Last updated: 2024-08-21
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Knit directory: CCL19_FRCs_lung_cancer/
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Rmd | 07a0991 | Pchryssa | 2024-08-21 | NSCLC TILS |
suppressPackageStartupMessages({
library(here)
library(purrr)
library(stringr)
library(patchwork)
library(Seurat)
library(Matrix)
library(dittoSeq)
library(gridExtra)
library(dplyr)
})
basedir <- here()
NSCLS_TIL_data <-readRDS(paste0(basedir,"/data/Human/NSCLC_TILs.rds"))
#Define color palet
palet <- c("#1B9E77", "#54B0E4","#E3BE00", "#E41A1C","#4DAF4A","#377EB8","#A65628","#222F75","#FB9A99")
names(palet) <- c("TRC","PRC","AdvFB" ,"SMC/PC", paste0("CD4", "\u207A ", "T cells"),"B cells", "Regulatory T cells" ,paste0("Cycling CD8", "\u207A ", "T cells"),paste0("CD8", "\u207A ", "T cells"))
palet <- palet[names(palet) %in% unique(NSCLS_TIL_data$cell_type)]
DimPlot(NSCLS_TIL_data, reduction = "umap", group.by = "cell_type",cols = palet)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2") + ggtitle("NSCLC infiltrating lymphocytes")
gg <- FeaturePlot(NSCLS_TIL_data, reduction = "umap",
features = get_full_gene_name("CD4",NSCLS_TIL_data)[4],
cols=c("lightgrey", "darkred"),
order = T,combin = FALSE)
gg[[1]]$labels$title <- "CD4"
gg
gg <-FeaturePlot(NSCLS_TIL_data, reduction = "umap",
features = get_full_gene_name("CD8A",NSCLS_TIL_data),
cols=c("lightgrey", "darkred"),
order = T,combin = FALSE)
gg[[1]]$labels$title <- "CD8A"
gg
gg <-FeaturePlot(NSCLS_TIL_data, reduction = "umap",
features = get_full_gene_name("CD79A",NSCLS_TIL_data),
cols=c("lightgrey", "darkred"),
order = T,combin = FALSE)
gg[[1]]$labels$title <- "CD79A"
gg
merged_data <-readRDS(paste0(basedir,"/data/Human/NSCLC_TILs_SI3.rds"))
NCLS_TIL_FRC_cd8 <- subset(merged_data, cell_type %in% c(paste0("CD8", "\u207A ", "T cells"), paste0("Cycling CD8", "\u207A ", "T cells")))
#Preprocessing
resolution <- c(0.1, 0.25, 0.4, 0.6,0.7, 0.8, 0.9, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0)
NCLS_TIL_FRC_cd8 <- FindVariableFeatures(NCLS_TIL_FRC_cd8, selection.method = "vst", nfeatures = 2000)
NCLS_TIL_FRC_cd8 <- ScaleData(NCLS_TIL_FRC_cd8)
NCLS_TIL_FRC_cd8 <- RunPCA(object = NCLS_TIL_FRC_cd8, npcs = 30, verbose = FALSE,seed.use = 8734)
NCLS_TIL_FRC_cd8 <- RunTSNE(object = NCLS_TIL_FRC_cd8, reduction = "pca", dims = 1:20, seed.use = 8734)
NCLS_TIL_FRC_cd8 <- RunUMAP(object = NCLS_TIL_FRC_cd8, reduction = "pca", dims = 1:20, seed.use = 8734)
NCLS_TIL_FRC_cd8 <- FindNeighbors(object = NCLS_TIL_FRC_cd8, reduction = "pca", dims = 1:20, seed.use = 8734)
for(k in 1:length(resolution)){
NCLS_TIL_FRC_cd8 <- FindClusters(object = NCLS_TIL_FRC_cd8, resolution = resolution[k], random.seed = 8734)
}
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 4918
Number of edges: 176244
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9314
Number of communities: 5
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 4918
Number of edges: 176244
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8926
Number of communities: 9
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 4918
Number of edges: 176244
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8638
Number of communities: 10
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 4918
Number of edges: 176244
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8382
Number of communities: 12
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 4918
Number of edges: 176244
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8263
Number of communities: 14
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 4918
Number of edges: 176244
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8160
Number of communities: 14
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 4918
Number of edges: 176244
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8057
Number of communities: 14
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 4918
Number of edges: 176244
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7955
Number of communities: 15
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 4918
Number of edges: 176244
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7793
Number of communities: 16
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 4918
Number of edges: 176244
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7641
Number of communities: 18
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 4918
Number of edges: 176244
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7507
Number of communities: 21
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 4918
Number of edges: 176244
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7374
Number of communities: 21
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 4918
Number of edges: 176244
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7254
Number of communities: 22
Elapsed time: 0 seconds
NCLS_TIL_FRC_cd8$cell_type <- NULL
NCLS_TIL_FRC_cd8$cell_type <--1
NCLS_TIL_FRC_cd8$cell_type[which(NCLS_TIL_FRC_cd8$RNA_snn_res.0.4 == 0)] <- paste0("Stem-like/Naive CD8", "\u207A ", "T cells")
NCLS_TIL_FRC_cd8$cell_type[which(NCLS_TIL_FRC_cd8$RNA_snn_res.0.4 == 1)] <-paste0("Stem-like/Naive CD8", "\u207A ", "T cells")
NCLS_TIL_FRC_cd8$cell_type[which(NCLS_TIL_FRC_cd8$RNA_snn_res.0.4 == 2)] <- paste0("Effector-memory CD8", "\u207A ", "T cells")
NCLS_TIL_FRC_cd8$cell_type[which(NCLS_TIL_FRC_cd8$RNA_snn_res.0.4 == 3)] <- paste0("Effector-memory CD8", "\u207A ", "T cells")
NCLS_TIL_FRC_cd8$cell_type[which(NCLS_TIL_FRC_cd8$RNA_snn_res.0.4 == 4)] <- paste0("Exhausted CD8", "\u207A ", "T cells")
NCLS_TIL_FRC_cd8$cell_type[which(NCLS_TIL_FRC_cd8$RNA_snn_res.0.4 == 5)] <- paste0("Effector CD8", "\u207A ", "T cells")
NCLS_TIL_FRC_cd8$cell_type[which(NCLS_TIL_FRC_cd8$RNA_snn_res.0.4 == 6)] <-paste0("Exhausted CD8", "\u207A ", "T cells")
NCLS_TIL_FRC_cd8$cell_type[which(NCLS_TIL_FRC_cd8$RNA_snn_res.0.4 == 7)] <- paste0("Exhausted CD8", "\u207A ", "T cells")
NCLS_TIL_FRC_cd8$cell_type[which(NCLS_TIL_FRC_cd8$RNA_snn_res.0.4 == 8)] <- paste0("Cycling CD8", "\u207A ", "T cells")
NCLS_TIL_FRC_cd8$cell_type[which(NCLS_TIL_FRC_cd8$RNA_snn_res.0.4 == 9)] <- paste0("Effector CD8", "\u207A ", "T cells")
#Extend palet for CD8 T cell subsets
palet <- c("#1B9E77", "#54B0E4","#E3BE00", "#E41A1C","#F8766D","#00C08B","#7CAE00","#00B4F0","#F564E3")
names(palet) <- c("TRC","PRC","AdvFB" ,"SMC/PC",paste0("Stem-like/Naive CD8", "\u207A ", "T cells"),paste0("Exhausted CD8", "\u207A ", "T cells"), paste0("Effector-memory CD8", "\u207A ", "T cells"),paste0("Effector CD8", "\u207A ", "T cells"),paste0("Cycling CD8", "\u207A ", "T cells"))
palet <- palet[names(palet) %in% unique(NCLS_TIL_FRC_cd8$cell_type)]
DimPlot(NCLS_TIL_FRC_cd8, reduction = "umap", group.by = "cell_type", cols = palet)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2") + ggtitle(paste0("CD8", "\U207A ", "T cells (NSCLC)"))
saveRDS(NCLS_TIL_FRC_cd8,paste0(basedir,"/data/Human/NSCLC_TILs_CD8_pop.rds"))
genes <-c("CCR7","SELL","TCF7","NKG7","GZMA","GZMB","GZMM","GZMK","CX3CR1","GNLY","FCGR3A", "KIR3DL2","KLRF1","TIGIT","CTLA4","LAG3","ZEB2","TOP2A","MKI67","PCLAF")
data_conv <-Remove_ensebl_id(NCLS_TIL_FRC_cd8)
data_conv$cell_type <- factor(data_conv$cell_type, levels = rev(c(paste0("Cycling CD8", "\u207A ", "T cells"),
paste0("Effector CD8", "\u207A ", "T cells"),
paste0("Exhausted CD8", "\u207A ", "T cells"),
paste0("Effector-memory CD8", "\u207A ", "T cells"),
paste0("Stem-like/Naive CD8", "\u207A ", "T cells"))))
gg <-dittoDotPlot(data_conv, vars = genes, group.by = "cell_type", size = 4)
gg + coord_fixed(ratio=0.8) + ylab("")
sessionInfo()
R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.6.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Europe/Zurich
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] dplyr_1.1.2 gridExtra_2.3 dittoSeq_1.12.1 ggplot2_3.4.2
[5] Matrix_1.6-0 SeuratObject_4.1.3 Seurat_4.3.0.1 patchwork_1.1.2
[9] stringr_1.5.0 purrr_1.0.1 here_1.0.1 magrittr_2.0.3
[13] circlize_0.4.15 tidyr_1.3.0 tibble_3.2.1 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 rstudioapi_0.15.0
[3] jsonlite_1.8.7 shape_1.4.6
[5] spatstat.utils_3.1-0 farver_2.1.1
[7] rmarkdown_2.23 ragg_1.2.5
[9] zlibbioc_1.46.0 GlobalOptions_0.1.2
[11] fs_1.6.3 vctrs_0.6.3
[13] ROCR_1.0-11 spatstat.explore_3.2-1
[15] RCurl_1.98-1.12 S4Arrays_1.2.1
[17] htmltools_0.5.5 SparseArray_1.2.4
[19] sass_0.4.7 sctransform_0.3.5
[21] parallelly_1.36.0 KernSmooth_2.23-22
[23] bslib_0.5.0 htmlwidgets_1.6.2
[25] ica_1.0-3 plyr_1.8.8
[27] plotly_4.10.2 zoo_1.8-12
[29] cachem_1.0.8 whisker_0.4.1
[31] igraph_1.5.0.1 mime_0.12
[33] lifecycle_1.0.3 pkgconfig_2.0.3
[35] R6_2.5.1 fastmap_1.1.1
[37] GenomeInfoDbData_1.2.10 MatrixGenerics_1.12.3
[39] fitdistrplus_1.1-11 future_1.33.0
[41] shiny_1.7.4.1 digest_0.6.33
[43] colorspace_2.1-0 S4Vectors_0.38.1
[45] ps_1.7.5 rprojroot_2.0.3
[47] tensor_1.5 irlba_2.3.5.1
[49] textshaping_0.3.6 GenomicRanges_1.52.0
[51] labeling_0.4.2 progressr_0.13.0
[53] fansi_1.0.4 spatstat.sparse_3.0-2
[55] httr_1.4.6 polyclip_1.10-4
[57] abind_1.4-5 compiler_4.3.1
[59] withr_2.5.0 highr_0.10
[61] MASS_7.3-60 DelayedArray_0.28.0
[63] tools_4.3.1 lmtest_0.9-40
[65] httpuv_1.6.11 future.apply_1.11.0
[67] goftest_1.2-3 glue_1.6.2
[69] callr_3.7.3 nlme_3.1-162
[71] promises_1.2.0.1 grid_4.3.1
[73] Rtsne_0.16 getPass_0.2-4
[75] cluster_2.1.4 reshape2_1.4.4
[77] generics_0.1.3 gtable_0.3.3
[79] spatstat.data_3.0-1 data.table_1.14.8
[81] XVector_0.40.0 sp_2.0-0
[83] utf8_1.2.3 BiocGenerics_0.46.0
[85] spatstat.geom_3.2-4 RcppAnnoy_0.0.21
[87] ggrepel_0.9.3 RANN_2.6.1
[89] pillar_1.9.0 later_1.3.1
[91] splines_4.3.1 lattice_0.21-8
[93] survival_3.5-5 deldir_1.0-9
[95] tidyselect_1.2.0 SingleCellExperiment_1.22.0
[97] miniUI_0.1.1.1 pbapply_1.7-2
[99] knitr_1.43 git2r_0.33.0
[101] IRanges_2.34.1 SummarizedExperiment_1.30.2
[103] scattermore_1.2 stats4_4.3.1
[105] xfun_0.39 Biobase_2.60.0
[107] matrixStats_1.0.0 pheatmap_1.0.12
[109] stringi_1.7.12 lazyeval_0.2.2
[111] yaml_2.3.7 evaluate_0.21
[113] codetools_0.2-19 cli_3.6.1
[115] uwot_0.1.16 systemfonts_1.0.4
[117] xtable_1.8-4 reticulate_1.36.1
[119] munsell_0.5.0 processx_3.8.2
[121] jquerylib_0.1.4 GenomeInfoDb_1.36.1
[123] Rcpp_1.0.11 globals_0.16.2
[125] spatstat.random_3.1-5 png_0.1-8
[127] parallel_4.3.1 ellipsis_0.3.2
[129] bitops_1.0-7 listenv_0.9.0
[131] viridisLite_0.4.2 scales_1.2.1
[133] ggridges_0.5.4 crayon_1.5.2
[135] leiden_0.4.3 rlang_1.1.1
[137] cowplot_1.1.1
date()
[1] "Wed Aug 21 09:20:31 2024"
sessionInfo()
R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.6.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Europe/Zurich
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] dplyr_1.1.2 gridExtra_2.3 dittoSeq_1.12.1 ggplot2_3.4.2
[5] Matrix_1.6-0 SeuratObject_4.1.3 Seurat_4.3.0.1 patchwork_1.1.2
[9] stringr_1.5.0 purrr_1.0.1 here_1.0.1 magrittr_2.0.3
[13] circlize_0.4.15 tidyr_1.3.0 tibble_3.2.1 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 rstudioapi_0.15.0
[3] jsonlite_1.8.7 shape_1.4.6
[5] spatstat.utils_3.1-0 farver_2.1.1
[7] rmarkdown_2.23 ragg_1.2.5
[9] zlibbioc_1.46.0 GlobalOptions_0.1.2
[11] fs_1.6.3 vctrs_0.6.3
[13] ROCR_1.0-11 spatstat.explore_3.2-1
[15] RCurl_1.98-1.12 S4Arrays_1.2.1
[17] htmltools_0.5.5 SparseArray_1.2.4
[19] sass_0.4.7 sctransform_0.3.5
[21] parallelly_1.36.0 KernSmooth_2.23-22
[23] bslib_0.5.0 htmlwidgets_1.6.2
[25] ica_1.0-3 plyr_1.8.8
[27] plotly_4.10.2 zoo_1.8-12
[29] cachem_1.0.8 whisker_0.4.1
[31] igraph_1.5.0.1 mime_0.12
[33] lifecycle_1.0.3 pkgconfig_2.0.3
[35] R6_2.5.1 fastmap_1.1.1
[37] GenomeInfoDbData_1.2.10 MatrixGenerics_1.12.3
[39] fitdistrplus_1.1-11 future_1.33.0
[41] shiny_1.7.4.1 digest_0.6.33
[43] colorspace_2.1-0 S4Vectors_0.38.1
[45] ps_1.7.5 rprojroot_2.0.3
[47] tensor_1.5 irlba_2.3.5.1
[49] textshaping_0.3.6 GenomicRanges_1.52.0
[51] labeling_0.4.2 progressr_0.13.0
[53] fansi_1.0.4 spatstat.sparse_3.0-2
[55] httr_1.4.6 polyclip_1.10-4
[57] abind_1.4-5 compiler_4.3.1
[59] withr_2.5.0 highr_0.10
[61] MASS_7.3-60 DelayedArray_0.28.0
[63] tools_4.3.1 lmtest_0.9-40
[65] httpuv_1.6.11 future.apply_1.11.0
[67] goftest_1.2-3 glue_1.6.2
[69] callr_3.7.3 nlme_3.1-162
[71] promises_1.2.0.1 grid_4.3.1
[73] Rtsne_0.16 getPass_0.2-4
[75] cluster_2.1.4 reshape2_1.4.4
[77] generics_0.1.3 gtable_0.3.3
[79] spatstat.data_3.0-1 data.table_1.14.8
[81] XVector_0.40.0 sp_2.0-0
[83] utf8_1.2.3 BiocGenerics_0.46.0
[85] spatstat.geom_3.2-4 RcppAnnoy_0.0.21
[87] ggrepel_0.9.3 RANN_2.6.1
[89] pillar_1.9.0 later_1.3.1
[91] splines_4.3.1 lattice_0.21-8
[93] survival_3.5-5 deldir_1.0-9
[95] tidyselect_1.2.0 SingleCellExperiment_1.22.0
[97] miniUI_0.1.1.1 pbapply_1.7-2
[99] knitr_1.43 git2r_0.33.0
[101] IRanges_2.34.1 SummarizedExperiment_1.30.2
[103] scattermore_1.2 stats4_4.3.1
[105] xfun_0.39 Biobase_2.60.0
[107] matrixStats_1.0.0 pheatmap_1.0.12
[109] stringi_1.7.12 lazyeval_0.2.2
[111] yaml_2.3.7 evaluate_0.21
[113] codetools_0.2-19 cli_3.6.1
[115] uwot_0.1.16 systemfonts_1.0.4
[117] xtable_1.8-4 reticulate_1.36.1
[119] munsell_0.5.0 processx_3.8.2
[121] jquerylib_0.1.4 GenomeInfoDb_1.36.1
[123] Rcpp_1.0.11 globals_0.16.2
[125] spatstat.random_3.1-5 png_0.1-8
[127] parallel_4.3.1 ellipsis_0.3.2
[129] bitops_1.0-7 listenv_0.9.0
[131] viridisLite_0.4.2 scales_1.2.1
[133] ggridges_0.5.4 crayon_1.5.2
[135] leiden_0.4.3 rlang_1.1.1
[137] cowplot_1.1.1