Annotation cluster res06
NadineBestard
30/03/2021
library(scater) #feature plots # the devel version for repel.
library(here) # reproducible paths
library(patchwork) # agregate plots
library(scran) # for findmarkers
library(pals) # for palettes with large n #kelly()22, #polychrome()#36, unname(alphabet())project <- "fire-mice"cols25 <- unname(cols25())
# remove the black and white from the pallete, and the similars to cols25
# assessed with pal.bands and pal.cube
kelly_col <- unname(kelly()[-c(1,2,5,6)])
# remove the colours that are similar to the
cols25 <-
cols <- c(kelly_col, cols25)if (!file.exists(here("processed", project, "sce_anno_01.RDS"))) {
sce <- readRDS(here("processed", project, "sce_clusters_01.RDS"))
}else{
sce <- readRDS(here("processed", project, "sce_anno_01.RDS"))
}Annotation
We make a first rough annotation using known markers for different celltypes.
plotTSNE(sce, colour_by = "originalexp_snn_res.0.6", text_by = "originalexp_snn_res.0.6", force = 0) + scale_color_manual(values = cols)## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
Click to expand the Neurons marker plots
#Neurons:
list_plots <- lapply(c("Snap25", "Stmn2", "Rbfox3", "Gabrb2"),
function(x)plotTSNE(sce, colour_by = x ))
wrap_plots(list_plots) + plot_annotation(title = "Neurons")
plotExpression(sce, features=c("Snap25", "Stmn2", "Rbfox3", "Gabrb2") ,
x="originalexp_snn_res.0.6", colour_by = "originalexp_snn_res.0.6", ncol=1) + scale_color_manual(values = cols)## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
#Inhibitory neurons
list_plots <- lapply(c("Gad1", "Gad2", "Slc32a1", "Pvalb"),
function(x)plotTSNE(sce, colour_by = x ))
wrap_plots(list_plots) + plot_annotation(title = "Inhibitory Neurons")
plotExpression(sce, features=c("Gad1", "Gad2", "Slc32a1", "Pvalb"),
x="originalexp_snn_res.0.6", colour_by = "originalexp_snn_res.0.6", ncol=1) + scale_color_manual(values = cols)## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
# Excitatory neurons
list_plots <- lapply(c("Satb2", "Slc12a6", "Slc17a7"),
function(x)plotTSNE(sce, colour_by = x ))
wrap_plots(list_plots) + plot_annotation(title = "Exitatory Neurons")
plotExpression(sce, features=c("Satb2", "Slc12a6", "Slc17a7"),
x="originalexp_snn_res.0.6", colour_by = "originalexp_snn_res.0.6", ncol=1) + scale_color_manual(values = cols)## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
# RBFOX3 (and other Granulate cell markers)
list_plots <- lapply(c("Cdh15", "Calb2", "Rbfox3", "Reln"),
function(x)plotTSNE(sce, colour_by = x ))
wrap_plots(list_plots) + plot_annotation(title = "RBFO3+ Neurons")
plotExpression(sce, features=c("Cdh15", "Calb2", "Rbfox3", "Reln"),
x="originalexp_snn_res.0.6", colour_by = "originalexp_snn_res.0.6", ncol=1) + scale_color_manual(values = cols)## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
Click to expand the Stromal marker plots
#Stromal
list_plots <- lapply(c( "Lamb1" ,
"Hspg2",
"Col4a1",
"Fn1",
"Lama2"),
function(x) plotTSNE(sce, colour_by = x ))
wrap_plots(list_plots) + plot_annotation(title = "Stromal")
plotExpression(sce, features=c( "Lamb1" ,
"Hspg2",
"Col4a1",
"Fn1",
"Lama2"),
x="originalexp_snn_res.0.6", colour_by = "originalexp_snn_res.0.6", ncol=1) + scale_color_manual(values = cols)## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
# Endothelial cells and pericytes
list_plots <- lapply(c( "Cldn5",
"Icam2",
"Pdgfrb",
"Notch3",
"Vwf",
"Flt1",
"Mecom"),
function(x)plotTSNE(sce, colour_by = x ))
wrap_plots(list_plots) + plot_annotation(title = " Endothelial cells and pericytes")
plotExpression(sce, features=c( "Cldn5",
"Icam2",
"Pdgfrb",
"Notch3",
"Vwf",
"Flt1",
"Mecom"),
x="originalexp_snn_res.0.6", colour_by = "originalexp_snn_res.0.6", ncol=1) + scale_color_manual(values = cols)## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
Click to expand the Epithelial cells
list_plots <- lapply(c("Ttr", "Kcnj13", "Krt18"),
function(x)plotTSNE(sce, colour_by = x ))
wrap_plots(list_plots) + plot_annotation(title = "Choroid plexus epithelial cells")
plotExpression(sce, features=c("Ttr", "Kcnj13", "Krt18"),
x="originalexp_snn_res.0.6", colour_by = "originalexp_snn_res.0.6", ncol=1) + scale_color_manual(values = cols)## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
Click to expand the Astrocytes marker plots
# Astrocytes
list_plots <- lapply(c("Gja1",
"Aqp4",
"Glul",
"Sox9",
"Ndrg2",
"Gfap",
"Aldh1a1",
"Aldh1l1",
"Vim",
"Apoe",
"Fgfr3"),
function(x)plotTSNE(sce, colour_by = x ))
wrap_plots(list_plots) + plot_annotation(title = "Astrocyte")
plotExpression(sce, features=c("Gja1",
"Aqp4",
"Glul",
"Sox9",
"Ndrg2",
"Gfap",
"Aldh1a1",
"Aldh1l1",
"Vim",
"Apoe",
"Fgfr3"),
x="originalexp_snn_res.0.6", colour_by = "originalexp_snn_res.0.6", ncol=1) + scale_color_manual(values = cols)## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
#Astrocyte markers as described in Zeisel et al. 2018 in the mouse for telencephalon and non-telencephalon astrocytes
list_plots <- lapply(c( "Agt",
"Mfge8",
"Slc6a11",
"Slc6a9",
"Gdf10",
"Islr",
"Gfap",
"Aqp4") ,
function(x)plotTSNE(sce, colour_by = x ))
wrap_plots(list_plots) + plot_annotation(title = "Astrocyte mouse telecephalon")
Click to expand the Immune cells plots
#Microglia and macrophages
list_plots <- lapply(c( "Cd74",
"Spi1",
"Mrc1",
"Tmem119",
"Cx3cr1",
"Aif1",
"P2ry12",
"C1qc",
"C1qa"),
function(x)plotTSNE(sce, colour_by = x ))
wrap_plots(list_plots) + plot_annotation(title = "Microglia and macrophages")
plotExpression(sce, features=c( "Cd74",
"Spi1",
"Mrc1",
"Tmem119",
"Cx3cr1",
"Aif1",
"P2ry12",
"C1qc",
"C1qa"),
x="originalexp_snn_res.0.6", colour_by = "originalexp_snn_res.0.6", ncol=1) + scale_color_manual(values = cols)## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
# Border associated mcrophages
list_plots <- lapply(c( "Mrc1", "Ms4a7", "Apoe"),
function(x)plotTSNE(sce, colour_by = x ))
wrap_plots(list_plots) + plot_annotation(title = "Border associated macrophages")
plotExpression(sce, features=c( "Mrc1", "Ms4a7", "Apoe"),
x="originalexp_snn_res.0.6", colour_by = "originalexp_snn_res.0.6", ncol=1) + scale_color_manual(values = cols)## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
# Immune cells
list_plots <- lapply(c(
# Tcells
"Cd3e",
# Bcells
"Cd19",
# Natural killer
"Klrb1c", "Cd209a",
# all immune cells (CD45)
"Ptprc"),
function(x)plotTSNE(sce,
colour_by = x ,
point_alpha=0.3,
point_size = 0.5))
wrap_plots(list_plots) + plot_annotation(title = "Immune cells")
plotExpression(sce, features=c(
# Tcells
"Cd3e",
# Bcells
"Cd19",
# Natural killer
"Klrb1c", "Cd209a",
# all immune cells (CD45)
"Ptprc"),
x="originalexp_snn_res.0.6", colour_by = "originalexp_snn_res.0.6", ncol=1) + scale_color_manual(values = cols)## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
#monocybes/neutrophils
list_plots <- lapply(c(
# monocytes
"S100a9",
#neutrophils
"Ly6g", "Camp"),
function(x)plotTSNE(sce,
colour_by = x ,
point_alpha=0.3,
point_size = 0.5))
wrap_plots(list_plots) + plot_annotation(title = "Monocytes and Neutorphils cells")
plotExpression(sce, features=c(
# monocytes
"S100a9",
#neutrophils
"Ly6g", "Camp"),
x="originalexp_snn_res.0.6", colour_by = "originalexp_snn_res.0.6", ncol=1) + scale_color_manual(values = cols)## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
Click to expand the Oligodendroglia marker plots
#OPCs
list_plots <- lapply(c("Pdgfra",
"Cspg4",
"Gpr17",
"Ptprz1",
"Olig1",
"Olig2",
"Pcdh15",
"Ptgds",
"Bcan"),
function(x)plotTSNE(sce, colour_by = x ))
wrap_plots(list_plots) + plot_annotation(title = "OPCS")
plotExpression(sce, features=c("Pdgfra",
"Cspg4",
"Gpr17",
"Ptprz1",
"Olig1",
"Olig2",
"Pcdh15",
"Ptgds",
"Bcan"),
x="originalexp_snn_res.0.6", colour_by = "originalexp_snn_res.0.6", ncol=1) + scale_color_manual(values = cols)## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
#Oligodendrocytes
list_plots <- lapply(c("Plp1",
"Cnp",
"Mag",
"Mog",
"Mobp",
"Mbp",
"Sox10" ),
function(x)plotTSNE(sce, colour_by = x ))
wrap_plots(list_plots) + plot_annotation(title = "Oligodendrocytes")
plotExpression(sce, features=c("Plp1",
"Cnp",
"Mag",
"Mog",
"Mobp",
"Mbp",
"Sox10" ),
x="originalexp_snn_res.0.6", colour_by = "originalexp_snn_res.0.6", ncol=1) + scale_color_manual(values = cols)## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
Click to expand the ependymal marker plots
list_plots <- lapply(c("Vit", "Sox9", "Dynlrb2", "Ccdc153", "Rsph1", "Tm4sf1", "Pcp4l1", "Pcp4", "Hspa2", "Cd24a", "Mt2", "Chchd10"),
function(x)plotTSNE(sce, colour_by = x ))
wrap_plots(list_plots) + plot_annotation(title = "Ependymal")
â™
Rename the Clusters with assigned Cell Types
With the help of the known markers plotted above the celltype identity of every cluster has benn identified.
Renaming the clusters accordingly:
plotTSNE(sce, colour_by = "originalexp_snn_res.0.6", text_by = "originalexp_snn_res.0.6", force = 0) + scale_color_manual(values = cols)## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
if (!file.exists(here("processed", project, "sce_anno_01.RDS"))) {
annotation <- read.csv(here("data","young_data_cluster_names_anno_01.csv"))
celltype <- annotation$Celltype
sce$celltype <- as.factor(sce$originalexp_snn_res.0.6)
# add the celltypes as described above in the correct order to replace the levels.
levels(sce$celltype) <- celltype
# same with the cluster_names
cluster_names <- annotation$ClusterName
sce$cluster_names <- as.factor(sce$originalexp_snn_res.0.6)
levels(sce$cluster_names) <- cluster_names
saveRDS(sce, here("processed", project, "sce_anno_01.RDS"))
}
plotTSNE(sce, colour_by = "celltype", text_by = "celltype", text_size = 3, force = 1) + scale_color_manual(values = cols)## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
plotTSNE(sce, colour_by = "cluster_names", text_by = "cluster_names", text_size = 3, force = 1) + scale_color_manual(values = cols)## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
Session Info
Click to expand
sessionInfo()## R version 4.1.1 (2021-08-10)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19043)
##
## 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] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] pals_1.7 scran_1.22.1
## [3] patchwork_1.1.1 here_1.0.1
## [5] scater_1.23.5 ggplot2_3.3.5
## [7] scuttle_1.4.0 SingleCellExperiment_1.16.0
## [9] SummarizedExperiment_1.24.0 Biobase_2.54.0
## [11] GenomicRanges_1.46.1 GenomeInfoDb_1.30.1
## [13] IRanges_2.28.0 S4Vectors_0.32.3
## [15] BiocGenerics_0.40.0 MatrixGenerics_1.6.0
## [17] matrixStats_0.61.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 rprojroot_2.0.2
## [3] tools_4.1.1 bslib_0.3.1
## [5] utf8_1.2.2 R6_2.5.1
## [7] irlba_2.3.5 vipor_0.4.5
## [9] DBI_1.1.2 colorspace_2.0-2
## [11] withr_2.4.3 tidyselect_1.1.1
## [13] gridExtra_2.3 compiler_4.1.1
## [15] cli_3.2.0 BiocNeighbors_1.12.0
## [17] DelayedArray_0.20.0 labeling_0.4.2
## [19] sass_0.4.0 scales_1.1.1
## [21] stringr_1.4.0 digest_0.6.29
## [23] rmarkdown_2.11 XVector_0.34.0
## [25] dichromat_2.0-0 pkgconfig_2.0.3
## [27] htmltools_0.5.2 sparseMatrixStats_1.6.0
## [29] highr_0.9 maps_3.4.0
## [31] fastmap_1.1.0 limma_3.50.0
## [33] rlang_1.0.1 rstudioapi_0.13
## [35] DelayedMatrixStats_1.16.0 farver_2.1.0
## [37] jquerylib_0.1.4 generics_0.1.2
## [39] jsonlite_1.7.3 BiocParallel_1.28.3
## [41] dplyr_1.0.8 RCurl_1.98-1.6
## [43] magrittr_2.0.2 BiocSingular_1.10.0
## [45] GenomeInfoDbData_1.2.7 Matrix_1.4-0
## [47] Rcpp_1.0.8 ggbeeswarm_0.6.0
## [49] munsell_0.5.0 fansi_1.0.2
## [51] viridis_0.6.2 lifecycle_1.0.1
## [53] stringi_1.7.6 yaml_2.2.2
## [55] edgeR_3.36.0 zlibbioc_1.40.0
## [57] grid_4.1.1 parallel_4.1.1
## [59] ggrepel_0.9.1 dqrng_0.3.0
## [61] crayon_1.5.0 lattice_0.20-45
## [63] cowplot_1.1.1 beachmat_2.10.0
## [65] mapproj_1.2.8 locfit_1.5-9.4
## [67] metapod_1.2.0 knitr_1.37
## [69] pillar_1.7.0 igraph_1.2.11
## [71] codetools_0.2-18 ScaledMatrix_1.2.0
## [73] glue_1.6.1 evaluate_0.14
## [75] vctrs_0.3.8 gtable_0.3.0
## [77] purrr_0.3.4 assertthat_0.2.1
## [79] xfun_0.29 rsvd_1.0.5
## [81] viridisLite_0.4.0 tibble_3.1.6
## [83] beeswarm_0.4.0 cluster_2.1.2
## [85] statmod_1.4.36 bluster_1.4.0
## [87] ellipsis_0.3.2