.3.5 htmltools_0.5.2
[135] memoise_2.0.1 BiocIO_1.4.0
[137] locfit_1.5-9.4 digest_0.6.29
[139] mime_0.12 rappdirs_0.3.3
[141] registry_0.5-1 N2R_1.0.1
[143] RSQLite_2.2.9 future.apply_1.8.1
[145] remotes_2.4.2 blob_1.2.2
[147] vegan_2.5-7 R.oo_1.24.0
[149] drat_0.2.2 labeling_0.4.2
[151] splines_4.1.2 Rhdf5lib_1.16.0
[153] RCurl_1.98-1.6 broom_0.7.10
[155] hms_1.1.1 modelr_0.1.8
[157] rhdf5_2.38.0 colorspace_2.0-3
[159] BiocManager_1.30.16 shape_1.4.6
[161] sass_0.4.0 Rcpp_1.0.8.3
[163] RANN_2.6.1 mvtnorm_1.1-3
[165] fansi_1.0.2 tzdb_0.2.0
[167] parallelly_1.29.0 R6_2.5.1
[169] ggridges_0.5.3 lifecycle_1.0.1
[171] minqa_1.2.4 testthat_3.1.1
[173] leiden_0.3.9 jquerylib_0.1.4
[175] snakecase_0.11.0 desc_1.4.0
[177] RcppAnnoy_0.0.19 TH.data_1.1-0
[179] iterators_1.0.13 TMB_1.7.22
[181] htmlwidgets_1.5.4 beachmat_2.10.0
[183] polyclip_1.10-0 triebeard_0.3.0
[185] RMTstat_0.3 rvest_1.0.2
[187] mgcv_1.8-38 globals_0.14.0
[189] insight_0.16.0 codetools_0.2-18
[191] lubridate_1.8.0 gtools_3.9.2
[193] prettyunits_1.1.1 dbplyr_2.1.1
[195] basilisk.utils_1.6.0 R.methodsS3_1.8.1
[197] gtable_0.3.0 DBI_1.1.1
[199] git2r_0.29.0 tensor_1.5
[201] httr_1.4.2 highr_0.9
[203] KernSmooth_2.23-20 stringi_1.7.6
[205] progress_1.2.2 farver_2.1.0
[207] annotate_1.72.0 hexbin_1.28.2
[209] magick_2.7.3 xml2_1.3.3
[211] sccore_1.0.1 grr_0.9.5
[213] boot_1.3-28 BiocNeighbors_1.12.0
[215] lme4_1.1-27.1 restfulr_0.0.13
[217] ade4_1.7-18 geneplotter_1.72.0
[219] scattermore_0.7 DESeq2_1.34.0
[221] bit_4.0.4 spatstat.data_2.1-0
[223] janitor_2.1.0 pkgconfig_2.0.3
[225] lmerTest_3.1-3 corrplot_0.92
[227] knitr_1.37