# (PART) Case studies {-}

# Human PBMCs (10X Genomics)

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## Introduction

This performs an analysis of the public PBMC ID dataset generated by 10X Genomics [@zheng2017massively],
starting from the filtered count matrix.

## Data loading


``` r
library(TENxPBMCData)
all.sce <- list(
    pbmc3k=TENxPBMCData('pbmc3k'),
    pbmc4k=TENxPBMCData('pbmc4k'),
    pbmc8k=TENxPBMCData('pbmc8k')
)
```

## Quality control


``` r
unfiltered <- all.sce
```

Cell calling implicitly serves as a QC step to remove libraries with low total counts and number of detected genes.
Thus, we will only filter on the mitochondrial proportion.


``` r
library(scater)
stats <- high.mito <- list()
for (n in names(all.sce)) {
    current <- all.sce[[n]]
    is.mito <- grep("MT", rowData(current)$Symbol_TENx)
    stats[[n]] <- perCellQCMetrics(current, subsets=list(Mito=is.mito))
    high.mito[[n]] <- isOutlier(stats[[n]]$subsets_Mito_percent, type="higher")
    all.sce[[n]] <- current[,!high.mito[[n]]]
}
```


``` r
qcplots <- list()
for (n in names(all.sce)) {
    current <- unfiltered[[n]]
    colData(current) <- cbind(colData(current), stats[[n]])
    current$discard <- high.mito[[n]]
    qcplots[[n]] <- plotColData(current, x="sum", y="subsets_Mito_percent",
        colour_by="discard") + scale_x_log10()
}
do.call(gridExtra::grid.arrange, c(qcplots, ncol=3))
```

<div class="figure">
<img src="tenx-filtered-pbmc3k-4k-8k_files/figure-html/unref-pbmc-filtered-var-1.png" alt="Percentage of mitochondrial reads in each cell in each of the 10X PBMC datasets, compared to the total count. Each point represents a cell and is colored according to whether that cell was discarded." width="672" />
<p class="caption">(\#fig:unref-pbmc-filtered-var)Percentage of mitochondrial reads in each cell in each of the 10X PBMC datasets, compared to the total count. Each point represents a cell and is colored according to whether that cell was discarded.</p>
</div>


``` r
lapply(high.mito, summary)
```

```
## $pbmc3k
##    Mode   FALSE    TRUE 
## logical    2609      91 
## 
## $pbmc4k
##    Mode   FALSE    TRUE 
## logical    4182     158 
## 
## $pbmc8k
##    Mode   FALSE    TRUE 
## logical    8157     224
```

## Normalization

We perform library size normalization, simply for convenience when dealing with file-backed matrices.


``` r
all.sce <- lapply(all.sce, logNormCounts)
```


``` r
lapply(all.sce, function(x) summary(sizeFactors(x)))
```

```
## $pbmc3k
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.234   0.748   0.926   1.000   1.157   6.604 
## 
## $pbmc4k
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.315   0.711   0.890   1.000   1.127  11.027 
## 
## $pbmc8k
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.296   0.704   0.877   1.000   1.118   6.794
```

## Variance modelling


``` r
library(scran)
all.dec <- lapply(all.sce, modelGeneVar)
all.hvgs <- lapply(all.dec, getTopHVGs, prop=0.1)
```


``` r
par(mfrow=c(1,3))
for (n in names(all.dec)) {
    curdec <- all.dec[[n]]
    plot(curdec$mean, curdec$total, pch=16, cex=0.5, main=n,
        xlab="Mean of log-expression", ylab="Variance of log-expression")
    curfit <- metadata(curdec)
    curve(curfit$trend(x), col='dodgerblue', add=TRUE, lwd=2)
}
```

<div class="figure">
<img src="tenx-filtered-pbmc3k-4k-8k_files/figure-html/unref-filtered-pbmc-variance-1.png" alt="Per-gene variance as a function of the mean for the log-expression values in each PBMC dataset. Each point represents a gene (black) with the mean-variance trend (blue) fitted to the variances." width="672" />
<p class="caption">(\#fig:unref-filtered-pbmc-variance)Per-gene variance as a function of the mean for the log-expression values in each PBMC dataset. Each point represents a gene (black) with the mean-variance trend (blue) fitted to the variances.</p>
</div>

## Dimensionality reduction

For various reasons, we will first analyze each PBMC dataset separately rather than merging them together.
We use randomized SVD, which is more efficient for file-backed matrices.


``` r
library(BiocSingular)
set.seed(10000)
all.sce <- mapply(FUN=runPCA, x=all.sce, subset_row=all.hvgs, 
    MoreArgs=list(ncomponents=25, BSPARAM=RandomParam()), 
    SIMPLIFY=FALSE)

set.seed(100000)
all.sce <- lapply(all.sce, runTSNE, dimred="PCA")

set.seed(1000000)
all.sce <- lapply(all.sce, runUMAP, dimred="PCA")
```

## Clustering


``` r
for (n in names(all.sce)) {
    g <- buildSNNGraph(all.sce[[n]], k=10, use.dimred='PCA')
    clust <- igraph::cluster_walktrap(g)$membership
    colLabels(all.sce[[n]])  <- factor(clust)
}
```


``` r
lapply(all.sce, function(x) table(colLabels(x)))
```

```
## $pbmc3k
## 
##   1   2   3   4   5   6   7   8   9  10 
## 475 636 153 476 164  31 159 164 340  11 
## 
## $pbmc4k
## 
##   1   2   3   4   5   6   7   8   9  10  11  12 
## 127 594 518 775 211 394 187 993  55 201  91  36 
## 
## $pbmc8k
## 
##    1    2    3    4    5    6    7    8    9   10   11   12   13   14   15 
##  292 1603  388   94  738 1035 1049  156  203  153 2098  261   64   14    9
```


``` r
all.tsne <- list()
for (n in names(all.sce)) {
    all.tsne[[n]] <- plotTSNE(all.sce[[n]], colour_by="label") + ggtitle(n)
}
do.call(gridExtra::grid.arrange, c(all.tsne, list(ncol=2)))
```

<div class="figure">
<img src="tenx-filtered-pbmc3k-4k-8k_files/figure-html/unref-filtered-pbmc-tsne-1.png" alt="Obligatory $t$-SNE plots of each PBMC dataset, where each point represents a cell in the corresponding dataset and is colored according to the assigned cluster." width="672" />
<p class="caption">(\#fig:unref-filtered-pbmc-tsne)Obligatory $t$-SNE plots of each PBMC dataset, where each point represents a cell in the corresponding dataset and is colored according to the assigned cluster.</p>
</div>

## Data integration

With the per-dataset analyses out of the way, we will now repeat the analysis after merging together the three batches.


``` r
# Intersecting the common genes.
universe <- Reduce(intersect, lapply(all.sce, rownames))
all.sce2 <- lapply(all.sce, "[", i=universe,)
all.dec2 <- lapply(all.dec, "[", i=universe,)

# Renormalizing to adjust for differences in depth.
library(batchelor)
normed.sce <- do.call(multiBatchNorm, all.sce2)

# Identifying a set of HVGs using stats from all batches.
combined.dec <- do.call(combineVar, all.dec2)
combined.hvg <- getTopHVGs(combined.dec, n=5000)

set.seed(1000101)
merged.pbmc <- do.call(fastMNN, c(normed.sce, 
    list(subset.row=combined.hvg, BSPARAM=RandomParam())))
```

We use the percentage of lost variance as a diagnostic measure.


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

```
##         pbmc3k    pbmc4k   pbmc8k
## [1,] 7.044e-03 3.129e-03 0.000000
## [2,] 6.876e-05 4.912e-05 0.003008
```

We proceed to clustering:


``` r
g <- buildSNNGraph(merged.pbmc, use.dimred="corrected")
colLabels(merged.pbmc) <- factor(igraph::cluster_louvain(g)$membership)
table(colLabels(merged.pbmc), merged.pbmc$batch)
```

```
##     
##      pbmc3k pbmc4k pbmc8k
##   1     535    426    830
##   2     331    588   1126
##   3     182    122    217
##   4     150    179    292
##   5     170    345    573
##   6     292    538   1020
##   7     342    630   1236
##   8     437    749   1538
##   9       9     18     95
##   10     97    365    782
##   11     34    120    201
##   12     11     54    159
##   13     11      3      9
##   14      4     36     64
##   15      4      9     15
```

And visualization:


``` r
set.seed(10101010)
merged.pbmc <- runTSNE(merged.pbmc, dimred="corrected")
gridExtra::grid.arrange(
    plotTSNE(merged.pbmc, colour_by="label", text_by="label", text_colour="red"),
    plotTSNE(merged.pbmc, colour_by="batch")
)
```

<div class="figure">
<img src="tenx-filtered-pbmc3k-4k-8k_files/figure-html/unref-filtered-pbmc-merged-tsne-1.png" alt="Obligatory $t$-SNE plots for the merged PBMC datasets, where each point represents a cell and is colored by cluster (top) or batch (bottom)." width="672" />
<p class="caption">(\#fig:unref-filtered-pbmc-merged-tsne)Obligatory $t$-SNE plots for the merged PBMC datasets, where each point represents a cell and is colored by cluster (top) or batch (bottom).</p>
</div>

## Session Info {-}

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<div class="rebook-content">
```
R version 4.5.0 (2025-04-11)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.2 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_1.25.0            BiocSingular_1.25.0        
 [3] scran_1.37.0                scater_1.37.0              
 [5] ggplot2_3.5.2               scuttle_1.19.0             
 [7] TENxPBMCData_1.27.0         HDF5Array_1.37.0           
 [9] h5mread_1.1.0               rhdf5_2.53.0               
[11] DelayedArray_0.35.1         SparseArray_1.9.0          
[13] S4Arrays_1.9.0              abind_1.4-8                
[15] Matrix_1.7-3                SingleCellExperiment_1.31.0
[17] SummarizedExperiment_1.39.0 Biobase_2.69.0             
[19] GenomicRanges_1.61.0        GenomeInfoDb_1.45.0        
[21] IRanges_2.43.0              S4Vectors_0.47.0           
[23] BiocGenerics_0.55.0         generics_0.1.3             
[25] MatrixGenerics_1.21.0       matrixStats_1.5.0          
[27] BiocStyle_2.37.0            rebook_1.19.0              

loaded via a namespace (and not attached):
  [1] DBI_1.2.3                 gridExtra_2.3            
  [3] httr2_1.1.2               CodeDepends_0.6.6        
  [5] rlang_1.1.6               magrittr_2.0.3           
  [7] RcppAnnoy_0.0.22          compiler_4.5.0           
  [9] RSQLite_2.3.9             DelayedMatrixStats_1.31.0
 [11] dir.expiry_1.17.0         png_0.1-8                
 [13] vctrs_0.6.5               pkgconfig_2.0.3          
 [15] crayon_1.5.3              fastmap_1.2.0            
 [17] dbplyr_2.5.0              XVector_0.49.0           
 [19] labeling_0.4.3            rmarkdown_2.29           
 [21] graph_1.87.0              UCSC.utils_1.5.0         
 [23] ggbeeswarm_0.7.2          purrr_1.0.4              
 [25] bit_4.6.0                 bluster_1.19.0           
 [27] xfun_0.52                 cachem_1.1.0             
 [29] beachmat_2.25.0           jsonlite_2.0.0           
 [31] blob_1.2.4                rhdf5filters_1.21.0      
 [33] Rhdf5lib_1.31.0           BiocParallel_1.43.0      
 [35] cluster_2.1.8.1           irlba_2.3.5.1            
 [37] parallel_4.5.0            R6_2.6.1                 
 [39] bslib_0.9.0               limma_3.65.0             
 [41] jquerylib_0.1.4           Rcpp_1.0.14              
 [43] bookdown_0.43             knitr_1.50               
 [45] FNN_1.1.4.1               igraph_2.1.4             
 [47] tidyselect_1.2.1          viridis_0.6.5            
 [49] yaml_2.3.10               codetools_0.2-20         
 [51] curl_6.2.2                lattice_0.22-7           
 [53] tibble_3.2.1              withr_3.0.2              
 [55] KEGGREST_1.49.0           Rtsne_0.17               
 [57] evaluate_1.0.3            BiocFileCache_2.99.0     
 [59] ExperimentHub_2.99.0      Biostrings_2.77.0        
 [61] pillar_1.10.2             BiocManager_1.30.25      
 [63] filelock_1.0.3            BiocVersion_3.22.0       
 [65] sparseMatrixStats_1.21.0  munsell_0.5.1            
 [67] scales_1.3.0              glue_1.8.0               
 [69] metapod_1.17.0            tools_4.5.0              
 [71] AnnotationHub_3.99.0      BiocNeighbors_2.3.0      
 [73] ScaledMatrix_1.17.0       locfit_1.5-9.12          
 [75] XML_3.99-0.18             cowplot_1.1.3            
 [77] grid_4.5.0                edgeR_4.7.0              
 [79] AnnotationDbi_1.71.0      colorspace_2.1-1         
 [81] GenomeInfoDbData_1.2.14   beeswarm_0.4.0           
 [83] vipor_0.4.7               cli_3.6.4                
 [85] rsvd_1.0.5                rappdirs_0.3.3           
 [87] viridisLite_0.4.2         dplyr_1.1.4              
 [89] ResidualMatrix_1.19.0     uwot_0.2.3               
 [91] gtable_0.3.6              sass_0.4.10              
 [93] digest_0.6.37             dqrng_0.4.1              
 [95] ggrepel_0.9.6             farver_2.1.2             
 [97] memoise_2.0.1             htmltools_0.5.8.1        
 [99] lifecycle_1.0.4           httr_1.4.7               
[101] statmod_1.5.0             bit64_4.6.0-1            
```
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