# Filtered 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     113    387    825
##   2     507    395    806
##   3     175    344    581
##   4     295    539   1018
##   5     346    638   1210
##   6      11      3      9
##   7      17     27    111
##   8      33    113    185
##   9     423    754   1546
##   10      4     36     67
##   11    197    124    221
##   12    150    180    293
##   13    327    588   1125
##   14     11     54    160
```

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 4.0.4 (2021-02-15)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.2 LTS

Matrix products: default
BLAS:   /home/biocbuild/bbs-3.12-books/R/lib/libRblas.so
LAPACK: /home/biocbuild/bbs-3.12-books/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        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] parallel  stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] batchelor_1.6.2             BiocSingular_1.6.0         
 [3] scran_1.18.5                scater_1.18.6              
 [5] ggplot2_3.3.3               TENxPBMCData_1.8.0         
 [7] HDF5Array_1.18.1            rhdf5_2.34.0               
 [9] DelayedArray_0.16.2         Matrix_1.3-2               
[11] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
[13] Biobase_2.50.0              GenomicRanges_1.42.0       
[15] GenomeInfoDb_1.26.4         IRanges_2.24.1             
[17] S4Vectors_0.28.1            BiocGenerics_0.36.0        
[19] MatrixGenerics_1.2.1        matrixStats_0.58.0         
[21] BiocStyle_2.18.1            rebook_1.0.0               

loaded via a namespace (and not attached):
  [1] AnnotationHub_2.22.0          BiocFileCache_1.14.0         
  [3] igraph_1.2.6                  BiocParallel_1.24.1          
  [5] digest_0.6.27                 htmltools_0.5.1.1            
  [7] viridis_0.5.1                 fansi_0.4.2                  
  [9] magrittr_2.0.1                memoise_2.0.0                
 [11] limma_3.46.0                  colorspace_2.0-0             
 [13] blob_1.2.1                    rappdirs_0.3.3               
 [15] xfun_0.22                     dplyr_1.0.5                  
 [17] callr_3.5.1                   crayon_1.4.1                 
 [19] RCurl_1.98-1.3                jsonlite_1.7.2               
 [21] graph_1.68.0                  glue_1.4.2                   
 [23] gtable_0.3.0                  zlibbioc_1.36.0              
 [25] XVector_0.30.0                Rhdf5lib_1.12.1              
 [27] scales_1.1.1                  DBI_1.1.1                    
 [29] edgeR_3.32.1                  Rcpp_1.0.6                   
 [31] viridisLite_0.3.0             xtable_1.8-4                 
 [33] dqrng_0.2.1                   bit_4.0.4                    
 [35] rsvd_1.0.3                    ResidualMatrix_1.0.0         
 [37] httr_1.4.2                    FNN_1.1.3                    
 [39] ellipsis_0.3.1                pkgconfig_2.0.3              
 [41] XML_3.99-0.6                  farver_2.1.0                 
 [43] scuttle_1.0.4                 CodeDepends_0.6.5            
 [45] sass_0.3.1                    uwot_0.1.10                  
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 [49] utf8_1.2.1                    tidyselect_1.1.0             
 [51] labeling_0.4.2                rlang_0.4.10                 
 [53] later_1.1.0.1                 AnnotationDbi_1.52.0         
 [55] munsell_0.5.0                 BiocVersion_3.12.0           
 [57] tools_4.0.4                   cachem_1.0.4                 
 [59] generics_0.1.0                RSQLite_2.2.4                
 [61] ExperimentHub_1.16.0          evaluate_0.14                
 [63] stringr_1.4.0                 fastmap_1.1.0                
 [65] yaml_2.2.1                    processx_3.4.5               
 [67] knitr_1.31                    bit64_4.0.5                  
 [69] purrr_0.3.4                   sparseMatrixStats_1.2.1      
 [71] mime_0.10                     compiler_4.0.4               
 [73] beeswarm_0.3.1                curl_4.3                     
 [75] interactiveDisplayBase_1.28.0 tibble_3.1.0                 
 [77] statmod_1.4.35                bslib_0.2.4                  
 [79] stringi_1.5.3                 highr_0.8                    
 [81] ps_1.6.0                      RSpectra_0.16-0              
 [83] lattice_0.20-41               bluster_1.0.0                
 [85] vctrs_0.3.6                   pillar_1.5.1                 
 [87] lifecycle_1.0.0               rhdf5filters_1.2.0           
 [89] BiocManager_1.30.10           jquerylib_0.1.3              
 [91] RcppAnnoy_0.0.18              BiocNeighbors_1.8.2          
 [93] cowplot_1.1.1                 bitops_1.0-6                 
 [95] irlba_2.3.3                   httpuv_1.5.5                 
 [97] R6_2.5.0                      bookdown_0.21                
 [99] promises_1.2.0.1              gridExtra_2.3                
[101] vipor_0.4.5                   codetools_0.2-18             
[103] assertthat_0.2.1              withr_2.4.1                  
[105] GenomeInfoDbData_1.2.4        grid_4.0.4                   
[107] beachmat_2.6.4                rmarkdown_2.7                
[109] DelayedMatrixStats_1.12.3     Rtsne_0.15                   
[111] shiny_1.6.0                   ggbeeswarm_0.6.0             
```
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