Chapter 5 Grun human pancreas (CEL-seq2)

5.1 Introduction

This workflow performs an analysis of the Grun et al. (2016) CEL-seq2 dataset consisting of human pancreas cells from various donors.

5.2 Data loading

library(scRNAseq)
sce.grun <- GrunPancreasData()

We convert to Ensembl identifiers, and we remove duplicated genes or genes without Ensembl IDs.

library(org.Hs.eg.db)
gene.ids <- mapIds(org.Hs.eg.db, keys=rowData(sce.grun)$symbol,
    keytype="SYMBOL", column="ENSEMBL")

keep <- !is.na(gene.ids) & !duplicated(gene.ids)
sce.grun <- sce.grun[keep,]
rownames(sce.grun) <- gene.ids[keep]

5.3 Quality control

unfiltered <- sce.grun

This dataset lacks mitochondrial genes so we will do without them for quality control. We compute the median and MAD while blocking on the donor; for donors where the assumption of a majority of high-quality cells seems to be violated (Figure 5.1), we compute an appropriate threshold using the other donors as specified in the subset= argument.

library(scater)
stats <- perCellQCMetrics(sce.grun)

qc <- quickPerCellQC(stats, percent_subsets="altexps_ERCC_percent",
    batch=sce.grun$donor,
    subset=sce.grun$donor %in% c("D17", "D7", "D2"))

sce.grun <- sce.grun[,!qc$discard]
colData(unfiltered) <- cbind(colData(unfiltered), stats)
unfiltered$discard <- qc$discard

gridExtra::grid.arrange(
    plotColData(unfiltered, x="donor", y="sum", colour_by="discard") +
        scale_y_log10() + ggtitle("Total count"),
    plotColData(unfiltered, x="donor", y="detected", colour_by="discard") +
        scale_y_log10() + ggtitle("Detected features"),
    plotColData(unfiltered, x="donor", y="altexps_ERCC_percent",
        colour_by="discard") + ggtitle("ERCC percent"),
    ncol=2
)
Distribution of each QC metric across cells from each donor of the Grun pancreas dataset. Each point represents a cell and is colored according to whether that cell was discarded.

Figure 5.1: Distribution of each QC metric across cells from each donor of the Grun pancreas dataset. Each point represents a cell and is colored according to whether that cell was discarded.

colSums(as.matrix(qc), na.rm=TRUE)
##              low_lib_size            low_n_features high_altexps_ERCC_percent 
##                       447                       511                       605 
##                   discard 
##                       664

5.4 Normalization

library(scran)
set.seed(1000) # for irlba. 
clusters <- quickCluster(sce.grun)
sce.grun <- computeSumFactors(sce.grun, clusters=clusters)
sce.grun <- logNormCounts(sce.grun)
summary(sizeFactors(sce.grun))
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0949  0.5015  0.7962  1.0000  1.2263  9.4546
plot(librarySizeFactors(sce.grun), sizeFactors(sce.grun), pch=16,
    xlab="Library size factors", ylab="Deconvolution factors", log="xy")
Relationship between the library size factors and the deconvolution size factors in the Grun pancreas dataset.

Figure 5.2: Relationship between the library size factors and the deconvolution size factors in the Grun pancreas dataset.

5.5 Variance modelling

We block on a combined plate and donor factor.

block <- paste0(sce.grun$sample, "_", sce.grun$donor)
dec.grun <- modelGeneVarWithSpikes(sce.grun, spikes="ERCC", block=block)
top.grun <- getTopHVGs(dec.grun, prop=0.1)

We examine the number of cells in each level of the blocking factor.

table(block)
## block
##                  CD13+ sorted cells_D17       CD24+ CD44+ live sorted cells_D17 
##                                      86                                      87 
##                  CD63+ sorted cells_D10                TGFBR3+ sorted cells_D17 
##                                      40                                      90 
## exocrine fraction, live sorted cells_D2 exocrine fraction, live sorted cells_D3 
##                                      82                                       7 
##        live sorted cells, library 1_D10        live sorted cells, library 1_D17 
##                                      33                                      88 
##         live sorted cells, library 1_D3         live sorted cells, library 1_D7 
##                                      25                                      85 
##        live sorted cells, library 2_D10        live sorted cells, library 2_D17 
##                                      35                                      83 
##         live sorted cells, library 2_D3         live sorted cells, library 2_D7 
##                                      27                                      84 
##         live sorted cells, library 3_D3         live sorted cells, library 3_D7 
##                                      17                                      83 
##         live sorted cells, library 4_D3         live sorted cells, library 4_D7 
##                                      29                                      83
par(mfrow=c(6,3))
blocked.stats <- dec.grun$per.block
for (i in colnames(blocked.stats)) {
    current <- blocked.stats[[i]]
    plot(current$mean, current$total, main=i, pch=16, cex=0.5,
        xlab="Mean of log-expression", ylab="Variance of log-expression")
    curfit <- metadata(current)
    points(curfit$mean, curfit$var, col="red", pch=16)
    curve(curfit$trend(x), col='dodgerblue', add=TRUE, lwd=2)
}
Per-gene variance as a function of the mean for the log-expression values in the Grun pancreas dataset. Each point represents a gene (black) with the mean-variance trend (blue) fitted to the spike-in transcripts (red) separately for each donor.

Figure 1.4: Per-gene variance as a function of the mean for the log-expression values in the Grun pancreas dataset. Each point represents a gene (black) with the mean-variance trend (blue) fitted to the spike-in transcripts (red) separately for each donor.

5.6 Data integration

library(batchelor)
set.seed(1001010)
merged.grun <- fastMNN(sce.grun, subset.row=top.grun, batch=sce.grun$donor)
metadata(merged.grun)$merge.info$lost.var
##           D10      D17       D2      D3      D7
## [1,] 0.029320 0.030835 0.000000 0.00000 0.00000
## [2,] 0.007790 0.011984 0.038270 0.00000 0.00000
## [3,] 0.004175 0.005056 0.008115 0.05288 0.00000
## [4,] 0.014759 0.017827 0.016893 0.01534 0.05513

5.7 Dimensionality reduction

set.seed(100111)
merged.grun <- runTSNE(merged.grun, dimred="corrected")

5.8 Clustering

snn.gr <- buildSNNGraph(merged.grun, use.dimred="corrected")
colLabels(merged.grun) <- factor(igraph::cluster_walktrap(snn.gr)$membership)
table(Cluster=colLabels(merged.grun), Donor=merged.grun$batch)
##        Donor
## Cluster D10 D17  D2  D3  D7
##      1   12 128   0   0  62
##      2   32  70  31  81  29
##      3   11  73  31   2  70
##      4   14  33   3   2  68
##      5    2   8   3   3   6
##      6    4   4   2   4   2
##      7    3  41   0   0   9
##      8   16  34  12  11  45
##      9    5  13   0   0  10
##      10   4  13   0   0   1
##      11   5  17   0   2  33
gridExtra::grid.arrange(
    plotTSNE(merged.grun, colour_by="label"),
    plotTSNE(merged.grun, colour_by="batch"),
    ncol=2
)
Obligatory $t$-SNE plots of the Grun pancreas dataset. Each point represents a cell that is colored by cluster (left) or batch (right).

Figure 5.3: Obligatory \(t\)-SNE plots of the Grun pancreas dataset. Each point represents a cell that is colored by cluster (left) or batch (right).

Session Info

R version 4.5.0 RC (2025-04-04 r88126)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.2 LTS

Matrix products: default
BLAS:   /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0  LAPACK version 3.12.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_GB              LC_COLLATE=C              
 [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       

time zone: America/New_York
tzcode source: system (glibc)

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] batchelor_1.24.0            scran_1.36.0               
 [3] scater_1.36.0               ggplot2_3.5.2              
 [5] scuttle_1.18.0              org.Hs.eg.db_3.21.0        
 [7] AnnotationDbi_1.70.0        scRNAseq_2.21.1            
 [9] SingleCellExperiment_1.30.0 SummarizedExperiment_1.38.0
[11] Biobase_2.68.0              GenomicRanges_1.60.0       
[13] GenomeInfoDb_1.44.0         IRanges_2.42.0             
[15] S4Vectors_0.46.0            BiocGenerics_0.54.0        
[17] generics_0.1.3              MatrixGenerics_1.20.0      
[19] matrixStats_1.5.0           BiocStyle_2.36.0           
[21] rebook_1.18.0              

loaded via a namespace (and not attached):
  [1] jsonlite_2.0.0            CodeDepends_0.6.6        
  [3] magrittr_2.0.3            ggbeeswarm_0.7.2         
  [5] GenomicFeatures_1.60.0    gypsum_1.4.0             
  [7] farver_2.1.2              rmarkdown_2.29           
  [9] BiocIO_1.18.0             vctrs_0.6.5              
 [11] DelayedMatrixStats_1.30.0 memoise_2.0.1            
 [13] Rsamtools_2.24.0          RCurl_1.98-1.17          
 [15] htmltools_0.5.8.1         S4Arrays_1.8.0           
 [17] AnnotationHub_3.16.0      curl_6.2.2               
 [19] BiocNeighbors_2.2.0       Rhdf5lib_1.30.0          
 [21] SparseArray_1.8.0         rhdf5_2.52.0             
 [23] sass_0.4.10               alabaster.base_1.8.0     
 [25] bslib_0.9.0               alabaster.sce_1.8.0      
 [27] httr2_1.1.2               cachem_1.1.0             
 [29] ResidualMatrix_1.18.0     GenomicAlignments_1.44.0 
 [31] igraph_2.1.4              lifecycle_1.0.4          
 [33] pkgconfig_2.0.3           rsvd_1.0.5               
 [35] Matrix_1.7-3              R6_2.6.1                 
 [37] fastmap_1.2.0             GenomeInfoDbData_1.2.14  
 [39] digest_0.6.37             colorspace_2.1-1         
 [41] dqrng_0.4.1               irlba_2.3.5.1            
 [43] ExperimentHub_2.16.0      RSQLite_2.3.9            
 [45] beachmat_2.24.0           labeling_0.4.3           
 [47] filelock_1.0.3            httr_1.4.7               
 [49] abind_1.4-8               compiler_4.5.0           
 [51] bit64_4.6.0-1             withr_3.0.2              
 [53] BiocParallel_1.42.0       viridis_0.6.5            
 [55] DBI_1.2.3                 HDF5Array_1.36.0         
 [57] alabaster.ranges_1.8.0    alabaster.schemas_1.8.0  
 [59] rappdirs_0.3.3            DelayedArray_0.34.0      
 [61] bluster_1.18.0            rjson_0.2.23             
 [63] tools_4.5.0               vipor_0.4.7              
 [65] beeswarm_0.4.0            glue_1.8.0               
 [67] h5mread_1.0.0             restfulr_0.0.15          
 [69] rhdf5filters_1.20.0       grid_4.5.0               
 [71] Rtsne_0.17                cluster_2.1.8.1          
 [73] gtable_0.3.6              ensembldb_2.32.0         
 [75] metapod_1.16.0            BiocSingular_1.24.0      
 [77] ScaledMatrix_1.16.0       XVector_0.48.0           
 [79] ggrepel_0.9.6             BiocVersion_3.21.1       
 [81] pillar_1.10.2             limma_3.64.0             
 [83] dplyr_1.1.4               BiocFileCache_2.16.0     
 [85] lattice_0.22-7            rtracklayer_1.68.0       
 [87] bit_4.6.0                 tidyselect_1.2.1         
 [89] locfit_1.5-9.12           Biostrings_2.76.0        
 [91] knitr_1.50                gridExtra_2.3            
 [93] bookdown_0.43             ProtGenerics_1.40.0      
 [95] edgeR_4.6.0               xfun_0.52                
 [97] statmod_1.5.0             UCSC.utils_1.4.0         
 [99] lazyeval_0.2.2            yaml_2.3.10              
[101] evaluate_1.0.3            codetools_0.2-20         
[103] tibble_3.2.1              alabaster.matrix_1.8.0   
[105] BiocManager_1.30.25       graph_1.86.0             
[107] cli_3.6.4                 munsell_0.5.1            
[109] jquerylib_0.1.4           Rcpp_1.0.14              
[111] dir.expiry_1.16.0         dbplyr_2.5.0             
[113] png_0.1-8                 XML_3.99-0.18            
[115] parallel_4.5.0            blob_1.2.4               
[117] AnnotationFilter_1.32.0   sparseMatrixStats_1.20.0 
[119] bitops_1.0-9              viridisLite_0.4.2        
[121] alabaster.se_1.8.0        scales_1.3.0             
[123] crayon_1.5.3              rlang_1.1.6              
[125] cowplot_1.1.3             KEGGREST_1.48.0          

References

Grun, D., M. J. Muraro, J. C. Boisset, K. Wiebrands, A. Lyubimova, G. Dharmadhikari, M. van den Born, et al. 2016. β€œDe Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data.” Cell Stem Cell 19 (2): 266–77.