Last updated: 2024-08-21
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Knit directory: CCL19_FRCs_lung_cancer/
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suppressPackageStartupMessages({
library(here)
library(purrr)
library(dplyr)
library(stringr)
library(patchwork)
library(Seurat)
library(Matrix)
library(dittoSeq)
library(gridExtra)
library(gsubfn)
library(ggsci)
library(bigmds)
library(tidyverse)
})
basedir <- here()
NCLS_FRCS <- readRDS(paste0(basedir,"/data/Human/NSCLC_Ccl19_FB_FRCs_trc_prc_final.rds"))
Tons_FRC_data <-readRDS(paste0(basedir,"/data/Human/mergedHumanTonsilExtendedDataset_incAcuteTonsilitis_mapped_wocl11+12+14_seuratFRC.rds"))
cols<- pal_igv()(51)
names(cols) <- c(0:50)
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "ACTA2+PRC_1"] <- paste0("ACTA2", expression("\U207A"),"PRC_1")
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "ACTA2+PRC_2"] <- paste0("ACTA2", expression("\U207A"),"PRC_2")
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "ACTA2+PRC_3"] <- paste0("ACTA2", expression("\U207A"),"PRC_3")
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "ACTA2+PRC_4"] <- paste0("ACTA2", expression("\U207A"),"PRC_4")
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "ACTA2+PRC_5"] <- paste0("ACTA2", expression("\U207A"),"PRC_5")
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "FDC_6"] <- "FDC"
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "PI16+RC_10"] <- paste0("PI16", expression("\U207A"),"RC_1")
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "PI16+RC_11"] <- paste0("PI16", expression("\U207A"),"RC_2")
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "PI16+RC_12"] <- paste0("PI16", expression("\U207A"),"RC_3")
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "TRC_7"] <- "TRC_1"
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "TRC_8"] <- "TRC_2"
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "TRC_9"] <- "TRC_3"
colDataset <- cols[3:15]
names(colDataset) <- unique(Tons_FRC_data$clusterLabel)
DimPlot(Tons_FRC_data, reduction = "umap", group.by = "clusterLabel",cols=colDataset)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2") + ggtitle("Tonsilar FRCs (De Martin et al 2023)")
NCLS_FRCS$Disease_short <-rep("NSCLC",nrow(NCLS_FRCS@meta.data))
Tons_FRC_data$Disease_short <-rep("Tonsil",nrow(Tons_FRC_data@meta.data))
colnames(Tons_FRC_data@meta.data)[names(Tons_FRC_data@meta.data) == 'clusterLabel'] <- 'cell_type'
data_merge <- merge(NCLS_FRCS, y = c(Tons_FRC_data),
add.cell.ids = c("NCLS_FRCS","Tons_FRC_data"),
project = "merge_nsclc_tonsils")
#Preprocessing
resolution <- c(0.1, 0.25, 0.4, 0.6,0.8, 1.)
data_merge <- preprocessing(data_merge,resolution)
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 33594
Number of edges: 1098322
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9641
Number of communities: 6
Elapsed time: 6 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 33594
Number of edges: 1098322
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9342
Number of communities: 12
Elapsed time: 6 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 33594
Number of edges: 1098322
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9153
Number of communities: 13
Elapsed time: 6 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 33594
Number of edges: 1098322
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8977
Number of communities: 17
Elapsed time: 6 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 33594
Number of edges: 1098322
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8821
Number of communities: 21
Elapsed time: 6 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 33594
Number of edges: 1098322
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8698
Number of communities: 27
Elapsed time: 5 seconds
#saveRDS(data_merge, paste0(basedir,"/data/Human/NSCLC_Ccl19_tonsil_merged.rds"))
obj.list <-SplitObject(data_merge, split.by = 'cell_type')
#For each object in list we see to run normalization and identify highly variable features
for (i in 1:length(obj.list)){
#Normalization
obj.list[[i]] <- NormalizeData(obj.list[[i]], normalization.method = "LogNormalize", scale.factor = 10000)
#Find high variable genes
obj.list[[i]] <- FindVariableFeatures(obj.list[[i]], selection.method = "vst", nfeatures = 2000)
}
#select features that are repeatedly variable across datasets for integration
features <- SelectIntegrationFeatures(object.list = obj.list)
#Find anchors to integrate the data across different patients (Canonical correlation analysis)
anchors <- FindIntegrationAnchors(object.list = obj.list, anchor.features = features)
# Create an 'integrated' data assay
seurat_integrated <- IntegrateData(anchorset = anchors)
# We run a single integrated analysis on all cells!
DefaultAssay(seurat_integrated) <- "integrated"
# Run the standard workflow for visualization and clustering
seurat_integrated <- ScaleData(seurat_integrated, verbose = FALSE)
seurat_integrated <- RunPCA(object = seurat_integrated, npcs = 30, verbose = FALSE,seed.use = 8734)
seurat_integrated <- RunTSNE(object = seurat_integrated, reduction = "pca", dims = 1:20, seed.use = 8734)
seurat_integrated<- RunUMAP(object = seurat_integrated, reduction = "pca", dims = 1:20, seed.use = 8734)
seurat_integrated <- FindNeighbors(object = seurat_integrated, reduction = "pca", dims = 1:20, seed.use = 8734)
#Clustering
resolution <- c(0.1, 0.25, 0.4, 0.6,0.8, 1.,1.2,1.4,1.8)
for(k in 1:length(resolution)){
seurat_integrated <- FindClusters(object = seurat_integrated, resolution = resolution[k], random.seed = 8734)
}
DefaultAssay(seurat_integrated) <-'integrated'
#MDS computation
mds <- divide_conquer_mds(x = t(GetAssayData(seurat_integrated, slot = 'scale.data')), l = 200, c_points = 5 * 2, r = 2, n_cores = 1)$points
colnames(mds) <- paste0("MDSDIVCONQ_", 1:2)
# Store MDS representation as a custom dimensional reduction field
seurat_integrated[['mds_div_conq']] <- CreateDimReducObject(embeddings = mds, key = 'MDSDIVCONQ_', assay = DefaultAssay(seurat_integrated))
Multidimensional scaling (MDS) visualizes the level of similarity of variables in a data set. MDS recognizes the structure of the dataset in 2D, as it maintains the pairwise distances between data points.
Due to the large size of the integrated dataset of Tonsilar and NSCLC FRCs, the classical MDS algorithm suffers from computational problems and thus MDS configuration can not be obtained. To resolve this issue, we used the Divide-and-conquer MDS algorithm proposed by Delicado P. and C. Pachón-García (2021) for large data sets from the bigmds R package.
In the MDS plot:
Gaussian kernel function flexibly measures the similarity between data points in a high-dimensional space, given its ability to capture complex relationships that may not be linear or easily separable in the original feature space. When calculating Euclidean distance, the value increases with distance, thus the kernel function weights these observations accordingly.
Please note that we provide the integrated object with the MDS representation given that it takes some time to be generated.
seurat_integrated <- readRDS(paste0(basedir,"/data/Human/Tonsil_Ccl19_TRC_PRC_final_mds.rds"))
mds_tx_condition <- seurat_integrated@reductions$mds_div_conq@cell.embeddings %>%
as.data.frame() %>% cbind(tx = seurat_integrated@meta.data$Disease_short)
mds_tx_celltype <- seurat_integrated@reductions$mds_div_conq@cell.embeddings %>%
as.data.frame() %>% cbind(tx = seurat_integrated@meta.data$cell_type)
mds_tx_TOTAL <- merge(mds_tx_condition, mds_tx_celltype, by=c("MDSDIVCONQ_1", "MDSDIVCONQ_2"), all.x=T, all.y=T)
colnames(mds_tx_TOTAL) <-c("MDS_1", "MDS_2", "Condition","Celltype")
#Color palette
colDataset <- cols[1:15]
names(colDataset) <- unique(seurat_integrated$cell_type)
# Use mean gaussian kernel
mds_tx_TOTAL_gk <- mds_tx_TOTAL %>%
group_by(Celltype,Condition) %>%
mutate(count_mds1 = mean(GK(MDS_1))) %>%
mutate(count_mds2 = mean(GK(MDS_2)))
ggplot(mds_tx_TOTAL_gk, aes(x=count_mds1, y=count_mds2, color=Celltype, shape = Condition)) + geom_point(stroke = 1.5) + ylab("MDS2") + xlab("MDS1") + coord_cartesian(xlim = c(0, max(mds_tx_TOTAL_gk$count_mds1,mds_tx_TOTAL_gk$count_mds2)), ylim = c(0, max(mds_tx_TOTAL_gk$count_mds1,mds_tx_TOTAL_gk$count_mds2)) ) +
scale_color_manual(values=colDataset) + scale_shape_manual(values = c(2, 3)) +
theme(aspect.ratio = 2,axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.text.y = element_text(angle = 0, vjust = 0.5,colour = "black",size = 10),
axis.text.x = element_text(angle = 0, vjust = 0.5,colour = "black",size = 10))
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] lubridate_1.9.2 forcats_1.0.0 readr_2.1.4 tidyverse_2.0.0
[5] bigmds_3.0.0 ggsci_3.0.0 gsubfn_0.7 proto_1.0.0
[9] gridExtra_2.3 dittoSeq_1.12.1 ggplot2_3.4.2 Matrix_1.6-0
[13] SeuratObject_4.1.3 Seurat_4.3.0.1 patchwork_1.1.2 stringr_1.5.0
[17] dplyr_1.1.2 purrr_1.0.1 here_1.0.1 magrittr_2.0.3
[21] circlize_0.4.15 tidyr_1.3.0 tibble_3.2.1 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.21 splines_4.3.1
[3] later_1.3.1 bitops_1.0-7
[5] polyclip_1.10-4 lifecycle_1.0.3
[7] tcltk_4.3.1 rprojroot_2.0.3
[9] globals_0.16.2 processx_3.8.2
[11] lattice_0.21-8 MASS_7.3-60
[13] plotly_4.10.2 sass_0.4.7
[15] rmarkdown_2.23 jquerylib_0.1.4
[17] yaml_2.3.7 httpuv_1.6.11
[19] sctransform_0.3.5 sp_2.0-0
[21] spatstat.sparse_3.0-2 reticulate_1.36.1
[23] cowplot_1.1.1 pbapply_1.7-2
[25] RColorBrewer_1.1-3 abind_1.4-5
[27] zlibbioc_1.46.0 Rtsne_0.16
[29] GenomicRanges_1.52.0 BiocGenerics_0.46.0
[31] RCurl_1.98-1.12 pracma_2.4.4
[33] git2r_0.33.0 GenomeInfoDbData_1.2.10
[35] IRanges_2.34.1 S4Vectors_0.38.1
[37] ggrepel_0.9.3 svd_0.5.5
[39] irlba_2.3.5.1 listenv_0.9.0
[41] spatstat.utils_3.1-0 pheatmap_1.0.12
[43] goftest_1.2-3 spatstat.random_3.1-5
[45] fitdistrplus_1.1-11 parallelly_1.36.0
[47] leiden_0.4.3 codetools_0.2-19
[49] DelayedArray_0.28.0 tidyselect_1.2.0
[51] shape_1.4.6 farver_2.1.1
[53] matrixStats_1.0.0 stats4_4.3.1
[55] spatstat.explore_3.2-1 jsonlite_1.8.7
[57] ellipsis_0.3.2 progressr_0.13.0
[59] ggridges_0.5.4 survival_3.5-5
[61] systemfonts_1.0.4 tools_4.3.1
[63] ragg_1.2.5 ica_1.0-3
[65] Rcpp_1.0.11 glue_1.6.2
[67] SparseArray_1.2.4 xfun_0.39
[69] MatrixGenerics_1.12.3 GenomeInfoDb_1.36.1
[71] withr_2.5.0 fastmap_1.1.1
[73] fansi_1.0.4 callr_3.7.3
[75] digest_0.6.33 timechange_0.2.0
[77] R6_2.5.1 mime_0.12
[79] textshaping_0.3.6 colorspace_2.1-0
[81] scattermore_1.2 tensor_1.5
[83] spatstat.data_3.0-1 utf8_1.2.3
[85] generics_0.1.3 data.table_1.14.8
[87] httr_1.4.6 htmlwidgets_1.6.2
[89] S4Arrays_1.2.1 whisker_0.4.1
[91] uwot_0.1.16 pkgconfig_2.0.3
[93] gtable_0.3.3 lmtest_0.9-40
[95] SingleCellExperiment_1.22.0 XVector_0.40.0
[97] htmltools_0.5.5 scales_1.2.1
[99] Biobase_2.60.0 png_0.1-8
[101] knitr_1.43 rstudioapi_0.15.0
[103] tzdb_0.4.0 reshape2_1.4.4
[105] nlme_3.1-162 cachem_1.0.8
[107] zoo_1.8-12 GlobalOptions_0.1.2
[109] KernSmooth_2.23-22 parallel_4.3.1
[111] miniUI_0.1.1.1 pillar_1.9.0
[113] grid_4.3.1 vctrs_0.6.3
[115] RANN_2.6.1 promises_1.2.0.1
[117] xtable_1.8-4 cluster_2.1.4
[119] evaluate_0.21 cli_3.6.1
[121] compiler_4.3.1 rlang_1.1.1
[123] crayon_1.5.2 future.apply_1.11.0
[125] labeling_0.4.2 ps_1.7.5
[127] getPass_0.2-4 plyr_1.8.8
[129] fs_1.6.3 stringi_1.7.12
[131] viridisLite_0.4.2 deldir_1.0-9
[133] munsell_0.5.0 lazyeval_0.2.2
[135] spatstat.geom_3.2-4 hms_1.1.3
[137] future_1.33.0 shiny_1.7.4.1
[139] highr_0.10 SummarizedExperiment_1.30.2
[141] ROCR_1.0-11 igraph_1.5.0.1
[143] bslib_0.5.0
date()
[1] "Wed Aug 21 09:08:51 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] lubridate_1.9.2 forcats_1.0.0 readr_2.1.4 tidyverse_2.0.0
[5] bigmds_3.0.0 ggsci_3.0.0 gsubfn_0.7 proto_1.0.0
[9] gridExtra_2.3 dittoSeq_1.12.1 ggplot2_3.4.2 Matrix_1.6-0
[13] SeuratObject_4.1.3 Seurat_4.3.0.1 patchwork_1.1.2 stringr_1.5.0
[17] dplyr_1.1.2 purrr_1.0.1 here_1.0.1 magrittr_2.0.3
[21] circlize_0.4.15 tidyr_1.3.0 tibble_3.2.1 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.21 splines_4.3.1
[3] later_1.3.1 bitops_1.0-7
[5] polyclip_1.10-4 lifecycle_1.0.3
[7] tcltk_4.3.1 rprojroot_2.0.3
[9] globals_0.16.2 processx_3.8.2
[11] lattice_0.21-8 MASS_7.3-60
[13] plotly_4.10.2 sass_0.4.7
[15] rmarkdown_2.23 jquerylib_0.1.4
[17] yaml_2.3.7 httpuv_1.6.11
[19] sctransform_0.3.5 sp_2.0-0
[21] spatstat.sparse_3.0-2 reticulate_1.36.1
[23] cowplot_1.1.1 pbapply_1.7-2
[25] RColorBrewer_1.1-3 abind_1.4-5
[27] zlibbioc_1.46.0 Rtsne_0.16
[29] GenomicRanges_1.52.0 BiocGenerics_0.46.0
[31] RCurl_1.98-1.12 pracma_2.4.4
[33] git2r_0.33.0 GenomeInfoDbData_1.2.10
[35] IRanges_2.34.1 S4Vectors_0.38.1
[37] ggrepel_0.9.3 svd_0.5.5
[39] irlba_2.3.5.1 listenv_0.9.0
[41] spatstat.utils_3.1-0 pheatmap_1.0.12
[43] goftest_1.2-3 spatstat.random_3.1-5
[45] fitdistrplus_1.1-11 parallelly_1.36.0
[47] leiden_0.4.3 codetools_0.2-19
[49] DelayedArray_0.28.0 tidyselect_1.2.0
[51] shape_1.4.6 farver_2.1.1
[53] matrixStats_1.0.0 stats4_4.3.1
[55] spatstat.explore_3.2-1 jsonlite_1.8.7
[57] ellipsis_0.3.2 progressr_0.13.0
[59] ggridges_0.5.4 survival_3.5-5
[61] systemfonts_1.0.4 tools_4.3.1
[63] ragg_1.2.5 ica_1.0-3
[65] Rcpp_1.0.11 glue_1.6.2
[67] SparseArray_1.2.4 xfun_0.39
[69] MatrixGenerics_1.12.3 GenomeInfoDb_1.36.1
[71] withr_2.5.0 fastmap_1.1.1
[73] fansi_1.0.4 callr_3.7.3
[75] digest_0.6.33 timechange_0.2.0
[77] R6_2.5.1 mime_0.12
[79] textshaping_0.3.6 colorspace_2.1-0
[81] scattermore_1.2 tensor_1.5
[83] spatstat.data_3.0-1 utf8_1.2.3
[85] generics_0.1.3 data.table_1.14.8
[87] httr_1.4.6 htmlwidgets_1.6.2
[89] S4Arrays_1.2.1 whisker_0.4.1
[91] uwot_0.1.16 pkgconfig_2.0.3
[93] gtable_0.3.3 lmtest_0.9-40
[95] SingleCellExperiment_1.22.0 XVector_0.40.0
[97] htmltools_0.5.5 scales_1.2.1
[99] Biobase_2.60.0 png_0.1-8
[101] knitr_1.43 rstudioapi_0.15.0
[103] tzdb_0.4.0 reshape2_1.4.4
[105] nlme_3.1-162 cachem_1.0.8
[107] zoo_1.8-12 GlobalOptions_0.1.2
[109] KernSmooth_2.23-22 parallel_4.3.1
[111] miniUI_0.1.1.1 pillar_1.9.0
[113] grid_4.3.1 vctrs_0.6.3
[115] RANN_2.6.1 promises_1.2.0.1
[117] xtable_1.8-4 cluster_2.1.4
[119] evaluate_0.21 cli_3.6.1
[121] compiler_4.3.1 rlang_1.1.1
[123] crayon_1.5.2 future.apply_1.11.0
[125] labeling_0.4.2 ps_1.7.5
[127] getPass_0.2-4 plyr_1.8.8
[129] fs_1.6.3 stringi_1.7.12
[131] viridisLite_0.4.2 deldir_1.0-9
[133] munsell_0.5.0 lazyeval_0.2.2
[135] spatstat.geom_3.2-4 hms_1.1.3
[137] future_1.33.0 shiny_1.7.4.1
[139] highr_0.10 SummarizedExperiment_1.30.2
[141] ROCR_1.0-11 igraph_1.5.0.1
[143] bslib_0.5.0