Differentiation anaylsis in NSCLC
Chrysa Papadopoulou, Almut Lütge
Last updated: 2024-08-22
Checks: 7 0
Knit directory: CCL19_FRCs_lung_cancer/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 7246b50 | Pchryssa | 2024-08-22 | NSCLC differentiation |
| html | 6857394 | Pchryssa | 2024-08-22 | Build site. |
| Rmd | d9b3536 | Pchryssa | 2024-08-22 | trajectory analysis |
Load packages
suppressPackageStartupMessages({
library(here)
library(purrr)
library(dplyr)
library(stringr)
library(patchwork)
library(Seurat)
library(Matrix)
library(ggsci)
library(monocle3)
library(SeuratWrappers)
library(ggplot2)
library(scCustomize)
})Trajectory analysis in NSCLC
Set directory
basedir <- here()Read CCL19⁺ FRC data
NSCLC_CCL19_data <- readRDS(paste0(basedir,"/data/Human/NSCLC_CCL19_FRCs_CAFs.rds"))Color palette per celltype
palet_CCL19_FRC <- c("#1B9E77", "#54B0E4","#E3BE00", "#E41A1C")
names(palet_CCL19_FRC) <- c("CAF2/TRC","CAF1/PRC","AdvFB" ,"SMC/PC")CCL19⁺ fibroblasts
palet_region <- c("#C77CFF","#B79F00","#F8766D")
names(palet_region) <- c("Central Margin","Subpleural Margin","Lung (unaffected)")
DimPlot(NSCLC_CCL19_data, reduction = "umap", group.by = "group",cols = palet_region)+
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("CCL19", "\U207A ", "fibroblasts"))-1.png)
Differentiation trajectories on NSCLC CCL19⁺ cells based on Monocle3
Monocle3 preparation
sceSub <-as.SingleCellExperiment(NSCLC_CCL19_data)
sce_list <- list("all" = sceSub)
sce_names <- names(sce_list)
test = rownames(rowData(sce_list$all))
test_new = as.data.frame(test)
test_new <- gsub("^.*\\.", "", test)
cds_list <- lapply(sce_names, prep_sce) %>% set_names(sce_names)Visulaize partitions
for (sce_nam in sce_names) {
p <-plot_cells(cds_list[[sce_nam]], color_cells_by = "partition")
print(p)
}
Learn graph
cds_list <- lapply(cds_list, function(cds){
cds <- learn_graph(cds, learn_graph_control = list(minimal_branch_len = 30,
L1.gamma = 0.08,
L1.sigma = 0.01,
nn.n_trees = 20
))
})
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|======================================================================| 100%Learn pseudotime
# Written by Almut Lütge
cds_list <- lapply(cds_list, function(cds){
mu_start <- get_earliest_principal_node(cds, "SMC/PC")
lu_start <- get_earliest_principal_node(cds, "AdvFB")
cds <- order_cells(cds, root_pr_nodes=c(mu_start, lu_start))
}) %>% set_names(sce_names)Visualize trajectories
Origin (Figure 4A)
for (sce_nam in sce_names) {
p <- plot_trajectory(col_var = "group", cds = cds_list[[sce_nam]], palet_region)
print(p)
}
Visualize pseudotime (Figure 4B)
for (sce_nam in sce_names) {
p <- plot_pseudo(cds_list[[sce_nam]])
print(p)
}
Celltype
for (sce_nam in sce_names) {
p <- plot_trajectory(col_var = "cell_type", cds = cds_list[[sce_nam]], palet_CCL19_FRC)
print(p)
}
Visualize genes along pseudotime
DES MYH11 (Figure 4C)
for (sce_nam in sce_names) {
p <- plot_top_de(sce_nam, "1", gene_list = c("DES", "MYH11"))
print(p)
}
RGS5 NOTCH3 ACTA2 (Figure 4E)
for (sce_nam in sce_names) {
p <- plot_top_de(sce_nam, "1", gene_list = c("RGS5","NOTCH3","ACTA2"))
print(p)
}
TNC CCL21 CXCL12 (Figure 4G)
for (sce_nam in sce_names) {
p <- plot_top_de(sce_nam, "1", gene_list = c("TNC","CCL21","CXCL12"))
print(p)
}
CLU POSTN SULF1 (Figure 4I)
for (sce_nam in sce_names) {
p <- plot_top_de(sce_nam, "1", gene_list = c("CLU","POSTN","SULF1"))
print(p)
}
Unaffected Lung vs Tumor (Supplementary Figure 5A)
Umap
DimPlot(NSCLC_CCL19_data, reduction = "umap", group.by = "type", split.by = "type")+
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")
DES
FeaturePlot(NSCLC_CCL19_data, reduction = "umap", split.by = "type",
features = get_full_gene_name('DES',NSCLC_CCL19_data),raster=FALSE,
cols=c("lightgrey", "darkred"), min.cutoff = 0, max.cutoff = 4.5)
Signatures (Supplementary Figure 5B)
CD34
FeaturePlot(NSCLC_CCL19_data, reduction = "umap",
features = get_full_gene_name('CD34',NSCLC_CCL19_data),raster=FALSE,
cols=c("lightgrey", "darkred"), min.cutoff = 0, max.cutoff = 4.5) + ggtitle("CD34")
LEPR
FeaturePlot(NSCLC_CCL19_data, reduction = "umap",
features = get_full_gene_name('LEPR',NSCLC_CCL19_data),raster=FALSE,
cols=c("lightgrey", "darkred"), min.cutoff = 0, max.cutoff = 4.5) + ggtitle("LEPR")
Session info
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] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] scCustomize_1.1.3 ggplot2_3.4.2
[3] SeuratWrappers_0.2.0 monocle3_1.3.7
[5] SingleCellExperiment_1.22.0 SummarizedExperiment_1.30.2
[7] GenomicRanges_1.52.0 GenomeInfoDb_1.36.1
[9] IRanges_2.34.1 S4Vectors_0.38.1
[11] MatrixGenerics_1.12.3 matrixStats_1.0.0
[13] Biobase_2.60.0 BiocGenerics_0.46.0
[15] ggsci_3.0.0 Matrix_1.6-0
[17] SeuratObject_4.1.3 Seurat_4.3.0.1
[19] patchwork_1.1.2 stringr_1.5.0
[21] dplyr_1.1.2 purrr_1.0.1
[23] here_1.0.1 magrittr_2.0.3
[25] circlize_0.4.15 tidyr_1.3.0
[27] tibble_3.2.1 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.21 splines_4.3.1 later_1.3.1
[4] bitops_1.0-7 polyclip_1.10-4 janitor_2.2.0
[7] lifecycle_1.0.3 rprojroot_2.0.3 globals_0.16.2
[10] processx_3.8.2 lattice_0.21-8 MASS_7.3-60
[13] plotly_4.10.2 sass_0.4.7 rmarkdown_2.23
[16] jquerylib_0.1.4 yaml_2.3.7 remotes_2.4.2.1
[19] httpuv_1.6.11 sctransform_0.3.5 sp_2.0-0
[22] spatstat.sparse_3.0-2 reticulate_1.36.1 cowplot_1.1.1
[25] pbapply_1.7-2 minqa_1.2.5 RColorBrewer_1.1-3
[28] lubridate_1.9.2 abind_1.4-5 zlibbioc_1.46.0
[31] Rtsne_0.16 RCurl_1.98-1.12 git2r_0.33.0
[34] GenomeInfoDbData_1.2.10 ggrepel_0.9.3 irlba_2.3.5.1
[37] listenv_0.9.0 spatstat.utils_3.1-0 goftest_1.2-3
[40] spatstat.random_3.1-5 fitdistrplus_1.1-11 parallelly_1.36.0
[43] leiden_0.4.3 codetools_0.2-19 DelayedArray_0.28.0
[46] tidyselect_1.2.0 shape_1.4.6 farver_2.1.1
[49] viridis_0.6.4 lme4_1.1-34 spatstat.explore_3.2-1
[52] jsonlite_1.8.7 ellipsis_0.3.2 progressr_0.13.0
[55] ggridges_0.5.4 survival_3.5-5 systemfonts_1.0.4
[58] tools_4.3.1 ragg_1.2.5 ica_1.0-3
[61] Rcpp_1.0.11 glue_1.6.2 gridExtra_2.3
[64] SparseArray_1.2.4 xfun_0.39 withr_2.5.0
[67] BiocManager_1.30.21.1 fastmap_1.1.1 boot_1.3-28.1
[70] fansi_1.0.4 callr_3.7.3 digest_0.6.33
[73] rsvd_1.0.5 timechange_0.2.0 R6_2.5.1
[76] mime_0.12 textshaping_0.3.6 ggprism_1.0.4
[79] colorspace_2.1-0 scattermore_1.2 tensor_1.5
[82] spatstat.data_3.0-1 utf8_1.2.3 generics_0.1.3
[85] data.table_1.14.8 httr_1.4.6 htmlwidgets_1.6.2
[88] S4Arrays_1.2.1 whisker_0.4.1 uwot_0.1.16
[91] pkgconfig_2.0.3 gtable_0.3.3 lmtest_0.9-40
[94] XVector_0.40.0 htmltools_0.5.5 scales_1.2.1
[97] png_0.1-8 snakecase_0.11.1 knitr_1.43
[100] rstudioapi_0.15.0 reshape2_1.4.4 nlme_3.1-162
[103] nloptr_2.0.3 proxy_0.4-27 cachem_1.0.8
[106] zoo_1.8-12 GlobalOptions_0.1.2 KernSmooth_2.23-22
[109] vipor_0.4.5 parallel_4.3.1 miniUI_0.1.1.1
[112] ggrastr_1.0.2 pillar_1.9.0 grid_4.3.1
[115] vctrs_0.6.3 RANN_2.6.1 promises_1.2.0.1
[118] xtable_1.8-4 cluster_2.1.4 paletteer_1.5.0
[121] beeswarm_0.4.0 evaluate_0.21 cli_3.6.1
[124] compiler_4.3.1 rlang_1.1.1 crayon_1.5.2
[127] future.apply_1.11.0 labeling_0.4.2 rematch2_2.1.2
[130] ps_1.7.5 forcats_1.0.0 getPass_0.2-4
[133] plyr_1.8.8 fs_1.6.3 ggbeeswarm_0.7.2
[136] stringi_1.7.12 viridisLite_0.4.2 deldir_1.0-9
[139] assertthat_0.2.1 munsell_0.5.0 lazyeval_0.2.2
[142] spatstat.geom_3.2-4 future_1.33.0 shiny_1.7.4.1
[145] highr_0.10 ROCR_1.0-11 igraph_1.5.0.1
[148] bslib_0.5.0 date()[1] "Thu Aug 22 23:23:25 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] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] scCustomize_1.1.3 ggplot2_3.4.2
[3] SeuratWrappers_0.2.0 monocle3_1.3.7
[5] SingleCellExperiment_1.22.0 SummarizedExperiment_1.30.2
[7] GenomicRanges_1.52.0 GenomeInfoDb_1.36.1
[9] IRanges_2.34.1 S4Vectors_0.38.1
[11] MatrixGenerics_1.12.3 matrixStats_1.0.0
[13] Biobase_2.60.0 BiocGenerics_0.46.0
[15] ggsci_3.0.0 Matrix_1.6-0
[17] SeuratObject_4.1.3 Seurat_4.3.0.1
[19] patchwork_1.1.2 stringr_1.5.0
[21] dplyr_1.1.2 purrr_1.0.1
[23] here_1.0.1 magrittr_2.0.3
[25] circlize_0.4.15 tidyr_1.3.0
[27] tibble_3.2.1 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.21 splines_4.3.1 later_1.3.1
[4] bitops_1.0-7 polyclip_1.10-4 janitor_2.2.0
[7] lifecycle_1.0.3 rprojroot_2.0.3 globals_0.16.2
[10] processx_3.8.2 lattice_0.21-8 MASS_7.3-60
[13] plotly_4.10.2 sass_0.4.7 rmarkdown_2.23
[16] jquerylib_0.1.4 yaml_2.3.7 remotes_2.4.2.1
[19] httpuv_1.6.11 sctransform_0.3.5 sp_2.0-0
[22] spatstat.sparse_3.0-2 reticulate_1.36.1 cowplot_1.1.1
[25] pbapply_1.7-2 minqa_1.2.5 RColorBrewer_1.1-3
[28] lubridate_1.9.2 abind_1.4-5 zlibbioc_1.46.0
[31] Rtsne_0.16 RCurl_1.98-1.12 git2r_0.33.0
[34] GenomeInfoDbData_1.2.10 ggrepel_0.9.3 irlba_2.3.5.1
[37] listenv_0.9.0 spatstat.utils_3.1-0 goftest_1.2-3
[40] spatstat.random_3.1-5 fitdistrplus_1.1-11 parallelly_1.36.0
[43] leiden_0.4.3 codetools_0.2-19 DelayedArray_0.28.0
[46] tidyselect_1.2.0 shape_1.4.6 farver_2.1.1
[49] viridis_0.6.4 lme4_1.1-34 spatstat.explore_3.2-1
[52] jsonlite_1.8.7 ellipsis_0.3.2 progressr_0.13.0
[55] ggridges_0.5.4 survival_3.5-5 systemfonts_1.0.4
[58] tools_4.3.1 ragg_1.2.5 ica_1.0-3
[61] Rcpp_1.0.11 glue_1.6.2 gridExtra_2.3
[64] SparseArray_1.2.4 xfun_0.39 withr_2.5.0
[67] BiocManager_1.30.21.1 fastmap_1.1.1 boot_1.3-28.1
[70] fansi_1.0.4 callr_3.7.3 digest_0.6.33
[73] rsvd_1.0.5 timechange_0.2.0 R6_2.5.1
[76] mime_0.12 textshaping_0.3.6 ggprism_1.0.4
[79] colorspace_2.1-0 scattermore_1.2 tensor_1.5
[82] spatstat.data_3.0-1 utf8_1.2.3 generics_0.1.3
[85] data.table_1.14.8 httr_1.4.6 htmlwidgets_1.6.2
[88] S4Arrays_1.2.1 whisker_0.4.1 uwot_0.1.16
[91] pkgconfig_2.0.3 gtable_0.3.3 lmtest_0.9-40
[94] XVector_0.40.0 htmltools_0.5.5 scales_1.2.1
[97] png_0.1-8 snakecase_0.11.1 knitr_1.43
[100] rstudioapi_0.15.0 reshape2_1.4.4 nlme_3.1-162
[103] nloptr_2.0.3 proxy_0.4-27 cachem_1.0.8
[106] zoo_1.8-12 GlobalOptions_0.1.2 KernSmooth_2.23-22
[109] vipor_0.4.5 parallel_4.3.1 miniUI_0.1.1.1
[112] ggrastr_1.0.2 pillar_1.9.0 grid_4.3.1
[115] vctrs_0.6.3 RANN_2.6.1 promises_1.2.0.1
[118] xtable_1.8-4 cluster_2.1.4 paletteer_1.5.0
[121] beeswarm_0.4.0 evaluate_0.21 cli_3.6.1
[124] compiler_4.3.1 rlang_1.1.1 crayon_1.5.2
[127] future.apply_1.11.0 labeling_0.4.2 rematch2_2.1.2
[130] ps_1.7.5 forcats_1.0.0 getPass_0.2-4
[133] plyr_1.8.8 fs_1.6.3 ggbeeswarm_0.7.2
[136] stringi_1.7.12 viridisLite_0.4.2 deldir_1.0-9
[139] assertthat_0.2.1 munsell_0.5.0 lazyeval_0.2.2
[142] spatstat.geom_3.2-4 future_1.33.0 shiny_1.7.4.1
[145] highr_0.10 ROCR_1.0-11 igraph_1.5.0.1
[148] bslib_0.5.0