Last updated: 2024-03-21

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Rmd e6213a5 FloWuenne 2024-03-21 Publish all updated files.
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Rmd 82f107f FloWuenne 2024-01-21 Updates to Molkart analysis.
Rmd f4d5c82 FloWuenne 2024-01-15 Latest update to Seurat analysis with reprocessed data. 
plan(multisession, workers = 8)

## Custom functions
interaction_communities_info <- function(misty.results, concat.views, view,
                                         trim = 0, trim.measure = "gain.R2", 
                                         cutoff = 1, resolution = 1) {
  
  inv <- sign((stringr::str_detect(trim.measure, "gain") |
                 stringr::str_detect(trim.measure, "RMSE", negate = TRUE)) - 0.5)
  
  targets <- misty.results$improvements.stats %>%
    dplyr::filter(
      measure == trim.measure,
      inv * mean >= inv * trim
    ) %>%
    dplyr::pull(target)
  
  view.wide <- misty.results$importances.aggregated %>%
    filter(view == !!view) %>%
    pivot_wider(
      names_from = "Target", values_from = "Importance",
      id_cols = -c(view, nsamples)
    ) %>% mutate(across(-c(Predictor,all_of(targets)), \(x) x = NA))
  
  mistarget <- setdiff(view.wide$Predictor, colnames(view.wide)[-1])
  mispred <- setdiff(colnames(view.wide)[-1], view.wide$Predictor)
  
  if(length(mispred) != 0){
    view.wide.aug <- view.wide %>% add_row(Predictor = mispred)
  } else {
    view.wide.aug <- view.wide
  }
  
  if(length(mistarget) != 0){
    view.wide.aug <- view.wide.aug %>% 
      bind_cols(mistarget %>% 
                  map_dfc(~tibble(!!.x := NA)))
  }
  
  A <- view.wide.aug %>%
    column_to_rownames("Predictor") %>%
    as.matrix()
  A[A < cutoff | is.na(A)] <- 0
  
  ## !!! Was buggy
  G <- graph.adjacency(A[,rownames(A)], mode = "plus", weighted = TRUE) %>%
    set.vertex.attribute("name", value = names(V(.))) %>%
    delete.vertices(which(degree(.) == 0))
  
  Gdir <- graph.adjacency(A[,rownames(A)], "directed", weighted = TRUE) %>%
    set.vertex.attribute("name", value = names(V(.))) %>%
    delete.vertices(which(degree(.) == 0))
  
  C <- cluster_leiden(G, resolution_parameter = resolution, n_iterations = -1)
  
  mem <- membership(C)
  
  Gdir <- set_vertex_attr(Gdir, "community", names(mem), as.numeric(mem))
  
  # careful here the first argument is the predictor and the second the target, 
  # it might need to come from different view
  corrs <- as_edgelist(Gdir) %>% apply(1, \(x) cor(
    concat.views[[view]][, x[1]],
    concat.views[["intraview"]][, x[2]]
  )) %>% replace_na(0)
  
  Gdir <- set_edge_attr(Gdir, "cor", value = corrs)
  return(Gdir)
}

cellular_neighborhoods <- function(sample.cells, sample.pos, n, k){
  misty.views  <- create_initial_view(sample.cells) %>% add_paraview(sample.pos, family = "constant", l = n)
  clust <- KMeans_rcpp(misty.views[[paste0("paraview.",n)]], k)
  return(clust$clusters) 
}

Introduction

In this markdown we will utilize MistyR to perform global spatial analysis on the cell-type encodings for the Molecular Cartography data.

Make sure to have the latest development version (15.01.2024) : https://github.com/jtanevski/mistyR

Analyze data using MistyR with low level cell phenotypes

size_param <- 125
all.data <- read_tsv("./output/molkart/molkart.misty_celltype_table.lowres.tsv")
Rows: 69028 Columns: 12
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (8): sample_ID, timepoint, replicate, anno_cell_type_lvl1, anno_cell_typ...
dbl (4): X_centroid, Y_centroid, Area, nCount_RNA

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
samples <- all.data %>%
  pull(sample_ID) %>%
  unique()

cts <- all.data %>%
  pull(misty_cts) %>%
  unique()

cts.names <- make.names(cts, allow_ = FALSE)

## Count number of cells per type
# ct_numbers <- all.data %>%
#   group_by(sample_ID, misty_cts) %>%
#   summarise(n = n()) %>%
#   pivot_wider(names_from = misty_cts, values_from = n) %>%
#   column_to_rownames("sample_ID") %>%
#   as.matrix()

samples %>% walk(\(sample){
  
  sample.cells <- all.data %>%
    filter(sample_ID == sample) %>%
    pull(misty_cts) %>%
    map(~ .x == cts) %>%
    list.rbind() %>%
    `colnames<-`(cts.names) %>%
    as_tibble()

  sample.pos <- all.data %>%
    filter(sample_ID == sample) %>%
    select(X_centroid, Y_centroid)

  l <- size_param / 0.138

  misty.views.cts <- create_initial_view(sample.cells) %>%
    add_paraview(sample.pos, l) %>%
    rename_view(paste0("paraview.", l), "paraview") %>%
    select_markers("intraview", where(~ sd(.) != 0))
  
  db_name <- paste("results_cts.lowres.",size_param,".sqm",sep="")
  
  run_misty(misty.views.cts, sample, db_name, bypass.intra = TRUE)
})

Generating paraview

Training models
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Myeloid.cells
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Cardiac.fibroblasts
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Pericytes
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Endothelial.cells
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Cardiomyocytes
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Smooth.muscle.cells
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Cardiomyocytes.Nppa.
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Endocardial.cells
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Lymphoid.cells

Generating paraview

Training models
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Myeloid.cells
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Cardiac.fibroblasts
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Pericytes
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Endothelial.cells
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Cardiomyocytes
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Smooth.muscle.cells
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Cardiomyocytes.Nppa.
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Endocardial.cells
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Lymphoid.cells

Generating paraview

Training models
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Myeloid.cells
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Cardiac.fibroblasts
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Pericytes
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Endothelial.cells
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Cardiomyocytes
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Smooth.muscle.cells
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Cardiomyocytes.Nppa.
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Endocardial.cells
Warning in predict.lm(meta.multi, oob.predictions %>% dplyr::slice(test.fold)):
prediction from rank-deficient fit; attr(*, "non-estim") has doubtful cases
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Lymphoid.cells

Generating paraview

Training models
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Myeloid.cells
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Cardiac.fibroblasts
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Pericytes
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Endothelial.cells
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Cardiomyocytes
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Smooth.muscle.cells
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Cardiomyocytes.Nppa.
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Endocardial.cells
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Lymphoid.cells

Generating paraview

Training models
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Myeloid.cells
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Cardiac.fibroblasts
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Pericytes
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Endothelial.cells
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Cardiomyocytes
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Smooth.muscle.cells
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Cardiomyocytes.Nppa.
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Endocardial.cells
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Lymphoid.cells

Generating paraview

Training models
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Myeloid.cells
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Cardiac.fibroblasts
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Pericytes
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Endothelial.cells
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Cardiomyocytes
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Smooth.muscle.cells
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Cardiomyocytes.Nppa.
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Endocardial.cells
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Lymphoid.cells

Generating paraview

Training models
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Myeloid.cells
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Cardiac.fibroblasts
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Pericytes
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Endothelial.cells
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Cardiomyocytes
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Smooth.muscle.cells
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Cardiomyocytes.Nppa.
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Endocardial.cells
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Lymphoid.cells

Generating paraview

Training models
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Myeloid.cells
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Cardiac.fibroblasts
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Pericytes
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Endothelial.cells
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Cardiomyocytes
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Smooth.muscle.cells
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Cardiomyocytes.Nppa.
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Endocardial.cells
Warning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target Lymphoid.cells
l <- size_param / 0.138
db_name <- paste("results_cts.lowres.",size_param,".sqm",sep="")
groups <- samples %>% str_extract("(?<=sample_).+(?=_r)") %>% unique()

misty.results.g <- groups %>% map(~ collect_results(db_name, .x))

Collecting improvements

Collecting contributions

Collecting importances

Aggregating

Collecting improvements

Collecting contributions

Collecting importances

Aggregating

Collecting improvements

Collecting contributions

Collecting importances

Aggregating

Collecting improvements

Collecting contributions

Collecting importances

Aggregating
#misty.results.g <- groups %>% map(~ collect_results(paste("results_cts_",as.character(size_param),".sqm",sep=""), .x,)) ##
names(misty.results.g) <- groups

outdir <- paste("./plots/misty_figures",sep="")

misty.results.g %>% iwalk(\(misty.results, cond){
  plot.list <- list()
  plot_improvement_stats(misty.results, "gain.R2")
  plot.list <- list.append(plot.list, last_plot())
  plot_interaction_heatmap(misty.results, "paraview", cutoff = 0.4, clean = TRUE, trim = 5)
  plot.list <- list.append(plot.list, last_plot())
  plot_grid(plotlist = plot.list, ncol = 2)
  ggsave(paste0(outdir,"/", cond, "_stats.pdf"), width = 10, height = 10)
})

## Save misty results in R object for easier faster loading
saveRDS(misty.results.g,
        file = paste0("./output/molkart/misty_results.lowres.",size_param,".rds"))

sessionInfo()
R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.1.2

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Europe/Berlin
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] RColorBrewer_1.1-3 here_1.0.1         ggsci_3.0.0        viridis_0.6.4     
 [5] viridisLite_0.4.2  ClusterR_1.3.2     igraph_1.6.0       cowplot_1.1.2     
 [9] future_1.33.1      FNN_1.1.4          rlist_0.4.6.2      mistyR_1.99.9     
[13] lubridate_1.9.3    forcats_1.0.0      stringr_1.5.1      dplyr_1.1.4       
[17] purrr_1.0.2        readr_2.1.5        tidyr_1.3.0        tibble_3.2.1      
[21] ggplot2_3.4.4      tidyverse_2.0.0    workflowr_1.7.1   

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.0    farver_2.1.1        blob_1.2.4         
 [4] filelock_1.0.3      R.utils_2.12.3      fastmap_1.1.1      
 [7] promises_1.2.1      digest_0.6.34       timechange_0.2.0   
[10] lifecycle_1.0.4     processx_3.8.3      RSQLite_2.3.4      
[13] magrittr_2.0.3      compiler_4.3.1      rlang_1.1.3        
[16] sass_0.4.8          tools_4.3.1         utf8_1.2.4         
[19] yaml_2.3.8          data.table_1.14.10  knitr_1.45         
[22] labeling_0.4.3      bit_4.0.5           withr_2.5.2        
[25] R.oo_1.25.0         grid_4.3.1          fansi_1.0.6        
[28] git2r_0.33.0        colorspace_2.1-0    globals_0.16.2     
[31] scales_1.3.0        cli_3.6.2           rmarkdown_2.25     
[34] crayon_1.5.2        ragg_1.2.7          generics_0.1.3     
[37] rstudioapi_0.15.0   httr_1.4.7          tzdb_0.4.0         
[40] DBI_1.2.0           cachem_1.0.8        assertthat_0.2.1   
[43] parallel_4.3.1      BiocManager_1.30.22 vctrs_0.6.5        
[46] jsonlite_1.8.8      callr_3.7.3         hms_1.1.3          
[49] distances_0.1.10    bit64_4.0.5         listenv_0.9.0      
[52] systemfonts_1.0.5   jquerylib_0.1.4     glue_1.7.0         
[55] parallelly_1.36.0   codetools_0.2-19    ps_1.7.6           
[58] stringi_1.8.3       gtable_0.3.4        later_1.3.2        
[61] gmp_0.7-4           munsell_0.5.0       pillar_1.9.0       
[64] furrr_0.3.1         htmltools_0.5.7     R6_2.5.1           
[67] textshaping_0.3.7   rprojroot_2.0.4     vroom_1.6.5        
[70] evaluate_0.23       highr_0.10          R.methodsS3_1.8.2  
[73] memoise_2.0.1       renv_1.0.3          httpuv_1.6.14      
[76] bslib_0.6.1         Rcpp_1.0.12         gridExtra_2.3      
[79] whisker_0.4.1       xfun_0.41           fs_1.6.3           
[82] getPass_0.2-4       pkgconfig_2.0.3