# Chimeric mouse embryo (10X Genomics)

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## Introduction

This performs an analysis of the @pijuansala2019single dataset on mouse gastrulation.
Here, we examine chimeric embryos at the E8.5 stage of development 
where td-Tomato-positive embryonic stem cells (ESCs) were injected into a wild-type blastocyst.

## Data loading


```r
library(MouseGastrulationData)
sce.chimera <- WTChimeraData(samples=5:10)
sce.chimera
```

```
## class: SingleCellExperiment 
## dim: 29453 20935 
## metadata(0):
## assays(1): counts
## rownames(29453): ENSMUSG00000051951 ENSMUSG00000089699 ...
##   ENSMUSG00000095742 tomato-td
## rowData names(2): ENSEMBL SYMBOL
## colnames(20935): cell_9769 cell_9770 ... cell_30702 cell_30703
## colData names(11): cell barcode ... doub.density sizeFactor
## reducedDimNames(2): pca.corrected.E7.5 pca.corrected.E8.5
## mainExpName: NULL
## altExpNames(0):
```




```r
library(scater)
rownames(sce.chimera) <- uniquifyFeatureNames(
    rowData(sce.chimera)$ENSEMBL, rowData(sce.chimera)$SYMBOL)
```

## Quality control

Quality control on the cells has already been performed by the authors, so we will not repeat it here.
We additionally remove cells that are labelled as stripped nuclei or doublets.


```r
drop <- sce.chimera$celltype.mapped %in% c("stripped", "Doublet")
sce.chimera <- sce.chimera[,!drop]
```

## Normalization

We use the pre-computed size factors in `sce.chimera`.


```r
sce.chimera <- logNormCounts(sce.chimera)
```

## Variance modelling

We retain all genes with any positive biological component, to preserve as much signal as possible across a very heterogeneous dataset.


```r
library(scran)
dec.chimera <- modelGeneVar(sce.chimera, block=sce.chimera$sample)
chosen.hvgs <- dec.chimera$bio > 0
```


```r
par(mfrow=c(1,2))
blocked.stats <- dec.chimera$per.block
for (i in colnames(blocked.stats)) {
    current <- blocked.stats[[i]]
    plot(current$mean, current$total, main=i, pch=16, cex=0.5,
        xlab="Mean of log-expression", ylab="Variance of log-expression")
    curfit <- metadata(current)
    curve(curfit$trend(x), col='dodgerblue', add=TRUE, lwd=2)
}
```

<div class="figure">
<img src="pijuan-embryo_files/figure-html/unref-pijuan-var-1.png" alt="Per-gene variance as a function of the mean for the log-expression values in the Pijuan-Sala chimeric mouse embryo dataset. Each point represents a gene (black) with the mean-variance trend (blue) fitted to the variances." width="672" />
<p class="caption">(\#fig:unref-pijuan-var-1)Per-gene variance as a function of the mean for the log-expression values in the Pijuan-Sala chimeric mouse embryo dataset. Each point represents a gene (black) with the mean-variance trend (blue) fitted to the variances.</p>
</div><div class="figure">
<img src="pijuan-embryo_files/figure-html/unref-pijuan-var-2.png" alt="Per-gene variance as a function of the mean for the log-expression values in the Pijuan-Sala chimeric mouse embryo dataset. Each point represents a gene (black) with the mean-variance trend (blue) fitted to the variances." width="672" />
<p class="caption">(\#fig:unref-pijuan-var-2)Per-gene variance as a function of the mean for the log-expression values in the Pijuan-Sala chimeric mouse embryo dataset. Each point represents a gene (black) with the mean-variance trend (blue) fitted to the variances.</p>
</div><div class="figure">
<img src="pijuan-embryo_files/figure-html/unref-pijuan-var-3.png" alt="Per-gene variance as a function of the mean for the log-expression values in the Pijuan-Sala chimeric mouse embryo dataset. Each point represents a gene (black) with the mean-variance trend (blue) fitted to the variances." width="672" />
<p class="caption">(\#fig:unref-pijuan-var-3)Per-gene variance as a function of the mean for the log-expression values in the Pijuan-Sala chimeric mouse embryo dataset. Each point represents a gene (black) with the mean-variance trend (blue) fitted to the variances.</p>
</div>

## Merging

We use a hierarchical merge to first merge together replicates with the same genotype, 
and then merge samples across different genotypes.


```r
library(batchelor)
set.seed(01001001)
merged <- correctExperiments(sce.chimera, 
    batch=sce.chimera$sample, 
    subset.row=chosen.hvgs,
    PARAM=FastMnnParam(
        merge.order=list(
            list(1,3,5), # WT (3 replicates)
            list(2,4,6)  # td-Tomato (3 replicates)
        )
    )
)
```

We use the percentage of variance lost as a diagnostic:


```r
metadata(merged)$merge.info$lost.var
```

```
##              5         6         7         8        9       10
## [1,] 0.000e+00 0.0204238 0.000e+00 0.0169321 0.000000 0.000000
## [2,] 0.000e+00 0.0007403 0.000e+00 0.0004431 0.000000 0.015455
## [3,] 3.089e-02 0.0000000 2.012e-02 0.0000000 0.000000 0.000000
## [4,] 9.042e-05 0.0000000 8.298e-05 0.0000000 0.018044 0.000000
## [5,] 4.318e-03 0.0072489 4.123e-03 0.0078254 0.003827 0.007779
```

## Clustering


```r
g <- buildSNNGraph(merged, use.dimred="corrected")
clusters <- igraph::cluster_louvain(g)
colLabels(merged) <- factor(clusters$membership)
```

We examine the distribution of cells across clusters and samples.


```r
table(Cluster=colLabels(merged), Sample=merged$sample)
```

```
##        Sample
## Cluster   5   6   7   8   9  10
##      1   86  20  62  53 151  74
##      2  147  37 132 111 230 216
##      3   99  16 164 128 371 275
##      4  141 104 198 459 385 471
##      5   96  36 291 377 171 232
##      6  216  53 353 209 562 653
##      7  149  73  85  85 163 377
##      8  133  95 110  66 160 312
##      9   82  20  74  33 165 203
##      10  97  19  36  18  50  35
##      11 110  41  47  38  40 147
##      12 122  65  62  51  63 140
##      13 157  79 131 102 133 405
##      14 110  69  73  96 128 256
##      15  84  47 159 351 200 620
##      16  43  35  82  81  86 357
##      17 165  44 208 174 200 365
##      18  78  43 189 118 329 489
##      19  47  22  84  50  89 128
##      20  38  41  50  49 128 125
##      21   1   5   0  84   0  66
##      22  18   7  13  17  19  37
##      23  57  29  92  78  82 190
##      24   9   7  18  13  30  27
##      25  11  16  20   9  47  58
##      26   2   1   7   3  75 138
##      27   0   2   0  51   0   5
```

## Dimensionality reduction

We use an external algorithm to compute nearest neighbors for greater speed.


```r
merged <- runTSNE(merged, dimred="corrected", external_neighbors=TRUE)
merged <- runUMAP(merged, dimred="corrected", external_neighbors=TRUE)
```


```r
gridExtra::grid.arrange(
    plotTSNE(merged, colour_by="label", text_by="label", text_colour="red"),
    plotTSNE(merged, colour_by="batch")
)
```

<div class="figure">
<img src="pijuan-embryo_files/figure-html/unref-pijuan-tsne-1.png" alt="Obligatory $t$-SNE plots of the Pijuan-Sala chimeric mouse embryo dataset, where each point represents a cell and is colored according to the assigned cluster (top) or sample of origin (bottom)." width="672" />
<p class="caption">(\#fig:unref-pijuan-tsne)Obligatory $t$-SNE plots of the Pijuan-Sala chimeric mouse embryo dataset, where each point represents a cell and is colored according to the assigned cluster (top) or sample of origin (bottom).</p>
</div>

## Session Info {-}

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<div class="rebook-content">
```
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] batchelor_1.20.0             scran_1.32.0                
 [3] scater_1.32.0                ggplot2_3.5.1               
 [5] scuttle_1.14.0               MouseGastrulationData_1.17.1
 [7] SpatialExperiment_1.14.0     SingleCellExperiment_1.26.0 
 [9] SummarizedExperiment_1.34.0  Biobase_2.64.0              
[11] GenomicRanges_1.56.0         GenomeInfoDb_1.40.0         
[13] IRanges_2.38.0               S4Vectors_0.42.0            
[15] BiocGenerics_0.50.0          MatrixGenerics_1.16.0       
[17] matrixStats_1.3.0            BiocStyle_2.32.0            
[19] rebook_1.14.0               

loaded via a namespace (and not attached):
  [1] jsonlite_1.8.8            CodeDepends_0.6.6        
  [3] magrittr_2.0.3            ggbeeswarm_0.7.2         
  [5] magick_2.8.3              farver_2.1.1             
  [7] rmarkdown_2.26            zlibbioc_1.50.0          
  [9] vctrs_0.6.5               memoise_2.0.1            
 [11] DelayedMatrixStats_1.26.0 htmltools_0.5.8.1        
 [13] S4Arrays_1.4.0            AnnotationHub_3.12.0     
 [15] curl_5.2.1                BiocNeighbors_1.22.0     
 [17] SparseArray_1.4.0         sass_0.4.9               
 [19] bslib_0.7.0               cachem_1.0.8             
 [21] ResidualMatrix_1.14.0     igraph_2.0.3             
 [23] mime_0.12                 lifecycle_1.0.4          
 [25] pkgconfig_2.0.3           rsvd_1.0.5               
 [27] Matrix_1.7-0              R6_2.5.1                 
 [29] fastmap_1.1.1             GenomeInfoDbData_1.2.12  
 [31] digest_0.6.35             colorspace_2.1-0         
 [33] AnnotationDbi_1.66.0      dqrng_0.3.2              
 [35] irlba_2.3.5.1             ExperimentHub_2.12.0     
 [37] RSQLite_2.3.6             beachmat_2.20.0          
 [39] labeling_0.4.3            filelock_1.0.3           
 [41] fansi_1.0.6               httr_1.4.7               
 [43] abind_1.4-5               compiler_4.4.0           
 [45] bit64_4.0.5               withr_3.0.0              
 [47] BiocParallel_1.38.0       viridis_0.6.5            
 [49] DBI_1.2.2                 highr_0.10               
 [51] rappdirs_0.3.3            DelayedArray_0.30.0      
 [53] rjson_0.2.21              bluster_1.14.0           
 [55] tools_4.4.0               vipor_0.4.7              
 [57] beeswarm_0.4.0            glue_1.7.0               
 [59] grid_4.4.0                Rtsne_0.17               
 [61] cluster_2.1.6             generics_0.1.3           
 [63] gtable_0.3.5              BiocSingular_1.20.0      
 [65] ScaledMatrix_1.12.0       metapod_1.12.0           
 [67] utf8_1.2.4                XVector_0.44.0           
 [69] ggrepel_0.9.5             BiocVersion_3.19.1       
 [71] pillar_1.9.0              limma_3.60.0             
 [73] BumpyMatrix_1.12.0        dplyr_1.1.4              
 [75] BiocFileCache_2.12.0      lattice_0.22-6           
 [77] bit_4.0.5                 tidyselect_1.2.1         
 [79] locfit_1.5-9.9            Biostrings_2.72.0        
 [81] knitr_1.46                gridExtra_2.3            
 [83] bookdown_0.39             edgeR_4.2.0              
 [85] xfun_0.43                 statmod_1.5.0            
 [87] UCSC.utils_1.0.0          yaml_2.3.8               
 [89] evaluate_0.23             codetools_0.2-20         
 [91] tibble_3.2.1              BiocManager_1.30.22      
 [93] graph_1.82.0              cli_3.6.2                
 [95] uwot_0.2.2                munsell_0.5.1            
 [97] jquerylib_0.1.4           Rcpp_1.0.12              
 [99] dir.expiry_1.12.0         dbplyr_2.5.0             
[101] png_0.1-8                 XML_3.99-0.16.1          
[103] parallel_4.4.0            blob_1.2.4               
[105] sparseMatrixStats_1.16.0  viridisLite_0.4.2        
[107] scales_1.3.0              purrr_1.0.2              
[109] crayon_1.5.2              rlang_1.1.3              
[111] cowplot_1.1.3             KEGGREST_1.44.0          
```
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