Chapter 39 Pijuan-Sala chimeric mouse embryo (10X Genomics)
39.1 Introduction
This performs an analysis of the Pijuan-Sala et al. (2019) 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.
39.2 Data loading
## 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
## altExpNames(0):39.3 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.
39.4 Normalization
We use the pre-computed size factors in sce.chimera.
39.5 Variance modelling
We retain all genes with any positive biological component, to preserve as much signal as possible across a very heterogeneous dataset.
library(scran)
dec.chimera <- modelGeneVar(sce.chimera, block=sce.chimera$sample)
chosen.hvgs <- dec.chimera$bio > 0par(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)
}
Figure 39.1: 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.
Figure 39.2: 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.
Figure 39.3: 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.
39.6 Merging
We use a hierarchical merge to first merge together replicates with the same genotype, and then merge samples across different genotypes.
library(batchelor)
set.seed(01001001)
merged <- correctExperiments(sce.chimera,
batch=sce.chimera$sample,
subset.row=chosen.hvgs,
PARAM=FastMnnParam(
merge.order=list(
list(1,3,5), # WT (3 replicates)
list(2,4,6) # td-Tomato (3 replicates)
)
)
)We use the percentage of variance lost as a diagnostic:
## 5 6 7 8 9 10
## [1,] 0.000e+00 0.0204433 0.000e+00 0.0169567 0.000000 0.000000
## [2,] 0.000e+00 0.0007389 0.000e+00 0.0004409 0.000000 0.015474
## [3,] 3.090e-02 0.0000000 2.012e-02 0.0000000 0.000000 0.000000
## [4,] 9.024e-05 0.0000000 8.272e-05 0.0000000 0.018047 0.000000
## [5,] 4.321e-03 0.0072518 4.124e-03 0.0078280 0.003831 0.00778639.7 Clustering
g <- buildSNNGraph(merged, use.dimred="corrected")
clusters <- igraph::cluster_louvain(g)
colLabels(merged) <- factor(clusters$membership)We examine the distribution of cells across clusters and samples.
## Sample
## Cluster 5 6 7 8 9 10
## 1 152 72 85 88 164 386
## 2 19 7 13 17 20 36
## 3 130 96 109 63 159 311
## 4 43 35 81 81 87 353
## 5 68 31 120 107 83 197
## 6 122 65 64 52 63 141
## 7 187 113 322 587 458 541
## 8 47 22 84 50 90 131
## 9 182 47 231 192 216 391
## 10 95 19 36 18 50 34
## 11 9 7 18 13 30 27
## 12 110 69 73 96 127 252
## 13 0 2 0 51 0 5
## 14 38 39 50 47 126 123
## 15 98 16 164 125 368 273
## 16 146 37 132 110 231 216
## 17 114 43 44 37 40 154
## 18 78 45 189 119 340 493
## 19 86 20 64 54 153 77
## 20 159 77 137 101 147 401
## 21 2 1 7 3 65 133
## 22 11 16 20 9 47 57
## 23 1 5 0 84 0 66
## 24 170 47 282 173 426 542
## 25 109 23 117 55 271 285
## 26 122 72 298 572 296 77639.8 Dimensionality reduction
We use an external algorithm to compute nearest neighbors for greater speed.
merged <- runTSNE(merged, dimred="corrected", external_neighbors=TRUE)
merged <- runUMAP(merged, dimred="corrected", external_neighbors=TRUE)gridExtra::grid.arrange(
plotTSNE(merged, colour_by="label", text_by="label", text_col="red"),
plotTSNE(merged, colour_by="batch")
)
Figure 39.4: 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).
Session Info
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 scran_1.18.5
[3] scater_1.18.6 ggplot2_3.3.3
[5] MouseGastrulationData_1.4.0 SingleCellExperiment_1.12.0
[7] SummarizedExperiment_1.20.0 Biobase_2.50.0
[9] GenomicRanges_1.42.0 GenomeInfoDb_1.26.4
[11] IRanges_2.24.1 S4Vectors_0.28.1
[13] BiocGenerics_0.36.0 MatrixGenerics_1.2.1
[15] matrixStats_0.58.0 BiocStyle_2.18.1
[17] rebook_1.0.0
loaded via a namespace (and not attached):
[1] Rtsne_0.15 ggbeeswarm_0.6.0
[3] colorspace_2.0-0 ellipsis_0.3.1
[5] scuttle_1.0.4 bluster_1.0.0
[7] XVector_0.30.0 BiocNeighbors_1.8.2
[9] farver_2.1.0 bit64_4.0.5
[11] interactiveDisplayBase_1.28.0 AnnotationDbi_1.52.0
[13] fansi_0.4.2 codetools_0.2-18
[15] sparseMatrixStats_1.2.1 cachem_1.0.4
[17] knitr_1.31 jsonlite_1.7.2
[19] ResidualMatrix_1.0.0 dbplyr_2.1.0
[21] uwot_0.1.10 graph_1.68.0
[23] shiny_1.6.0 BiocManager_1.30.10
[25] compiler_4.0.4 httr_1.4.2
[27] dqrng_0.2.1 assertthat_0.2.1
[29] Matrix_1.3-2 fastmap_1.1.0
[31] limma_3.46.0 later_1.1.0.1
[33] BiocSingular_1.6.0 htmltools_0.5.1.1
[35] tools_4.0.4 rsvd_1.0.3
[37] igraph_1.2.6 gtable_0.3.0
[39] glue_1.4.2 GenomeInfoDbData_1.2.4
[41] dplyr_1.0.5 rappdirs_0.3.3
[43] Rcpp_1.0.6 jquerylib_0.1.3
[45] vctrs_0.3.6 ExperimentHub_1.16.0
[47] DelayedMatrixStats_1.12.3 xfun_0.22
[49] stringr_1.4.0 ps_1.6.0
[51] beachmat_2.6.4 mime_0.10
[53] lifecycle_1.0.0 irlba_2.3.3
[55] statmod_1.4.35 XML_3.99-0.6
[57] edgeR_3.32.1 AnnotationHub_2.22.0
[59] zlibbioc_1.36.0 scales_1.1.1
[61] promises_1.2.0.1 yaml_2.2.1
[63] curl_4.3 memoise_2.0.0
[65] gridExtra_2.3 sass_0.3.1
[67] stringi_1.5.3 RSQLite_2.2.4
[69] highr_0.8 BiocVersion_3.12.0
[71] BiocParallel_1.24.1 rlang_0.4.10
[73] pkgconfig_2.0.3 bitops_1.0-6
[75] evaluate_0.14 lattice_0.20-41
[77] purrr_0.3.4 labeling_0.4.2
[79] CodeDepends_0.6.5 cowplot_1.1.1
[81] bit_4.0.4 processx_3.4.5
[83] tidyselect_1.1.0 magrittr_2.0.1
[85] bookdown_0.21 R6_2.5.0
[87] generics_0.1.0 DelayedArray_0.16.2
[89] DBI_1.1.1 pillar_1.5.1
[91] withr_2.4.1 RCurl_1.98-1.3
[93] tibble_3.1.0 crayon_1.4.1
[95] utf8_1.2.1 BiocFileCache_1.14.0
[97] rmarkdown_2.7 viridis_0.5.1
[99] locfit_1.5-9.4 grid_4.0.4
[101] blob_1.2.1 callr_3.5.1
[103] digest_0.6.27 xtable_1.8-4
[105] httpuv_1.5.5 munsell_0.5.0
[107] beeswarm_0.3.1 viridisLite_0.3.0
[109] vipor_0.4.5 bslib_0.2.4 Bibliography
Pijuan-Sala, B., J. A. Griffiths, C. Guibentif, T. W. Hiscock, W. Jawaid, F. J. Calero-Nieto, C. Mulas, et al. 2019. “A Single-Cell Molecular Map of Mouse Gastrulation and Early Organogenesis.” Nature 566 (7745): 490–95.