Last updated: 2025-06-05

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Here we will prepare the single-cell RNA-seq data from Stancill et al 2021 for analysis with fastTopics and flashier. The data files were obtained by downloading and extracting tar file GSE183010_RAW.tar from GEO accession GSE183010.

Load the R packages used to perform the data processing and analysis:

library(data.table)
library(Matrix)
library(tools)
library(rsvd)
library(uwot)
library(ggplot2)
library(cowplot)

Set the seed for reproducibility:

set.seed(1)

Import the count data from the “matrix market” format:

read_geo_data <- function (i, r, prefix = "GSM000000", dir = ".") {
  infile   <- sprintf("%s_Rep%d_S%d_barcodes.tsv.gz",prefix,r,i)
  barcodes <- fread(file.path(dir,infile),quote = FALSE,header = FALSE,
                    stringsAsFactors = FALSE)
  class(barcodes) <- "data.frame"
  barcodes <- barcodes[,1]
  infile <- sprintf("%s_Rep%d_S%d_features.tsv.gz",prefix,r,i)
  genes  <- fread(file.path(dir,infile),sep = "\t",quote = FALSE,
                  header = FALSE,stringsAsFactors = FALSE)
  class(genes) <- "data.frame"
  names(genes) <- c("ensembl","symbol","type")
  genes        <- transform(genes,type = factor(type))
  infile <- sprintf("%s_Rep%d_S%d_matrix.mtx.gz",prefix,r,i)
  counts <- fread(file.path(dir,infile),quote = FALSE,header = FALSE,
                  skip = 2)
  class(counts) <- "data.frame"
  names(counts) <- c("row","col","value")
  n <- max(counts$row)
  m <- max(counts$col)
  counts <- sparseMatrix(i = counts$row,j = counts$col,x = counts$value,
                         dims = c(n,m))
  rownames(counts) <- genes$ensembl
  colnames(counts) <- barcodes
  return(list(genes  = genes,
              counts = counts))
}
dat     <- vector("list",8)
dataset <- 0
samples <- NULL
for (r in 1:2) {
  for (i in 1:4) {
    dataset <- dataset + 1
    dat[[dataset]] <-
      read_geo_data(i,r,prefix = paste0("GSM55486",23 + dataset),
                    dir = "../data/GSE183010")
    samples <- rbind(samples,
                     data.frame(barcode   = colnames(dat[[dataset]]$counts),
                                mouse     = paste0("S",i),
                                replicate = r,
                                stringsAsFactors = FALSE))
  }
}
samples <- transform(samples,
                     mouse     = factor(mouse),
                     replicate = factor(replicate))
features <- Reduce(intersect,lapply(dat,function (x) x$genes$ensembl))
genes    <- subset(dat[[1]]$genes,is.element(ensembl,features))
counts   <- do.call("cbind",lapply(dat,function (x) x$counts[features,]))
counts   <- t(counts)

A good fraction of cells have fewer expressed genes. Let’s remove them:

par(mar = c(4,4,1,1))
x <- rowSums(counts > 0)
i <- which(x > 2000)
samples <- samples[i,]
counts  <- counts[i,]
hist(x,n = 64,main = "",xlab = "number of genes",ylab = "number of cells")

A small number of cells have a large proportion of mitochondrial genes. Let’s remove those cells as well.

par(mar = c(4,4,1,1))
mito_genes <- which(substr(genes$symbol,1,2) == "mt")
s          <- rowSums(counts)
s_mito     <- counts[,mito_genes]
prop_mito  <- rowSums(s_mito)/s
i          <- which(prop_mito < 0.1)
samples <- samples[i,]
counts  <- counts[i,]
hist(prop_mito,n = 64,main = "",xlab = "proportion mitochondrial",
     ylab = "number of cells")

Finally, a bunch of genes are not expressed in any cells. Let’s remove those as well:

x      <- colSums(counts > 0)
j      <- which(x > 0)
genes  <- genes[j,]
counts <- counts[,j]

Here’s an overview of the scRNA-seq data after these data filtering steps:

nrow(samples)
nrow(genes)
dim(counts)
mean(counts > 0)
# [1] 9354
# [1] 21906
# [1]  9354 21906
# [1] 0.2065823

Now let’s generate a 2-d nonlinear embedding of the cells using t-SNE. First, transform the counts into “shifted log counts”:

a <- 1
s <- s/mean(s)
shifted_log_counts <- MatrixExtra::mapSparse(counts/(a*s),log1p)
# Warning in .Ops.recycle.ind(e1, len = l2): longer object length
#   is not a multiple of shorter object length

Then project the cells onto the top 30 PCs:

set.seed(1)
U <- rsvd(shifted_log_counts,k = 30)$u

Now run UMAP on the 30 PCs:

set.seed(1)
Y <- umap(U,n_neighbors = 30,metric = "cosine",min_dist = 0.3,
          n_threads = 8,verbose = FALSE)
x <- Y[,1]
y <- Y[,2]
samples$umap1 <- x
samples$umap2 <- y

Specify the clusters based on the UMAP plot:

samples$cluster <- "islet2"
samples$cluster[x < -3 & y > 5]   <- "islet1" 
samples$cluster[x < -6 & y < -7]  <- "macrophage" # Ccr5
samples$cluster[x < -2 & y < -15] <- "endothelial" # Esam, Pecam1
samples$cluster[x > 14] <- "duct" # Krt19
samples$cluster[sqrt((x - 9)^2 + (y + 8)^2) < 2] <- "acinar" # Prss1
samples$cluster[sqrt((x + 1)^2 + (y + 13)^2) < 3] <- "mesenchyme" # Col1a1
samples <- transform(samples,cluster = factor(cluster))

UMAP plot by cluster:

cluster_colors <- c("darkmagenta","royalblue","limegreen","red",
                    "darkorange","gold","sienna")
ggplot(samples,aes(x = umap1,y = umap2,color = cluster)) +
  geom_point(size = 1.5) +
  scale_color_manual(values = cluster_colors) +
  theme_cowplot(font_size = 10)

UMAP plot by mouse:

sample_colors <- c("limegreen","orange","darkblue","magenta")
ggplot(samples,aes(x = umap1,y = umap2,color = mouse)) +
  geom_point(size = 1.5) +
  scale_color_manual(values = sample_colors) +
  theme_cowplot(font_size = 10)

Save the processed data to an RData file:

save(list = c("samples","genes","counts"),
     file = "pancreas_cytokine.RData")
resaveRdaFiles("pancreas_cytokine.RData")

sessionInfo()
# R version 4.3.3 (2024-02-29)
# Platform: aarch64-apple-darwin20 (64-bit)
# Running under: macOS 15.4.1
# 
# 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: America/Chicago
# tzcode source: internal
# 
# attached base packages:
# [1] tools     stats     graphics  grDevices utils     datasets  methods  
# [8] base     
# 
# other attached packages:
# [1] cowplot_1.1.3     ggplot2_3.5.0     uwot_0.2.3        rsvd_1.0.5       
# [5] Matrix_1.6-5      data.table_1.17.4
# 
# loaded via a namespace (and not attached):
#  [1] sass_0.4.9          utf8_1.2.4          generics_0.1.3     
#  [4] stringi_1.8.3       lattice_0.22-5      digest_0.6.34      
#  [7] magrittr_2.0.3      evaluate_1.0.3      grid_4.3.3         
# [10] float_0.3-2         fastmap_1.1.1       R.oo_1.26.0        
# [13] rprojroot_2.0.4     workflowr_1.7.1     jsonlite_1.8.8     
# [16] R.utils_2.12.3      whisker_0.4.1       promises_1.2.1     
# [19] fansi_1.0.6         scales_1.3.0        RhpcBLASctl_0.23-42
# [22] codetools_0.2-19    jquerylib_0.1.4     cli_3.6.4          
# [25] rlang_1.1.5         R.methodsS3_1.8.2   RcppAnnoy_0.0.22   
# [28] munsell_0.5.0       withr_3.0.2         cachem_1.0.8       
# [31] yaml_2.3.8          parallel_4.3.3      dplyr_1.1.4        
# [34] colorspace_2.1-0    httpuv_1.6.14       vctrs_0.6.5        
# [37] R6_2.5.1            lifecycle_1.0.4     git2r_0.33.0       
# [40] stringr_1.5.1       fs_1.6.5            irlba_2.3.5.1      
# [43] MatrixExtra_0.1.15  pkgconfig_2.0.3     pillar_1.9.0       
# [46] bslib_0.6.1         later_1.3.2         gtable_0.3.4       
# [49] glue_1.8.0          Rcpp_1.0.12         highr_0.10         
# [52] xfun_0.42           tibble_3.2.1        tidyselect_1.2.1   
# [55] knitr_1.45          farver_2.1.1        htmltools_0.5.8.1  
# [58] labeling_0.4.3      rmarkdown_2.26      compiler_4.3.3