Last updated: 2022-01-21

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Knit directory: MS_lesions/

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  • calc_sel_genes
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  • fig_factor2
  • fig_factor3
  • fig_factor4
  • fig_factor5
  • fig_interesting_gs
  • fig_mofa_factors_diagnosis
  • fig_mofa_factors_lesions
  • fig_muscat_and_sd_vs_lof
  • fig_muscat_vs_sd
  • fig_overview_expression
  • fig_random_effects_example
  • get_var_exp
  • init_mofa
  • load_muscat_inputs
  • load_n_cells
  • load_soup_logcpms
  • plot_age_and_duration
  • plot_cytokines
  • plot_factor_weight_corrs_by_factor
  • plot_factor_weight_corrs_by_type
  • plot_factors_heatmap_all
  • plot_factors_heatmap_few
  • plot_factors_over_mds
  • plot_factors_pairwise
  • plot_factors_univariate
  • plot_gsea_dotplots
  • plot_mofa_vs_logcpm
  • plot_mofa_vs_logcpm_soup
  • plot_mofa_vs_n_cells
  • plot_mofa_weight_distributions
  • plot_muscat_vs_mofa
  • plot_muscat_vs_sd
  • plot_muscat_vs_sd_magma
  • plot_overlap
  • plot_overlap_propns
  • plot_overview
  • plot_top_coeffs_by_factor
  • plot_top_expression_all_factors
  • plot_top_expression
  • plot_var_exp
  • run_gsea
  • save_interesting_gs
  • session_info
  • session-info-chunk-inserted-by-workflowr
  • setup_input
  • setup_outputs
  • train_mofa

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Ignored files:
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    Ignored:    .Rproj.user/
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Untracked files:
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Unstaged changes:
    Modified:   analysis/fig_muscat.Rmd
    Modified:   analysis/ms10_muscat_template_broad_slim.Rmd
    Modified:   analysis/ms13_labelling.Rmd
    Modified:   analysis/ms14_lesions.Rmd
    Modified:   analysis/ms15_mofa_sample_gm_w_layers_final_meta.Rmd
    Modified:   analysis/ms15_mofa_sample_wm_final_meta.Rmd
    Modified:   code/dev_de_w_contamation_2021-10-25.R
    Modified:   code/dev_edger_on_mofa_20210804.R
    Modified:   code/fig_muscat.R
    Modified:   code/ms10_muscat_fns.R
    Modified:   code/ms10_muscat_runs.R
    Modified:   code/ms10_slurm_jobs.py
    Modified:   code/ms14_lesions.R
    Modified:   code/ms15_mofa.R
    Modified:   code/supp10_muscat.R

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/ms15_mofa_sample_wm_final_meta.Rmd) and HTML (public/ms15_mofa_sample_wm_final_meta.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd 5f3bbe9 wmacnair 2022-01-19 Add additional gene selection plots to MOFA analysis
html 5f3bbe9 wmacnair 2022-01-19 Add additional gene selection plots to MOFA analysis
Rmd 2372e65 wmacnair 2022-01-18 Add ANOVA analysis to MOFA factors
html 2372e65 wmacnair 2022-01-18 Add ANOVA analysis to MOFA factors
Rmd 24d709c wmacnair 2022-01-18 Add proportion of genes overlapping to MOFA outputs
html 24d709c wmacnair 2022-01-18 Add proportion of genes overlapping to MOFA outputs
Rmd 5a1c0c2 wmacnair 2022-01-06 Tweak oligo barplots and MOFA heatmaps
html 5a1c0c2 wmacnair 2022-01-06 Tweak oligo barplots and MOFA heatmaps
Rmd e94e407 wmacnair 2021-11-25 Update index, also mofa code
html 7fb1b95 wmacnair 2021-11-25 Host with GitLab.
Rmd c013785 Macnair 2021-10-23 Update MOFA
Rmd 21ca0d3 Macnair 2021-10-19 Tweaks to MOFA barplots
Rmd 1fe7571 Macnair 2021-10-19 Fix error in WM MOFA barplots
Rmd 8a15881 Macnair 2021-10-18 Update index.Rmd with final MOFA results`
Rmd 47af3ff Macnair 2021-10-18 Finalise MOFA analysis
Rmd 1552617 Macnair 2021-10-05 Save MOFA genes to xls
Rmd c1a0bff Macnair 2021-10-05 Update GM MOFA with final metadata
Rmd ad82ee3 Macnair 2021-10-04 Update ms09_ancombc with final metadata

Setup / definitions

Libraries

Helper functions

source('code/ms00_utils.R')
source('code/ms09_ancombc.R')
source('code/ms10_muscat_runs.R')
source('code/ms15_mofa.R')
knitr::knit_hooks$set(webgl = hook_webgl)

Inputs

# 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'
comp_grps_f = 'output/ms09_ancombc/clr_clustering_WM_2021-10-19.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_sum_broad_2021-10-11.rds')
pb_fine_f   = file.path(soup_dir, 'pb_sum_fine_2021-10-11.rds')
soup_f      = 'data/ambient/ambient.100UMI.txt'

# define run to load
run_tag     = 'run09'
time_stamp  = '2021-10-13'

# 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)

Outputs

# where to save
save_dir    = 'output/ms15_mofa'
date_tag    = '2021-10-14'
if (!dir.exists(save_dir))
  dir.create(save_dir)

# parameters for gene selection
min_sd      = log(2)
min_fc      = log(2)
max_p       = 0.01
n_factors   = 5
sel_cl      = c("OPCs / COPs", "Oligodendrocytes", "Astrocytes", "Microglia", 
  "Endothelial cells", "Pericytes", "Immune")
fgsea_cut   = 0.1
log_p_mad   = 2
n_paths     = 50
n_cores     = 8

# parameters for plotting
min_var     = 5
w_cut       = 0.2

# checking if metadata can explain factors
formula_str = '~ lesion_type + sex + age_scale + pmi_cat'
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  = 'Oligodendrocytes'
example_gs  = c("NHLH1_ENSG00000171786", "CASP7_ENSG00000165806", 
  "RELN_ENSG00000189056", "KLB_ENSG00000134962", "NRTN_ENSG00000171119", 
  "EVI5L_ENSG00000142459", "PWP2_ENSG00000241945", "GRID2_ENSG00000152208", 
  "MET_ENSG00000105976")

Load inputs

# 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 proportions
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)
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)
annots_dt   = add_compositional_groups(annots_dt, comp_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    97     20     2     75
2:  Oligodendrocytes   507    259    16    232
3:        Astrocytes   794    559    29    206
4:         Microglia   667    255    48    364
5: Endothelial cells    86      2     0     84
6:         Pericytes    27      1     0     26
7:            Immune    27      4     0     23
n_cells_dt  = calc_n_cells_dt(pb_fine_f, annots_dt, sel_cl)
soup_dt     = get_soup_logcpms(soup_f, pb)

Processing / calculations

mofa_obj    = make_mofa_obj_samples(pb, filtered_dt, sel_cl)
Creating MOFA object from a data.frame...
# 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
)
Checking data options...
Checking training options...
Checking model options...
model       = run_mofa(mofa_obj, mofa_f)
Warning: Output file output/ms15_mofa/mofa_run09_2021-10-14.hdf5 already exists, it will be replaced
Connecting to the mofapy2 python package using reticulate (use_basilisk = FALSE)... 
    Please make sure to manually specify the right python binary when loading R with reticulate::use_python(..., force=TRUE) or the right conda environment with reticulate::use_condaenv(..., force=TRUE)
    If you prefer to let us automatically install a conda environment with 'mofapy2' installed using the 'basilisk' package, please use the argument 'use_basilisk = TRUE'
Warning in .quality_control(object, verbose = verbose): Factor(s) 1 are strongly correlated with the total number of expressed features for at least one of your omics. Such factors appear when there are differences in the total 'levels' between your samples, *sometimes* because of poor normalisation in the preprocessing steps.
# 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)]
w_dt        = extract_weights(model, sd_dt)
fgsea_fs    = sapply(names(paths_list)[1:2], function(p) sprintf(fgsea_pat, p))
if (all(file.exists(fgsea_fs))) {
  # gsea_list   = lapply(fgsea_fs, fread)
  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[1:2], w_dt, fgsea_pat, fgsea_cut, bpparam)
  bpstop()
}

# restrict to interesting ones
gsea_main  = gsea_list %>% map( ~.x[ main_path == TRUE ]) %>% rbindlist
r2_dt       = calc_r2_for_factors(model, annots_dt, formula_str, random_var)
anova_dt    = calc_lrts(model, annots_dt, formula_str, random_var)

Analysis

muscat results vs SD

for (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')
}

log10_padj

Version Author Date
24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

log2FC

Version Author Date
24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Cytokine effects

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))

Ages vs duration of MS

(plot_age_duration(annots_dt))

Version Author Date
24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Data overview

(plot_data_overview(mofa_obj))
Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
"none")` instead.

Version Author Date
24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Overlapping genes

cat('### All genes\n')

All genes

  suppressWarnings(print(plot_gene_overlap(model)))

Version Author Date
5a1c0c2 wmacnair 2022-01-06
7fb1b95 wmacnair 2021-11-25
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')
}

Genes in Factor1

Version Author Date
5a1c0c2 wmacnair 2022-01-06
7fb1b95 wmacnair 2021-11-25

Genes in Factor2

Version Author Date
5a1c0c2 wmacnair 2022-01-06
7fb1b95 wmacnair 2021-11-25

Genes in Factor3

Version Author Date
5a1c0c2 wmacnair 2022-01-06
7fb1b95 wmacnair 2021-11-25

Genes in Factor4

Version Author Date
5a1c0c2 wmacnair 2022-01-06
7fb1b95 wmacnair 2021-11-25

Genes in Factor5

Version Author Date
5a1c0c2 wmacnair 2022-01-06
7fb1b95 wmacnair 2021-11-25

Overlapping genes (proportions)

cat('### All genes\n')

All genes

  suppressWarnings(print(plot_gene_overlap(model, what = 'prop')))

Version Author Date
24d709c wmacnair 2022-01-18
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')
}

Genes in Factor1

Version Author Date
24d709c wmacnair 2022-01-18

Genes in Factor2

Version Author Date
24d709c wmacnair 2022-01-18

Genes in Factor3

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Genes in Factor4

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Genes in Factor5

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Factor distributions

for (annot in c('lesion_type', 'diagnosis', 'sex', 'sample_source', 'smoker', 'oligo_grp', 'comp_grp')) {
  cat('### by ', annot, '\n', sep = '')
  print(plot_factors_univariate(model, annots_dt, pb, by = annot))
  cat('\n\n')
}

by lesion_type

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by diagnosis

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by sex

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by sample_source

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by smoker

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by oligo_grp

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by comp_grp

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24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Factor distributions - pairwise

for (annot in c('subject_id', 'lesion_type', 'diagnosis', 'sex', 'sample_source', 'smoker', 'oligo_grp', 'comp_grp')) {
  cat('### by ', annot, '\n', sep = '')
  print(plot_factors_pairwise(model, annots_dt, pb, by = annot))
  cat('\n\n')
}

by subject_id

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by lesion_type

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by diagnosis

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by sex

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by sample_source

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by smoker

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by oligo_grp

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by comp_grp

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24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Factors over MDS layouts

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')
}

OPCs / COPs

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Oligodendrocytes

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Astrocytes

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Microglia

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Endothelial cells

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Pericytes

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Immune

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24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Factor distributions with patient annotations - few

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')
}

score

Version Author Date
5a1c0c2 wmacnair 2022-01-06
7fb1b95 wmacnair 2021-11-25

score_scaled

Version Author Date
24d709c wmacnair 2022-01-18
5a1c0c2 wmacnair 2022-01-06
7fb1b95 wmacnair 2021-11-25

Factor distributions with patient annotations - all

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')
}

score

Version Author Date
5a1c0c2 wmacnair 2022-01-06
7fb1b95 wmacnair 2021-11-25

score_scaled

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24d709c wmacnair 2022-01-18
5a1c0c2 wmacnair 2022-01-06
7fb1b95 wmacnair 2021-11-25

Coefficients of top genes (by factor)

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')
}

Factor1

Version Author Date
5a1c0c2 wmacnair 2022-01-06
7fb1b95 wmacnair 2021-11-25

Factor2

Version Author Date
5a1c0c2 wmacnair 2022-01-06
7fb1b95 wmacnair 2021-11-25

Factor3

Version Author Date
5a1c0c2 wmacnair 2022-01-06
7fb1b95 wmacnair 2021-11-25

Factor4

Version Author Date
5a1c0c2 wmacnair 2022-01-06
7fb1b95 wmacnair 2021-11-25

Factor5

Version Author Date
5a1c0c2 wmacnair 2022-01-06
7fb1b95 wmacnair 2021-11-25

Expression of top genes per celltype

# 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), merge_legend = TRUE )
  cat('\n\n')
}

oligo-F1 (32%)

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24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

opc_cop-F1 (27%)

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endo-F1 (23%)

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micro-F1 (22%)

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astro-F1 (18%)

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immune-F1 (17%)

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peri-F1 (13%)

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peri-F2 (18%)

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micro-F2 (18%)

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astro-F2 (15%)

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opc_cop-F2 (10%)

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endo-F2 (7%)

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oligo-F2 (7%)

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oligo-F3 (14%)

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opc_cop-F3 (9%)

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micro-F3 (8%)

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astro-F3 (8%)

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peri-F3 (7%)

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immune-F3 (6%)

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astro-F4 (13%)

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oligo-F5 (9%)

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24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Expression of top genes across all factors per celltype

# 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), merge_legend = TRUE )
  cat('\n\n')
}

opc_cop

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24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

oligo

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24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

astro

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7fb1b95 wmacnair 2021-11-25

micro

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7fb1b95 wmacnair 2021-11-25

endo

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24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

peri

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24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

immune

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24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Factors vs number of cells

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')
}

Factor1

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24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Factor2

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24d709c wmacnair 2022-01-18
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Factor3

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24d709c wmacnair 2022-01-18
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Factor4

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24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Factor5

Version Author Date
24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Factors vs top genes

for (f in factors_names(model)) {
  cat('### ', f, '\n', sep = '')
  print(plot_mofa_vs_logcpm(model, annots_dt, sel_f = f))
  cat('\n\n')
}

Factor1

Version Author Date
24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Factor2

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24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Factor3

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24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Factor4

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24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Factor5

Version Author Date
24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Factors vs top genes - soup

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')
}

Factor1

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24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Factor2

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24d709c wmacnair 2022-01-18
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Factor3

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24d709c wmacnair 2022-01-18
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Factor4

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24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Factor5

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24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Distributions of factor weights

(plot_mofa_weights(model))

Version Author Date
5a1c0c2 wmacnair 2022-01-06
7fb1b95 wmacnair 2021-11-25

Factor weights vs muscat results

for (what in c('log10_padj', 'log2FC')) {
  cat('### ', what, '\n', sep = '')
  print(plot_muscat_vs_mofa(model, filter_dt, what = what))
  cat('\n\n')
}

log10_padj

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24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

log2FC

Version Author Date
24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Correlations between factor weights - split by celltype

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')
}

opc_cop

Version Author Date
5a1c0c2 wmacnair 2022-01-06
7fb1b95 wmacnair 2021-11-25

oligo

Version Author Date
5a1c0c2 wmacnair 2022-01-06
7fb1b95 wmacnair 2021-11-25

astro

Version Author Date
5a1c0c2 wmacnair 2022-01-06
7fb1b95 wmacnair 2021-11-25

micro

Version Author Date
5a1c0c2 wmacnair 2022-01-06
7fb1b95 wmacnair 2021-11-25

endo

Version Author Date
5a1c0c2 wmacnair 2022-01-06
7fb1b95 wmacnair 2021-11-25

peri

Version Author Date
5a1c0c2 wmacnair 2022-01-06
7fb1b95 wmacnair 2021-11-25

immune

Version Author Date
5a1c0c2 wmacnair 2022-01-06
7fb1b95 wmacnair 2021-11-25

Correlations between factor weights - split by factor

for (f in factors_names(model) ) {
  cat('### ', f, '\n', sep = '')
  print(plot_factor_weight_corrs(model, f, by = 'factor', how = 'point'))
  cat('\n\n')
}

Factor1

Version Author Date
24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Factor2

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24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Factor3

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24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Factor4

Version Author Date
24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Factor5

Version Author Date
24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Variance explained

(plot_var_exp(var_exp_dt))

Version Author Date
24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

GSEA for factors

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')
}

go_bp

Version Author Date
5a1c0c2 wmacnair 2022-01-06
7fb1b95 wmacnair 2021-11-25

hallmark

Version Author Date
5a1c0c2 wmacnair 2022-01-06
7fb1b95 wmacnair 2021-11-25

Outputs

Top filter genes

# merge filtered and weights
xls_dt    = calc_xls_dt(model, filtered_dt)

# save outputs
write_xlsx(list(mofa_weights = xls_dt), path = interesting_f)

Figures

Illustrative example

for (g in example_gs) {
  cat('### ', str_extract(g, '^[^_]+'), '\n', sep = '')
  suppressWarnings(print(plot_ranef_example(pb, example_cl, g)))
  cat('\n\n')
}

NHLH1

Version Author Date
24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

CASP7

Version Author Date
24d709c wmacnair 2022-01-18
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RELN

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KLB

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NRTN

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24d709c wmacnair 2022-01-18
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EVI5L

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24d709c wmacnair 2022-01-18
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PWP2

Version Author Date
24d709c wmacnair 2022-01-18
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GRID2

Version Author Date
24d709c wmacnair 2022-01-18
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MET

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24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Selection of interesting genes

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")
}

fc_vs_sd_all

Version Author Date
5f3bbe9 wmacnair 2022-01-19

fc_vs_sd_signif

Version Author Date
5f3bbe9 wmacnair 2022-01-19

ms_p_vs_lrt_p

Version Author Date
5f3bbe9 wmacnair 2022-01-19

muscat results vs SD, MAGMA hits only

magma_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))

Version Author Date
24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

muscat results vs LoFs

(plot_muscat_and_sd_vs_lof(res_dt, sd_dt, lof_dt, sel_cl))

Version Author Date
24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Expression heatmaps

Some notes:

  • pca has both rows and columns ordered in a sensible data-driven way.
  • clustered has the rows clustered by hierarchical clustering, and the columns the same as pca.
  • three_per_patient is the same as clustered but only showing patients where we have >=3 samples.
  • by_lesion has the rows ordered by lesion type, and the columns ordered by MS logFC (hopefully this shows the horseshoe a bit).
  • FactorX has the rows ordered by each sample’s factor score, and the columns ordered by each gene’s factor weight; I also exclude genes with small weights for that factor.
  • is_shared on top of the heatmap indicates whether a gene is unique to the celltype, or was also selected for another celltype.
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")
}

pca

Version Author Date
24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

clustered

three_per_patient

by_lesion

Factor1

Factor2

Factor3

Factor4

Factor5

MOFA+ factors - diagnosis

(plot_factors_univariate(model, annots_dt, pb, by = 'diagnosis'))

Version Author Date
24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

MOFA+ factors - lesions

(plot_factors_univariate(model, annots_dt, pb, by = 'lesion_type'))

Version Author Date
24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Factor 1 vs Factor 2

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')
}

diagnosis

Version Author Date
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lesion_type

Version Author Date
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subject_id

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24d709c wmacnair 2022-01-18
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Interactions between factors and model components

(plot_factor_r2s(r2_dt, var_exp_dt))

Version Author Date
24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Does metadata explain factors?

(plot_factor_anovas(anova_dt))

Version Author Date
2372e65 wmacnair 2022-01-18

GO terms for factors

print(plot_mofa_gsea_dotplot(gsea_list[['go_bp']], labels_dt, 
  fgsea_cut = fgsea_cut, n_total = 50))

Version Author Date
5a1c0c2 wmacnair 2022-01-06
7fb1b95 wmacnair 2021-11-25

Top genes for Factor 1

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) )

Version Author Date
24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Top genes for Factor 2

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 = 10) )

Version Author Date
24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Top genes for Factor 3

draw( plot_top_genes_expression(model, pb, annots_dt, 
  filter_dt, tfs_dt, var_exp_dt, 
  sel_f = 'Factor3', min_var = 5, min_w = 0.2, n_top = 10) )

Version Author Date
24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Top genes for Factor 4

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 = 20) )

Version Author Date
24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

Top genes for Factor 5

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) )

Version Author Date
24d709c wmacnair 2022-01-18
7fb1b95 wmacnair 2021-11-25

3D plots under duress

combn1

plot_factors_3d(model, annots_dt, sel_fs = c('Factor1', 'Factor2', 'Factor3'), 
  annot_v = 'oligo_grp')

combn2

plot_factors_3d(model, annots_dt, sel_fs = c('Factor1', 'Factor5', 'Factor3'), 
  annot_v = 'oligo_grp')

combn3

plot_factors_3d(model, annots_dt, sel_fs = c('Factor2', 'Factor5', 'Factor3'), 
  annot_v = 'oligo_grp')

combn4

plot_factors_3d(model, annots_dt, sel_fs = c('Factor1', 'Factor2', 'Factor5'), 
  annot_v = 'oligo_grp')

Barplots of celltype proportions ordered by factors

conos_dt    = load_labelled_dt(labelled_f, labels_f)
types       = c('OPCs / COPs', 'Oligodendrocytes')
m           = "WM"
oligos_dt   = conos_dt[ type_broad %in% types & str_detect(sample_id, "WM") ] %>%
  .[, N_sample  := .N, by = sample_id] %>%
  .[, .N, by = .(sample_id, N_sample, type_broad, type_fine)] %>%
  .[, prop      := N / sum(N), by = .(sample_id, type_broad)] %>%
  .[, type_fine := fct_relevel(type_fine, 'OPC')]
for (sel_f in factors_names(model)) {
  cat('### ', sel_f, '\n', sep = '')
  print(plot_barplots_ordered_by_factors(oligos_dt, model, sel_f))
  cat('\n\n')
}

Factor1

Version Author Date
5a1c0c2 wmacnair 2022-01-06
7fb1b95 wmacnair 2021-11-25

Factor2

Version Author Date
5a1c0c2 wmacnair 2022-01-06
7fb1b95 wmacnair 2021-11-25

Factor3

Version Author Date
5a1c0c2 wmacnair 2022-01-06
7fb1b95 wmacnair 2021-11-25

Factor4

Version Author Date
5a1c0c2 wmacnair 2022-01-06
7fb1b95 wmacnair 2021-11-25

Factor5

Version Author Date
5a1c0c2 wmacnair 2022-01-06
7fb1b95 wmacnair 2021-11-25
devtools::session_info()
- Session info ---------------------------------------------------------------
 setting  value                       
 version  R version 4.0.5 (2021-03-31)
 os       CentOS Linux 7 (Core)       
 system   x86_64, linux-gnu           
 ui       X11                         
 language (EN)                        
 collate  en_US.UTF-8                 
 ctype    C                           
 tz       Europe/Zurich               
 date     2022-01-18                  

- Packages -------------------------------------------------------------------
 package              * version    date       lib
 ade4                   1.7-18     2021-09-16 [1]
 ANCOMBC              * 1.0.5      2021-03-09 [1]
 annotate               1.68.0     2020-10-27 [1]
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 assertthat           * 0.2.1      2019-03-21 [2]
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 blme                   1.0-5      2021-01-05 [1]
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 bslib                  0.3.1      2021-10-06 [2]
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 CRAN (R 4.0.5)                    
 CRAN (R 4.0.1)                    
 CRAN (R 4.0.3)                    
 CRAN (R 4.0.3)                    
 CRAN (R 4.0.3)                    
 CRAN (R 4.0.3)                    
 CRAN (R 4.0.5)                    
 CRAN (R 4.0.5)                    
 CRAN (R 4.0.0)                    
 CRAN (R 4.0.0)                    
 Bioconductor                      
 CRAN (R 4.0.3)                    
 Bioconductor                      

[1] /pstore/home/macnairw/lib/conda_r3.12
[2] /pstore/home/macnairw/.conda/envs/r_4.0.3/lib/R/library

sessionInfo()
R version 4.0.5 (2021-03-31)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /pstore/home/macnairw/.conda/envs/r_4.0.3/lib/libopenblasp-r0.3.12.so

locale:
 [1] LC_CTYPE=C                 LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
 [1] grid      parallel  stats4    stats     graphics  grDevices utils    
 [8] datasets  methods   base     

other attached packages:
 [1] rgl_0.107.14                MOFA2_1.0.1                
 [3] rmarkdown_2.11              writexl_1.4.0              
 [5] ComplexHeatmap_2.6.2        fgsea_1.16.0               
 [7] tictoc_1.0.1                performance_0.8.0          
 [9] edgeR_3.32.1                limma_3.46.0               
[11] reshape2_1.4.4              scater_1.18.6              
[13] Matrix.utils_0.9.8          Matrix_1.3-4               
[15] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
[17] Biobase_2.50.0              MatrixGenerics_1.2.1       
[19] matrixStats_0.61.0          seriation_1.3.1            
[21] UpSetR_1.4.0                BiocParallel_1.24.1        
[23] muscat_1.5.1                dplyr_1.0.7                
[25] readr_2.0.2                 tidyr_1.1.4                
[27] tibble_3.1.5                tidyverse_1.3.1            
[29] rtracklayer_1.50.0          GenomicRanges_1.42.0       
[31] GenomeInfoDb_1.26.7         IRanges_2.24.1             
[33] S4Vectors_0.28.1            BiocGenerics_0.36.1        
[35] ggbeeswarm_0.6.0            ggrepel_0.9.1              
[37] reticulate_1.22             MASS_7.3-54                
[39] phyloseq_1.34.0             ANCOMBC_1.0.5              
[41] purrr_0.3.4                 patchwork_1.1.1            
[43] readxl_1.3.1                forcats_0.5.1              
[45] ggplot2_3.3.5               scales_1.1.1               
[47] viridis_0.6.2               viridisLite_0.4.0          
[49] assertthat_0.2.1            stringr_1.4.0              
[51] data.table_1.14.2           magrittr_2.0.1             
[53] circlize_0.4.13             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] rappdirs_0.3.3            R.methodsS3_1.8.1        
  [3] bit64_4.0.5               knitr_1.36               
  [5] R.utils_2.11.0            irlba_2.3.3              
  [7] DelayedArray_0.16.3       RCurl_1.98-1.5           
  [9] doParallel_1.0.16         generics_0.1.1           
 [11] callr_3.7.0               cowplot_1.1.1            
 [13] microbiome_1.12.0         usethis_2.1.2            
 [15] RSQLite_2.2.8             future_1.22.1            
 [17] bit_4.0.4                 tzdb_0.1.2               
 [19] xml2_1.3.2                lubridate_1.8.0          
 [21] httpuv_1.6.3              xfun_0.27                
 [23] hms_1.1.1                 jquerylib_0.1.4          
 [25] TSP_1.1-11                evaluate_0.14            
 [27] promises_1.2.0.1          fansi_0.5.0              
 [29] progress_1.2.2            caTools_1.18.2           
 [31] dbplyr_2.1.1              htmlwidgets_1.5.4        
 [33] igraph_1.2.7              DBI_1.1.1                
 [35] geneplotter_1.68.0        ellipsis_0.3.2           
 [37] corrplot_0.90             backports_1.2.1          
 [39] insight_0.14.5            permute_0.9-5            
 [41] annotate_1.68.0           sparseMatrixStats_1.2.1  
 [43] vctrs_0.3.8               remotes_2.4.1            
 [45] here_1.0.1                Cairo_1.5-12.2           
 [47] cachem_1.0.6              withr_2.4.2              
 [49] grr_0.9.5                 sctransform_0.3.2        
 [51] vegan_2.5-7               GenomicAlignments_1.26.0 
 [53] prettyunits_1.1.1         cluster_2.1.2            
 [55] ape_5.5                   crayon_1.4.1             
 [57] basilisk.utils_1.2.2      genefilter_1.72.1        
 [59] labeling_0.4.2            pkgconfig_2.0.3          
 [61] pkgload_1.2.3             nlme_3.1-153             
 [63] vipor_0.4.5               devtools_2.4.2           
 [65] blme_1.0-5                rlang_0.4.12             
 [67] globals_0.14.0            lifecycle_1.0.1          
 [69] filelock_1.0.2            registry_0.5-1           
 [71] modelr_0.1.8              rsvd_1.0.5               
 [73] cellranger_1.1.0          rprojroot_2.0.2          
 [75] Rhdf5lib_1.12.1           boot_1.3-28              
 [77] reprex_2.0.1              beeswarm_0.4.0           
 [79] processx_3.5.2            pheatmap_1.0.12          
 [81] whisker_0.4               GlobalOptions_0.1.2      
 [83] png_0.1-7                 rjson_0.2.20             
 [85] bitops_1.0-7              R.oo_1.24.0              
 [87] KernSmooth_2.23-20        rhdf5filters_1.2.1       
 [89] Biostrings_2.58.0         blob_1.2.2               
 [91] DelayedMatrixStats_1.12.3 shape_1.4.6              
 [93] parallelly_1.28.1         beachmat_2.6.4           
 [95] memoise_2.0.0             plyr_1.8.6               
 [97] gplots_3.1.1              zlibbioc_1.36.0          
 [99] compiler_4.0.5            clue_0.3-60              
[101] lme4_1.1-27.1             DESeq2_1.30.1            
[103] snakecase_0.11.0          Rsamtools_2.6.0          
[105] cli_3.0.1                 ade4_1.7-18              
[107] XVector_0.30.0            lmerTest_3.1-3           
[109] listenv_0.8.0             ps_1.6.0                 
[111] TMB_1.7.22                mgcv_1.8-38              
[113] tidyselect_1.1.1          stringi_1.7.4            
[115] highr_0.9                 yaml_2.2.1               
[117] BiocSingular_1.6.0        locfit_1.5-9.4           
[119] sass_0.4.0                fastmatch_1.1-3          
[121] tools_4.0.5               future.apply_1.8.1       
[123] rstudioapi_0.13           foreach_1.5.1            
[125] git2r_0.28.0              janitor_2.1.0            
[127] gridExtra_2.3             farver_2.1.0             
[129] Rtsne_0.15                digest_0.6.28            
[131] BiocManager_1.30.16       Rcpp_1.0.7               
[133] broom_0.7.9               scuttle_1.0.4            
[135] later_1.3.0               httr_1.4.2               
[137] AnnotationDbi_1.52.0      Rdpack_2.1.2             
[139] colorspace_2.0-2          rvest_1.0.2              
[141] XML_3.99-0.8              fs_1.5.0                 
[143] splines_4.0.5             uwot_0.1.10              
[145] basilisk_1.2.1            multtest_2.46.0          
[147] sessioninfo_1.1.1         xtable_1.8-4             
[149] jsonlite_1.7.2            nloptr_1.2.2.2           
[151] testthat_3.1.0            R6_2.5.1                 
[153] pillar_1.6.4              htmltools_0.5.2          
[155] glue_1.4.2                fastmap_1.1.0            
[157] minqa_1.2.4               BiocNeighbors_1.8.2      
[159] codetools_0.2-18          pkgbuild_1.2.0           
[161] utf8_1.2.2                lattice_0.20-45          
[163] bslib_0.3.1               numDeriv_2016.8-1.1      
[165] pbkrtest_0.5.1            colorRamps_2.3           
[167] gtools_3.9.2              magick_2.7.3             
[169] survival_3.2-13           glmmTMB_1.1.2.3          
[171] desc_1.4.0                biomformat_1.18.0        
[173] munsell_0.5.0             GetoptLong_1.0.5         
[175] rhdf5_2.34.0              GenomeInfoDbData_1.2.4   
[177] iterators_1.0.13          HDF5Array_1.18.1         
[179] variancePartition_1.20.0  haven_2.4.3              
[181] gtable_0.3.0              rbibutils_2.2.4