subluclustering and cell type definition
Daniel
19 12 2019
Last updated: 2022-02-10
Checks: 7 0
Knit directory: MelanomaIMC/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | b24a994 | toobiwankenobi | 2022-02-08 | update code to work under R 4.1.2 |
| Rmd | f9a3a83 | toobiwankenobi | 2022-02-08 | clean repo for release |
| html | 4109ff1 | toobiwankenobi | 2021-07-07 | delete html files and adapt gitignore |
| Rmd | 3203891 | toobiwankenobi | 2021-02-19 | change celltype names |
| html | 3203891 | toobiwankenobi | 2021-02-19 | change celltype names |
| Rmd | ee1595d | toobiwankenobi | 2021-02-12 | clean repo and adapt files |
| html | ee1595d | toobiwankenobi | 2021-02-12 | clean repo and adapt files |
| html | 3f5af3f | toobiwankenobi | 2021-02-09 | add .html files |
| Rmd | d8819f2 | toobiwankenobi | 2020-10-08 | read new data (nuclei expansion) and adapt scripts |
| Rmd | fb0f7cb | SchulzDan | 2020-08-24 | more paths adapted |
| Rmd | b68b4c3 | SchulzDan | 2020-08-17 | added scores and coxph models. not finished |
load packages
sapply(list.files("code/helper_functions/", full.names = TRUE), source)
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library(SingleCellExperiment)Loading required package: SummarizedExperimentLoading required package: MatrixGenericsLoading required package: matrixStats
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count
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colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
colWeightedMeans, colWeightedMedians, colWeightedSds,
colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
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order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
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collapse, desc, sliceLoading required package: GenomeInfoDbLoading required package: BiobaseWelcome to Bioconductor
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'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
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unionlibrary(CATALYST)
library(workflowr)load data
sce = readRDS(file = "data/data_for_analysis/sce_protein.rds")for(i in 1:10){
scatter_x_y(sce,x = "CD8",y = "pERK",imagenumber =i, xlim = c(0,5), ylim = c(0,5))
abline(h = 1.25)
abline(v=1.5)
abline(a = 0, b= 1)
}
mean expression of markers per celltype
# Aggregate the counts. calculateSummary is a function written from Nils
mean_sce <- calculateSummary(sce, split_by = "celltype",
exprs_values = "counts")
# Exclude DNA and Histone
mean_sce <- mean_sce[!grepl("DNA|Histone", rownames(mean_sce)),]
# Transform and scale
assay(mean_sce, "arcsinh") <- asinh(assay(mean_sce, "meanCounts"))
assay(mean_sce, "arcsinh_scaled") <- t(scale(t(asinh(assay(mean_sce, "meanCounts")))))
# Linear scale
plotHeatmap(mean_sce, exprs_values = "arcsinh",
features = c(rownames(mean_sce)),
colour_columns_by = "celltype",
color = viridis(100), cluster_rows = FALSE, gaps_row = 10)
scaled mean expression of markers per celltype
# Linear scale
plotHeatmap(mean_sce, exprs_values = "arcsinh_scaled",
features = c(rownames(mean_sce)),
colour_columns_by = "celltype",
color = colorRampPalette(c("dark blue", "white", "dark red"))(100),
zlim = c(-3,3), cluster_rows = TRUE, gaps_row = 10)
definition of markers used for subclustering the super-classes
# Select markers
marker_list <- list()
marker_list$Tumor <- c("Ki67", "PDL1", "PARP", "H3K27me3","CXCR2","HLADR","S100","Sox9","pERK","CD36",
"p75","MiTF","bCatenin","Collagen1","pS6","IDO1","SOX10")
marker_list$Neutrophil <- c("CD15", "PDL1", "MPO", "CD11b")
marker_list$`FOXP3+ T cell` <- c("IDO1", "PD1", "FOXP3", "ICOS", "CD4", "Ki67", "GrzB",
"CD45RA", "CD45RO")
marker_list$`CD8+ T cell` <- c("IDO1", "TOX1", "PD1", "TCF7", "ICOS", "CD8","Ki67", "GrzB",
"CD45RA", "CD45RO")
marker_list$`CD4+ T cell` <- c("IDO1", "TOX1", "PD1", "TCF7",
"FOXP3", "ICOS", "CD4", "Ki67", "GrzB",
"CD45RA", "CD45RO")
marker_list$`BnT cell` <- c("IDO1", "TOX1", "PD1", "TCF7",
"FOXP3", "ICOS", "CD8", "CD4", "Ki67", "GrzB",
"CD45RA", "CD45RO", "CD20","HLADR","H3K27me3","pS6","pERK", "CD7")
marker_list$pDC <- c("CD303","IDO1","GrzB","CXCR2","CD11b")
marker_list$`B cell` <- c("Ki67","CD45RA", "CD45RO", "CD20","HLADR","H3K27me3","pS6","pERK")
marker_list$Macrophage <- c("Caveolin1", "PDL1", "PARP", "H3K27me3","CXCR2","HLADR","CD45RO","CD45RA","CD68","pERK","CD36", "Collagen1","CD11c","pS6","IDO1","CD206")
marker_list$Stroma <- c("Caveolin1", "CD68","CD36", "Collagen1","SMA")
marker_list$unknown <- rownames(sce[rowData(sce)$good_marker,])cluster the whole dataset with FlowSOM
FlowSOM first because it is faster
## the FlowSOM function from CATALYST needs an another column in the rowData of the sce to work properly:
rowData(sce)$marker_class <- "state"
# vector for clustering
fs_clustering <- vector(length = ncol(sce))
# create the "exprs" slot in the assay data (needed for CATALYST)
assay(sce, "exprs") <- assay(sce,"asinh")
# Macrophage, B cells, CD4+ T cell, CD8+ T cell, Tother and BnT cells will be clustered for a total of 6 clustes each
set.seed(12345)
for(i in c("Macrophage","CD4+ T cell","CD8+ T cell")){
cur_sce <- sce[,sce$celltype == i]
cur_sce <- CATALYST::cluster(cur_sce,features = marker_list[i][[1]], ydim = 2,xdim = 3,maxK = 4)
fs_clustering[sce$celltype == i] <- cur_sce$cluster_id
}o running FlowSOM clustering...o running ConsensusClusterPlus metaclustering...o running FlowSOM clustering...o running ConsensusClusterPlus metaclustering...o running FlowSOM clustering...o running ConsensusClusterPlus metaclustering...# pDCs and Neutrophils will be clustered to 4 clusteres
for(i in c("pDC","Neutrophil","BnT cell","B cell","FOXP3+ T cell","Stroma", "unknown")){
cur_sce <- sce[,sce$celltype == i]
cur_sce <- CATALYST::cluster(cur_sce,features = marker_list[i][[1]],ydim = 2,xdim = 2,maxK = 3)
fs_clustering[sce$celltype == i] <- cur_sce$cluster_id
}o running FlowSOM clustering...
o running ConsensusClusterPlus metaclustering...o running FlowSOM clustering...o running ConsensusClusterPlus metaclustering...o running FlowSOM clustering...o running ConsensusClusterPlus metaclustering...o running FlowSOM clustering...o running ConsensusClusterPlus metaclustering...o running FlowSOM clustering...o running ConsensusClusterPlus metaclustering...o running FlowSOM clustering...o running ConsensusClusterPlus metaclustering...o running FlowSOM clustering...o running ConsensusClusterPlus metaclustering...# Tumor will be clustered into 9 clusters
cur_sce <- sce[,sce$celltype == "Tumor"]
cur_sce <- CATALYST::cluster(cur_sce,features = marker_list["Tumor"][[1]],ydim = 3,xdim = 3,maxK = 7)o running FlowSOM clustering...
o running ConsensusClusterPlus metaclustering...fs_clustering[sce$celltype == "Tumor"] <- cur_sce$cluster_id
# Save in SCE object
colData(sce)$celltype_clustered <- as.factor(fs_clustering)
sce$celltype_clustered <- paste0(sce$celltype, "_", sce$celltype_clustered)visulalize clustering results for FlowSOM
# Aggregate the counts
mean_sce <- calculateSummary(sce, split_by = c("celltype","celltype_clustered"),
exprs_values = "counts")
# Exclude DNA and Histone
mean_sce <- mean_sce[!grepl("DNA|Histone", rownames(mean_sce)),]
# Transform and scale
assay(mean_sce, "arcsinh") <- asinh(assay(mean_sce, "meanCounts"))
assay(mean_sce, "arcsinh_scaled") <- t(scale(t(asinh(assay(mean_sce, "meanCounts")))))
# Linear scale
plotHeatmap(mean_sce, exprs_values = "arcsinh",
features = rownames(sce)[rowData(sce)$good_marker],
colour_columns_by = c("celltype"),
color = viridis(100),
labels_col = mean_sce$celltype_clustered,
show_colnames = TRUE, annotation_legend = FALSE, borders_color = NA)
# Scaled
plotHeatmap(mean_sce, exprs_values = "arcsinh_scaled",
features = rownames(sce)[rowData(sce)$good_marker],
colour_columns_by = c("celltype"),
labels_col = mean_sce$celltype_clustered,
show_colnames = TRUE, annotation_legend = TRUE, borders_color = NA,
color = colorRampPalette(c("dark blue", "white", "dark red"))(100),
zlim = c(-4, 4),legend = TRUE)
numbers of cells per cluster
table(sce$celltype_clustered)
B cell_1 B cell_2 B cell_3 B cell_4 BnT cell_1
4315 11731 12914 21486 7024
BnT cell_2 BnT cell_3 BnT cell_4 CD4+ T cell_1 CD4+ T cell_2
7961 6527 9082 11334 11205
CD4+ T cell_3 CD4+ T cell_4 CD4+ T cell_5 CD4+ T cell_6 CD8+ T cell_1
9717 11569 13073 2997 3671
CD8+ T cell_2 CD8+ T cell_3 CD8+ T cell_4 CD8+ T cell_5 CD8+ T cell_6
11058 11808 12549 9536 4671
FOXP3+ T cell_1 FOXP3+ T cell_2 FOXP3+ T cell_3 FOXP3+ T cell_4 Macrophage_1
2976 2375 2827 1293 12010
Macrophage_2 Macrophage_3 Macrophage_4 Macrophage_5 Macrophage_6
8126 10022 8439 14226 9699
Neutrophil_1 Neutrophil_2 Neutrophil_3 Neutrophil_4 pDC_1
3339 2473 1770 3612 2508
pDC_2 pDC_3 pDC_4 Stroma_1 Stroma_2
1810 891 1882 14333 17709
Stroma_3 Stroma_4 Tumor_1 Tumor_2 Tumor_3
10161 18751 84616 76055 82520
Tumor_4 Tumor_5 Tumor_6 Tumor_7 Tumor_8
51185 72087 86565 43957 51597
Tumor_9 unknown_1 unknown_2 unknown_3 unknown_4
75909 2795 10315 1678 4665 assign names to clusters
annotations <- sce$celltype_clustered
# annotations[annotations == "other_4"] <- "Other_CD206Low_pS6Low"
# annotations[annotations == "other_2"] <- "Tumor_SOX9Low_SOX10+_MITF+_S100+_bCatenin++"
# annotations[annotations == "other_3"] <- "Other_SOX10Low_MITFLow_bCatenin+"
# annotations[annotations == "other_1"] <- "Other_CaveolinLow"
# annotations[annotations == "other_5"] <- "Vasculature_SMA++_Collagen+_Caveolin+"
# annotations[annotations == "other_6"] <- "Stroma_SMA+_Collagen++_Caveolin++_CD36++"
#
# annotations[annotations == "Tumor_2"] <- "Tumor_SOX9++_p75+"
# annotations[annotations == "Tumor_3"] <- "Tumor_SOX10+_MITFLow_CD36Low"
# annotations[annotations == "Tumor_4"] <- "Tumor_SOX10Low_S100+"
# annotations[annotations == "Tumor_5"] <- "Tumor_SOX9++_SOX10+_MITF+_S100Low"
# annotations[annotations == "Tumor_8"] <- "Tumor_SOX9Low_SOX10Low_MITFLow_p75+"
# annotations[annotations == "Tumor_9"] <- "Tumor_SOX10+_MITF+_S100++"
# annotations[annotations == "Tumor_6"] <- "Tumor_S100+"
# annotations[annotations == "Tumor_1"] <- "Tumor_SOX10Low_MITFLow_HLADR+"
# annotations[annotations == "Tumor_7"] <- "Tumor_SOX10+_MITF++_S100+_pS6+"
#
# annotations[annotations == "B cells_1"] <- "B cells_pErkLow_H3K27meLow"
# annotations[annotations == "B cells_4"] <- "B cells_pErkLow_H3K27meLow"
# annotations[annotations == "B cells_2"] <- "B cells_H3K27me_LowpS6Low"
# annotations[annotations == "B cells_3"] <- "B cells_CD19++_pErkLow_H3K27meLow"
# annotations[annotations == "B cells_5"] <- "B cells_Ki67++"
# annotations[annotations == "B cells_6"] <- "B cells_SMALow"
#
# annotations[annotations == "BnT_1"] <- "B cells_CD3Low_CD4Low"
# annotations[annotations == "BnT_2"] <- "B cells_pErkLow_H3K27meLow"
# annotations[annotations == "BnT_6"] <- "BnT_CD8+"
# annotations[annotations == "BnT_4"] <- "BnT_CD8+"
# annotations[annotations == "BnT_5"] <- "BnT_CD4+_TCF7+"
# annotations[annotations == "BnT_3"] <- "BnT_CD4+_ICOS++_PD1++_Ki67+"
#
# annotations[annotations == "T_other_4"] <- "Tcells_undefined"
# annotations[annotations == "T_other_5"] <- "Tcells_undefined"
# annotations[annotations == "T_other_3"] <- "Tcells_CD15_LowGrzB+"
# annotations[annotations == "T_other_1"] <- "Tcells_Ki67++"
# annotations[annotations == "T_other_2"] <- "Tcells_CaveolinLow_SMALow"
# annotations[annotations == "T_other_6"] <- "Tcells_undefined"
#
# annotations[annotations == "T_helper_2"] <- "CD4+ T cell_CD11cLow_HLADRLow"
# annotations[annotations == "T_helper_5"] <- "CD4+ T cell_SMALow_CaveolinLow"
# annotations[annotations == "T_helper_1"] <- "CD4+ T cell_TCF7++"
# annotations[annotations == "T_helper_6"] <- "CD4+ T cell_FoxP3++_ICOS+_CD11cLow_CD206Low"
# annotations[annotations == "T_helper_3"] <- "CD4+ T cell_FoxP3Low_pERKLow"
# annotations[annotations == "T_helper_4"] <- "CD4+ T cell_FoxP3Low"
#
# annotations[annotations == "T_cytotoxic_3"] <- "CD8+ T cell_PD1+_TOX1+_GrzBLow"
# annotations[annotations == "T_cytotoxic_5"] <- "CD8+ T cell_TCF7+_H3K27meLow"
# annotations[annotations == "T_cytotoxic_6"] <- "CD8+ T cell_PD1+_TOX1+_GrzB+_ICOSLow_Ki67+"
# annotations[annotations == "T_cytotoxic_1"] <- "CD8+ T cell_GrzBLow_SMALow_CaveolinLow"
# annotations[annotations == "T_cytotoxic_2"] <- "CD8+ T cell_GrzBLow_pS6Low"
# annotations[annotations == "T_cytotoxic_4"] <- "CD8+ T cell_PD1+_TOX1+_GrzBLow_ICOSLow_CD11cLow_CD206Low"
#
# annotations[annotations == "Macrophage_4"] <- "Macrophage_CD68Low_CaveolinLow"
# annotations[annotations == "Macrophage_6"] <- "Tumor_CD68Low_S100Low_bCateninLow_MITFLow_SOX10Low_HLADR+"
# annotations[annotations == "Macrophage_2"] <- "Macrophage_CD68Low_CD206++_CD36Low"
# annotations[annotations == "Macrophage_1"] <- "Macrophage_CD68Low_pS6Low_S100Low"
# annotations[annotations == "Macrophage_3"] <- "Macrophage_CD68+_CD11c+_CD206++_CXCR2+_PDL1+"
# annotations[annotations == "Macrophage_5"] <- "Macrophage_CD68++_CD11c+_CD36+_Caveolin+_Collagen+"
#
# annotations[annotations == "pDC_1"] <- "pDC_IDO1+_GrzB+"
# annotations[annotations == "pDC_2"] <- "pDC_IDO1+_TOX1+_CD11b+"
# annotations[annotations == "pDC_3"] <- "pDC_IDO1++_GrzB++"
# annotations[annotations == "pDC_4"] <- "pDC_Ido1+_PARP++"
#
# annotations[annotations == "Neutrophil_2"] <- "Neutrophil_CD11b+_CD15++_MPO+_PDL1++"
# annotations[annotations == "Neutrophil_4"] <- "Neutrophil_CD11b+_CD15+_MPO+_PDL1Low"
# annotations[annotations == "Neutrophil_3"] <- "Neutrophil_CD11b++_CD15++_MPO++_CXCR2Low_PDL1+"
# annotations[annotations == "Neutrophil_1"] <- "Neutrophil_CD11b+_CD15+_MPO++"
sce$cellAnnotation <- annotations
mean_sce <- calculateSummary(sce, split_by = c("celltype","cellAnnotation"),
exprs_values = "counts")
# Exclude DNA and Histone
mean_sce <- mean_sce[!grepl("DNA|Histone", rownames(mean_sce)),]
# Transform and scale
assay(mean_sce, "arcsinh") <- asinh(assay(mean_sce, "meanCounts"))
assay(mean_sce, "arcsinh_scaled") <- t(scale(t(asinh(assay(mean_sce, "meanCounts")))))
plotHeatmap(mean_sce, exprs_values = "arcsinh_scaled",
features = rownames(sce)[rowData(sce)$good_marker],
colour_columns_by = c("celltype"),
labels_col = mean_sce$cellAnnotation,
show_colnames = TRUE, annotation_legend = TRUE, borders_color = NA,
color = colorRampPalette(c("dark blue", "white", "dark red"))(100),
zlim = c(-4, 4),legend = TRUE)clustering does not really pick up PDL1 positive Tumor cells (which definitely exist). Potentially we have to investigate this with a manual cut off. arcsinh > 1.75 seems to be reasonable
Additionally, the clustering did not pick up proliferating and non prolifertating tumor clusters. this should also be investigated by manual cut offs. arcsinh > 1.25 seems reasonable.
celltype_counts <- sce$celltype
table(celltype_counts)save the single cell object with the clusters assigned to the cells
# delete the "exprs" slot from the single cell experiment again.
assay(sce,"exprs") <- NULL
saveRDS(sce,file = "data/data_for_analysis/sce_protein.rds")
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.3 LTS
Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] CATALYST_1.18.1 igraph_1.2.11
[3] viridis_0.6.2 viridisLite_0.4.0
[5] scater_1.22.0 scuttle_1.4.0
[7] ggplot2_3.3.5 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 LSD_4.1-0
[19] dplyr_1.0.7 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] utf8_1.2.2 tidyselect_1.1.1
[3] grid_4.1.2 BiocParallel_1.28.3
[5] Rtsne_0.15 aws.signature_0.6.0
[7] flowCore_2.6.0 munsell_0.5.0
[9] ScaledMatrix_1.2.0 codetools_0.2-18
[11] withr_2.4.3 colorspace_2.0-2
[13] highr_0.9 knitr_1.37
[15] rstudioapi_0.13 ggsignif_0.6.3
[17] git2r_0.29.0 GenomeInfoDbData_1.2.7
[19] polyclip_1.10-0 farver_2.1.0
[21] pheatmap_1.0.12 flowWorkspace_4.6.0
[23] rprojroot_2.0.2 vctrs_0.3.8
[25] generics_0.1.2 TH.data_1.1-0
[27] xfun_0.29 R6_2.5.1
[29] doParallel_1.0.16 ggbeeswarm_0.6.0
[31] clue_0.3-60 rsvd_1.0.5
[33] bitops_1.0-7 DelayedArray_0.20.0
[35] assertthat_0.2.1 promises_1.2.0.1
[37] scales_1.1.1 multcomp_1.4-18
[39] beeswarm_0.4.0 gtable_0.3.0
[41] beachmat_2.10.0 processx_3.5.2
[43] RProtoBufLib_2.6.0 sandwich_3.0-1
[45] rlang_1.0.0 GlobalOptions_0.1.2
[47] splines_4.1.2 rstatix_0.7.0
[49] hexbin_1.28.2 broom_0.7.12
[51] reshape2_1.4.4 yaml_2.2.2
[53] abind_1.4-5 backports_1.4.1
[55] httpuv_1.6.5 RBGL_1.70.0
[57] tools_4.1.2 ellipsis_0.3.2
[59] jquerylib_0.1.4 RColorBrewer_1.1-2
[61] ggridges_0.5.3 Rcpp_1.0.8
[63] plyr_1.8.6 base64enc_0.1-3
[65] sparseMatrixStats_1.6.0 zlibbioc_1.40.0
[67] purrr_0.3.4 RCurl_1.98-1.5
[69] ps_1.6.0 FlowSOM_2.2.0
[71] ggpubr_0.4.0 GetoptLong_1.0.5
[73] cowplot_1.1.1 zoo_1.8-9
[75] ggrepel_0.9.1 cluster_2.1.2
[77] colorRamps_2.3 fs_1.5.2
[79] magrittr_2.0.2 ncdfFlow_2.40.0
[81] data.table_1.14.2 scattermore_0.7
[83] circlize_0.4.13 mvtnorm_1.1-3
[85] whisker_0.4 ggnewscale_0.4.5
[87] evaluate_0.14 XML_3.99-0.8
[89] jpeg_0.1-9 gridExtra_2.3
[91] shape_1.4.6 ggcyto_1.22.0
[93] compiler_4.1.2 tibble_3.1.6
[95] crayon_1.4.2 ggpointdensity_0.1.0
[97] htmltools_0.5.2 later_1.3.0
[99] tidyr_1.2.0 RcppParallel_5.1.5
[101] aws.s3_0.3.21 DBI_1.1.2
[103] tweenr_1.0.2 ComplexHeatmap_2.10.0
[105] MASS_7.3-55 Matrix_1.4-0
[107] car_3.0-12 cli_3.1.1
[109] parallel_4.1.2 pkgconfig_2.0.3
[111] getPass_0.2-2 xml2_1.3.3
[113] foreach_1.5.2 vipor_0.4.5
[115] bslib_0.3.1 XVector_0.34.0
[117] drc_3.0-1 stringr_1.4.0
[119] callr_3.7.0 digest_0.6.29
[121] ConsensusClusterPlus_1.58.0 graph_1.72.0
[123] rmarkdown_2.11 DelayedMatrixStats_1.16.0
[125] curl_4.3.2 gtools_3.9.2
[127] rjson_0.2.21 lifecycle_1.0.1
[129] jsonlite_1.7.3 carData_3.0-5
[131] BiocNeighbors_1.12.0 fansi_1.0.2
[133] pillar_1.7.0 lattice_0.20-45
[135] plotrix_3.8-2 fastmap_1.1.0
[137] httr_1.4.2 survival_3.2-13
[139] glue_1.6.1 png_0.1-7
[141] iterators_1.0.13 Rgraphviz_2.38.0
[143] nnls_1.4 ggforce_0.3.3
[145] stringi_1.7.6 sass_0.4.0
[147] BiocSingular_1.10.0 CytoML_2.6.0
[149] latticeExtra_0.6-29 cytolib_2.6.1
[151] irlba_2.3.5