Last updated: 2021-05-06

Checks: 4 3

Knit directory: MS_lesions/

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  • plot_paga_all_wm_gm
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  • plot_paga_olg_wm_gm
  • setup_input
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File Version Author Date Message
Rmd eef8a1c Macnair 2021-04-29 Minor tweaks to allow rerunning on Roche servers
Rmd 129c53d Macnair 2021-04-16 Renamed a lot of things to add ms07_soup

Setup / definitions

Libraries

Helper functions

source('code/ms00_utils.R')
source('code/ms09_ancombc.R')
source('code/ms11_paga.R')
use_condaenv('scanpy', required=TRUE)
source_python('code/ms11_paga_fns.py')

Inputs

# define inputs
graph_f     = 'output/ms04_conos/conos_graph_2021-02-11.txt'
# merged_f    = 'output/ms06_sccaf/conos_merged_dt_2021-02-13.csv'
# merged_f    = 'output/ms06_sccaf/conos_merged_dt_2021-03-11.csv'
merged_f    = 'output/ms06_sccaf/conos_merged_dt_2021-03-18.csv'
meta_f      = 'data/metadata/metadata_updated_20201127.txt'
byhand_f    = 'data/byhand_markers/Copy of Copy of Marker_selection_for_validation_MS_snucseq_30102020  Ediinburgh.xlsx - final markers for Cartana panel.csv'

Outputs

save_dir    = 'output/ms11_paga'
if (!dir.exists(save_dir))
  dir.create(save_dir)
# date_tag    = '2021-03-03'
# seed        = 20210303
# date_tag    = '2021-03-12'
date_tag    = '2021-03-18'
seed        = 20210318

# define what is strange sample in neuro %
mad_cut     = 2

subg_pat    = sprintf('%s/subgraph_%s_%s.txt.gz', save_dir, '%s', date_tag)
subset_list = list(
  all         = list(), 
  healthy_WM  = list(lesion_type = c('WM'), neuro_ok = TRUE), 
  MS_WM       = list(lesion_type = c('NAWM', 'AL', 'CAL', 'CIL', 'RL'), neuro_ok = TRUE),
  healthy_GM  = list(lesion_type = c('GM'), neuro_ok = TRUE),
  MS_GM       = list(lesion_type = c('NAGM', 'GML'), neuro_ok = TRUE)
  )

lesion_list = lapply(lesion_ord, function(l) 
  list(lesion_type = l, neuro_ok = TRUE)) %>% 
    setNames(paste0('lesion_', lesion_ord)
  )
xy_ref_list = c(rep('WM', 6), rep('GM', 3)) %>% 
  paste0('lesion_', .) %>% setNames(names(lesion_list))

# what to look at when comparing edges across samples?
sel_broad     = c('Oligodendrocytes', 'OPCs / COPs')
assert_that(all(sel_broad %in% broad_ord))
[1] TRUE

Load inputs

# load conos
conos_dt    = merged_f %>% fread %>% 
  .[, .(cell_id, sample_id, conos = conos_merge)]
labels_dt   = conos_dt[, .(conos)] %>% unique
byhand_dt   = byhand_f %>% fread %>% janitor::clean_names(.) %>%
  .[, .(
    conos       = cluster_id, 
    type_broad  = broad_celltype, 
    type_fine   = proposed_cluster_label
  )]
labels_dt   = merge(labels_dt, byhand_dt, by = 'conos', all = TRUE) %>%
  .[, type_broad  := factor(type_broad, levels = broad_ord)] %>%
  .[, type_fine   := fct_reorder(type_fine, as.integer(type_broad))]
conos_dt    = merge(conos_dt, labels_dt, by = 'conos')
meta_dt     = load_meta_dt(meta_f, outlier_samples = NULL)
conos_dt    = merge(conos_dt, meta_dt, by = 'sample_id') %>%
  add_neuro_props(mad_cut)

Processing / calculations

# make list of all samples
samples     = unique(conos_dt$sample_id)
sample_list = lapply(samples, function(s) list(sample_id = s)) %>% 
  setNames(paste0('sample_', samples))

# make subgraphs for each specification
set.seed(seed)
calc_subgraphs(conos_dt, subset_list, graph_f, subg_pat, n_sample = 1e5)
already done!
NULL
calc_subgraphs(conos_dt, lesion_list, graph_f, subg_pat, n_sample = 1e5)
already done!
NULL
calc_subgraphs(conos_dt, sample_list, graph_f, subg_pat, n_sample = 0)
already done!
NULL
# run on each sample
if (!check_all_done(sample_list, save_dir, date_tag)) {
  bpparam   = MulticoreParam(workers = 16)
  bplapply(seq_along(sample_list), function(i) {
    spec_n    = names(sample_list)[[i]]
    subg_f    = sprintf(subg_pat, spec_n)
    run_paga(save_dir, spec_n, date_tag, r_to_py(as.data.frame(conos_dt)), 
      subg_f, group_var = 'type_fine')
  }, BPPARAM = bpparam)
  bpstop()  
}

# run on each lesion type
if (!check_all_done(lesion_list, save_dir, date_tag)) {
  bpparam   = MulticoreParam(workers = length(lesion_list))
  bplapply(seq_along(lesion_list), function(i) {
    spec_n    = names(lesion_list)[[i]]
    subg_f    = sprintf(subg_pat, spec_n)
    run_paga(save_dir, spec_n, date_tag, r_to_py(as.data.frame(conos_dt)), 
      subg_f, group_var = 'type_fine')
  }, BPPARAM = bpparam)
  bpstop()  
}

# run on each subset
if (!check_all_done(subset_list, save_dir, date_tag)) {
  bpparam   = MulticoreParam(workers = length(subset_list))
  bplapply(seq_along(subset_list), function(i) {
    # define things
    spec_n    = names(subset_list)[[i]]
    subg_f    = sprintf(subg_pat, spec_n)

    # run paga
    run_paga(save_dir, spec_n, date_tag, r_to_py(as.data.frame(conos_dt)), 
      subg_f, group_var = 'type_fine')
  }, BPPARAM = bpparam)
  bpstop()
}
edges_all   = calc_edges_over_samples(sample_list, date_tag, 
  sel_broad, labels_dt)
edges_all   = merge(edges_all, meta_dt, by = 'sample_id')

Analysis

PAGA on all celltypes

Split by GM / WM + condition

for (n in names(subset_list)) {
  cat('#### ', n, '\n')
  suppressWarnings({print(plot_paga_outputs(save_dir, n, date_tag, 'all', 
    conos_dt, subset_list[[n]], labels_dt, xy_ref = 'all'))})
  cat('\n\n')
}

all

healthy_WM

MS_WM

healthy_GM

MS_GM

Split by lesion type

for (n in names(lesion_list)) {
  cat('#### ', n, '\n')
  suppressWarnings({print(plot_paga_outputs(save_dir, n, date_tag, 'all', 
    conos_dt, lesion_list[[n]], labels_dt, xy_ref = xy_ref_list[[n]]))})
  cat('\n\n')
}

lesion_WM

lesion_NAWM

lesion_AL

lesion_CAL

lesion_CIL

lesion_RL

lesion_GM

lesion_NAGM

lesion_GML

PAGA on oligo + OPC compartment

Split by GM / WM + condition

for (n in names(subset_list)) {
  cat('#### ', n, '\n')
  suppressWarnings({print(plot_paga_outputs(save_dir, n, date_tag, 'olg', 
    conos_dt, subset_list[[n]], labels_dt, xy_ref = 'all'))})
  cat('\n\n')
}

all

healthy_WM

MS_WM

healthy_GM

MS_GM

Split by lesion type

for (n in names(lesion_list)) {
  cat('#### ', n, '\n')
  suppressWarnings({print(plot_paga_outputs(save_dir, n, date_tag, 'olg', 
    conos_dt, lesion_list[[n]], labels_dt, xy_ref = xy_ref_list[[n]]))})
  cat('\n\n')
}

lesion_WM

lesion_NAWM

lesion_AL

lesion_CAL

lesion_CIL

lesion_RL

lesion_GM

lesion_NAGM

lesion_GML

Heatmap of PAGA edges across samples, within oligo + OPC compartment

cat('### Data-driven')
draw(plot_edge_heatmap(edges_all, conos_dt, split = 'data'))
cat('\n')
cat('### Data-driven, no outliers')
draw(plot_edge_heatmap(edges_all, conos_dt[neuro_ok == TRUE], split = 'data'))
cat('\n')
cat('### Data-driven, WM no outliers')
draw(plot_edge_heatmap(edges_all, conos_dt[neuro_ok == TRUE & matter == 'WM'], 
  split = 'data'))
cat('\n')
cat('### Data-driven, GM no outliers')
draw(plot_edge_heatmap(edges_all, conos_dt[neuro_ok == TRUE & matter == 'GM'], 
  split = 'data'))
cat('\n')
cat('### Split by lesion')
draw(plot_edge_heatmap(edges_all, conos_dt, split = 'lesion_type'))
cat('\n')
cat('### Split by lesion, no outliers')
draw(plot_edge_heatmap(edges_all, conos_dt[neuro_ok == TRUE], split = 'lesion_type'))
cat('\n')

Barplot of oligo proportions split by sample

for (m in c('WM', 'GM')) {
  cat('### ', m, '\n')
  print(plot_sample_splits(conos_dt[matter == m], 
    types = c('Oligodendrocytes', 'OPCs / COPs')))
  cat('\n\n')
}

WM

GM

Barplot of neuron proportions split by sample

for (m in c('WM', 'GM')) {
  cat('### ', m, '\n')
  print(plot_sample_splits(conos_dt[matter == m], 
    types = c('Excitatory neurons', 'Inhibitory neurons')))
  cat('\n\n')
}

WM

GM

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     2021-05-06                  

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 stringr        * 1.4.0    2019-02-10 [2] CRAN (R 4.0.0)                    
 survival         3.2-10   2021-03-16 [2] CRAN (R 4.0.3)                    
 testthat         3.0.2    2021-02-14 [2] CRAN (R 4.0.3)                    
 tibble           3.1.1    2021-04-18 [2] CRAN (R 4.0.3)                    
 tidyr            1.1.3    2021-03-03 [2] CRAN (R 4.0.3)                    
 tidyselect       1.1.0    2020-05-11 [2] CRAN (R 4.0.0)                    
 usethis          2.0.1    2021-02-10 [1] CRAN (R 4.0.3)                    
 utf8             1.2.1    2021-03-12 [2] CRAN (R 4.0.3)                    
 vctrs            0.3.7    2021-03-29 [2] CRAN (R 4.0.3)                    
 vegan            2.5-7    2020-11-28 [1] CRAN (R 4.0.3)                    
 viridis        * 0.6.0    2021-04-15 [1] CRAN (R 4.0.3)                    
 viridisLite    * 0.4.0    2021-04-13 [2] CRAN (R 4.0.3)                    
 whisker          0.4      2019-08-28 [1] CRAN (R 4.0.3)                    
 withr            2.4.2    2021-04-18 [2] CRAN (R 4.0.3)                    
 workflowr      * 1.6.2    2020-04-30 [1] CRAN (R 4.0.3)                    
 xfun             0.22     2021-03-11 [1] CRAN (R 4.0.3)                    
 XVector          0.30.0   2020-10-27 [1] Bioconductor                      
 yaml             2.2.1    2020-02-01 [2] CRAN (R 4.0.3)                    
 zlibbioc         1.36.0   2020-10-27 [1] 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 4.0.3 (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      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] BiocParallel_1.24.1  ComplexHeatmap_2.6.2 ggrepel_0.9.1       
 [4] reticulate_1.18      MASS_7.3-53.1        phyloseq_1.34.0     
 [7] ANCOMBC_1.0.5        purrr_0.3.4          patchwork_1.1.1     
[10] forcats_0.5.1        ggplot2_3.3.3        scales_1.1.1        
[13] viridis_0.6.0        viridisLite_0.4.0    assertthat_0.2.1    
[16] stringr_1.4.0        data.table_1.14.0    magrittr_2.0.1      
[19] circlize_0.4.12      RColorBrewer_1.1-2   BiocStyle_2.18.1    
[22] colorout_1.2-2       workflowr_1.6.2     

loaded via a namespace (and not attached):
  [1] Rtsne_0.15          colorspace_2.0-0    rjson_0.2.20       
  [4] ellipsis_0.3.1      rprojroot_2.0.2     XVector_0.30.0     
  [7] GlobalOptions_0.1.2 fs_1.5.0            rstudioapi_0.13    
 [10] clue_0.3-59         farver_2.1.0        remotes_2.3.0      
 [13] fansi_0.4.2         codetools_0.2-18    splines_4.0.3      
 [16] cachem_1.0.4        knitr_1.32          pkgload_1.2.1      
 [19] ade4_1.7-16         jsonlite_1.7.2      nloptr_1.2.2.2     
 [22] Cairo_1.5-12.2      cluster_2.1.2       png_0.1-7          
 [25] BiocManager_1.30.12 compiler_4.0.3      fastmap_1.1.0      
 [28] Matrix_1.3-2        cli_2.4.0           later_1.1.0.1      
 [31] htmltools_0.5.1.1   prettyunits_1.1.1   tools_4.0.3        
 [34] igraph_1.2.6        gtable_0.3.0        glue_1.4.2         
 [37] reshape2_1.4.4      dplyr_1.0.5         rappdirs_0.3.3     
 [40] Rcpp_1.0.6          Biobase_2.50.0      jquerylib_0.1.3    
 [43] vctrs_0.3.7         Biostrings_2.58.0   rhdf5filters_1.2.0 
 [46] multtest_2.46.0     ape_5.4-1           nlme_3.1-152       
 [49] iterators_1.0.13    xfun_0.22           ps_1.6.0           
 [52] rbibutils_2.1       testthat_3.0.2      lifecycle_1.0.0    
 [55] devtools_2.4.0      zlibbioc_1.36.0     hms_1.0.0          
 [58] promises_1.2.0.1    parallel_4.0.3      biomformat_1.18.0  
 [61] rhdf5_2.34.0        yaml_2.2.1          memoise_2.0.0      
 [64] gridExtra_2.3       sass_0.3.1          stringi_1.5.3      
 [67] highr_0.9           desc_1.3.0          S4Vectors_0.28.1   
 [70] foreach_1.5.1       permute_0.9-5       BiocGenerics_0.36.1
 [73] pkgbuild_1.2.0      shape_1.4.5         Rdpack_2.1.1       
 [76] rlang_0.4.10        pkgconfig_2.0.3     matrixStats_0.58.0 
 [79] evaluate_0.14       lattice_0.20-41     Rhdf5lib_1.12.1    
 [82] processx_3.5.1      tidyselect_1.1.0    plyr_1.8.6         
 [85] R6_2.5.0            IRanges_2.24.1      generics_0.1.0     
 [88] DBI_1.1.1           pillar_1.6.0        whisker_0.4        
 [91] withr_2.4.2         mgcv_1.8-35         survival_3.2-10    
 [94] tibble_3.1.1        crayon_1.4.1        utf8_1.2.1         
 [97] microbiome_1.12.0   rmarkdown_2.7       usethis_2.0.1      
[100] GetoptLong_1.0.5    progress_1.2.2      callr_3.6.0        
[103] git2r_0.28.0        vegan_2.5-7         digest_0.6.27      
[106] tidyr_1.1.3         httpuv_1.5.5        stats4_4.0.3       
[109] munsell_0.5.0       bslib_0.2.4         sessioninfo_1.1.1