Annotation

We make a first rough annotation using known markers for different celltypes.

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Click to expand the Neurons marker plots 

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Click to expand the Stromal marker plots 

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Click to expand the Epithelial cells 

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Click to expand the Astrocytes marker plots 

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Click to expand the Immune cells plots 

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Click to expand the Oligodendroglia marker plots 

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Click to expand the ependymal marker plots

Top markers and differential expression

In order to help with the identification of we pull out the most expressed genes for each cluster. ( ignoring the mitochondrial ribosomal genes)

We compute here a pairwise differential expression between all the clusters. The results are saved in a list of dataframes, “markers_k20_02/markers_k20_02.RDS” one for each cluster. A .csv file (that can be opened with a spreadsheet program) for each cluster is available in “markers_k20_02”.

The default philosophy of findMarkers() is to identify a combination of marker genes that - together - uniquely define one cluster against the rest. To this end, we collect the top DE genes from each pairwise comparison involving a particular cluster to assemble a set of candidate markers for that cluster. Of particular interest is the Top field. The set of genes with Top \(≤X\) is the union of the top \(X\) genes (ranked by p-value) from each pairwise comparison involving the cluster of interest. For example, the set of all genes with Top values of 1 contains the gene with the lowest p-value from each comparison. Similarly, the set of genes with Top values less than or equal to 10 contains the top 10 genes from each comparison.

Rename the Clusters with assigned Cell Types

With the help of the known markers plotted above, the DE between clusters and the top expressed genes the celltype identity of every cluster has benn identified.

Renaming the clusters accordingly:

1 mNeurons

2 Endothelial

3 fEndothelia

4 Gran & Mono

5 OligoAstro

6 Microglia

7 Mural_cells

8 Lymphocytes

9 ChP_epithelial

10 OPCs

11 Astrocyte_1

12 Oligo_2

13 Astrocyte_2

14 Astrocyte_3

15 Oligo_1

16 DCs

17 Microglia

18 iNeuron & NRPs

19 BAMs

20 Ependymocytes

21 Microglia

22 OEG

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Marker plots

Exploring the shiny app and the DE between clusters specific markers for each one of the subtypes is identified.

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Session Info

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## R version 4.0.4 (2021-02-15)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19041)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=English_United Kingdom.1252 
## [2] LC_CTYPE=English_United Kingdom.1252   
## [3] LC_MONETARY=English_United Kingdom.1252
## [4] LC_NUMERIC=C                           
## [5] LC_TIME=English_United Kingdom.1252    
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] pals_1.7                    scran_1.18.7               
##  [3] patchwork_1.1.1             scater_1.18.6              
##  [5] ggplot2_3.3.5               SingleCellExperiment_1.12.0
##  [7] SummarizedExperiment_1.20.0 Biobase_2.50.0             
##  [9] GenomicRanges_1.42.0        GenomeInfoDb_1.26.7        
## [11] IRanges_2.24.1              S4Vectors_0.28.1           
## [13] BiocGenerics_0.36.1         MatrixGenerics_1.2.1       
## [15] matrixStats_0.59.0          here_1.0.1                 
## 
## loaded via a namespace (and not attached):
##  [1] bitops_1.0-7              rprojroot_2.0.2          
##  [3] tools_4.0.4               bslib_0.2.5.1            
##  [5] utf8_1.2.1                R6_2.5.0                 
##  [7] irlba_2.3.3               vipor_0.4.5              
##  [9] DBI_1.1.1                 colorspace_2.0-2         
## [11] withr_2.4.2               tidyselect_1.1.1         
## [13] gridExtra_2.3             compiler_4.0.4           
## [15] BiocNeighbors_1.8.2       DelayedArray_0.16.3      
## [17] labeling_0.4.2            sass_0.4.0               
## [19] scales_1.1.1              stringr_1.4.0            
## [21] digest_0.6.27             rmarkdown_2.9            
## [23] XVector_0.30.0            dichromat_2.0-0          
## [25] pkgconfig_2.0.3           htmltools_0.5.1.1        
## [27] sparseMatrixStats_1.2.1   highr_0.9                
## [29] limma_3.46.0              maps_3.3.0               
## [31] rlang_0.4.10              DelayedMatrixStats_1.12.3
## [33] farver_2.1.0              jquerylib_0.1.4          
## [35] generics_0.1.0            jsonlite_1.7.2           
## [37] BiocParallel_1.24.1       dplyr_1.0.7              
## [39] RCurl_1.98-1.3            magrittr_2.0.1           
## [41] BiocSingular_1.6.0        GenomeInfoDbData_1.2.4   
## [43] scuttle_1.0.4             Matrix_1.3-4             
## [45] Rcpp_1.0.6                ggbeeswarm_0.6.0         
## [47] munsell_0.5.0             fansi_0.5.0              
## [49] viridis_0.6.1             lifecycle_1.0.0          
## [51] stringi_1.7.3             yaml_2.2.1               
## [53] edgeR_3.32.1              zlibbioc_1.36.0          
## [55] grid_4.0.4                dqrng_0.3.0              
## [57] crayon_1.4.1              lattice_0.20-44          
## [59] cowplot_1.1.1             beachmat_2.6.4           
## [61] mapproj_1.2.7             locfit_1.5-9.4           
## [63] knitr_1.33                pillar_1.6.1             
## [65] igraph_1.2.6              codetools_0.2-18         
## [67] glue_1.4.2                evaluate_0.14            
## [69] vctrs_0.3.8               gtable_0.3.0             
## [71] purrr_0.3.4               assertthat_0.2.1         
## [73] xfun_0.21                 rsvd_1.0.5               
## [75] viridisLite_0.4.0         tibble_3.1.2             
## [77] beeswarm_0.4.0            bluster_1.0.0            
## [79] statmod_1.4.36            ellipsis_0.3.2