Last updated: 2021-07-02

Checks: 4 3

Knit directory: MS_lesions/

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The following chunks had caches available:
  • calc_oligo_pcs
  • calc_patient_level_gsea
  • calc_patient_level_muscat
  • calc_pseudo_bulk
  • calc_soup
  • calc_tweaked_inputs
  • load_outputs_for_plotting
  • load_standard_things
  • plot_de_barplot
  • plot_gsea_results_test
  • plot_olg_pcs
  • plot_olg_pcs_annot
  • plot_olg_pcs_vs_qc_stats
  • plot_oligo_pc_heatmap
  • plot_oligo_pc_scatters_down
  • plot_oligo_pc_scatters_up
  • plot_soup_vs_results
  • plot_top_genes_oligo_pc_common
  • plot_top_genes_oligo_pc_specific
  • save_outputs
  • session_info
  • session-info-chunk-inserted-by-workflowr
  • setup_input
  • setup_outputs

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Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rprofile
    Ignored:    .Rproj.user/
    Ignored:    ._.DS_Store
    Ignored:    ._MS_lesions.sublime-project
    Ignored:    .log/
    Ignored:    MS_lesions.sublime-project
    Ignored:    MS_lesions.sublime-workspace
    Ignored:    analysis/.__site.yml
    Ignored:    analysis/ms02_doublet_id_cache/
    Ignored:    analysis/ms03_SampleQC_cache/
    Ignored:    analysis/ms04_conos_cache/
    Ignored:    analysis/ms05_splitting_cache/
    Ignored:    analysis/ms06_sccaf_cache/
    Ignored:    analysis/ms07_soup_cache/
    Ignored:    analysis/ms08_modules_cache/
    Ignored:    analysis/ms09_ancombc_cache/
    Ignored:    analysis/ms09_ancombc_clean_1e3_cache/
    Ignored:    analysis/ms09_ancombc_clean_2e3_cache/
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    Ignored:    analysis/ms10_muscat_template_broad_cache/
    Ignored:    analysis/ms10_muscat_template_fine_cache/
    Ignored:    analysis/ms11_paga_cache/
    Ignored:    analysis/ms12_markers_cache/
    Ignored:    analysis/ms13_labelling_cache/
    Ignored:    analysis/ms14_lesions_cache/
    Ignored:    analysis/supp06_sccaf_cache/
    Ignored:    analysis/supp09_ancombc_cache/
    Ignored:    analysis/supp10_muscat_cache/
    Ignored:    analysis/supp10_muscat_heatmaps_cache/
    Ignored:    analysis/supp10_muscat_olg_pc1_cache/
    Ignored:    analysis/supp10_muscat_olg_pc2_cache/
    Ignored:    analysis/supp10_muscat_olg_pc_cache/
    Ignored:    code/muscat_plan.txt
    Ignored:    data/
    Ignored:    figures/
    Ignored:    output/

Untracked files:
    Untracked:  Rplots.pdf
    Untracked:  analysis/supp10_muscat_heatmaps.Rmd
    Untracked:  analysis/supp10_muscat_olg_pc1.Rmd
    Untracked:  analysis/supp10_muscat_olg_pc2.Rmd
    Untracked:  analysis/temp_igll5.R
    Untracked:  code/dev_muscat_fixing_zero_conditions_2021-06-28.R
    Untracked:  code/dev_stan_cauchy_fits_2021-06-28.R
    Untracked:  code/supp10_muscat_olg_pc.R

Unstaged changes:
    Modified:   analysis/index.Rmd
    Modified:   analysis/ms10_muscat_template_broad.Rmd
    Modified:   analysis/ms10_muscat_template_fine.Rmd
    Modified:   analysis/ms12_markers.Rmd
    Modified:   analysis/supp09_ancombc.Rmd
    Modified:   code/ms07_soup.R
    Modified:   code/ms09_ancombc.R
    Modified:   code/ms10_muscat_fns.R
    Modified:   code/ms10_muscat_runs.R
    Modified:   code/ms12_markers.R
    Deleted:    code/supp08_ancombc.R
    Modified:   code/supp10_muscat.R

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Setup / definitions

Libraries

Helper functions

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')

Inputs

# 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')

Outputs

# 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))

Load inputs

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')

Processing / calculations

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)

CLR plots

CLRs to show Oligo_D outliers

input_all = conos_dt[(matter == "WM") & 
  (type_broad %in% c('Oligodendrocytes', 'OPCs / COPs'))]
cat('### All\n')

All

  (plot_patient_clrs(input_all, props_wm_all))

cat('\n\n')
cat('### Oligo_D + COP_A2 excluded\n')

Oligo_D + COP_A2 excluded

  input_sel = input_all[ !(type_fine %in% d_types) ]
  (plot_patient_clrs(input_sel, props_wm_all))

cat('\n\n')

Same CLRs, annotated

cat('### All\n')

All

  (plot_patient_clrs_annots(input_all, props_wm_all, meta_tweak))

cat('\n\n')
cat('### Oligo_D + COP_A2 excluded\n')

Oligo_D + COP_A2 excluded

  input_sel = input_all[ !(type_fine %in% d_types) ]
  (plot_patient_clrs_annots(input_sel, props_wm_all, meta_tweak))

cat('\n\n')

Muscat summary

Barplot of significant genes by contrast

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='')

Top celltype_specific genes, test variables

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')
}

OPCs / COPs

Oligodendrocytes

Astrocytes

Microglia

Excitatory neurons

Inhibitory neurons

Endothelial cells

Pericytes

how_unique  = 'non_specific'
cat('# Top ', how_unique, ' genes, ', this_type, 
  ' variables{.tabset}\n', sep='')

Top non_specific genes, test variables

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')
}

OPCs / COPs

Oligodendrocytes

Astrocytes

Microglia

Excitatory neurons

Inhibitory neurons

Endothelial cells

Pericytes

Immune

this_type   = 'test'
cat('# GSEA results for ', this_type, ' variables{.tabset}\n', sep = '')

GSEA results for test variables

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')
}

go_bp

hallmark

TFs

celltype

hpo

mirs

curated

immune

position

Top genes positively correlated with oligo-PC1

for (cl in broad_ord) {
  # restrict to relevant GO terms
  cat('## ', cl, '\n', sep='')
  suppressWarnings(print(plot_oligo_pc_scatter(cl, signif_dt[ logFC > 0 ], 
    cols_dt, pb_olg_pc, this_var, top_n = 30)))
  cat('\n\n')
}

OPCs / COPs

Oligodendrocytes

Astrocytes

Microglia

Excitatory neurons

Inhibitory neurons

Endothelial cells

Pericytes

Immune

Top genes negatively correlated with oligo-PC1

for (cl in broad_ord) {
  # restrict to relevant GO terms
  cat('## ', cl, '\n', sep='')
  suppressWarnings(print(plot_oligo_pc_scatter(cl, signif_dt[ logFC < 0 ], 
    cols_dt, pb_olg_pc, this_var, top_n = 30)))
  cat('\n\n')
}

OPCs / COPs

Oligodendrocytes

Astrocytes

Microglia

Excitatory neurons

Inhibitory neurons

Endothelial cells

Pericytes

Immune

NULL

Heatmap of top oligo-PC1 genes

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')
}

OPCs / COPs

Oligodendrocytes

Astrocytes

Microglia

Excitatory neurons

Inhibitory neurons

Endothelial cells

Pericytes

Immune

Could this be contamination?

Soup proportions vs logFCs

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)

Oligo PCs vs QC stats

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)

Outputs

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                  

- Packages -------------------------------------------------------------------
 package              * version    date       lib
 ade4                   1.7-16     2020-10-28 [1]
 ANCOMBC              * 1.0.5      2021-03-09 [1]
 annotate               1.68.0     2020-10-27 [1]
 AnnotationDbi          1.52.0     2020-10-27 [1]
 ape                    5.5        2021-04-25 [1]
 assertthat           * 0.2.1      2019-03-21 [2]
 backports              1.2.1      2020-12-09 [2]
 beachmat               2.6.4      2020-12-20 [1]
 beeswarm               0.3.1      2021-03-07 [1]
 Biobase              * 2.50.0     2020-10-27 [1]
 BiocGenerics         * 0.36.1     2021-04-16 [1]
 BiocManager            1.30.15    2021-05-11 [1]
 BiocNeighbors          1.8.2      2020-12-07 [1]
 BiocParallel         * 1.24.1     2020-11-06 [1]
 BiocSingular           1.6.0      2020-10-27 [1]
 BiocStyle            * 2.18.1     2020-11-24 [1]
 biomformat             1.18.0     2020-10-27 [1]
 Biostrings             2.58.0     2020-10-27 [1]
 bit                    4.0.4      2020-08-04 [2]
 bit64                  4.0.5      2020-08-30 [2]
 bitops                 1.0-7      2021-04-24 [2]
 blme                   1.0-5      2021-01-05 [1]
 blob                   1.2.1      2020-01-20 [2]
 bluster                1.0.0      2020-10-27 [1]
 boot                   1.3-28     2021-05-03 [2]
 broom                  0.7.6      2021-04-05 [2]
 bslib                  0.2.5      2021-05-12 [2]
 cachem                 1.0.5      2021-05-15 [1]
 Cairo                  1.5-12.2   2020-07-07 [2]
 callr                  3.7.0      2021-04-20 [2]
 caTools                1.18.2     2021-03-28 [2]
 cellranger             1.1.0      2016-07-27 [2]
 circlize             * 0.4.12     2021-01-08 [1]
 cli                    2.5.0      2021-04-26 [2]
 clue                   0.3-59     2021-04-16 [1]
 cluster                2.1.2      2021-04-17 [2]
 codetools              0.2-18     2020-11-04 [2]
 colorout             * 1.2-2      2021-04-15 [1]
 colorRamps             2.3        2012-10-29 [1]
 colorspace             2.0-1      2021-05-04 [2]
 ComplexHeatmap       * 2.6.2      2020-11-12 [1]
 crayon                 1.4.1      2021-02-08 [2]
 data.table           * 1.14.0     2021-02-21 [2]
 DBI                    1.1.1      2021-01-15 [2]
 dbplyr                 2.1.1      2021-04-06 [2]
 DelayedArray           0.16.3     2021-03-24 [1]
 DelayedMatrixStats     1.12.3     2021-02-03 [1]
 desc                   1.3.0      2021-03-05 [2]
 DESeq2                 1.30.1     2021-02-19 [1]
 devtools               2.4.1      2021-05-05 [1]
 digest                 0.6.27     2020-10-24 [2]
 doParallel             1.0.16     2020-10-16 [1]
 dplyr                * 1.0.6      2021-05-05 [2]
 dqrng                  0.3.0      2021-05-01 [2]
 DropletUtils         * 1.10.3     2021-02-02 [1]
 edgeR                * 3.32.1     2021-01-14 [1]
 ellipsis               0.3.2      2021-04-29 [2]
 evaluate               0.14       2019-05-28 [2]
 fansi                  0.4.2      2021-01-15 [2]
 farver                 2.1.0      2021-02-28 [2]
 fastmap                1.1.0      2021-01-25 [2]
 fastmatch              1.1-0      2017-01-28 [1]
 fgsea                * 1.16.0     2020-10-27 [1]
 forcats              * 0.5.1      2021-01-27 [2]
 foreach                1.5.1      2020-10-15 [2]
 fs                     1.5.0      2020-07-31 [2]
 future                 1.21.0     2020-12-10 [2]
 future.apply           1.7.0      2021-01-04 [2]
 genefilter             1.72.1     2021-01-21 [1]
 geneplotter            1.68.0     2020-10-27 [1]
 generics               0.1.0      2020-10-31 [2]
 GenomeInfoDb         * 1.26.7     2021-04-08 [1]
 GenomeInfoDbData       1.2.4      2021-04-15 [1]
 GenomicAlignments      1.26.0     2020-10-27 [1]
 GenomicRanges        * 1.42.0     2020-10-27 [1]
 GetoptLong             1.0.5      2020-12-15 [1]
 ggbeeswarm             0.6.0      2017-08-07 [1]
 ggplot2              * 3.3.3      2020-12-30 [2]
 ggrepel              * 0.9.1      2021-01-15 [2]
 git2r                  0.28.0     2021-01-10 [1]
 glmmTMB                1.0.2.1    2020-07-02 [1]
 GlobalOptions          0.1.2      2020-06-10 [1]
 globals                0.14.0     2020-11-22 [2]
 glue                   1.4.2      2020-08-27 [2]
 googlesheets         * 0.3.0      2018-06-29 [1]
 gplots                 3.1.1      2020-11-28 [2]
 gridExtra              2.3        2017-09-09 [2]
 grr                    0.9.5      2016-08-26 [1]
 gtable                 0.3.0      2019-03-25 [2]
 gtools                 3.8.2      2020-03-31 [2]
 haven                  2.4.1      2021-04-23 [2]
 HDF5Array              1.18.1     2021-02-04 [1]
 highr                  0.9        2021-04-16 [2]
 hms                    1.1.0      2021-05-17 [2]
 htmltools              0.5.1.1    2021-01-22 [2]
 httpuv                 1.6.1      2021-05-07 [2]
 httr                   1.4.2      2020-07-20 [2]
 igraph                 1.2.6      2020-10-06 [2]
 IRanges              * 2.24.1     2020-12-12 [1]
 irlba                  2.3.3      2019-02-05 [2]
 iterators              1.0.13     2020-10-15 [2]
 janitor                2.1.0      2021-01-05 [1]
 jquerylib              0.1.4      2021-04-26 [2]
 jsonlite               1.7.2      2020-12-09 [2]
 KernSmooth             2.23-20    2021-05-03 [2]
 knitr                  1.33       2021-04-24 [1]
 labeling               0.4.2      2020-10-20 [2]
 later                  1.2.0      2021-04-23 [2]
 lattice                0.20-44    2021-05-02 [2]
 lifecycle              1.0.0      2021-02-15 [2]
 limma                * 3.46.0     2020-10-27 [1]
 listenv                0.8.0      2019-12-05 [2]
 lme4                   1.1-27     2021-05-15 [1]
 lmerTest               3.1-3      2020-10-23 [1]
 locfit                 1.5-9.4    2020-03-25 [1]
 lubridate              1.7.10     2021-02-26 [2]
 magrittr             * 2.0.1      2020-11-17 [1]
 MASS                 * 7.3-54     2021-05-03 [2]
 Matrix               * 1.3-3      2021-05-04 [2]
 Matrix.utils         * 0.9.8      2020-02-26 [1]
 MatrixGenerics       * 1.2.1      2021-01-30 [1]
 matrixStats          * 0.59.0     2021-06-01 [1]
 memoise                2.0.0      2021-01-26 [1]
 mgcv                   1.8-35     2021-04-18 [2]
 microbiome             1.12.0     2020-10-27 [1]
 minqa                  1.2.4      2014-10-09 [1]
 modelr                 0.1.8      2020-05-19 [2]
 multtest               2.46.0     2020-10-27 [1]
 munsell                0.5.0      2018-06-12 [2]
 muscat               * 1.5.1      2021-04-15 [1]
 nlme                   3.1-152    2021-02-04 [2]
 nloptr                 1.2.2.2    2020-07-02 [1]
 nnls                 * 1.4        2012-03-19 [1]
 numDeriv               2016.8-1.1 2019-06-06 [2]
 parallelly             1.25.0     2021-04-30 [2]
 patchwork            * 1.1.1      2020-12-17 [2]
 pbkrtest               0.5.1      2021-03-09 [1]
 permute                0.9-5      2019-03-12 [1]
 phyloseq             * 1.34.0     2020-10-27 [1]
 pillar                 1.6.1      2021-05-16 [2]
 pkgbuild               1.2.0      2020-12-15 [1]
 pkgconfig              2.0.3      2019-09-22 [2]
 pkgload                1.2.1      2021-04-06 [2]
 plyr                   1.8.6      2020-03-03 [2]
 png                    0.1-7      2013-12-03 [2]
 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