# Muraro human pancreas (CEL-seq)

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## Introduction

This performs an analysis of the @muraro2016singlecell CEL-seq dataset,
consisting of human pancreas cells from various donors.

## Data loading


``` r
library(scRNAseq)
sce.muraro <- MuraroPancreasData()
```

Converting back to Ensembl identifiers.


``` r
library(AnnotationHub)
edb <- AnnotationHub()[["AH73881"]]
gene.symb <- sub("__chr.*$", "", rownames(sce.muraro))
gene.ids <- mapIds(edb, keys=gene.symb, 
    keytype="SYMBOL", column="GENEID")

# Removing duplicated genes or genes without Ensembl IDs.
keep <- !is.na(gene.ids) & !duplicated(gene.ids)
sce.muraro <- sce.muraro[keep,]
rownames(sce.muraro) <- gene.ids[keep]
```

## Quality control


``` r
unfiltered <- sce.muraro
```

This dataset lacks mitochondrial genes so we will do without.
For the one batch that seems to have a high proportion of low-quality cells, we compute an appropriate filter threshold using a shared median and MAD from the other batches (Figure \@ref(fig:unref-muraro-qc-dist)).


``` r
library(scater)
stats <- perCellQCMetrics(sce.muraro)
qc <- quickPerCellQC(stats, percent_subsets="altexps_ERCC_percent",
    batch=sce.muraro$donor, subset=sce.muraro$donor!="D28")
sce.muraro <- sce.muraro[,!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="muraro-pancreas_files/figure-html/unref-muraro-qc-dist-1.png" alt="Distribution of each QC metric across cells from each donor in the Muraro pancreas dataset. Each point represents a cell and is colored according to whether that cell was discarded." width="672" />
<p class="caption">(\#fig:unref-muraro-qc-dist)Distribution of each QC metric across cells from each donor in the Muraro pancreas dataset. Each point represents a cell and is colored according to whether that cell was discarded.</p>
</div>

We have a look at the causes of removal:


``` r
colSums(as.matrix(qc))
```

```
##              low_lib_size            low_n_features high_altexps_ERCC_percent 
##                       663                       700                       738 
##                   discard 
##                       773
```

## Normalization


``` r
library(scran)
set.seed(1000)
clusters <- quickCluster(sce.muraro)
sce.muraro <- computeSumFactors(sce.muraro, clusters=clusters)
sce.muraro <- logNormCounts(sce.muraro)
```


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

```
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0878  0.5411  0.8208  1.0000  1.2108 13.9869
```


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

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

## Variance modelling

We block on a combined plate and donor factor.


``` r
block <- paste0(sce.muraro$plate, "_", sce.muraro$donor)
dec.muraro <- modelGeneVarWithSpikes(sce.muraro, "ERCC", block=block)
top.muraro <- getTopHVGs(dec.muraro, prop=0.1)
```


``` r
par(mfrow=c(8,4))
blocked.stats <- dec.muraro$per.block
for (i in colnames(blocked.stats)) {
    current <- blocked.stats[[i]]
    plot(current$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="muraro-pancreas_files/figure-html/unref-muraro-variance-1.png" alt="Per-gene variance as a function of the mean for the log-expression values in the Muraro 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-muraro-variance)Per-gene variance as a function of the mean for the log-expression values in the Muraro 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.muraro <- fastMNN(sce.muraro, subset.row=top.muraro, 
    batch=sce.muraro$donor)
```

We use the proportion of variance lost as a diagnostic measure:


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

```
##           D28      D29      D30     D31
## [1,] 0.060847 0.024121 0.000000 0.00000
## [2,] 0.002646 0.003018 0.062421 0.00000
## [3,] 0.003449 0.002641 0.002598 0.08162
```

## Dimensionality reduction


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

## Clustering


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


``` r
tab <- table(Cluster=colLabels(merged.muraro), CellType=sce.muraro$label)
library(pheatmap)
pheatmap(log10(tab+10), color=viridis::viridis(100))
```

<div class="figure">
<img src="muraro-pancreas_files/figure-html/unref-seger-heat-1.png" alt="Heatmap of the frequency of cells from each cell type label in each cluster." width="672" />
<p class="caption">(\#fig:unref-seger-heat)Heatmap of the frequency of cells from each cell type label in each cluster.</p>
</div>


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

```
##        Donor
## Cluster D28 D29 D30 D31
##      1  104   6  57 112
##      2   59  21  77  97
##      3   12  75  64  43
##      4   28 149 126 120
##      5   87 261 277 214
##      6   21   7  54  26
##      7    1   6   6  37
##      8    6   6   5   2
##      9   11  68   5  30
##      10   4   2   5   8
```


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

<div class="figure">
<img src="muraro-pancreas_files/figure-html/unref-muraro-tsne-1.png" alt="Obligatory $t$-SNE plots of the Muraro pancreas dataset. Each point represents a cell that is colored by cluster (left) or batch (right)." width="672" />
<p class="caption">(\#fig:unref-muraro-tsne)Obligatory $t$-SNE plots of the Muraro 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] pheatmap_1.0.13             batchelor_1.26.0           
 [3] scran_1.38.0                scater_1.38.0              
 [5] ggplot2_4.0.0               scuttle_1.20.0             
 [7] ensembldb_2.34.0            AnnotationFilter_1.34.0    
 [9] GenomicFeatures_1.62.0      AnnotationDbi_1.72.0       
[11] AnnotationHub_4.0.0         BiocFileCache_3.0.0        
[13] dbplyr_2.5.1                scRNAseq_2.24.0            
[15] SingleCellExperiment_1.32.0 SummarizedExperiment_1.40.0
[17] Biobase_2.70.0              GenomicRanges_1.62.0       
[19] Seqinfo_1.0.0               IRanges_2.44.0             
[21] S4Vectors_0.48.0            BiocGenerics_0.56.0        
[23] generics_0.1.4              MatrixGenerics_1.22.0      
[25] matrixStats_1.5.0           BiocStyle_2.38.0           
[27] 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          gypsum_1.6.0             
  [7] farver_2.1.2              rmarkdown_2.30           
  [9] BiocIO_1.20.0             vctrs_0.6.5              
 [11] DelayedMatrixStats_1.32.0 memoise_2.0.1            
 [13] Rsamtools_2.26.0          RCurl_1.98-1.17          
 [15] htmltools_0.5.8.1         S4Arrays_1.10.0          
 [17] curl_7.0.0                BiocNeighbors_2.4.0      
 [19] Rhdf5lib_1.32.0           SparseArray_1.10.1       
 [21] rhdf5_2.54.0              sass_0.4.10              
 [23] alabaster.base_1.10.0     bslib_0.9.0              
 [25] alabaster.sce_1.10.0      httr2_1.2.1              
 [27] cachem_1.1.0              ResidualMatrix_1.20.0    
 [29] GenomicAlignments_1.46.0  igraph_2.2.1             
 [31] lifecycle_1.0.4           pkgconfig_2.0.3          
 [33] rsvd_1.0.5                Matrix_1.7-4             
 [35] R6_2.6.1                  fastmap_1.2.0            
 [37] digest_0.6.37             dqrng_0.4.1              
 [39] irlba_2.3.5.1             ExperimentHub_3.0.0      
 [41] RSQLite_2.4.3             beachmat_2.26.0          
 [43] labeling_0.4.3            filelock_1.0.3           
 [45] httr_1.4.7                abind_1.4-8              
 [47] compiler_4.5.1            bit64_4.6.0-1            
 [49] withr_3.0.2               S7_0.2.0                 
 [51] BiocParallel_1.44.0       viridis_0.6.5            
 [53] DBI_1.2.3                 HDF5Array_1.38.0         
 [55] alabaster.ranges_1.10.0   alabaster.schemas_1.10.0 
 [57] rappdirs_0.3.3            DelayedArray_0.36.0      
 [59] bluster_1.20.0            rjson_0.2.23             
 [61] tools_4.5.1               vipor_0.4.7              
 [63] beeswarm_0.4.0            glue_1.8.0               
 [65] h5mread_1.2.0             restfulr_0.0.16          
 [67] rhdf5filters_1.22.0       grid_4.5.1               
 [69] Rtsne_0.17                cluster_2.1.8.1          
 [71] gtable_0.3.6              metapod_1.18.0           
 [73] BiocSingular_1.26.0       ScaledMatrix_1.18.0      
 [75] XVector_0.50.0            ggrepel_0.9.6            
 [77] BiocVersion_3.22.0        pillar_1.11.1            
 [79] limma_3.66.0              dplyr_1.1.4              
 [81] lattice_0.22-7            rtracklayer_1.70.0       
 [83] bit_4.6.0                 tidyselect_1.2.1         
 [85] locfit_1.5-9.12           Biostrings_2.78.0        
 [87] knitr_1.50                gridExtra_2.3            
 [89] bookdown_0.45             ProtGenerics_1.42.0      
 [91] edgeR_4.8.0               xfun_0.54                
 [93] statmod_1.5.1             UCSC.utils_1.6.0         
 [95] lazyeval_0.2.2            yaml_2.3.10              
 [97] evaluate_1.0.5            codetools_0.2-20         
 [99] cigarillo_1.0.0           tibble_3.3.0             
[101] alabaster.matrix_1.10.0   BiocManager_1.30.26      
[103] graph_1.88.0              cli_3.6.5                
[105] jquerylib_0.1.4           dichromat_2.0-0.1        
[107] Rcpp_1.1.0                GenomeInfoDb_1.46.0      
[109] dir.expiry_1.18.0         png_0.1-8                
[111] XML_3.99-0.19             parallel_4.5.1           
[113] blob_1.2.4                sparseMatrixStats_1.22.0 
[115] bitops_1.0-9              viridisLite_0.4.2        
[117] alabaster.se_1.10.0       scales_1.4.0             
[119] purrr_1.1.0               crayon_1.5.3             
[121] rlang_1.1.6               cowplot_1.2.0            
[123] KEGGREST_1.50.0          
```
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