Last updated: 2024-10-31

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Rmd 886cd01 Peter Carbonetto 2024-10-31 workflowr::wflow_publish("pancreas.Rmd", verbose = TRUE, view = FALSE)
html 528d5ef Peter Carbonetto 2024-10-30 First build of the pancreas workflowr page.
Rmd a1d7a17 Peter Carbonetto 2024-10-30 Added some background on the pancreas data set.
Rmd 3fa28da Peter Carbonetto 2024-10-30 Small edit to pancreas.Rmd.
Rmd 370a336 Peter Carbonetto 2024-10-30 Still working on pancreas.Rmd.
Rmd fbe0b62 Peter Carbonetto 2024-10-30 Made a few improvements to the pancreas analysis.
Rmd c090d1d Peter Carbonetto 2024-10-30 Working on initial examination of pancreas data.

The aim of this analysis is to take an initial look at the “pancreas” data set that was featured in in the Luecken et al 2022 benchmarking paper, and prepare the data in a convenient form for subsequent analyses in R.

In addition to being featured in the Luecken et al paper, it has been used in several papers on “data integration” methods for single-cell data (also known as “batch correction” or “harmonization” methods). See for example the MNN paper. The Supplementary Note in the Luecken et al paper has additional references.

See Supplementary Fig. 13, Supplementary Note 3 and Supplementary Data 7 of the Luecken et al paper for more information on this data set.

First, load the packages needed for this analysis.

library(tools)
library(Matrix)
library(MatrixExtra)
library(hdf5r)
library(uwot)
library(ggplot2)
library(cowplot)

Download the file “human_pancreas_norm_complexBatch.h5ad” from figshare and copy it to the “data” subdirectory of this git repository. Then load the count data, and encode them as a sparse matrix:

dat <- H5File$new("../data/human_pancreas_norm_complexBatch.h5ad",mode = "r")
counts <- dat[["layers"]][["counts"]][,]
counts <- as(counts,"CsparseMatrix")
counts <- t(counts)
sample_info <- data.frame(id          = dat[["obs"]][["_index"]][],
                          tech        = dat[["obs"]][["tech"]][],
                          celltype    = dat[["obs"]][["celltype"]][],
                          size_factor = dat[["obs"]][["size_factors"]][],
                          stringsAsFactors = FALSE)
sample_info <- transform(sample_info,
                         tech     = factor(tech),
                         celltype = factor(celltype))
levels(sample_info$tech)     <- dat[["obs"]][["__categories"]][["tech"]][]
levels(sample_info$celltype) <- dat[["obs"]][["__categories"]][["celltype"]][]
genes <- dat[["var"]][["_index"]][]
rownames(counts) <- sample_info$id
colnames(counts) <- genes

Note that some of the data are not actually counts, so perhaps calling this matrix “counts” is a bit misleading. Also note that in Luecken et al the counts were log-transformed, but here we taking the untransformed counts.

The matrix has 16,382 rows (cells) and 19,093 columns (genes), and about 18% of the entries are nonzeros:

nrow(counts)
ncol(counts)
mean(counts > 0)
# [1] 16382
# [1] 19093
# [1] 0.1779012

The pancreas data are actually several scRNA-seq data sets that were combined together:

table(sample_info$tech)
# 
#     celseq    celseq2 fluidigmc1    inDrop1    inDrop2    inDrop3    inDrop4 
#       1004       2285        638       1937       1724       3605       1303 
#    smarter  smartseq2 
#       1492       2394

The cells were previous annotated by cell type:

table(sample_info$celltype)
# 
#             acinar activated_stellate              alpha               beta 
#               1669                464               5493               4169 
#              delta             ductal        endothelial            epsilon 
#               1055               2142                313                 32 
#              gamma         macrophage               mast quiescent_stellate 
#                699                 79                 42                193 
#            schwann             t_cell 
#                 25                  7

Some of the cell types occur in only a very small number of cells.

Distribution of the size factors:

s <- rowSums(counts)
ggplot(data.frame(log_size_factor = log10(s)),aes(log_size_factor)) +
  geom_histogram(bins = 64,col = "black",fill = "black") +
  theme_cowplot(font_size = 10)

Genes with nonzero expression:

p <- colSums(counts)/sum(s)
sum(p > 0)
# [1] 18771

Distribution of the (relative) expression levels:

p <- p[p > 0]
ggplot(data.frame(log_rel_expression_level = log10(p)),
                  aes(log_rel_expression_level)) +
  geom_histogram(bins = 64,col = "black",fill = "black") +
  theme_cowplot(font_size = 10)

Now do UMAP… Compute the shifted log counts:

a <- 1
s <- rowSums(counts)
s <- s/mean(s)
Y <- mapSparse(counts/(a*s),log1p)

UMAP:

res <- umap(as.matrix(Y),n_components = 2,n_threads = 8,verbose = TRUE)

TO DO: Save the data in an .Rdata file for more convenient analysis with the matrix factorization methods.

save(list = c("sample_info","counts"),file = "pancreas.RData")
resaveRdaFiles("pancreas.RData")

sessionInfo()
# R version 4.3.3 (2024-02-29)
# Platform: aarch64-apple-darwin20 (64-bit)
# Running under: macOS Sonoma 14.6.1
# 
# 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_0.1.16        hdf5r_1.3.11      
# [5] MatrixExtra_0.1.15 Matrix_1.6-5      
# 
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#  [1] sass_0.4.8          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_0.23       grid_4.3.3         
# [10] float_0.3-2         fastmap_1.1.1       rprojroot_2.0.4    
# [13] workflowr_1.7.1     jsonlite_1.8.8      whisker_0.4.1      
# [16] promises_1.2.1      fansi_1.0.6         scales_1.3.0       
# [19] RhpcBLASctl_0.23-42 jquerylib_0.1.4     cli_3.6.2          
# [22] rlang_1.1.3         bit64_4.0.5         munsell_0.5.0      
# [25] withr_3.0.0         cachem_1.0.8        yaml_2.3.8         
# [28] parallel_4.3.3      dplyr_1.1.4         colorspace_2.1-0   
# [31] httpuv_1.6.14       vctrs_0.6.5         R6_2.5.1           
# [34] lifecycle_1.0.4     git2r_0.33.0        stringr_1.5.1      
# [37] fs_1.6.3            bit_4.0.5           pkgconfig_2.0.3    
# [40] pillar_1.9.0        bslib_0.6.1         later_1.3.2        
# [43] gtable_0.3.4        glue_1.7.0          Rcpp_1.0.12        
# [46] highr_0.10          xfun_0.42           tibble_3.2.1       
# [49] tidyselect_1.2.1    knitr_1.45          farver_2.1.1       
# [52] htmltools_0.5.7     labeling_0.4.3      rmarkdown_2.26     
# [55] compiler_4.3.3