# 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. 
##   0.264   0.520   0.752   1.000   1.207  10.790
```


```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 
## 550 847 639 477  54  88  39  22  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 4.4.0 beta (2024-04-15 r86425)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.4 LTS

Matrix products: default
BLAS:   /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

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] BiocSingular_1.20.0         scran_1.32.0               
 [3] AnnotationHub_3.12.0        BiocFileCache_2.12.0       
 [5] dbplyr_2.5.0                scater_1.32.0              
 [7] ggplot2_3.5.1               scuttle_1.14.0             
 [9] ensembldb_2.28.0            AnnotationFilter_1.28.0    
[11] GenomicFeatures_1.56.0      AnnotationDbi_1.66.0       
[13] scRNAseq_2.18.0             SingleCellExperiment_1.26.0
[15] SummarizedExperiment_1.34.0 Biobase_2.64.0             
[17] GenomicRanges_1.56.0        GenomeInfoDb_1.40.0        
[19] IRanges_2.38.0              S4Vectors_0.42.0           
[21] BiocGenerics_0.50.0         MatrixGenerics_1.16.0      
[23] matrixStats_1.3.0           BiocStyle_2.32.0           
[25] rebook_1.14.0              

loaded via a namespace (and not attached):
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  [3] magrittr_2.0.3            ggbeeswarm_0.7.2         
  [5] gypsum_1.0.0              farver_2.1.1             
  [7] rmarkdown_2.26            BiocIO_1.14.0            
  [9] zlibbioc_1.50.0           vctrs_0.6.5              
 [11] memoise_2.0.1             Rsamtools_2.20.0         
 [13] DelayedMatrixStats_1.26.0 RCurl_1.98-1.14          
 [15] htmltools_0.5.8.1         S4Arrays_1.4.0           
 [17] curl_5.2.1                BiocNeighbors_1.22.0     
 [19] Rhdf5lib_1.26.0           SparseArray_1.4.0        
 [21] rhdf5_2.48.0              sass_0.4.9               
 [23] alabaster.base_1.4.0      bslib_0.7.0              
 [25] alabaster.sce_1.4.0       httr2_1.0.1              
 [27] cachem_1.0.8              GenomicAlignments_1.40.0 
 [29] igraph_2.0.3              mime_0.12                
 [31] lifecycle_1.0.4           pkgconfig_2.0.3          
 [33] rsvd_1.0.5                Matrix_1.7-0             
 [35] R6_2.5.1                  fastmap_1.1.1            
 [37] GenomeInfoDbData_1.2.12   digest_0.6.35            
 [39] colorspace_2.1-0          paws.storage_0.5.0       
 [41] dqrng_0.3.2               irlba_2.3.5.1            
 [43] ExperimentHub_2.12.0      RSQLite_2.3.6            
 [45] beachmat_2.20.0           labeling_0.4.3           
 [47] filelock_1.0.3            fansi_1.0.6              
 [49] httr_1.4.7                abind_1.4-5              
 [51] compiler_4.4.0            bit64_4.0.5              
 [53] withr_3.0.0               BiocParallel_1.38.0      
 [55] viridis_0.6.5             DBI_1.2.2                
 [57] highr_0.10                HDF5Array_1.32.0         
 [59] alabaster.ranges_1.4.0    alabaster.schemas_1.4.0  
 [61] rappdirs_0.3.3            DelayedArray_0.30.0      
 [63] bluster_1.14.0            rjson_0.2.21             
 [65] tools_4.4.0               vipor_0.4.7              
 [67] beeswarm_0.4.0            glue_1.7.0               
 [69] restfulr_0.0.15           rhdf5filters_1.16.0      
 [71] grid_4.4.0                Rtsne_0.17               
 [73] cluster_2.1.6             generics_0.1.3           
 [75] gtable_0.3.5              metapod_1.12.0           
 [77] ScaledMatrix_1.12.0       utf8_1.2.4               
 [79] XVector_0.44.0            ggrepel_0.9.5            
 [81] BiocVersion_3.19.1        pillar_1.9.0             
 [83] limma_3.60.0              dplyr_1.1.4              
 [85] lattice_0.22-6            rtracklayer_1.64.0       
 [87] bit_4.0.5                 tidyselect_1.2.1         
 [89] paws.common_0.7.2         locfit_1.5-9.9           
 [91] Biostrings_2.72.0         knitr_1.46               
 [93] gridExtra_2.3             bookdown_0.39            
 [95] ProtGenerics_1.36.0       edgeR_4.2.0              
 [97] xfun_0.43                 statmod_1.5.0            
 [99] UCSC.utils_1.0.0          lazyeval_0.2.2           
[101] yaml_2.3.8                evaluate_0.23            
[103] codetools_0.2-20          tibble_3.2.1             
[105] alabaster.matrix_1.4.0    BiocManager_1.30.22      
[107] graph_1.82.0              cli_3.6.2                
[109] munsell_0.5.1             jquerylib_0.1.4          
[111] Rcpp_1.0.12               dir.expiry_1.12.0        
[113] png_0.1-8                 XML_3.99-0.16.1          
[115] parallel_4.4.0            blob_1.2.4               
[117] sparseMatrixStats_1.16.0  bitops_1.0-7             
[119] viridisLite_0.4.2         alabaster.se_1.4.0       
[121] scales_1.3.0              purrr_1.0.2              
[123] crayon_1.5.2              rlang_1.1.3              
[125] cowplot_1.1.3             KEGGREST_1.44.0          
```
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