# (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 
## 487 154 603 514  31 150 179 333 147  11 
## 
## $pbmc4k
## 
##    1    2    3    4    5    6    7    8    9   10   11   12   13 
##  497  185  569  786  373  232   44 1023   77  218   88   54   36 
## 
## $pbmc8k
## 
##    1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
## 1004  759 1073 1543  367  150  201 2067   59  154  244   67   76  285   20   15 
##   17   18 
##   64    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,] 7.003e-03 3.126e-03 0.000000
## [2,] 7.137e-05 5.125e-05 0.003003
```

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     523    405    826
##   2     327    588   1125
##   3     185    122    218
##   4     150    180    293
##   5     172    340    577
##   6     283    533   1002
##   7     346    638   1203
##   8     433    749   1533
##   9      17     27    111
##   10    112    387    832
##   11     34    120    204
##   12     11     54    160
##   13     12      3      9
##   14      4     36     64
```

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 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.4 LTS

Matrix products: default
BLAS:   /home/biocbuild/bbs-3.15-bioc/R/lib/libRblas.so
LAPACK: /home/biocbuild/bbs-3.15-bioc/R/lib/libRlapack.so

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       

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

other attached packages:
 [1] batchelor_1.12.0            BiocSingular_1.12.0        
 [3] scran_1.24.0                scater_1.24.0              
 [5] ggplot2_3.3.6               scuttle_1.6.2              
 [7] TENxPBMCData_1.14.0         HDF5Array_1.24.0           
 [9] rhdf5_2.40.0                DelayedArray_0.22.0        
[11] Matrix_1.4-1                SingleCellExperiment_1.18.0
[13] SummarizedExperiment_1.26.1 Biobase_2.56.0             
[15] GenomicRanges_1.48.0        GenomeInfoDb_1.32.2        
[17] IRanges_2.30.0              S4Vectors_0.34.0           
[19] BiocGenerics_0.42.0         MatrixGenerics_1.8.0       
[21] matrixStats_0.62.0          BiocStyle_2.24.0           
[23] rebook_1.6.0               

loaded via a namespace (and not attached):
  [1] AnnotationHub_3.4.0           BiocFileCache_2.4.0          
  [3] igraph_1.3.1                  BiocParallel_1.30.2          
  [5] digest_0.6.29                 htmltools_0.5.2              
  [7] viridis_0.6.2                 fansi_1.0.3                  
  [9] magrittr_2.0.3                memoise_2.0.1                
 [11] ScaledMatrix_1.4.0            cluster_2.1.3                
 [13] limma_3.52.1                  Biostrings_2.64.0            
 [15] colorspace_2.0-3              blob_1.2.3                   
 [17] rappdirs_0.3.3                ggrepel_0.9.1                
 [19] xfun_0.31                     dplyr_1.0.9                  
 [21] crayon_1.5.1                  RCurl_1.98-1.6               
 [23] jsonlite_1.8.0                graph_1.74.0                 
 [25] glue_1.6.2                    gtable_0.3.0                 
 [27] zlibbioc_1.42.0               XVector_0.36.0               
 [29] Rhdf5lib_1.18.2               scales_1.2.0                 
 [31] DBI_1.1.2                     edgeR_3.38.1                 
 [33] Rcpp_1.0.8.3                  viridisLite_0.4.0            
 [35] xtable_1.8-4                  dqrng_0.3.0                  
 [37] bit_4.0.4                     rsvd_1.0.5                   
 [39] ResidualMatrix_1.6.0          metapod_1.4.0                
 [41] httr_1.4.3                    FNN_1.1.3                    
 [43] dir.expiry_1.4.0              ellipsis_0.3.2               
 [45] pkgconfig_2.0.3               XML_3.99-0.9                 
 [47] farver_2.1.0                  uwot_0.1.11                  
 [49] CodeDepends_0.6.5             sass_0.4.1                   
 [51] dbplyr_2.1.1                  locfit_1.5-9.5               
 [53] utf8_1.2.2                    tidyselect_1.1.2             
 [55] labeling_0.4.2                rlang_1.0.2                  
 [57] later_1.3.0                   AnnotationDbi_1.58.0         
 [59] munsell_0.5.0                 BiocVersion_3.15.2           
 [61] tools_4.2.0                   cachem_1.0.6                 
 [63] cli_3.3.0                     generics_0.1.2               
 [65] RSQLite_2.2.14                ExperimentHub_2.4.0          
 [67] evaluate_0.15                 stringr_1.4.0                
 [69] fastmap_1.1.0                 yaml_2.3.5                   
 [71] knitr_1.39                    bit64_4.0.5                  
 [73] purrr_0.3.4                   KEGGREST_1.36.0              
 [75] sparseMatrixStats_1.8.0       mime_0.12                    
 [77] compiler_4.2.0                beeswarm_0.4.0               
 [79] filelock_1.0.2                curl_4.3.2                   
 [81] png_0.1-7                     interactiveDisplayBase_1.34.0
 [83] tibble_3.1.7                  statmod_1.4.36               
 [85] bslib_0.3.1                   stringi_1.7.6                
 [87] highr_0.9                     RSpectra_0.16-1              
 [89] lattice_0.20-45               bluster_1.6.0                
 [91] vctrs_0.4.1                   pillar_1.7.0                 
 [93] lifecycle_1.0.1               rhdf5filters_1.8.0           
 [95] BiocManager_1.30.18           jquerylib_0.1.4              
 [97] RcppAnnoy_0.0.19              BiocNeighbors_1.14.0         
 [99] cowplot_1.1.1                 bitops_1.0-7                 
[101] irlba_2.3.5                   httpuv_1.6.5                 
[103] R6_2.5.1                      bookdown_0.26                
[105] promises_1.2.0.1              gridExtra_2.3                
[107] vipor_0.4.5                   codetools_0.2-18             
[109] assertthat_0.2.1              withr_2.5.0                  
[111] GenomeInfoDbData_1.2.8        parallel_4.2.0               
[113] grid_4.2.0                    beachmat_2.12.0              
[115] rmarkdown_2.14                DelayedMatrixStats_1.18.0    
[117] Rtsne_0.16                    shiny_1.7.1                  
[119] ggbeeswarm_0.6.0             
```
</div>