Last updated: 2021-12-08

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Knit directory: MS_lesions/

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File Version Author Date Message
Rmd aa64e51 wmacnair 2021-12-06 Add comparison of abundances with Rowitch clusters
html aa64e51 wmacnair 2021-12-06 Add comparison of abundances with Rowitch clusters

Setup / definitions

Libraries

Helper functions

source('code/ms00_utils.R')
source('code/ms04_conos.R')
source('code/ms07_soup.R')
source('code/ms09_ancombc_mixed.R')

source('code/supp09_ancombc_rowitch.R')

Inputs

# define run
labels_f    = 'data/byhand_markers/validation_markers_2021-05-31.csv'
labelled_f  = 'output/ms13_labelling/conos_labelled_2021-05-31.txt.gz'
meta_f      = "data/metadata/metadata_checked_assumptions_2021-10-08.xlsx"

# define pseudobulk data
soup_dir    = 'output/ms07_soup'
pb_broad_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')
pb_f_ls     = c(broad = pb_broad_f, fine = pb_fine_f)
rowitch_f   = "data/rowitch/cross_study_neuron_labelling_2021-12-03.xlsx"

Outputs

# where to save?
save_dir    = 'output/ms09_ancombc'
date_tag    = '2021-12-07'
if (!dir.exists(save_dir))
    dir.create(save_dir)

# sample variables
sample_vars = c('sample_id', 'matter', 'lesion_type', 
  'neuro_ok', 'neuro_prop', 'sample_source', 'subject_id', 
  'sex', 'age_scale', 'pmi_cat', 'pmi_cat2')

# identifying strange samples
neuro_mad_cut = 2
log_n_mad_cut = 3

# define how to select PCs
cut_var_exp   = 0.01
cut_layer_cor = 0.2

# define how to select PCs
gm_pc_spec  = list(
  name_str    = 'lesions_GM_',
  subset      = list(matter = 'GM', neuro_ok = TRUE),
  size        = list(min_count = 10, min_prop = 0.1),
  exc_regex   = NULL,
  formula_pat = '~ lesion_type + %s + sex + age_scale + pmi_cat2',
  fixef_test  = 'lesion_type',
  fixef_covar = c('sex', 'age_scale', 'pmi_cat2'),
  ranef_var   = 'subject_id',
  broad_sel   = c("Excitatory neurons", "Inhibitory neurons"),
  lesion_ctrl = "GM",
  n_pcs       = NA
)
sel_pcs     = 4
ancom_pat   = file.path(save_dir, 'ancombc_rowitch_standard_%s_%s.rds')
boots_pat   = file.path(save_dir, 'ancombc_rowitch_bootstrap_%s_%s.txt.gz')

# bootstrapping parameters
n_boots     = 1e4
n_cores     = 16

Load inputs

meta_dt     = load_meta_dt_from_xls(meta_f)
labels_dt   = load_names_dt(labels_f) %>%
  .[, cluster_id := type_fine]
conos_dt    = load_labelled_dt(labelled_f, labels_f) %>%
  merge(meta_dt, by = 'sample_id') %>%
  add_neuro_props(mad_cut = neuro_mad_cut)
# load other groupings
rowitch_dt  = rowitch_f %>% read_excel(sheet = "Sheet1") %>% 
  as.data.table %>% 
  .[, .(type_broad, type_fine, 
    rowitch_guess = factor(rowitch_guess), 
    anna_group    = factor(anna_protein_group)
  )]

Processing / calculations

# check for any outliers
size_chks   = calc_size_outliers(conos_dt, mad_cut = log_n_mad_cut)
message("these samples excluded to outlier sample sizes:")
these samples excluded to outlier sample sizes:
print(size_chks[ size_ok == FALSE ])
   matter sample_id   N med_log_N mad_log_N size_ok
1:     GM     EU034 911  8.519391  0.423111   FALSE
2:     GM     EU043 687  8.519391  0.423111   FALSE
# exclude them from conos
ok_samples  = size_chks[ size_ok == TRUE ]$sample_id
conos_dt    = conos_dt[ (sample_id %in% ok_samples) & (neuro_ok == TRUE) ]
# calc neuronal proportions
props_neu   = conos_dt %>%
  .[ (type_broad %in% gm_pc_spec$broad_sel) ] %>% 
  calc_props_dt(sample_vars)

# calc PCAs
ctrl_pcs_dt = props_neu %>% 
  .[ lesion_type == gm_pc_spec$lesion_ctrl ] %>%
  calc_ctrl_pcs_dt(layers_dt)
props_dt    = calc_props_dt(conos_dt, sample_vars)
wide_dt     = calc_counts_wide(props_dt, sample_vars)
# apply pcs
all_pcs_dt  = apply_ctrl_pcs(props_neu[ matter == "GM" ], ctrl_pcs_dt, 
  cut_var_exp, cut_layer_cor)
wide_neu    = merge(all_pcs_dt, wide_dt, 'sample_id')
pc_vars     = str_subset(names(wide_neu), "ctrl_PC")
layer_spec  = make_layer_pc_spec(gm_pc_spec, pc_vars, n_pcs = sel_pcs)
# make wide things with alternative groupings
wide_rowitch  = conos_dt %>%
  add_rowitch_labels(rowitch_dt[, .(type_fine, new_group = rowitch_guess)],
    sample_vars, all_pcs_dt)
wide_anna  = conos_dt %>%
  add_rowitch_labels(rowitch_dt[, .(type_fine, new_group = anna_group)],
    sample_vars, all_pcs_dt)

# put into nice list
wide_ls   = list(
  type_fine     = wide_neu, 
  rowitch_guess = wide_rowitch, 
  anna_group    = wide_anna
  )
# loop through specified models
for (nn in names(wide_ls)) {
  # make file
  ancom_f   = sprintf(ancom_pat, nn, date_tag)
  
  # if necessary, run thing
  if (!file.exists(ancom_f)) {
    # do bootstrapping, save results
    message('running standard ANCOM-BC for ', nn)
    ancom_neu = calc_ancom_standard(wide_ls[[ nn ]], c(sample_vars, pc_vars),
      layer_spec$subset, layer_spec$size, layer_spec$exc_regex, 
      layer_spec$inc_regex, layer_spec$ref_type,
      layer_spec$fixef_test, layer_spec$fixef_covar)
    saveRDS(ancom_neu, file = ancom_f)
  }
}
for (nn in names(wide_ls)) {
  # make file
  boots_f   = sprintf(boots_pat, nn, date_tag)

  # if necessary, run thing
  if (file.exists(boots_f)) {
    message('bootstrapped ANCOM-BC for ', nn, ' already done')
  } else {
    # do bootstrapping, save resulst
    message('running bootstrapped ANCOM-BC for ', nn)
    t_start    = Sys.time()
    boots_neu = calc_ancom_bootstrap(wide_ls[[ nn ]], c(sample_vars, pc_vars),
      layer_spec$subset, layer_spec$size, layer_spec$exc_regex, 
      layer_spec$inc_regex, layer_spec$ref_type,
      layer_spec$fixef_test, layer_spec$fixef_covar, layer_spec$ranef_var, 
      seed = 1, n_boots, n_cores)
    t_stop    = Sys.time()
    fwrite(boots_neu, file = boots_f)

    # report how long it took
    t_elapsed = difftime(t_stop, t_start, units = 'mins') %>% unclass
    message(sprintf(
      paste0('  (bootstrapping %d boots with %d cores took %.1f minutes;',
        ' %.1f boots / min / core)'), 
      n_boots, n_cores, t_elapsed, n_boots / t_elapsed / n_cores))
  }
}
bootstrapped ANCOM-BC for type_fine already done
bootstrapped ANCOM-BC for rowitch_guess already done
bootstrapped ANCOM-BC for anna_group already done
# load std
ancom_ls    = lapply(names(wide_ls), function(nn) {
  # make file
  ancom_obj   = sprintf(ancom_pat, nn, date_tag) %>%
    readRDS
  
  return(ancom_obj)
  }) %>% setNames(names(wide_ls))

# load boots
boots_ls    = lapply(names(wide_ls), function(nn) {
  # make file
  boots_dt    = sprintf(boots_pat, nn, date_tag) %>% fread
  
  return(boots_dt)
  }) %>% setNames(names(wide_ls))

Analysis

Abundances and proportions, split by different groupings

conos_gm  = conos_dt[ matter == "GM" ] %>%
  .[, lesion_type := fct_drop(lesion_type) ]
sel_broad = c("Excitatory neurons", "Inhibitory neurons")
for (v in c('type_fine', 'rowitch_guess', 'anna_group')) {
  cat('### ', v, '\n')
  print(plot_alternative_celltypes(conos_gm, sel_broad, group_var = v))
  cat('\n\n')  
}

ANCOM-BC standard results, lesions only

for (nn in names(ancom_ls)) {
  cat('### ', nn, '\n')
  print(plot_ancombc_ci(ancom_ls[[nn]], 
    coef_filter = "lesion_type", q_cut = 0.05))
  cat('\n\n')  
}

type_fine

rowitch_guess

anna_group

ANCOM-BC bootstrap results, lesions only

for (nn in names(boots_ls)) {
  cat('### ', nn, '\n')
  print(plot_boots_dt(boots_ls[[nn]], 
    coef_filter = "lesion_type", min_effect = 0.2))
  cat('\n\n')  
}

type_fine

rowitch_guess

anna_group

Outputs

devtools::session_info()
Registered S3 method overwritten by 'cli':
  method     from         
  print.boxx spatstat.geom
- 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     2021-12-08                  

- Packages -------------------------------------------------------------------
 ! package              * version    date       lib
   abind                  1.4-5      2016-07-21 [2]
   ade4                   1.7-18     2021-09-16 [1]
   ANCOMBC              * 1.0.5      2021-03-09 [1]
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   lazyeval               0.2.2      2019-03-15 [2]
 R leiden                 0.3.8      <NA>       [2]
   leidenAlg              0.1.1      2021-03-03 [1]
   lifecycle              1.0.1      2021-09-24 [2]
   limma                * 3.46.0     2020-10-27 [1]
   listenv                0.8.0      2019-12-05 [2]
   lme4                   1.1-27.1   2021-06-22 [1]
   lmerTest               3.1-3      2020-10-23 [1]
   lmtest                 0.9-38     2020-09-09 [2]
   locfit                 1.5-9.4    2020-03-25 [1]
   lubridate              1.8.0      2021-10-07 [2]
   magrittr             * 2.0.1      2020-11-17 [1]
   MASS                 * 7.3-54     2021-05-03 [2]
   Matrix               * 1.3-4      2021-06-01 [2]
   Matrix.utils           0.9.8      2020-02-26 [1]
   MatrixGenerics       * 1.2.1      2021-01-30 [1]
   matrixStats          * 0.61.0     2021-09-17 [1]
   memoise                2.0.0      2021-01-26 [1]
   mgcv                   1.8-38     2021-10-06 [1]
   microbiome             1.12.0     2020-10-27 [1]
   mime                   0.12       2021-09-28 [1]
   miniUI                 0.1.1.1    2018-05-18 [2]
   minqa                  1.2.4      2014-10-09 [1]
   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                   3.1-153    2021-09-07 [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             1.28.1     2021-09-09 [2]
   patchwork            * 1.1.1      2020-12-17 [2]
   pbapply                1.5-0      2021-09-16 [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                 1.6.4      2021-10-18 [1]
   pkgbuild               1.2.0      2020-12-15 [1]
   pkgconfig              2.0.3      2019-09-22 [2]
   pkgload                1.2.3      2021-10-13 [2]
   plotly                 4.10.0     2021-10-09 [2]
   plyr                   1.8.6      2020-03-03 [2]
   png                    0.1-7      2013-12-03 [2]
   polyclip               1.10-0     2019-03-14 [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                2.11.0     2021-09-26 [1]
   R6                     2.5.1      2021-08-19 [2]
   RANN                   2.6.1      2019-01-08 [2]
   rbibutils              2.2.4      2021-10-11 [1]
   RColorBrewer         * 1.1-2      2014-12-07 [2]
   Rcpp                   1.0.7      2021-07-07 [1]
   RcppAnnoy              0.0.19     2021-07-30 [1]
   RCurl                  1.98-1.5   2021-09-17 [1]
   Rdpack                 2.1.2      2021-06-01 [1]
   readxl               * 1.3.1      2019-03-13 [2]
   registry               0.5-1      2019-03-05 [1]
   remotes                2.4.1      2021-09-29 [1]
   reshape2               1.4.4      2020-04-09 [2]
   reticulate           * 1.22       2021-09-17 [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                  0.4.12     2021-10-18 [2]
   rmarkdown              2.11       2021-09-14 [1]
   ROCR                   1.0-11     2020-05-02 [2]
   rpart                  4.1-15     2019-04-12 [2]
   rprojroot              2.0.2      2020-11-15 [2]
   RSQLite                2.2.8      2021-08-21 [1]
   rsvd                   1.0.5      2021-04-16 [1]
   Rtsne                  0.15       2018-11-10 [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]
   scattermore            0.7        2020-11-24 [2]
   sccore                 1.0.0      2021-10-07 [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            * 1.3.1      2021-10-16 [1]
   sessioninfo            1.1.1      2018-11-05 [1]
   Seurat               * 4.0.5      2021-10-17 [2]
   SeuratObject         * 4.0.2      2021-06-09 [2]
   shape                  1.4.6      2021-05-19 [1]
   shiny                  1.7.1      2021-10-02 [2]
   SingleCellExperiment * 1.12.0     2020-10-27 [1]
   snakecase              0.11.0     2019-05-25 [1]
   sparseMatrixStats      1.2.1      2021-02-02 [1]
   spatstat.core          2.3-0      2021-07-16 [2]
   spatstat.data          2.1-0      2021-03-21 [2]
   spatstat.geom          2.3-0      2021-10-09 [2]
   spatstat.sparse        2.0-0      2021-03-16 [2]
   spatstat.utils         2.2-0      2021-06-14 [2]
   statmod                1.4.36     2021-05-10 [1]
   stringi                1.7.4      2021-08-25 [1]
   stringr              * 1.4.0      2019-02-10 [2]
   SummarizedExperiment * 1.20.0     2020-10-27 [1]
   survival               3.2-13     2021-08-24 [2]
   tensor                 1.5        2012-05-05 [2]
   testthat               3.1.0      2021-10-04 [2]
   tibble                 3.1.5      2021-09-30 [1]
   tidyr                  1.1.4      2021-09-27 [2]
   tidyselect             1.1.1      2021-04-30 [2]
   TMB                    1.7.22     2021-09-28 [1]
   TSP                    1.1-11     2021-10-06 [1]
   usethis                2.1.2      2021-10-25 [1]
   utf8                   1.2.2      2021-07-24 [1]
   uwot                 * 0.1.10     2020-12-15 [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              * 0.6.2      2021-10-13 [1]
   viridisLite          * 0.4.0      2021-04-13 [1]
   whisker                0.4        2019-08-28 [1]
   withr                  2.4.2      2021-04-18 [2]
   workflowr            * 1.6.2      2020-04-30 [1]
   xfun                   0.27       2021-10-18 [1]
   XML                    3.99-0.8   2021-09-17 [1]
   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]
   zoo                    1.8-9      2021-03-09 [2]
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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

 R -- Package was removed from disk.

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] parallel  stats4    grid      stats     graphics  grDevices utils    
 [8] datasets  methods   base     

other attached packages:
 [1] ggbeeswarm_0.6.0            ggrepel_0.9.1              
 [3] reticulate_1.22             MASS_7.3-54                
 [5] phyloseq_1.34.0             ANCOMBC_1.0.5              
 [7] ica_1.0-2                   purrr_0.3.4                
 [9] nnls_1.4                    muscat_1.5.1               
[11] DropletUtils_1.10.3         edgeR_3.32.1               
[13] limma_3.46.0                googlesheets_0.3.0         
[15] scran_1.18.7                uwot_0.1.10                
[17] scater_1.18.6               SingleCellExperiment_1.12.0
[19] SummarizedExperiment_1.20.0 Biobase_2.50.0             
[21] GenomicRanges_1.42.0        GenomeInfoDb_1.26.7        
[23] IRanges_2.24.1              S4Vectors_0.28.1           
[25] BiocGenerics_0.36.1         MatrixGenerics_1.2.1       
[27] matrixStats_0.61.0          BiocParallel_1.24.1        
[29] ggplot.multistats_1.0.0     patchwork_1.1.1            
[31] seriation_1.3.1             ComplexHeatmap_2.6.2       
[33] SeuratObject_4.0.2          Seurat_4.0.5               
[35] conos_1.4.3                 igraph_1.2.7               
[37] Matrix_1.3-4                readxl_1.3.1               
[39] forcats_0.5.1               ggplot2_3.3.5              
[41] scales_1.1.1                viridis_0.6.2              
[43] viridisLite_0.4.0           assertthat_0.2.1           
[45] stringr_1.4.0               data.table_1.14.2          
[47] magrittr_2.0.1              circlize_0.4.13            
[49] RColorBrewer_1.1-2          BiocStyle_2.18.1           
[51] colorout_1.2-2              workflowr_1.6.2            

loaded via a namespace (and not attached):
  [1] rsvd_1.0.5                ps_1.6.0                 
  [3] foreach_1.5.1             lmtest_0.9-38            
  [5] rprojroot_2.0.2           crayon_1.4.1             
  [7] spatstat.core_2.3-0       rbibutils_2.2.4          
  [9] rhdf5filters_1.2.1        Matrix.utils_0.9.8       
 [11] nlme_3.1-153              backports_1.2.1          
 [13] rlang_0.4.12              XVector_0.30.0           
 [15] ROCR_1.0-11               microbiome_1.12.0        
 [17] irlba_2.3.3               callr_3.7.0              
 [19] nloptr_1.2.2.2            rjson_0.2.20             
 [21] bit64_4.0.5               glue_1.4.2               
 [23] sctransform_0.3.2         processx_3.5.2           
 [25] pbkrtest_0.5.1            vipor_0.4.5              
 [27] spatstat.sparse_2.0-0     AnnotationDbi_1.52.0     
 [29] spatstat.geom_2.3-0       tidyselect_1.1.1         
 [31] usethis_2.1.2             fitdistrplus_1.1-6       
 [33] variancePartition_1.20.0  XML_3.99-0.8             
 [35] tidyr_1.1.4               zoo_1.8-9                
 [37] xtable_1.8-4              evaluate_0.14            
 [39] cli_3.0.1                 Rdpack_2.1.2             
 [41] scuttle_1.0.4             zlibbioc_1.36.0          
 [43] miniUI_0.1.1.1            whisker_0.4              
 [45] bslib_0.3.1               rpart_4.1-15             
 [47] shiny_1.7.1               BiocSingular_1.6.0       
 [49] xfun_0.27                 clue_0.3-60              
 [51] pkgbuild_1.2.0            multtest_2.46.0          
 [53] cluster_2.1.2             caTools_1.18.2           
 [55] TSP_1.1-11                biomformat_1.18.0        
 [57] tibble_3.1.5              ape_5.5                  
 [59] listenv_0.8.0             Biostrings_2.58.0        
 [61] png_0.1-7                 permute_0.9-5            
 [63] future_1.22.1             withr_2.4.2              
 [65] bitops_1.0-7              plyr_1.8.6               
 [67] cellranger_1.1.0          dqrng_0.3.0              
 [69] pillar_1.6.4              gplots_3.1.1             
 [71] GlobalOptions_0.1.2       cachem_1.0.6             
 [73] fs_1.5.0                  GetoptLong_1.0.5         
 [75] DelayedMatrixStats_1.12.3 vctrs_0.3.8              
 [77] ellipsis_0.3.2            generics_0.1.1           
 [79] devtools_2.4.2            tools_4.0.5              
 [81] beeswarm_0.4.0            munsell_0.5.0            
 [83] DelayedArray_0.16.3       pkgload_1.2.3            
 [85] fastmap_1.1.0             compiler_4.0.5           
 [87] abind_1.4-5               httpuv_1.6.3             
 [89] sessioninfo_1.1.1         plotly_4.10.0            
 [91] GenomeInfoDbData_1.2.4    gridExtra_2.3            
 [93] glmmTMB_1.1.2.3           lattice_0.20-45          
 [95] deldir_1.0-6              utf8_1.2.2               
 [97] later_1.3.0               dplyr_1.0.7              
 [99] jsonlite_1.7.2            pbapply_1.5-0            
[101] sparseMatrixStats_1.2.1   genefilter_1.72.1        
[103] lazyeval_0.2.2            promises_1.2.0.1         
[105] doParallel_1.0.16         R.utils_2.11.0           
[107] goftest_1.2-3             spatstat.utils_2.2-0     
[109] rmarkdown_2.11            cowplot_1.1.1            
[111] blme_1.0-5                statmod_1.4.36           
[113] Rtsne_0.15                HDF5Array_1.18.1         
[115] survival_3.2-13           numDeriv_2016.8-1.1      
[117] yaml_2.2.1                htmltools_0.5.2          
[119] memoise_2.0.0             locfit_1.5-9.4           
[121] digest_0.6.28             mime_0.12                
[123] registry_0.5-1            RSQLite_2.2.8            
[125] future.apply_1.8.1        remotes_2.4.1            
[127] blob_1.2.2                vegan_2.5-7              
[129] R.oo_1.24.0               splines_4.0.5            
[131] Rhdf5lib_1.12.1           Cairo_1.5-12.2           
[133] RCurl_1.98-1.5            broom_0.7.9              
[135] hms_1.1.1                 rhdf5_2.34.0             
[137] colorspace_2.0-2          BiocManager_1.30.16      
[139] shape_1.4.6               sass_0.4.0               
[141] Rcpp_1.0.7                RANN_2.6.1               
[143] fansi_0.5.0               parallelly_1.28.1        
[145] R6_2.5.1                  ggridges_0.5.3           
[147] lifecycle_1.0.1           bluster_1.0.0            
[149] minqa_1.2.4               testthat_3.1.0           
[151] leiden_0.3.8              jquerylib_0.1.4          
[153] snakecase_0.11.0          desc_1.4.0               
[155] RcppAnnoy_0.0.19          iterators_1.0.13         
[157] TMB_1.7.22                htmlwidgets_1.5.4        
[159] beachmat_2.6.4            polyclip_1.10-0          
[161] mgcv_1.8-38               globals_0.14.0           
[163] leidenAlg_0.1.1           codetools_0.2-18         
[165] lubridate_1.8.0           gtools_3.9.2             
[167] prettyunits_1.1.1         R.methodsS3_1.8.1        
[169] gtable_0.3.0              DBI_1.1.1                
[171] git2r_0.28.0              tensor_1.5               
[173] httr_1.4.2                highr_0.9                
[175] KernSmooth_2.23-20        stringi_1.7.4            
[177] progress_1.2.2            reshape2_1.4.4           
[179] farver_2.1.0              annotate_1.68.0          
[181] hexbin_1.28.2             colorRamps_2.3           
[183] sccore_1.0.0              boot_1.3-28              
[185] grr_0.9.5                 BiocNeighbors_1.8.2      
[187] lme4_1.1-27.1             ade4_1.7-18              
[189] geneplotter_1.68.0        scattermore_0.7          
[191] DESeq2_1.30.1             bit_4.0.4                
[193] spatstat.data_2.1-0       janitor_2.1.0            
[195] pkgconfig_2.0.3           lmerTest_3.1-3           
[197] knitr_1.36