Last updated: 2021-04-29

Checks: 6 1

Knit directory: MS_lesions/

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Rmd fd8a5bc wmacnair 2021-02-10 Running and checking conos outputs

Setup / definitions

Libraries

Helper functions

source('code/ms00_utils.R')
source('code/ms03_SampleQC.R')
source('code/ms04_conos.R')

Inputs

sce_f       = 'data/sce_raw/ms_sce.rds'
meta_f      = 'data/metadata/metadata_updated_20201127.txt'

# qc info
dbl_dir     = 'output/ms02_doublet_id'
dbl_f       = file.path(dbl_dir, 'scDblFinder_outputs.txt')
loom_dir    = 'data/loom_files'
qc_dir      = 'output/ms03_SampleQC'
qc_f        = file.path(qc_dir, 'ms_qc_dt.txt')
keep_f      = sprintf('%s/keep_dt_%s.txt', qc_dir, '2021-02-10')

Outputs

# define save directory
save_dir    = 'output/ms04_conos'
date_tag    = '2021-02-11'
if (!dir.exists(save_dir))
    dir.create(save_dir)

# define conos run
res_list    = c(1, 2, 4, 6, 8)
sel_res     = 8
min_cells   = 100
conos_f     = sprintf('%s/conos_clusts_%s.txt', save_dir, date_tag)
viz_f       = sprintf('%s/conos_viz_%s.txt', save_dir, date_tag)
# umap_f      = sprintf('%s/conos_umap_%s.txt', save_dir, date_tag)

n_per_conos = 200
graph_f     = sprintf('%s/conos_graph_%s.txt', save_dir, date_tag)
umap_many_f = sprintf('%s/conos_umap_sub_%s.txt', save_dir, date_tag)
spread_list = c(1, 2, 4, 8)
min_list    = c(1e-2, 1e-1, 1e-0)

# define file to save these
prior_f     = sprintf('%s/prior_marker_means_%s.txt', save_dir, date_tag)

# define outputs
annot_f     = sprintf('%s/conos_annot_%s.txt', save_dir, date_tag)

# define list of groups to use
fm_groups   = list(
  oligo         = "Oligodendrocytes", 
  opc           = "Oligodendrocyte precursor cells", 
  astro         = "Astrocytes", 
  micro_immune  = c("Microglia", "Plasma B cells"), 
  excit_neuron  = "Excitatory neurons", 
  inhib_neuron  = "Inhibitory neurons", 
  endo_peri     = c("Endothelial cells", "Pericytes")
)
fm_pat      = sprintf("%s/fm_%s_%s_%s.txt", save_dir, '%s', '%s', date_tag)
n_cells     = 500

Load inputs

dbl_dt      = dbl_f %>% fread %>% 
  .[, .(cell_id, cxds = pmax(log10(cxds_score), -5), dbl_logscore = log10(score))]
qc_dt       = get_qc_dt(qc_dir, qc_f) %>%
  merge(dbl_dt, by = 'cell_id')
meta_dt     = meta_f %>% fread %>%
  .[, .(sample_id = library_id, patient_id)]
keep_dt     = keep_f %>% fread
calc_conos(sce_f, save_dir, date_tag, keep_dt$cell_id, res_list,
  n_cores = 48)
done!
NULL
conos_dt    = load_conos_dt(conos_f, sel_res, min_cells)
umap_dt     = viz_f %>% fread %>%
  set_colnames(c('cell_id', 'umap1', 'umap2'))
prior_dt    = get_prior_dt()

Processing / calculations

# extract counts, means
marker_list     = prior_dt$symbol
marker_exp_dt   = calc_marker_exp_dt(marker_list, sce_f, prior_f, qc_f, conos_dt,
  cell_ids = conos_dt$cell_id) %>%
  merge(prior_dt, by = 'symbol')
already done! loading
labels_dt     = calc_labels_dt(marker_exp_dt)
marker_exp_dt = merge(marker_exp_dt, labels_dt, by = 'conos')
conos_dt      = merge(conos_dt, labels_dt, by = 'conos')
tests     = c('binom', 'wilcox', 't')
for (n in names(fm_groups)) {
  # define filename
  fm_fs       = tests %>% sapply(function(t) 
    sprintf(fm_pat, n, t) %>% file.path(save_dir, .))
  broad_types = fm_groups[[n]]

  # run findMarkers
  calc_find_markers_broad(sce_f, fm_fs, n, broad_types, tests, 
    conos_dt, n_cells, n_cores = 8)
}
set.seed(20210212)
conos_sub   = calc_conos_sub(conos_dt, n_per_conos)
umap_all    = calc_umap_sub(conos_sub, graph_f, umap_many_f, spread_list, min_list, 
  seed = 20210212)
set.seed(20210212)
umap_sub    = umap_dt[ sample(nrow(umap_dt), 2e4) ] %>%
  merge(conos_dt, by='cell_id')
umap_qc_dt  = umap_sub[, .(cell_id, umap1, umap2)] %>% 
  merge(qc_dt, by='cell_id') %>%
  melt(id=c('cell_id', 'sample_id', 'umap1', 'umap2'),
    variable.name = 'qc_var', value.name = 'qc_val')

Analysis

Density over UMAP

# plot densities
(plot_umap_dens(umap_dt))

Broad celltypes over UMAP

(plot_umap_broad(umap_sub))

Fine celltypes over UMAP

set.seed(20210202)
for (b in broad_ord) {
  cat('### ', b, ' \n')
  print(plot_umap_split(conos_dt, umap_dt, b))
  cat('\n\n')
}

Oligodendrocytes

OPCs / COPs

Astrocytes

Microglia

Excitatory neurons

Inhibitory neurons

Endothelial cells

Pericytes

Immune

Expression of known marker genes

draw(marker_heatmap_fn(marker_exp_dt), merge_legend = TRUE, 
  heatmap_legend_side = "bottom", annotation_legend_side = "bottom")

Celltype mixing across samples

(plot_conos_mixing(conos_dt, meta_dt))

QC metrics split by conos celltype

(plot_qc_violins(qc_dt, conos_dt))

QC metrics celltypes over UMAP

for (v in levels(umap_qc_dt$qc_var)) {
  cat('### ', v, '\n')
  print(plot_umap_qc(umap_qc_dt, v))
  cat('\n\n')
}

log_counts

log_feats

logit_mito

splice_ratio

cxds

dbl_logscore

Outputs

conos_dt %>% fwrite(file = annot_f)
devtools::session_info()
Registered S3 method overwritten by 'cli':
  method     from         
  print.boxx spatstat.geom
- 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     2021-04-29                  

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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] scran_1.18.7                uwot_0.1.10                
 [3] scater_1.18.6               BiocParallel_1.24.1        
 [5] ggplot.multistats_1.0.0     seriation_1.2-9            
 [7] ComplexHeatmap_2.6.2        SeuratObject_4.0.0         
 [9] Seurat_4.0.1                conos_1.4.0                
[11] igraph_1.2.6                SampleQC_0.4.5             
[13] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
[15] Biobase_2.50.0              GenomicRanges_1.42.0       
[17] GenomeInfoDb_1.26.7         IRanges_2.24.1             
[19] S4Vectors_0.28.1            BiocGenerics_0.36.1        
[21] MatrixGenerics_1.2.1        matrixStats_0.58.0         
[23] Matrix_1.3-2                loomR_0.2.0                
[25] itertools_0.1-3             iterators_1.0.13           
[27] hdf5r_1.3.3                 R6_2.5.0                   
[29] patchwork_1.1.1             forcats_0.5.1              
[31] ggplot2_3.3.3               scales_1.1.1               
[33] viridis_0.6.0               viridisLite_0.4.0          
[35] assertthat_0.2.1            stringr_1.4.0              
[37] data.table_1.14.0           magrittr_2.0.1             
[39] circlize_0.4.12             RColorBrewer_1.1-2         
[41] BiocStyle_2.18.1            colorout_1.2-2             
[43] workflowr_1.6.2            

loaded via a namespace (and not attached):
  [1] scattermore_0.7           tidyr_1.1.3              
  [3] bit64_4.0.5               knitr_1.32               
  [5] irlba_2.3.3               DelayedArray_0.16.3      
  [7] rpart_4.1-15              RCurl_1.98-1.3           
  [9] generics_0.1.0            callr_3.6.0              
 [11] mvnfast_0.2.5.1           cowplot_1.1.1            
 [13] usethis_2.0.1             RANN_2.6.1               
 [15] future_1.21.0             bit_4.0.4                
 [17] spatstat.data_2.1-0       httpuv_1.5.5             
 [19] xfun_0.22                 jquerylib_0.1.3          
 [21] evaluate_0.14             promises_1.2.0.1         
 [23] TSP_1.1-10                fansi_0.4.2              
 [25] DBI_1.1.1                 htmlwidgets_1.5.3        
 [27] spatstat.geom_2.1-0       purrr_0.3.4              
 [29] ellipsis_0.3.1            dplyr_1.0.5              
 [31] deldir_0.2-10             sparseMatrixStats_1.2.1  
 [33] vctrs_0.3.7               remotes_2.3.0            
 [35] Cairo_1.5-12.2            ROCR_1.0-11              
 [37] abind_1.4-5               cachem_1.0.4             
 [39] withr_2.4.2               grr_0.9.5                
 [41] sctransform_0.3.2         prettyunits_1.1.1        
 [43] mclust_5.4.7              goftest_1.2-2            
 [45] cluster_2.1.2             segmented_1.3-3          
 [47] lazyeval_0.2.2            crayon_1.4.1             
 [49] edgeR_3.32.1              pkgconfig_2.0.3          
 [51] labeling_0.4.2            pkgload_1.2.1            
 [53] nlme_3.1-152              vipor_0.4.5              
 [55] devtools_2.4.0            rlang_0.4.10             
 [57] globals_0.14.0            lifecycle_1.0.0          
 [59] miniUI_0.1.1.1            registry_0.5-1           
 [61] rsvd_1.0.5                rprojroot_2.0.2          
 [63] polyclip_1.10-0           lmtest_0.9-38            
 [65] zoo_1.8-9                 Matrix.utils_0.9.8       
 [67] beeswarm_0.3.1            processx_3.5.1           
 [69] whisker_0.4               ggridges_0.5.3           
 [71] GlobalOptions_0.1.2       png_0.1-7                
 [73] rjson_0.2.20              bitops_1.0-6             
 [75] KernSmooth_2.23-18        DelayedMatrixStats_1.12.3
 [77] shape_1.4.5               parallelly_1.24.0        
 [79] sccore_0.1.2              beachmat_2.6.4           
 [81] memoise_2.0.0             plyr_1.8.6               
 [83] hexbin_1.28.2             ica_1.0-2                
 [85] zlibbioc_1.36.0           compiler_4.0.3           
 [87] dqrng_0.2.1               clue_0.3-59              
 [89] fitdistrplus_1.1-3        cli_2.4.0                
 [91] XVector_0.30.0            listenv_0.8.0            
 [93] ps_1.6.0                  pbapply_1.4-3            
 [95] MASS_7.3-53.1             mgcv_1.8-35              
 [97] tidyselect_1.1.0          stringi_1.5.3            
 [99] highr_0.9                 yaml_2.2.1               
[101] BiocSingular_1.6.0        locfit_1.5-9.4           
[103] ggrepel_0.9.1             sass_0.3.1               
[105] tools_4.0.3               future.apply_1.7.0       
[107] rstudioapi_0.13           bluster_1.0.0            
[109] foreach_1.5.1             git2r_0.28.0             
[111] gridExtra_2.3             farver_2.1.0             
[113] Rtsne_0.15                digest_0.6.27            
[115] BiocManager_1.30.12       shiny_1.6.0              
[117] Rcpp_1.0.6                scuttle_1.0.4            
[119] later_1.1.0.1             RcppAnnoy_0.0.18         
[121] httr_1.4.2                kernlab_0.9-29           
[123] colorspace_2.0-0          fs_1.5.0                 
[125] tensor_1.5                reticulate_1.18          
[127] splines_4.0.3             statmod_1.4.35           
[129] spatstat.utils_2.1-0      sessioninfo_1.1.1        
[131] plotly_4.9.3              xtable_1.8-4             
[133] jsonlite_1.7.2            leidenAlg_0.1.1          
[135] testthat_3.0.2            pillar_1.6.0             
[137] htmltools_0.5.1.1         mime_0.10                
[139] glue_1.4.2                fastmap_1.1.0            
[141] BiocNeighbors_1.8.2       codetools_0.2-18         
[143] pkgbuild_1.2.0            mvtnorm_1.1-1            
[145] utf8_1.2.1                lattice_0.20-41          
[147] bslib_0.2.4               spatstat.sparse_2.0-0    
[149] tibble_3.1.1              mixtools_1.2.0           
[151] ggbeeswarm_0.6.0          leiden_0.3.7             
[153] gtools_3.8.2              survival_3.2-10          
[155] limma_3.46.0              rmarkdown_2.7            
[157] desc_1.3.0                munsell_0.5.0            
[159] GetoptLong_1.0.5          GenomeInfoDbData_1.2.4   
[161] reshape2_1.4.4            gtable_0.3.0             
[163] spatstat.core_2.1-2