merge ST data human LN - batch2

Author

Mechthild Lütge

Published

June 12, 2023

Load packages

library(tidyverse)
library(SpatialExperiment)
library(here)
library(ggspavis)
library(scater)
library(scran)
library(ggpubr)
library(ggsci)
library(pheatmap)

Load and merge spe objects

basedir <- here()
metaDat <- read_tsv(paste0(basedir, "/data/ST/metadataB2.txt"), col_names = T)

assignSamples <- function(smpNam, basedirSmp){
  smpNamFull <- list.files(path = paste0(basedirSmp, "/data/ST/"),
                 pattern = paste0(smpNam, ".*_spe.rds"))
  speSmp <- readRDS(paste0(basedirSmp, "/data/ST/", smpNamFull))
  return(speSmp)
}

####################################################################

for(i in 1:length(metaDat$Sample)){
  speX <- assignSamples(smpNam = metaDat$Sample[i],
                           basedirSmp = basedir)
  if(exists("spe")){
    spe <- cbind(spe, speX)
  }else{
    spe <- speX
  }
}

remove(speX)

Examine samples before removal of low quality spots

## before QC
table(spe@colData$sample_id)


334131_11-1_20231103_Hu_pat03_07_squA 334131_12-2_20231103_Hu_pat03_10_squB 
                                 4992                                  4992 
334131_13-3_20231103_Hu_pat03_9a_squC 334131_14-4_20231103_Hu_pat03_11_squD 
                                 4992                                  4992

dim(spe)

[1] 60666 19968

plotSpots(spe) + facet_grid(~ sample_id)

# subset to keep only spots over tissue
spe <- spe[, colData(spe)$in_tissue == 1]
dim(spe)

[1] 60666  7632

table(spe@colData$sample_id)


334131_11-1_20231103_Hu_pat03_07_squA 334131_12-2_20231103_Hu_pat03_10_squB 
                                 1143                                  2823 
334131_13-3_20231103_Hu_pat03_9a_squC 334131_14-4_20231103_Hu_pat03_11_squD 
                                 1997                                  1669

plotSpots(spe) + facet_grid(~ sample_id)

run QC

# identify mitochondrial genes
is_mito <- grepl("(^MT-)|(^mt-)", rowData(spe)$symbol)
table(is_mito)

is_mito
FALSE  TRUE 
60629    37

rowData(spe)$symbol[is_mito]

 [1] "MT-TF"   "MT-RNR1" "MT-TV"   "MT-RNR2" "MT-TL1"  "MT-ND1"  "MT-TI"  
 [8] "MT-TQ"   "MT-TM"   "MT-ND2"  "MT-TW"   "MT-TA"   "MT-TN"   "MT-TC"  
[15] "MT-TY"   "MT-CO1"  "MT-TS1"  "MT-TD"   "MT-CO2"  "MT-TK"   "MT-ATP8"
[22] "MT-ATP6" "MT-CO3"  "MT-TG"   "MT-ND3"  "MT-TR"   "MT-ND4L" "MT-ND4" 
[29] "MT-TH"   "MT-TS2"  "MT-TL2"  "MT-ND5"  "MT-ND6"  "MT-TE"   "MT-CYB" 
[36] "MT-TT"   "MT-TP"

# run QC parameters per sample
speList <- lapply(unique(colData(spe)$sample_id), function(sid){
  speSub <- spe[, which(colData(spe)$sample_id == sid)]
  
  speSub <- addPerCellQC(speSub, subsets = list(mito = is_mito))
  hist(colData(speSub)$sum, breaks = 50,
       main = paste0("sum - ", sid), xlab = "sum")
  hist(colData(speSub)$detected, breaks = 50,
       main = paste0("detected - ", sid), xlab = "detected")
  hist(colData(speSub)$subsets_mito_percent, breaks = 50,
       main = paste0("subsets_mito_percent - ", sid),
       xlab = "subsets_mito_percent")
  
  ## remove spots with high deviation from median
  speSub@colData$total_counts_drop <- isOutlier(speSub@colData$sum, nmads = 5,
                                     type = "both", log = TRUE)
  speSub@colData$total_features_drop <- isOutlier(speSub@colData$detected,
                                                  nmads = 5, type = "both",
                                                  log = TRUE)
  speSub@colData$mito_drop <- isOutlier(speSub@colData$subsets_mito_percent,
                                        nmads = 10, type = "higher",
                                        log = FALSE)
  speSub@colData$mito_drop <- FALSE
  speSub <- speSub
})

## vis spots with low quality
lapply(speList, function(speSub){
  p <- plotQC(speSub, type = "spots", 
       discard = "total_counts_drop") +
    ggtitle(speSub@colData$sample_id)
  p
})

[[1]]


[[2]]


[[3]]


[[4]]

lapply(speList, function(speSub){
  p <- plotQC(speSub, type = "spots", 
       discard = "total_features_drop") +
    ggtitle(speSub@colData$sample_id)
  p
})

[[1]]


[[2]]


[[3]]


[[4]]

lapply(speList, function(speSub){
  p <- plotQC(speSub, type = "spots", 
       discard = "mito_drop") +
    ggtitle(speSub@colData$sample_id)
  p
})

[[1]]


[[2]]


[[3]]


[[4]]

## cnt spots with low quality
lapply(speList, function(speSub){
  table(speSub@colData$total_counts_drop)
})

[[1]]

FALSE 
 1143 

[[2]]

FALSE  TRUE 
 2820     3 

[[3]]

FALSE  TRUE 
 1992     5 

[[4]]

FALSE 
 1669

lapply(speList, function(speSub){
  table(speSub@colData$total_features_drop)
})

[[1]]

FALSE 
 1143 

[[2]]

FALSE  TRUE 
 2809    14 

[[3]]

FALSE  TRUE 
 1992     5 

[[4]]

FALSE 
 1669

lapply(speList, function(speSub){
  table(speSub@colData$mito_drop)
})

[[1]]

FALSE 
 1143 

[[2]]

FALSE 
 2823 

[[3]]

FALSE 
 1997 

[[4]]

FALSE 
 1669

## remove plots with low quality
speList <- lapply(speList, function(speSub){
  speSub <- speSub[, !(speSub@colData$total_counts_drop |
                         speSub@colData$total_features_drop |
                         speSub@colData$mito_drop)]
  sid <- unique(colData(speSub)$sample_id)
  ## after QC
  hist(colData(speSub)$sum, breaks = 50,
       main = paste0("sum after QC - ", sid), xlab = "sum")
  hist(colData(speSub)$detected, breaks = 50,
       main = paste0("detected after QC - ", sid), xlab = "detected")
  hist(colData(speSub)$subsets_mito_percent, breaks = 50,
       main = paste0("subsets_mito_percent after QC - ", sid),
       xlab = "subsets_mito_percent")
  speSub <- speSub
})

names(speList) <- unique(colData(spe)$sample_id)

Examine samples after removal of low quality spots

## vis spots after QC
lapply(speList, function(speSub){
  p <- plotSpots(speSub) +
    ggtitle(speSub@colData$sample_id)
  p

})

$`334131_11-1_20231103_Hu_pat03_07_squA`


$`334131_12-2_20231103_Hu_pat03_10_squB`


$`334131_13-3_20231103_Hu_pat03_9a_squC`


$`334131_14-4_20231103_Hu_pat03_11_squD`

## cnt spots after QC
lapply(speList, function(speSub){
  dim(speSub)
})

$`334131_11-1_20231103_Hu_pat03_07_squA`
[1] 60666  1143

$`334131_12-2_20231103_Hu_pat03_10_squB`
[1] 60666  2809

$`334131_13-3_20231103_Hu_pat03_9a_squC`
[1] 60666  1992

$`334131_14-4_20231103_Hu_pat03_11_squD`
[1] 60666  1669

Normalization

speList <- lapply(speList, function(speSub){
  speSub <- computeLibraryFactors(speSub)
  summary(sizeFactors(speSub))
  sid <- unique(colData(speSub)$sample_id)
  hist(sizeFactors(speSub), breaks = 20, main = sid)
  speSub <- logNormCounts(speSub)
})

Feature selection

hvgList <- lapply(speList, function(speSub){
  # fit mean-variance relationship
  dec <- modelGeneVar(speSub)

  # visualize mean-variance relationship
  fit <- metadata(dec)
  plot(fit$mean, fit$var, 
     xlab = "mean of log-expression", ylab = "variance of log-expression")
  curve(fit$trend(x), col = "dodgerblue", add = TRUE, lwd = 2)
  top_hvgs <- getTopHVGs(dec, prop = 0.1)
})

names(hvgList) <- names(speList)
lengths(hvgList)

334131_11-1_20231103_Hu_pat03_07_squA 334131_12-2_20231103_Hu_pat03_10_squB 
                                 1150                                  1592 
334131_13-3_20231103_Hu_pat03_9a_squC 334131_14-4_20231103_Hu_pat03_11_squD 
                                 1620                                  1055

Dimensionality reduction

speList <- lapply(speList, function(speSub){
  sid <- unique(colData(speSub)$sample_id)
  top_hvgs <- hvgList[[sid]]
  speSub <- runPCA(speSub, subset_row = top_hvgs)
  speSub <- runUMAP(speSub, dimred = "PCA")
  colnames(reducedDim(speSub, "UMAP")) <- paste0("UMAP", 1:2)
  speSub <- speSub
})

lapply(speList, function(speSub){
  sid <- unique(colData(speSub)$sample_id)
  plotDimRed(speSub, type = "PCA") + 
    ggtitle(sid)
})

$`334131_11-1_20231103_Hu_pat03_07_squA`


$`334131_12-2_20231103_Hu_pat03_10_squB`


$`334131_13-3_20231103_Hu_pat03_9a_squC`


$`334131_14-4_20231103_Hu_pat03_11_squD`

lapply(speList, function(speSub){
  sid <- unique(colData(speSub)$sample_id)
  plotDimRed(speSub, type = "UMAP") + 
    ggtitle(sid)
})

$`334131_11-1_20231103_Hu_pat03_07_squA`


$`334131_12-2_20231103_Hu_pat03_10_squB`


$`334131_13-3_20231103_Hu_pat03_9a_squC`


$`334131_14-4_20231103_Hu_pat03_11_squD`

clustering

speList <- lapply(speList, function(speSub){
  # graph-based clustering
  k <- 15
  g <- buildSNNGraph(speSub, k = k, use.dimred = "PCA")
  g_walk <- igraph::cluster_walktrap(g)
  clus <- g_walk$membership
  table(clus)
  colLabels(speSub) <- factor(clus)
  speSub <- speSub
})

colPal <- c("#F0027F", "#377EB8", "#4DAF4A", "#984EA3", "#FFD700", "#FF7F00",
            "#1A1A1A", "#666666", pal_futurama()(10))
lapply(speList, function(speSub){
  sid <- unique(colData(speSub)$sample_id)
  plotSpots(speSub, annotate = "label", 
          palette = colPal) + 
    ggtitle(sid)
})

$`334131_11-1_20231103_Hu_pat03_07_squA`


$`334131_12-2_20231103_Hu_pat03_10_squB`


$`334131_13-3_20231103_Hu_pat03_9a_squC`


$`334131_14-4_20231103_Hu_pat03_11_squD`

lapply(speList, function(speSub){
  sid <- unique(colData(speSub)$sample_id)
  plotDimRed(speSub, type = "UMAP", 
           annotate = "label", palette = colPal) + 
    ggtitle(sid)
})

$`334131_11-1_20231103_Hu_pat03_07_squA`


$`334131_12-2_20231103_Hu_pat03_10_squB`


$`334131_13-3_20231103_Hu_pat03_9a_squC`


$`334131_14-4_20231103_Hu_pat03_11_squD`

lapply(speList, function(speSub){
  sid <- unique(colData(speSub)$sample_id)
  plotDimRed(speSub, type = "PCA", 
           annotate = "label", palette = colPal) + 
    ggtitle(sid)
})

$`334131_11-1_20231103_Hu_pat03_07_squA`


$`334131_12-2_20231103_Hu_pat03_10_squB`


$`334131_13-3_20231103_Hu_pat03_9a_squC`


$`334131_14-4_20231103_Hu_pat03_11_squD`

Marker genes

markerList <- lapply(speList, function(speSub){
  rownames(speSub) <- rowData(speSub)$symbol
  markers <- findMarkers(speSub, test = "binom", direction = "up")
})

names(markerList) <- names(speList)

Save data

saveRDS(speList, file = paste0(basedir, "/data/speAllLNB2.rds"))
saveRDS(markerList, file = paste0(basedir, "/data/markerAllLNB2.rds"))

session info

sessionInfo()

R version 4.3.0 (2023-04-21)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.4.1

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/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] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] Matrix_1.6-5                pheatmap_1.0.12            
 [3] ggsci_3.0.1                 ggpubr_0.6.0               
 [5] scran_1.28.2                scater_1.28.0              
 [7] scuttle_1.10.3              ggspavis_1.6.0             
 [9] here_1.0.1                  SpatialExperiment_1.10.0   
[11] SingleCellExperiment_1.22.0 SummarizedExperiment_1.30.2
[13] Biobase_2.60.0              GenomicRanges_1.52.1       
[15] GenomeInfoDb_1.36.4         IRanges_2.36.0             
[17] S4Vectors_0.40.1            BiocGenerics_0.48.0        
[19] MatrixGenerics_1.12.3       matrixStats_1.2.0          
[21] lubridate_1.9.3             forcats_1.0.0              
[23] stringr_1.5.1               dplyr_1.1.4                
[25] purrr_1.0.2                 readr_2.1.5                
[27] tidyr_1.3.1                 tibble_3.2.1               
[29] ggplot2_3.5.0               tidyverse_2.0.0            

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3        rstudioapi_0.15.0        
  [3] jsonlite_1.8.8            magrittr_2.0.3           
  [5] ggbeeswarm_0.7.2          magick_2.8.3             
  [7] farver_2.1.1              rmarkdown_2.26           
  [9] zlibbioc_1.46.0           vctrs_0.6.5              
 [11] DelayedMatrixStats_1.22.6 RCurl_1.98-1.14          
 [13] rstatix_0.7.2             htmltools_0.5.7          
 [15] S4Arrays_1.0.6            BiocNeighbors_1.18.0     
 [17] broom_1.0.5               Rhdf5lib_1.22.1          
 [19] rhdf5_2.44.0              htmlwidgets_1.6.4        
 [21] igraph_2.0.2              lifecycle_1.0.4          
 [23] ggside_0.3.1              pkgconfig_2.0.3          
 [25] rsvd_1.0.5                R6_2.5.1                 
 [27] fastmap_1.1.1             GenomeInfoDbData_1.2.10  
 [29] digest_0.6.34             colorspace_2.1-0         
 [31] rprojroot_2.0.4           dqrng_0.3.2              
 [33] irlba_2.3.5.1             beachmat_2.16.0          
 [35] labeling_0.4.3            fansi_1.0.6              
 [37] timechange_0.3.0          abind_1.4-5              
 [39] compiler_4.3.0            bit64_4.0.5              
 [41] withr_3.0.0               backports_1.4.1          
 [43] BiocParallel_1.34.2       carData_3.0-5            
 [45] viridis_0.6.5             HDF5Array_1.28.1         
 [47] R.utils_2.12.3            ggsignif_0.6.4           
 [49] DelayedArray_0.26.7       rjson_0.2.21             
 [51] bluster_1.10.0            tools_4.3.0              
 [53] vipor_0.4.7               beeswarm_0.4.0           
 [55] R.oo_1.26.0               glue_1.7.0               
 [57] rhdf5filters_1.12.1       grid_4.3.0               
 [59] cluster_2.1.6             generics_0.1.3           
 [61] gtable_0.3.4              tzdb_0.4.0               
 [63] R.methodsS3_1.8.2         hms_1.1.3                
 [65] BiocSingular_1.16.0       ScaledMatrix_1.8.1       
 [67] metapod_1.8.0             car_3.1-2                
 [69] utf8_1.2.4                XVector_0.40.0           
 [71] ggrepel_0.9.5             pillar_1.9.0             
 [73] vroom_1.6.5               limma_3.56.2             
 [75] lattice_0.22-5            FNN_1.1.4                
 [77] bit_4.0.5                 tidyselect_1.2.0         
 [79] locfit_1.5-9.9            knitr_1.45               
 [81] gridExtra_2.3             edgeR_3.42.4             
 [83] xfun_0.42                 statmod_1.5.0            
 [85] DropletUtils_1.20.0       stringi_1.8.3            
 [87] yaml_2.3.8                evaluate_0.23            
 [89] codetools_0.2-19          cli_3.6.2                
 [91] uwot_0.1.16               munsell_0.5.0            
 [93] Rcpp_1.0.12               parallel_4.3.0           
 [95] sparseMatrixStats_1.12.2  bitops_1.0-7             
 [97] viridisLite_0.4.2         scales_1.3.0             
 [99] crayon_1.5.2              rlang_1.1.3