Figures for DEG analysis - WM
Will Macnair
Institute for Molecular Life Sciences, University of Zurich, SwitzerlandSwiss Institute of Bioinformatics (SIB), University of Zurich, SwitzerlandMarch 22, 2022
Last updated: 2022-03-22
Checks: 4 3
Knit directory: MS_lesions/
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- plot_de_barplot_fine
- plot_de_barplot_gm_wm
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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')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 runs?
wm_broad = list(
run_tag = 'run09',
time_stamp = '2021-10-13',
sel_cl = c("OPCs / COPs", "Oligodendrocytes", "Astrocytes",
"Microglia", "Endothelial cells", "Pericytes", "Immune")
)
wm_fine = list(
run_tag = 'run11',
time_stamp = '2021-10-21',
sel_cl = c("OPCs / COPs", "Oligodendrocytes", "Astrocytes",
"Microglia", "Endothelial cells", "Pericytes", "Immune")
)
gm_broad = list(
run_tag = 'run23',
time_stamp = '2021-11-15',
sel_cl = c("OPCs / COPs", "Oligodendrocytes", "Astrocytes",
"Microglia", "Excitatory neurons", "Inhibitory neurons",
"Endothelial cells", "Pericytes")
)
gm_fine = 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
wm_hm_cls = c("OPCs / COPs", "Oligodendrocytes")
# sel_terms = c(
# type_i = "GOBP_RESPONSE_TO_TYPE_I_INTERFERON",
# type_ii = "GOBP_RESPONSE_TO_INTERFERON_GAMMA"
# )
# sel_set = 'go_bp'
sel_terms = c(
alpha = "HALLMARK_INTERFERON_ALPHA_RESPONSE",
gamma = "HALLMARK_INTERFERON_GAMMA_RESPONSE"
)
sel_set = 'hallmark'
smret_les = names(sel_terms) %>% setNames(sel_terms)
# sel genes
gm_hm_cl = "Excitatory neurons"
sel_gs = list(
glu_signaling = c('GRIA1', 'GRIA2', 'GRIA4', 'GRIN2B', 'GRM1', 'GRM5'),
glucose_homeo = c('SLC2A12', 'SLC22A10'),
ion_channels = c('SCN1A', 'SCN1B', 'SCN2B', 'SCN4B', 'KCNA1', 'KCNA2', 'KCNC1'),
ox_phos = 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)
min_in_cl = 5Load inputs
# unpack
run_tag = wm_broad$run_tag
time_stamp = wm_broad$time_stamp
sel_cl = wm_broad$sel_cl
# define files
model_dir = file.path('output/ms10_muscat', run_tag)
muscat_f = '%s/muscat_res_dt_%s_%s.txt.gz' %>%
sprintf(model_dir, run_tag, time_stamp)
anova_f = '%s/muscat_goodness_dt_%s_%s.txt.gz' %>%
sprintf(model_dir, run_tag, time_stamp)
params_f = '%s/muscat_params_%s_%s.rds' %>%
sprintf(model_dir, run_tag, time_stamp)
ranef_dt_f = sprintf('%s/muscat_ranef_dt_%s_%s.txt.gz',
model_dir, run_tag, time_stamp)
# which sets to show?
gsea_pat_wm = sprintf('%s/fgsea_dt_%s_%s_%s.txt.gz',
model_dir, run_tag, '%s', time_stamp)
gsea_pat_gm = sprintf('%s/fgsea_dt_%s_%s_%s.txt.gz',
file.path('output/ms10_muscat', gm_broad$run_tag),
gm_broad$run_tag, '%s', gm_broad$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)no samples have half or more of celltypes with very extreme (2 > MADs)
log proportionsok_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)
ranef_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_wm = lapply(sel_sets, function(s)
s %>% sprintf(gsea_pat_wm, .) %>% fread) %>% rbindlist %>%
.[ cluster_id %in% sel_cl ] %>% .[ var_type == 'test' ]
fgsea_gm = lapply(sel_sets, function(s)
s %>% sprintf(gsea_pat_gm, .) %>% fread) %>% rbindlist %>%
.[ cluster_id %in% gm_broad$sel_cl ] %>% .[ var_type == 'test' ]
# edit cluster names
labs_short = copy(labels_dt) %>%
.[, cluster_id := unlist(broad_short)[ cluster_id ] %>%
factor(levels = broad_short) ]
fgsea_wm[, cluster_id := unlist(broad_short)[ cluster_id ] %>%
factor(levels = broad_short) ]
fgsea_gm[, cluster_id := unlist(broad_short)[ cluster_id ] %>%
factor(levels = broad_short) ]# get random effects
sd_dt = ranef_dt %>% calc_ranef_melt %>% calc_sd_dt
filter_dt = calc_filter_dt(res_dt, sd_dt, pb, anova_dt,
max_p = max_p, min_sd = min_sd, min_fc = min_fc)
causes_dt = filter_dt[ cluster_id %in% sel_cl ] %>% calc_gene_causes_dtProcessing / calculations
fc_cl_dt = res_dt[ (var_type == 'test') & (cluster_id %in% sel_cl) ] %>%
calc_fc_clusters( max_fdr = max_p, logfc_cut = logfc_cut,
min_in_cl = min_in_cl )removing genes with best FDR > 5%# get ifn genes
ifn_gs_dt = load_genes_from_genesets(sel_set, sel_terms) %>%
.[, pathway := smret_les[ pathway ] ] %>% .[, dummy := 1 ] %>%
dcast.data.table(symbol ~ pathway, value.var = 'dummy', fill = 0)
signif_ls = signif_dt[ (var_type == 'test') & (cluster_id %in% wm_hm_cls) ]$symbol %>% unique
sel_gs = intersect(ifn_gs_dt$symbol, signif_ls)
# get these genes
sel_dt = res_dt[ (var_type == 'test') & (cluster_id %in% wm_hm_cls) ] %>%
.[ symbol %in% sel_gs ]Analysis
DE barplot, broad
tmp_spec = copy(wm_broad)
tmp_spec$sel_cl = c("OPCs / COPs", "Oligodendrocytes", "Astrocytes",
"Microglia", "Immune")
(plot_de_barplot_sel(tmp_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 observed
DE darplot, GM + WM
suppressMessages({
g_gm = plot_de_barplot_sel(gm_broad, padj_cut = max_p, facet_by = 'cluster_id') +
labs( x = NULL, y = "GM lesion type" ) +
guides(alpha = "none") +
theme( strip.text = element_text( size = 7 ),
panel.spacing = unit(0.1, "lines") )
tmp_spec = copy(wm_broad)
tmp_spec$sel_cl = c("OPCs / COPs", "Oligodendrocytes", "Astrocytes",
"Microglia")
g_wm = plot_de_barplot_sel(tmp_spec, padj_cut = max_p, facet_by = 'cluster_id') +
labs( y = "WM lesion type" ) +
theme( strip.text = element_text( size = 7 ),
panel.spacing = unit(0.1, "lines") )
})
g = (g_gm / g_wm) + plot_layout(heights = c(2, 2))
print(g)
DE darplot, fine
(plot_de_barplot_sel(wm_fine, 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(wm_broad,
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
Selected
(plot_fc_cluster_profiles(fc_cl_dt[ cluster_id %in% c("OPCs / COPs",
"Oligodendrocytes", "Astrocytes", "Microglia")]))
GSEA dotplots
for (s in sel_sets) {
cat('### ', s, '\n')
print(plot_gsea_dotplot(fgsea_wm[ path_set == s ], labs_short,
n_top_per_celltype = 5, fgsea_cut = 0.1))
cat('\n\n')
}hallmark
go_bp
GSEA dotplot, GM and WM combined
sel_s = 'hallmark'
g_gm = plot_gsea_dotplot(fgsea_gm[ path_set == sel_s ], labs_short,
n_top_per_celltype = 5, fgsea_cut = 0.1, n_chars = 45) +
guides( fill = "none" ) +
labs( x = NULL, y = NULL, title = "GM" ) +
theme( strip.background = element_blank() )
g_wm = plot_gsea_dotplot(fgsea_wm[ path_set == sel_s ], labs_short,
n_top_per_celltype = 5, fgsea_cut = 0.1, n_chars = 45) +
guides( alpha = "none", size = "none" ) +
labs( x = NULL, y = NULL, title = "WM" ) +
theme( strip.background = element_blank() )
g = (g_gm / g_wm)
print(g)
Heatmap of selected gene logFCs
hm_obj = plot_ifn_genes_heatmap(sel_dt, labs_short, ifn_gs_dt,
max_fc = log(4))
draw(hm_obj, heatmap_legend_side = "bottom")
# draw(hm_obj, padding = unit(c(0.5, 0.1, 0.1, 0.1), "in"),
# heatmap_legend_side = "bottom")
# .add_fdr_legend('log2fc')Outputs
devtools::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.1.3 (2022-03-10)
os macOS Big Sur/Monterey 10.16
system x86_64, darwin17.0
ui X11
language (EN)
collate C
ctype UTF-8
tz Europe/Zurich
date 2022-03-22
pandoc 2.12 @ /Users/macnairw/opt/anaconda3/bin/ (via rmarkdown)
─ Packages ───────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
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GenomeInfoDbData 1.2.7 2022-03-21 [1] Bioconductor
GenomicAlignments 1.30.0 2021-10-26 [1] Bioconductor
GenomicRanges * 1.46.1 2021-11-18 [1] Bioconductor
GetoptLong 1.0.5 2020-12-15 [1] CRAN (R 4.1.0)
ggbeeswarm * 0.6.0 2017-08-07 [1] CRAN (R 4.1.0)
ggplot2 * 3.3.5 2021-06-25 [1] CRAN (R 4.1.0)
ggrepel * 0.9.1 2021-01-15 [1] CRAN (R 4.1.0)
git2r 0.30.1 2022-03-16 [1] CRAN (R 4.1.2)
glmmTMB 1.1.3 2022-03-13 [1] CRAN (R 4.1.2)
GlobalOptions 0.1.2 2020-06-10 [1] CRAN (R 4.1.0)
globals 0.14.0 2020-11-22 [1] CRAN (R 4.1.0)
glue 1.6.2 2022-02-24 [1] CRAN (R 4.1.2)
gplots 3.1.1 2020-11-28 [1] CRAN (R 4.1.0)
gridExtra 2.3 2017-09-09 [1] CRAN (R 4.1.0)
grr 0.9.5 2016-08-26 [1] CRAN (R 4.1.0)
gtable 0.3.0 2019-03-25 [1] CRAN (R 4.1.0)
gtools 3.9.2 2021-06-06 [1] CRAN (R 4.1.0)
haven 2.4.3 2021-08-04 [1] CRAN (R 4.1.0)
HDF5Array 1.22.1 2021-11-14 [1] Bioconductor
highr 0.9 2021-04-16 [1] CRAN (R 4.1.0)
hms 1.1.1 2021-09-26 [1] CRAN (R 4.1.0)
htmltools 0.5.2 2021-08-25 [1] CRAN (R 4.1.0)
httpuv 1.6.5 2022-01-05 [1] CRAN (R 4.1.2)
httr 1.4.2 2020-07-20 [1] CRAN (R 4.1.0)
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 [1] Bioconductor
irlba 2.3.5 2021-12-06 [1] CRAN (R 4.1.0)
iterators 1.0.14 2022-02-05 [1] CRAN (R 4.1.2)
janitor 2.1.0 2021-01-05 [1] CRAN (R 4.1.0)
jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.1.0)
jsonlite 1.8.0 2022-02-22 [1] CRAN (R 4.1.2)
KEGGREST 1.34.0 2021-10-26 [1] Bioconductor
KernSmooth 2.23-20 2021-05-03 [1] CRAN (R 4.1.3)
knitr 1.37 2021-12-16 [1] CRAN (R 4.1.0)
labeling 0.4.2 2020-10-20 [1] CRAN (R 4.1.0)
later 1.3.0 2021-08-18 [1] CRAN (R 4.1.0)
lattice 0.20-45 2021-09-22 [1] CRAN (R 4.1.3)
lifecycle 1.0.1 2021-09-24 [1] CRAN (R 4.1.0)
limma * 3.50.1 2022-02-17 [1] Bioconductor
listenv 0.8.0 2019-12-05 [1] CRAN (R 4.1.0)
lme4 1.1-28 2022-02-05 [1] CRAN (R 4.1.2)
lmerTest 3.1-3 2020-10-23 [1] CRAN (R 4.1.0)
locfit 1.5-9.5 2022-03-03 [1] CRAN (R 4.1.2)
lubridate 1.8.0 2021-10-07 [1] CRAN (R 4.1.0)
magrittr * 2.0.2 2022-01-26 [1] CRAN (R 4.1.2)
MASS * 7.3-55 2022-01-13 [1] CRAN (R 4.1.2)
Matrix * 1.4-0 2021-12-08 [1] CRAN (R 4.1.0)
Matrix.utils * 0.9.8 2020-02-26 [1] CRAN (R 4.1.0)
MatrixGenerics * 1.6.0 2021-10-26 [1] Bioconductor
matrixStats * 0.61.0 2021-09-17 [1] CRAN (R 4.1.0)
memoise 2.0.1 2021-11-26 [1] CRAN (R 4.1.0)
mgcv 1.8-39 2022-02-24 [1] CRAN (R 4.1.3)
microbiome 1.16.0 2021-10-26 [1] Bioconductor
minqa 1.2.4 2014-10-09 [1] CRAN (R 4.1.0)
modelr 0.1.8 2020-05-19 [1] CRAN (R 4.1.0)
MOFA2 * 1.4.0 2021-10-26 [1] Bioconductor
multtest 2.50.0 2021-10-26 [1] Bioconductor
munsell 0.5.0 2018-06-12 [1] CRAN (R 4.1.0)
muscat * 1.8.2 2022-03-10 [1] Bioconductor
nlme 3.1-155 2022-01-16 [1] CRAN (R 4.1.3)
nloptr 2.0.0 2022-01-26 [1] CRAN (R 4.1.2)
numDeriv 2016.8-1.1 2019-06-06 [1] CRAN (R 4.1.0)
parallelly 1.30.0 2021-12-17 [1] CRAN (R 4.1.0)
patchwork * 1.1.1 2020-12-17 [1] CRAN (R 4.1.0)
pbkrtest 0.5.1 2021-03-09 [1] CRAN (R 4.1.0)
performance * 0.8.0 2021-10-01 [1] CRAN (R 4.1.0)
permute 0.9-7 2022-01-27 [1] CRAN (R 4.1.2)
pheatmap 1.0.12 2019-01-04 [1] CRAN (R 4.1.0)
phyloseq * 1.38.0 2021-10-26 [1] Bioconductor
pillar 1.7.0 2022-02-01 [1] CRAN (R 4.1.2)
pkgbuild 1.3.1 2021-12-20 [1] CRAN (R 4.1.0)
pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.1.0)
pkgload 1.2.4 2021-11-30 [1] CRAN (R 4.1.0)
plyr 1.8.6 2020-03-03 [1] CRAN (R 4.1.0)
png 0.1-7 2013-12-03 [1] CRAN (R 4.1.0)
prettyunits 1.1.1 2020-01-24 [1] CRAN (R 4.1.0)
processx 3.5.2 2021-04-30 [1] CRAN (R 4.1.0)
progress 1.2.2 2019-05-16 [1] CRAN (R 4.1.0)
promises 1.2.0.1 2021-02-11 [1] CRAN (R 4.1.0)
ps 1.6.0 2021-02-28 [1] CRAN (R 4.1.0)
purrr * 0.3.4 2020-04-17 [1] CRAN (R 4.1.0)
R.methodsS3 1.8.1 2020-08-26 [1] CRAN (R 4.1.0)
R.oo 1.24.0 2020-08-26 [1] CRAN (R 4.1.0)
R.utils 2.11.0 2021-09-26 [1] CRAN (R 4.1.0)
R6 2.5.1 2021-08-19 [1] CRAN (R 4.1.0)
rbibutils 2.2.7 2021-12-07 [1] CRAN (R 4.1.0)
RColorBrewer * 1.1-2 2014-12-07 [1] CRAN (R 4.1.0)
Rcpp 1.0.8.3 2022-03-17 [1] CRAN (R 4.1.2)
RCurl 1.98-1.6 2022-02-08 [1] CRAN (R 4.1.2)
Rdpack 2.2 2022-03-19 [1] CRAN (R 4.1.3)
readr * 2.1.2 2022-01-30 [1] CRAN (R 4.1.2)
readxl * 1.3.1 2019-03-13 [1] CRAN (R 4.1.0)
registry 0.5-1 2019-03-05 [1] CRAN (R 4.1.0)
remotes 2.4.2 2021-11-30 [1] CRAN (R 4.1.0)
reprex 2.0.1 2021-08-05 [1] CRAN (R 4.1.0)
reshape2 * 1.4.4 2020-04-09 [1] CRAN (R 4.1.0)
restfulr 0.0.13 2017-08-06 [1] CRAN (R 4.1.0)
reticulate * 1.24 2022-01-26 [1] CRAN (R 4.1.2)
rhdf5 2.38.1 2022-03-10 [1] Bioconductor
rhdf5filters 1.6.0 2021-10-26 [1] Bioconductor
Rhdf5lib 1.16.0 2021-10-26 [1] Bioconductor
rjson 0.2.21 2022-01-09 [1] 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)
rprojroot 2.0.2 2020-11-15 [1] CRAN (R 4.1.0)
Rsamtools 2.10.0 2021-10-26 [1] Bioconductor
RSQLite 2.2.10 2022-02-17 [1] CRAN (R 4.1.2)
rstudioapi 0.13 2020-11-12 [1] CRAN (R 4.1.0)
rsvd 1.0.5 2021-04-16 [1] CRAN (R 4.1.0)
rtracklayer * 1.54.0 2021-10-26 [1] Bioconductor
Rtsne 0.15 2018-11-10 [1] CRAN (R 4.1.0)
rvest 1.0.2 2021-10-16 [1] CRAN (R 4.1.0)
S4Vectors * 0.32.3 2021-11-21 [1] Bioconductor
sass 0.4.0 2021-05-12 [1] CRAN (R 4.1.0)
ScaledMatrix 1.2.0 2021-10-26 [1] Bioconductor
scales * 1.1.1 2020-05-11 [1] CRAN (R 4.1.0)
scater * 1.22.0 2021-10-26 [1] Bioconductor
sctransform 0.3.3 2022-01-13 [1] CRAN (R 4.1.2)
scuttle * 1.4.0 2021-10-26 [1] Bioconductor
seriation * 1.3.4 2022-03-17 [1] CRAN (R 4.1.2)
sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.1.0)
shape 1.4.6 2021-05-19 [1] CRAN (R 4.1.0)
SingleCellExperiment * 1.16.0 2021-10-26 [1] Bioconductor
snakecase 0.11.0 2019-05-25 [1] CRAN (R 4.1.0)
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 [1] CRAN (R 4.1.0)
SummarizedExperiment * 1.24.0 2021-10-26 [1] Bioconductor
survival 3.3-1 2022-03-03 [1] CRAN (R 4.1.2)
testthat 3.1.2 2022-01-20 [1] CRAN (R 4.1.2)
tibble * 3.1.6 2021-11-07 [1] CRAN (R 4.1.0)
tictoc * 1.0.1 2021-04-19 [1] CRAN (R 4.1.0)
tidyr * 1.2.0 2022-02-01 [1] CRAN (R 4.1.2)
tidyselect 1.1.2 2022-02-21 [1] CRAN (R 4.1.2)
tidyverse * 1.3.1 2021-04-15 [1] CRAN (R 4.1.0)
TMB 1.8.0 2022-03-07 [1] CRAN (R 4.1.2)
TSP 1.2-0 2022-02-21 [1] CRAN (R 4.1.2)
tzdb 0.2.0 2021-10-27 [1] CRAN (R 4.1.0)
UpSetR * 1.4.0 2019-05-22 [1] CRAN (R 4.1.0)
usethis 2.1.5 2021-12-09 [1] CRAN (R 4.1.0)
utf8 1.2.2 2021-07-24 [1] CRAN (R 4.1.0)
uwot 0.1.11 2021-12-02 [1] CRAN (R 4.1.0)
variancePartition 1.24.0 2021-10-26 [1] Bioconductor
vctrs 0.3.8 2021-04-29 [1] CRAN (R 4.1.0)
vegan 2.5-7 2020-11-28 [1] CRAN (R 4.1.0)
vipor 0.4.5 2017-03-22 [1] CRAN (R 4.1.0)
viridis * 0.6.2 2021-10-13 [1] CRAN (R 4.1.0)
viridisLite * 0.4.0 2021-04-13 [1] CRAN (R 4.1.0)
whisker 0.4 2019-08-28 [1] CRAN (R 4.1.0)
withr 2.5.0 2022-03-03 [1] CRAN (R 4.1.2)
workflowr 1.7.0 2021-12-21 [1] CRAN (R 4.1.0)
writexl * 1.4.0 2021-04-20 [1] CRAN (R 4.1.0)
xfun 0.30 2022-03-02 [1] CRAN (R 4.1.2)
XML 3.99-0.9 2022-02-24 [1] CRAN (R 4.1.2)
xml2 1.3.3 2021-11-30 [1] CRAN (R 4.1.0)
xtable 1.8-4 2019-04-21 [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 4.1.2)
zlibbioc 1.40.0 2021-10-26 [1] Bioconductor
[1] /Library/Frameworks/R.framework/Versions/4.1/Resources/library
──────────────────────────────────────────────────────────────────────────────
sessionInfo()R version 4.1.3 (2022-03-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur/Monterey 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
locale:
[1] C/UTF-8/C/C/C/C
attached base packages:
[1] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] rmarkdown_2.13 writexl_1.4.0
[3] ComplexHeatmap_2.10.0 fgsea_1.20.0
[5] tictoc_1.0.1 performance_0.8.0
[7] edgeR_3.36.0 limma_3.50.1
[9] reshape2_1.4.4 scater_1.22.0
[11] scuttle_1.4.0 Matrix.utils_0.9.8
[13] Matrix_1.4-0 SingleCellExperiment_1.16.0
[15] SummarizedExperiment_1.24.0 Biobase_2.54.0
[17] MatrixGenerics_1.6.0 matrixStats_0.61.0
[19] UpSetR_1.4.0 BiocParallel_1.28.3
[21] muscat_1.8.2 dplyr_1.0.8
[23] readr_2.1.2 tidyr_1.2.0
[25] tibble_3.1.6 tidyverse_1.3.1
[27] rtracklayer_1.54.0 GenomicRanges_1.46.1
[29] GenomeInfoDb_1.30.1 IRanges_2.28.0
[31] S4Vectors_0.32.3 BiocGenerics_0.40.0
[33] fastcluster_1.2.3 seriation_1.3.4
[35] MOFA2_1.4.0 ggbeeswarm_0.6.0
[37] ggrepel_0.9.1 reticulate_1.24
[39] MASS_7.3-55 phyloseq_1.38.0
[41] ANCOMBC_1.4.0 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_2.0.2 circlize_0.4.14
[55] RColorBrewer_1.1-2 BiocStyle_2.22.0
loaded via a namespace (and not attached):
[1] R.methodsS3_1.8.1 bit64_4.0.5
[3] knitr_1.37 R.utils_2.11.0
[5] irlba_2.3.5 DelayedArray_0.20.0
[7] KEGGREST_1.34.0 RCurl_1.98-1.6
[9] doParallel_1.0.17 generics_0.1.2
[11] ScaledMatrix_1.2.0 callr_3.7.0
[13] cowplot_1.1.1 microbiome_1.16.0
[15] usethis_2.1.5 RSQLite_2.2.10
[17] future_1.24.0 bit_4.0.4
[19] tzdb_0.2.0 xml2_1.3.3
[21] lubridate_1.8.0 httpuv_1.6.5
[23] xfun_0.30 hms_1.1.1
[25] jquerylib_0.1.4 evaluate_0.15
[27] promises_1.2.0.1 TSP_1.2-0
[29] fansi_1.0.2 restfulr_0.0.13
[31] progress_1.2.2 caTools_1.18.2
[33] dbplyr_2.1.1 igraph_1.2.11
[35] DBI_1.1.2 geneplotter_1.72.0
[37] ellipsis_0.3.2 corrplot_0.92
[39] backports_1.4.1 insight_0.16.0
[41] permute_0.9-7 annotate_1.72.0
[43] sparseMatrixStats_1.6.0 vctrs_0.3.8
[45] remotes_2.4.2 cachem_1.0.6
[47] withr_2.5.0 grr_0.9.5
[49] sctransform_0.3.3 vegan_2.5-7
[51] GenomicAlignments_1.30.0 prettyunits_1.1.1
[53] cluster_2.1.2 ape_5.6-2
[55] dir.expiry_1.2.0 crayon_1.5.0
[57] basilisk.utils_1.6.0 genefilter_1.76.0
[59] labeling_0.4.2 pkgconfig_2.0.3
[61] pkgload_1.2.4 nlme_3.1-155
[63] vipor_0.4.5 devtools_2.4.3
[65] blme_1.0-5 globals_0.14.0
[67] rlang_1.0.2 lifecycle_1.0.1
[69] registry_0.5-1 filelock_1.0.2
[71] modelr_0.1.8 rsvd_1.0.5
[73] cellranger_1.1.0 rprojroot_2.0.2
[75] Rhdf5lib_1.16.0 boot_1.3-28
[77] reprex_2.0.1 beeswarm_0.4.0
[79] processx_3.5.2 whisker_0.4
[81] GlobalOptions_0.1.2 pheatmap_1.0.12
[83] png_0.1-7 rjson_0.2.21
[85] bitops_1.0-7 R.oo_1.24.0
[87] KernSmooth_2.23-20 rhdf5filters_1.6.0
[89] Biostrings_2.62.0 blob_1.2.2
[91] DelayedMatrixStats_1.16.0 workflowr_1.7.0
[93] shape_1.4.6 parallelly_1.30.0
[95] beachmat_2.10.0 memoise_2.0.1
[97] plyr_1.8.6 gplots_3.1.1
[99] zlibbioc_1.40.0 compiler_4.1.3
[101] BiocIO_1.4.0 clue_0.3-60
[103] lme4_1.1-28 DESeq2_1.34.0
[105] snakecase_0.11.0 Rsamtools_2.10.0
[107] cli_3.2.0 ade4_1.7-18
[109] XVector_0.34.0 listenv_0.8.0
[111] lmerTest_3.1-3 ps_1.6.0
[113] TMB_1.8.0 mgcv_1.8-39
[115] tidyselect_1.1.2 stringi_1.7.6
[117] highr_0.9 yaml_2.3.5
[119] BiocSingular_1.10.0 locfit_1.5-9.5
[121] sass_0.4.0 fastmatch_1.1-3
[123] tools_4.1.3 future.apply_1.8.1
[125] parallel_4.1.3 rstudioapi_0.13
[127] foreach_1.5.2 git2r_0.30.1
[129] janitor_2.1.0 gridExtra_2.3
[131] farver_2.1.0 Rtsne_0.15
[133] digest_0.6.29 BiocManager_1.30.16
[135] Rcpp_1.0.8.3 broom_0.7.12
[137] later_1.3.0 httr_1.4.2
[139] AnnotationDbi_1.56.2 Rdpack_2.2
[141] colorspace_2.0-3 brio_1.1.3
[143] rvest_1.0.2 XML_3.99-0.9
[145] fs_1.5.2 splines_4.1.3
[147] uwot_0.1.11 basilisk_1.6.0
[149] multtest_2.50.0 sessioninfo_1.2.2
[151] xtable_1.8-4 jsonlite_1.8.0
[153] nloptr_2.0.0 testthat_3.1.2
[155] R6_2.5.1 pillar_1.7.0
[157] htmltools_0.5.2 glue_1.6.2
[159] fastmap_1.1.0 minqa_1.2.4
[161] BiocNeighbors_1.12.0 codetools_0.2-18
[163] pkgbuild_1.3.1 utf8_1.2.2
[165] lattice_0.20-45 bslib_0.3.1
[167] pbkrtest_0.5.1 numDeriv_2016.8-1.1
[169] gtools_3.9.2 survival_3.3-1
[171] glmmTMB_1.1.3 desc_1.4.1
[173] biomformat_1.22.0 munsell_0.5.0
[175] GetoptLong_1.0.5 rhdf5_2.38.1
[177] GenomeInfoDbData_1.2.7 iterators_1.0.14
[179] variancePartition_1.24.0 HDF5Array_1.22.1
[181] haven_2.4.3 gtable_0.3.0
[183] rbibutils_2.2.7