# Bach mouse mammary gland (10X Genomics)

<script>
document.addEventListener("click", function (event) {
    if (event.target.classList.contains("rebook-collapse")) {
        event.target.classList.toggle("active");
        var content = event.target.nextElementSibling;
        if (content.style.display === "block") {
            content.style.display = "none";
        } else {
            content.style.display = "block";
        }
    }
})
</script>

<style>
.rebook-collapse {
  background-color: #eee;
  color: #444;
  cursor: pointer;
  padding: 18px;
  width: 100%;
  border: none;
  text-align: left;
  outline: none;
  font-size: 15px;
}

.rebook-content {
  padding: 0 18px;
  display: none;
  overflow: hidden;
  background-color: #f1f1f1;
}
</style>

## 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 {-}

<button class="rebook-collapse">View session info</button>
<div class="rebook-content">
```
R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.2 LTS

Matrix products: default
BLAS:   /home/biocbuild/bbs-3.13-bioc/R/lib/libRblas.so
LAPACK: /home/biocbuild/bbs-3.13-bioc/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_1.8.0          scran_1.20.0               
 [3] AnnotationHub_3.0.0         BiocFileCache_2.0.0        
 [5] dbplyr_2.1.1                scater_1.20.0              
 [7] ggplot2_3.3.3               scuttle_1.2.0              
 [9] ensembldb_2.16.0            AnnotationFilter_1.16.0    
[11] GenomicFeatures_1.44.0      AnnotationDbi_1.54.0       
[13] scRNAseq_2.6.0              SingleCellExperiment_1.14.0
[15] SummarizedExperiment_1.22.0 Biobase_2.52.0             
[17] GenomicRanges_1.44.0        GenomeInfoDb_1.28.0        
[19] IRanges_2.26.0              S4Vectors_0.30.0           
[21] BiocGenerics_0.38.0         MatrixGenerics_1.4.0       
[23] matrixStats_0.58.0          BiocStyle_2.20.0           
[25] rebook_1.2.0               

loaded via a namespace (and not attached):
  [1] igraph_1.2.6                  lazyeval_0.2.2               
  [3] BiocParallel_1.26.0           digest_0.6.27                
  [5] htmltools_0.5.1.1             viridis_0.6.1                
  [7] fansi_0.4.2                   magrittr_2.0.1               
  [9] memoise_2.0.0                 ScaledMatrix_1.0.0           
 [11] cluster_2.1.2                 limma_3.48.0                 
 [13] Biostrings_2.60.0             prettyunits_1.1.1            
 [15] colorspace_2.0-1              blob_1.2.1                   
 [17] rappdirs_0.3.3                xfun_0.23                    
 [19] dplyr_1.0.6                   crayon_1.4.1                 
 [21] RCurl_1.98-1.3                jsonlite_1.7.2               
 [23] graph_1.70.0                  glue_1.4.2                   
 [25] gtable_0.3.0                  zlibbioc_1.38.0              
 [27] XVector_0.32.0                DelayedArray_0.18.0          
 [29] scales_1.1.1                  edgeR_3.34.0                 
 [31] DBI_1.1.1                     Rcpp_1.0.6                   
 [33] viridisLite_0.4.0             xtable_1.8-4                 
 [35] progress_1.2.2                dqrng_0.3.0                  
 [37] bit_4.0.4                     rsvd_1.0.5                   
 [39] metapod_1.0.0                 httr_1.4.2                   
 [41] dir.expiry_1.0.0              ellipsis_0.3.2               
 [43] pkgconfig_2.0.3               XML_3.99-0.6                 
 [45] farver_2.1.0                  CodeDepends_0.6.5            
 [47] sass_0.4.0                    locfit_1.5-9.4               
 [49] utf8_1.2.1                    tidyselect_1.1.1             
 [51] labeling_0.4.2                rlang_0.4.11                 
 [53] later_1.2.0                   munsell_0.5.0                
 [55] BiocVersion_3.13.1            tools_4.1.0                  
 [57] cachem_1.0.5                  generics_0.1.0               
 [59] RSQLite_2.2.7                 ExperimentHub_2.0.0          
 [61] evaluate_0.14                 stringr_1.4.0                
 [63] fastmap_1.1.0                 yaml_2.2.1                   
 [65] knitr_1.33                    bit64_4.0.5                  
 [67] purrr_0.3.4                   KEGGREST_1.32.0              
 [69] sparseMatrixStats_1.4.0       mime_0.10                    
 [71] biomaRt_2.48.0                compiler_4.1.0               
 [73] beeswarm_0.3.1                filelock_1.0.2               
 [75] curl_4.3.1                    png_0.1-7                    
 [77] interactiveDisplayBase_1.30.0 statmod_1.4.36               
 [79] tibble_3.1.2                  bslib_0.2.5.1                
 [81] stringi_1.6.2                 highr_0.9                    
 [83] bluster_1.2.0                 lattice_0.20-44              
 [85] ProtGenerics_1.24.0           Matrix_1.3-3                 
 [87] vctrs_0.3.8                   pillar_1.6.1                 
 [89] lifecycle_1.0.0               BiocManager_1.30.15          
 [91] jquerylib_0.1.4               BiocNeighbors_1.10.0         
 [93] cowplot_1.1.1                 bitops_1.0-7                 
 [95] irlba_2.3.3                   httpuv_1.6.1                 
 [97] rtracklayer_1.52.0            R6_2.5.0                     
 [99] BiocIO_1.2.0                  bookdown_0.22                
[101] promises_1.2.0.1              gridExtra_2.3                
[103] vipor_0.4.5                   codetools_0.2-18             
[105] assertthat_0.2.1              rjson_0.2.20                 
[107] withr_2.4.2                   GenomicAlignments_1.28.0     
[109] Rsamtools_2.8.0               GenomeInfoDbData_1.2.6       
[111] hms_1.1.0                     grid_4.1.0                   
[113] beachmat_2.8.0                rmarkdown_2.8                
[115] DelayedMatrixStats_1.14.0     Rtsne_0.15                   
[117] shiny_1.6.0                   ggbeeswarm_0.6.0             
[119] restfulr_0.0.13              
```
</div>