# Grun human pancreas (CEL-seq2) 

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## Introduction

This workflow performs an analysis of the @grun2016denovo CEL-seq2 dataset consisting of human pancreas cells from various donors.

## Data loading


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

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


``` r
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]
```

## Quality control


``` r
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 \@ref(fig:unref-grun-qc-dist)),
we compute an appropriate threshold using the other donors as specified in the `subset=` argument.


``` r
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]
```


``` r
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
)
```

<div class="figure">
<img src="grun-pancreas_files/figure-html/unref-grun-qc-dist-1.png" alt="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." width="672" />
<p class="caption">(\#fig:unref-grun-qc-dist)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.</p>
</div>


``` r
colSums(as.matrix(qc), na.rm=TRUE)
```

```
##              low_lib_size            low_n_features high_altexps_ERCC_percent 
##                       451                       510                       606 
##                   discard 
##                       664
```

## Normalization 


``` r
library(scran)
set.seed(1000) # for irlba. 
clusters <- quickCluster(sce.grun)
sce.grun <- computeSumFactors(sce.grun, clusters=clusters)
sce.grun <- logNormCounts(sce.grun)
```


``` r
summary(sizeFactors(sce.grun))
```

```
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0894  0.5065  0.7913  1.0000  1.2287 10.9872
```


``` r
plot(librarySizeFactors(sce.grun), sizeFactors(sce.grun), pch=16,
    xlab="Library size factors", ylab="Deconvolution factors", log="xy")
```

<div class="figure">
<img src="grun-pancreas_files/figure-html/unref-grun-norm-1.png" alt="Relationship between the library size factors and the deconvolution size factors in the Grun pancreas dataset." width="672" />
<p class="caption">(\#fig:unref-grun-norm)Relationship between the library size factors and the deconvolution size factors in the Grun pancreas dataset.</p>
</div>

## Variance modelling

We block on a combined plate and donor factor.


``` r
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.


``` r
table(block)
```

```
## block
##                  CD13+ sorted cells_D17       CD24+ CD44+ live sorted cells_D17 
##                                      87                                      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 
##                                      16                                      83 
##         live sorted cells, library 4_D3         live sorted cells, library 4_D7 
##                                      29                                      83
```


``` r
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)
}
```

<div class="figure">
<img src="grun-pancreas_files/figure-html/unref-416b-variance-1.png" alt="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." width="672" />
<p class="caption">(\#fig:unref-416b-variance)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.</p>
</div>

## Data integration


``` r
library(batchelor)
set.seed(1001010)
merged.grun <- fastMNN(sce.grun, subset.row=top.grun, batch=sce.grun$donor)
```


``` r
metadata(merged.grun)$merge.info$lost.var
```

```
##           D10      D17      D2      D3      D7
## [1,] 0.029802 0.031531 0.00000 0.00000 0.00000
## [2,] 0.007644 0.012248 0.03811 0.00000 0.00000
## [3,] 0.004034 0.005225 0.00759 0.05263 0.00000
## [4,] 0.014121 0.016981 0.01607 0.01501 0.05541
```

## Dimensionality reduction


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

## Clustering


``` r
snn.gr <- buildSNNGraph(merged.grun, use.dimred="corrected")
colLabels(merged.grun) <- factor(igraph::cluster_walktrap(snn.gr)$membership)
```


``` r
table(Cluster=colLabels(merged.grun), Donor=merged.grun$batch)
```

```
##        Donor
## Cluster D10 D17  D2  D3  D7
##      1   32  71  31  80  28
##      2    2   8   3   3   7
##      3    1  10   0   0   8
##      4    4   4   2   4   2
##      5   13  72  29   3  72
##      6   11 119   0   0  55
##      7    3  42   0   0   9
##      8    5  18   0   2  34
##      9   14  30   3   2  66
##      10  14  35  14  10  43
##      11   5  13   0   0  10
##      12   4  13   0   0   1
```


``` r
gridExtra::grid.arrange(
    plotTSNE(merged.grun, colour_by="label"),
    plotTSNE(merged.grun, colour_by="batch"),
    ncol=2
)
```

<div class="figure">
<img src="grun-pancreas_files/figure-html/unref-grun-tsne-1.png" alt="Obligatory $t$-SNE plots of the Grun pancreas dataset. Each point represents a cell that is colored by cluster (left) or batch (right)." width="672" />
<p class="caption">(\#fig:unref-grun-tsne)Obligatory $t$-SNE plots of the Grun pancreas dataset. Each point represents a cell that is colored by cluster (left) or batch (right).</p>
</div>

## Session Info {-}

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```
R version 4.5.1 Patched (2025-08-23 r88802)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.3 LTS

Matrix products: default
BLAS:   /home/biocbuild/bbs-3.22-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_1.26.0            scran_1.38.0               
 [3] scater_1.38.0               ggplot2_4.0.0              
 [5] scuttle_1.20.0              org.Hs.eg.db_3.22.0        
 [7] AnnotationDbi_1.72.0        scRNAseq_2.24.0            
 [9] SingleCellExperiment_1.32.0 SummarizedExperiment_1.40.0
[11] Biobase_2.70.0              GenomicRanges_1.62.0       
[13] Seqinfo_1.0.0               IRanges_2.44.0             
[15] S4Vectors_0.48.0            BiocGenerics_0.56.0        
[17] generics_0.1.4              MatrixGenerics_1.22.0      
[19] matrixStats_1.5.0           BiocStyle_2.38.0           
[21] rebook_1.20.0              

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3        jsonlite_2.0.0           
  [3] CodeDepends_0.6.6         magrittr_2.0.4           
  [5] ggbeeswarm_0.7.2          GenomicFeatures_1.62.0   
  [7] gypsum_1.6.0              farver_2.1.2             
  [9] rmarkdown_2.30            BiocIO_1.20.0            
 [11] vctrs_0.6.5               DelayedMatrixStats_1.32.0
 [13] memoise_2.0.1             Rsamtools_2.26.0         
 [15] RCurl_1.98-1.17           htmltools_0.5.8.1        
 [17] S4Arrays_1.10.0           AnnotationHub_4.0.0      
 [19] curl_7.0.0                BiocNeighbors_2.4.0      
 [21] Rhdf5lib_1.32.0           SparseArray_1.10.1       
 [23] rhdf5_2.54.0              sass_0.4.10              
 [25] alabaster.base_1.10.0     bslib_0.9.0              
 [27] alabaster.sce_1.10.0      httr2_1.2.1              
 [29] cachem_1.1.0              ResidualMatrix_1.20.0    
 [31] GenomicAlignments_1.46.0  igraph_2.2.1             
 [33] lifecycle_1.0.4           pkgconfig_2.0.3          
 [35] rsvd_1.0.5                Matrix_1.7-4             
 [37] R6_2.6.1                  fastmap_1.2.0            
 [39] digest_0.6.37             dqrng_0.4.1              
 [41] irlba_2.3.5.1             ExperimentHub_3.0.0      
 [43] RSQLite_2.4.3             beachmat_2.26.0          
 [45] labeling_0.4.3            filelock_1.0.3           
 [47] httr_1.4.7                abind_1.4-8              
 [49] compiler_4.5.1            bit64_4.6.0-1            
 [51] withr_3.0.2               S7_0.2.0                 
 [53] BiocParallel_1.44.0       viridis_0.6.5            
 [55] DBI_1.2.3                 HDF5Array_1.38.0         
 [57] alabaster.ranges_1.10.0   alabaster.schemas_1.10.0 
 [59] rappdirs_0.3.3            DelayedArray_0.36.0      
 [61] bluster_1.20.0            rjson_0.2.23             
 [63] tools_4.5.1               vipor_0.4.7              
 [65] beeswarm_0.4.0            glue_1.8.0               
 [67] h5mread_1.2.0             restfulr_0.0.16          
 [69] rhdf5filters_1.22.0       grid_4.5.1               
 [71] Rtsne_0.17                cluster_2.1.8.1          
 [73] gtable_0.3.6              ensembldb_2.34.0         
 [75] metapod_1.18.0            BiocSingular_1.26.0      
 [77] ScaledMatrix_1.18.0       XVector_0.50.0           
 [79] ggrepel_0.9.6             BiocVersion_3.22.0       
 [81] pillar_1.11.1             limma_3.66.0             
 [83] dplyr_1.1.4               BiocFileCache_3.0.0      
 [85] lattice_0.22-7            rtracklayer_1.70.0       
 [87] bit_4.6.0                 tidyselect_1.2.1         
 [89] locfit_1.5-9.12           Biostrings_2.78.0        
 [91] knitr_1.50                gridExtra_2.3            
 [93] bookdown_0.45             ProtGenerics_1.42.0      
 [95] edgeR_4.8.0               xfun_0.54                
 [97] statmod_1.5.1             UCSC.utils_1.6.0         
 [99] lazyeval_0.2.2            yaml_2.3.10              
[101] evaluate_1.0.5            codetools_0.2-20         
[103] cigarillo_1.0.0           tibble_3.3.0             
[105] alabaster.matrix_1.10.0   BiocManager_1.30.26      
[107] graph_1.88.0              cli_3.6.5                
[109] jquerylib_0.1.4           dichromat_2.0-0.1        
[111] Rcpp_1.1.0                GenomeInfoDb_1.46.0      
[113] dir.expiry_1.18.0         dbplyr_2.5.1             
[115] png_0.1-8                 XML_3.99-0.19            
[117] parallel_4.5.1            blob_1.2.4               
[119] AnnotationFilter_1.34.0   sparseMatrixStats_1.22.0 
[121] bitops_1.0-9              viridisLite_0.4.2        
[123] alabaster.se_1.10.0       scales_1.4.0             
[125] crayon_1.5.3              rlang_1.1.6              
[127] cowplot_1.2.0             KEGGREST_1.50.0          
```
</div>
