# Bach mouse mammary gland (10X Genomics)

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## Introduction

This performs an analysis of the @bach2017differentiation 10X Genomics dataset,
from which we will consider a single sample of epithelial cells from the mouse mammary gland during gestation.

## Data loading


```r
library(scRNAseq)
sce.mam <- BachMammaryData(samples="G_1")
```


```r
library(scater)
rownames(sce.mam) <- uniquifyFeatureNames(
    rowData(sce.mam)$Ensembl, rowData(sce.mam)$Symbol)

library(AnnotationHub)
ens.mm.v97 <- AnnotationHub()[["AH73905"]]
rowData(sce.mam)$SEQNAME <- mapIds(ens.mm.v97, keys=rowData(sce.mam)$Ensembl,
    keytype="GENEID", column="SEQNAME")
```

## Quality control


```r
unfiltered <- sce.mam
```


```r
is.mito <- rowData(sce.mam)$SEQNAME == "MT"
stats <- perCellQCMetrics(sce.mam, subsets=list(Mito=which(is.mito)))
qc <- quickPerCellQC(stats, percent_subsets="subsets_Mito_percent")
sce.mam <- sce.mam[,!qc$discard]
```


```r
colData(unfiltered) <- cbind(colData(unfiltered), stats)
unfiltered$discard <- qc$discard

gridExtra::grid.arrange(
    plotColData(unfiltered, y="sum", colour_by="discard") + 
        scale_y_log10() + ggtitle("Total count"),
    plotColData(unfiltered, y="detected", colour_by="discard") + 
        scale_y_log10() + ggtitle("Detected features"),
    plotColData(unfiltered, y="subsets_Mito_percent", 
        colour_by="discard") + ggtitle("Mito percent"),
    ncol=2
)
```

<div class="figure">
<img src="bach-mammary_files/figure-html/unref-bach-qc-dist-1.png" alt="Distribution of each QC metric across cells in the Bach mammary gland dataset. Each point represents a cell and is colored according to whether that cell was discarded." width="672" />
<p class="caption">(\#fig:unref-bach-qc-dist)Distribution of each QC metric across cells in the Bach mammary gland dataset. Each point represents a cell and is colored according to whether that cell was discarded.</p>
</div>


```r
plotColData(unfiltered, x="sum", y="subsets_Mito_percent", 
    colour_by="discard") + scale_x_log10()
```

<div class="figure">
<img src="bach-mammary_files/figure-html/unref-bach-qc-comp-1.png" alt="Percentage of mitochondrial reads in each cell in the Bach mammary gland dataset compared to its total count. Each point represents a cell and is colored according to whether that cell was discarded." width="672" />
<p class="caption">(\#fig:unref-bach-qc-comp)Percentage of mitochondrial reads in each cell in the Bach mammary gland dataset compared to its total count. Each point represents a cell and is colored according to whether that cell was discarded.</p>
</div>


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

```
##              low_lib_size            low_n_features high_subsets_Mito_percent 
##                         0                         0                       143 
##                   discard 
##                       143
```

## Normalization 


```r
library(scran)
set.seed(101000110)
clusters <- quickCluster(sce.mam)
sce.mam <- computeSumFactors(sce.mam, clusters=clusters)
sce.mam <- logNormCounts(sce.mam)
```


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

```
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.271   0.522   0.758   1.000   1.204  10.958
```


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

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

## Variance modelling

We use a Poisson-based technical trend to capture more genuine biological variation in the biological component.


```r
set.seed(00010101)
dec.mam <- modelGeneVarByPoisson(sce.mam)
top.mam <- getTopHVGs(dec.mam, prop=0.1)
```


```r
plot(dec.mam$mean, dec.mam$total, pch=16, cex=0.5,
    xlab="Mean of log-expression", ylab="Variance of log-expression")
curfit <- metadata(dec.mam)
curve(curfit$trend(x), col='dodgerblue', add=TRUE, lwd=2)
```

<div class="figure">
<img src="bach-mammary_files/figure-html/unref-bach-var-1.png" alt="Per-gene variance as a function of the mean for the log-expression values in the Bach mammary gland dataset. Each point represents a gene (black) with the mean-variance trend (blue) fitted to simulated Poisson counts." width="672" />
<p class="caption">(\#fig:unref-bach-var)Per-gene variance as a function of the mean for the log-expression values in the Bach mammary gland dataset. Each point represents a gene (black) with the mean-variance trend (blue) fitted to simulated Poisson counts.</p>
</div>

## Dimensionality reduction


```r
library(BiocSingular)
set.seed(101010011)
sce.mam <- denoisePCA(sce.mam, technical=dec.mam, subset.row=top.mam)
sce.mam <- runTSNE(sce.mam, dimred="PCA")
```


```r
ncol(reducedDim(sce.mam, "PCA"))
```

```
## [1] 15
```

## Clustering 

We use a higher `k` to obtain coarser clusters (for use in `doubletCluster()` later).


```r
snn.gr <- buildSNNGraph(sce.mam, use.dimred="PCA", k=25)
colLabels(sce.mam) <- factor(igraph::cluster_walktrap(snn.gr)$membership)
```


```r
table(colLabels(sce.mam))
```

```
## 
##   1   2   3   4   5   6   7   8   9  10 
## 550 799 716 452  24  84  52  39  32  24
```


```r
plotTSNE(sce.mam, colour_by="label")
```

<div class="figure">
<img src="bach-mammary_files/figure-html/unref-bach-tsne-1.png" alt="Obligatory $t$-SNE plot of the Bach mammary gland dataset, where each point represents a cell and is colored according to the assigned cluster." width="672" />
<p class="caption">(\#fig:unref-bach-tsne)Obligatory $t$-SNE plot of the Bach mammary gland dataset, where each point represents a cell and is colored according to the assigned cluster.</p>
</div>

## Session Info {-}

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```
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] BiocSingular_1.6.0          scran_1.18.5               
 [3] AnnotationHub_2.22.0        BiocFileCache_1.14.0       
 [5] dbplyr_2.1.0                scater_1.18.6              
 [7] ggplot2_3.3.3               ensembldb_2.14.0           
 [9] AnnotationFilter_1.14.0     GenomicFeatures_1.42.2     
[11] AnnotationDbi_1.52.0        scRNAseq_2.4.0             
[13] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
[15] Biobase_2.50.0              GenomicRanges_1.42.0       
[17] GenomeInfoDb_1.26.4         IRanges_2.24.1             
[19] S4Vectors_0.28.1            BiocGenerics_0.36.0        
[21] MatrixGenerics_1.2.1        matrixStats_0.58.0         
[23] BiocStyle_2.18.1            rebook_1.0.0               

loaded via a namespace (and not attached):
  [1] igraph_1.2.6                  lazyeval_0.2.2               
  [3] BiocParallel_1.24.1           digest_0.6.27                
  [5] htmltools_0.5.1.1             viridis_0.5.1                
  [7] fansi_0.4.2                   magrittr_2.0.1               
  [9] memoise_2.0.0                 limma_3.46.0                 
 [11] Biostrings_2.58.0             askpass_1.1                  
 [13] prettyunits_1.1.1             colorspace_2.0-0             
 [15] blob_1.2.1                    rappdirs_0.3.3               
 [17] xfun_0.22                     dplyr_1.0.5                  
 [19] callr_3.5.1                   crayon_1.4.1                 
 [21] RCurl_1.98-1.3                jsonlite_1.7.2               
 [23] graph_1.68.0                  glue_1.4.2                   
 [25] gtable_0.3.0                  zlibbioc_1.36.0              
 [27] XVector_0.30.0                DelayedArray_0.16.2          
 [29] scales_1.1.1                  edgeR_3.32.1                 
 [31] DBI_1.1.1                     Rcpp_1.0.6                   
 [33] viridisLite_0.3.0             xtable_1.8-4                 
 [35] progress_1.2.2                dqrng_0.2.1                  
 [37] bit_4.0.4                     rsvd_1.0.3                   
 [39] httr_1.4.2                    ellipsis_0.3.1               
 [41] pkgconfig_2.0.3               XML_3.99-0.6                 
 [43] farver_2.1.0                  scuttle_1.0.4                
 [45] CodeDepends_0.6.5             sass_0.3.1                   
 [47] locfit_1.5-9.4                utf8_1.2.1                   
 [49] tidyselect_1.1.0              labeling_0.4.2               
 [51] rlang_0.4.10                  later_1.1.0.1                
 [53] munsell_0.5.0                 BiocVersion_3.12.0           
 [55] tools_4.0.4                   cachem_1.0.4                 
 [57] generics_0.1.0                RSQLite_2.2.4                
 [59] ExperimentHub_1.16.0          evaluate_0.14                
 [61] stringr_1.4.0                 fastmap_1.1.0                
 [63] yaml_2.2.1                    processx_3.4.5               
 [65] knitr_1.31                    bit64_4.0.5                  
 [67] purrr_0.3.4                   sparseMatrixStats_1.2.1      
 [69] mime_0.10                     xml2_1.3.2                   
 [71] biomaRt_2.46.3                compiler_4.0.4               
 [73] beeswarm_0.3.1                curl_4.3                     
 [75] interactiveDisplayBase_1.28.0 statmod_1.4.35               
 [77] tibble_3.1.0                  bslib_0.2.4                  
 [79] stringi_1.5.3                 highr_0.8                    
 [81] ps_1.6.0                      lattice_0.20-41              
 [83] bluster_1.0.0                 ProtGenerics_1.22.0          
 [85] Matrix_1.3-2                  vctrs_0.3.6                  
 [87] pillar_1.5.1                  lifecycle_1.0.0              
 [89] BiocManager_1.30.10           jquerylib_0.1.3              
 [91] BiocNeighbors_1.8.2           cowplot_1.1.1                
 [93] bitops_1.0-6                  irlba_2.3.3                  
 [95] httpuv_1.5.5                  rtracklayer_1.50.0           
 [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              openssl_1.4.3                
[105] withr_2.4.1                   GenomicAlignments_1.26.0     
[107] Rsamtools_2.6.0               GenomeInfoDbData_1.2.4       
[109] hms_1.0.0                     grid_4.0.4                   
[111] beachmat_2.6.4                rmarkdown_2.7                
[113] DelayedMatrixStats_1.12.3     Rtsne_0.15                   
[115] shiny_1.6.0                   ggbeeswarm_0.6.0             
```
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