Last updated: 2022-02-22
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Knit directory: MelanomaIMC/
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| Rmd | 3da15db | toobiwankenobi | 2021-11-24 | changes for revision |
This script generates plots for Supplementary Figure 7.
In this script we will sequentially load all melanoma datasets from http://tisch.comp-genomics.org/
we will then calculate the proportions of each cell type expressing individual chemokines. under the hypothesis that the scRNAseq data is the ground truth. We will then compare these proportions to the observed proportions in IMC and thereby estimate whether we likely observe spatial spill over in IMC.
set.seed(12345)
sapply(list.files("code/helper_functions/", full.names = TRUE), source) code/helper_functions//calculateSummary.R
value ?
visible FALSE
code/helper_functions//censor_dat.R
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code/helper_functions//detect_mRNA_expression.R
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code/helper_functions//DistanceToClusterCenter.R
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visible FALSE
code/helper_functions//findMilieu.R code/helper_functions//findPatch.R
value ? ?
visible FALSE FALSE
code/helper_functions//getInfoFromString.R
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visible FALSE
code/helper_functions//getSpotnumber.R
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visible FALSE
code/helper_functions//plotCellCounts.R
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code/helper_functions//plotCellFractions.R
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code/helper_functions//plotDist.R code/helper_functions//read_Data.R
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visible FALSE FALSE
code/helper_functions//scatter_function.R
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code/helper_functions//sceChecks.R
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visible FALSE
code/helper_functions//validityChecks.R
value ?
visible FALSE library(Seurat)
library(hdf5r)
library(SingleCellExperiment)
library(scater)
library(dittoSeq)
library(scran)
library(ComplexHeatmap)
library(outliers)
library(purrr)
library(data.table)
library(dplyr)
library(tidyr)
library(cowplot)
library(ggpubr)sce_rna <- readRDS(file = "data/data_for_analysis/sce_RNA.rds")
SKCM_GSE115978 <- read_Data(data = "data/data_for_analysis/scRNAseq/SKCM_GSE115978_aPD1_expression.h5",
metadata_file = "data/data_for_analysis/scRNAseq/SKCM_GSE115978_aPD1_CellMetainfo_table.tsv",
name = "SKCM_GSE115978",
sorting = "all")Warning in sparseMatrix(i = indices[] + 1, p = indptr[], x = as.numeric(x =
counts[]), : 'giveCsparse' has been deprecated; setting 'repr = "T"' for youNote: Using an external vector in selections is ambiguous.
ℹ Use `all_of(targets)` instead of `targets` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.HNSC_GSE139324 <- read_Data(data = "data/data_for_analysis/scRNAseq/HNSC_GSE139324_expression.h5",
metadata_file = "data/data_for_analysis/scRNAseq/HNSC_GSE139324_CellMetainfo_table.tsv",
name = "HNSC_GSE139324",
sorting = "immune")Warning in sparseMatrix(i = indices[] + 1, p = indptr[], x = as.numeric(x =
counts[]), : 'giveCsparse' has been deprecated; setting 'repr = "T"' for youNSCLC_GSE131907 <- read_Data(data = "data/data_for_analysis/scRNAseq/NSCLC_GSE131907_expression.h5",
metadata_file = "data/data_for_analysis/scRNAseq/NSCLC_GSE131907_CellMetainfo_table.tsv",
name = "NSCLC_GSE131907",
sorting = "all")Warning in sparseMatrix(i = indices[] + 1, p = indptr[], x = as.numeric(x =
counts[]), : 'giveCsparse' has been deprecated; setting 'repr = "T"' for youPAAD_CRA001160 <- read_Data(data = "data/data_for_analysis/scRNAseq/PAAD_CRA001160_expression.h5",
metadata_file = "data/data_for_analysis/scRNAseq/PAAD_CRA001160_CellMetainfo_table.tsv",
name = "PAAD_CRA001160",
sorting = "all")Warning in sparseMatrix(i = indices[] + 1, p = indptr[], x = as.numeric(x =
counts[]), : 'giveCsparse' has been deprecated; setting 'repr = "T"' for youUVM_GSE139829 <- read_Data(data = "data/data_for_analysis/scRNAseq/UVM_GSE139829_expression.h5",
metadata_file = "data/data_for_analysis/scRNAseq/UVM_GSE139829_CellMetainfo_table.tsv",
name = "UVM_GSE139829",
sorting = "all")Warning in sparseMatrix(i = indices[] + 1, p = indptr[], x = as.numeric(x =
counts[]), : 'giveCsparse' has been deprecated; setting 'repr = "T"' for youSKCM_GSE72056 <- read_Data(data = "data/data_for_analysis/scRNAseq/SKCM_GSE72056_expression.h5",
metadata_file = "data/data_for_analysis/scRNAseq/SKCM_GSE72056_CellMetainfo_table.tsv",
name = "SKCM_GSE72056",
sorting = "all")Warning in sparseMatrix(i = indices[] + 1, p = indptr[], x = as.numeric(x =
counts[]), : 'giveCsparse' has been deprecated; setting 'repr = "T"' for you# merge the datasets
sc_dat <- rbind(HNSC_GSE139324, NSCLC_GSE131907, PAAD_CRA001160,SKCM_GSE115978, SKCM_GSE72056, UVM_GSE139829)
comp_dataset <- c("HNSC_GSE139324","NSCLC_GSE131907","PAAD_CRA001160","SKCM_GSE115978","SKCM_GSE72056","UVM_GSE139829")cur_dat <- as_tibble(colData(sce_rna))
cur_dat <- cur_dat %>%
group_by(celltype,.drop = FALSE) %>%
mutate(total_celltype_count = n()) %>%
select(celltype,total_celltype_count,cellID, CCL2,CCL4,CCL8,CCL18,CCL19,CCL22,CXCL8,CXCL9,CXCL10,CXCL12,CXCL13)
imc_long <- cur_dat %>%
pivot_longer(cols = c(CCL2,CCL4,CCL8,CCL18,CCL19,CCL22,CXCL8,CXCL9,CXCL10,CXCL12,CXCL13),names_to = "chemokine")
imc_long <- imc_long %>%
group_by(chemokine,.drop = FALSE) %>%
mutate(total_chem_count = sum(value)) %>%
ungroup() %>%
group_by(celltype,chemokine,.drop = FALSE) %>%
mutate(celltype_chemokine_sum=sum(value)) %>%
ungroup() %>%
select(celltype,celltype_chemokine_sum,chemokine,total_celltype_count,total_chem_count) %>%
distinct() %>%
mutate(frac_of_chemokine_pos = celltype_chemokine_sum/total_chem_count,
frac_of_celltype = celltype_chemokine_sum/total_celltype_count) %>%
ungroup()
imc_long$sorting <- "all"
imc_long$dataset <- "IMC"here we will unify the naming of cell types that are available in both datasets.
unique(sc_dat$celltype) [1] "B" "CD8- T cell" "CD8+ T cell" "DC"
[5] "Mast" "Mono/Macro" "NK" "Plasma"
[9] "Tprolif" "Endothelial" "Epithelial" "Fibroblasts"
[13] "Oligodendrocyte" "Acinar" "Ductal" "Endocrine"
[17] "Malignant" "Stellate" #sc_dat[which(sc_dat$celltype == "B"),]$celltype <- "HLA-DR"
#sc_dat[which(sc_dat$celltype == "CD4Tconv"),]$celltype <- "CD8- T cell"
#sc_dat[which(sc_dat$celltype == "CD8Tex"),]$celltype <- "CD8+ T cell"
#sc_dat[which(sc_dat$celltype == "CD8T"),]$celltype <- "CD8+ T cell"
sc_dat[which(sc_dat$celltype == "Endothelial"),]$celltype <- "Vasculature"
sc_dat[which(sc_dat$celltype == "Fibroblasts"),]$celltype <- "Stroma"
sc_dat[which(sc_dat$celltype == "Malignant"),]$celltype <- "Tumor"
sc_dat[which(sc_dat$celltype == "Mono/Macro"),]$celltype <- "Macrophage"
#sc_dat[which(sc_dat$celltype == "Treg"),]$celltype <- "CD8- T cell"
#sc_dat[which(sc_dat$celltype == "Plasma"),]$celltype <- "CD38"
unique(sc_dat$celltype) [1] "B" "CD8- T cell" "CD8+ T cell" "DC"
[5] "Mast" "Macrophage" "NK" "Plasma"
[9] "Tprolif" "Vasculature" "Epithelial" "Stroma"
[13] "Oligodendrocyte" "Acinar" "Ductal" "Endocrine"
[17] "Tumor" "Stellate" celltypes <- c("CD8- T cell","CD8+ T cell","Vasculature", "Stroma","Macrophage","Tumor")we will also unify the cell type names wherever possible
plot_dat <- sc_dat %>%
filter(celltype %in% celltypes, dataset %in% comp_dataset)
# order IMC data correct for merging
imc_long <- imc_long[,colnames(plot_dat)]
all_dat <- rbind(plot_dat,imc_long)
all_dat$datatype <- "scRNAseq"
all_dat[which(all_dat$dataset == "IMC"),]$datatype <- "IMC"test_dat <- all_dat %>%
group_by(chemokine, celltype) %>%
nest() %>%
mutate(n = map_dbl(data, ~ nrow(.x)), # number of entries
G = map(data, ~ grubbs.test(.x$frac_of_chemokine_pos)$statistic[[1]]), # G statistic
U = map(data, ~ grubbs.test(.x$frac_of_chemokine_pos)$statistic[[2]]), # U statistic
grubbs = map(data, ~ grubbs.test(.x$frac_of_chemokine_pos)$alternative), # Alternative hypotesis
p_grubbs = map_dbl(data, ~ grubbs.test(.x$frac_of_chemokine_pos)$p.value)) %>% # p-value
mutate(G = signif(unlist(G), 3),
U = signif(unlist(U), 3),
grubbs = unlist(grubbs),
p_grubbs = signif(p_grubbs, 3)) %>%
select(-data) %>%
arrange(p_grubbs)Warning in pt(t, n - 2): NaNs produced
Warning in pt(t, n - 2): NaNs produced
Warning in pt(t, n - 2): NaNs produced
Warning in pt(t, n - 2): NaNs produced
Warning in pt(t, n - 2): NaNs produced
Warning in pt(t, n - 2): NaNs produced
Warning in pt(t, n - 2): NaNs produced
Warning in pt(t, n - 2): NaNs produced
Warning in pt(t, n - 2): NaNs produced
Warning in pt(t, n - 2): NaNs produced
Warning in pt(t, n - 2): NaNs produced
Warning in pt(t, n - 2): NaNs produced
Warning in pt(t, n - 2): NaNs produced
Warning in pt(t, n - 2): NaNs produced
Warning in pt(t, n - 2): NaNs produced
Warning in pt(t, n - 2): NaNs produced# merge the test_dat data with the IMC data
test_dat <- left_join(test_dat,imc_long[,c("celltype","chemokine","frac_of_chemokine_pos")],by=c("celltype","chemokine"))
# define whether a detected outlier is an IMC datapoint and apply 0.05 significant value cut-off
sig_dat <- test_dat %>%
mutate(value = gsub("[^0-9.]", "", grubbs),
is_IMC = value == frac_of_chemokine_pos,
sig = p_grubbs <= 0.05) %>%
filter(sig == TRUE,is_IMC == TRUE)ggplot()+
geom_boxplot(data = all_dat,aes(x=celltype,y=frac_of_chemokine_pos))+
geom_point(data = all_dat[which(all_dat$dataset != "IMC"),],aes(x=celltype,y=frac_of_chemokine_pos,col=as.factor(dataset)))+
geom_point(data = all_dat[which(all_dat$datatype == "IMC"),],aes(x=celltype,y=frac_of_chemokine_pos),col="red",shape=18, size=5)+
geom_text(data = sig_dat,aes(x=celltype,y=0.75,label = p_grubbs), color= "red", angle=90) +
facet_wrap(.~chemokine)+
theme_bw()+
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
text = element_text(size=14)) +
ylab("Fraction of chemokine+ cells") +
guides(col=guide_legend(title="Dataset")) +
xlab("")
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] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] ggpubr_0.4.0 cowplot_1.1.1
[3] tidyr_1.2.0 data.table_1.14.2
[5] purrr_0.3.4 outliers_0.14
[7] ComplexHeatmap_2.10.0 scran_1.22.1
[9] dittoSeq_1.6.0 scater_1.22.0
[11] ggplot2_3.3.5 scuttle_1.4.0
[13] SingleCellExperiment_1.16.0 SummarizedExperiment_1.24.0
[15] Biobase_2.54.0 GenomicRanges_1.46.1
[17] GenomeInfoDb_1.30.1 IRanges_2.28.0
[19] S4Vectors_0.32.3 BiocGenerics_0.40.0
[21] MatrixGenerics_1.6.0 matrixStats_0.61.0
[23] hdf5r_1.3.5 SeuratObject_4.0.4
[25] Seurat_4.1.0 dplyr_1.0.7
[27] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] utf8_1.2.2 reticulate_1.24
[3] tidyselect_1.1.1 htmlwidgets_1.5.4
[5] BiocParallel_1.28.3 Rtsne_0.15
[7] munsell_0.5.0 ScaledMatrix_1.2.0
[9] codetools_0.2-18 ica_1.0-2
[11] statmod_1.4.36 future_1.23.0
[13] miniUI_0.1.1.1 withr_2.4.3
[15] colorspace_2.0-2 highr_0.9
[17] knitr_1.37 rstudioapi_0.13
[19] ROCR_1.0-11 ggsignif_0.6.3
[21] tensor_1.5 listenv_0.8.0
[23] labeling_0.4.2 git2r_0.29.0
[25] GenomeInfoDbData_1.2.7 polyclip_1.10-0
[27] farver_2.1.0 bit64_4.0.5
[29] pheatmap_1.0.12 rprojroot_2.0.2
[31] parallelly_1.30.0 vctrs_0.3.8
[33] generics_0.1.2 xfun_0.29
[35] doParallel_1.0.16 R6_2.5.1
[37] clue_0.3-60 ggbeeswarm_0.6.0
[39] rsvd_1.0.5 locfit_1.5-9.4
[41] bitops_1.0-7 spatstat.utils_2.3-0
[43] DelayedArray_0.20.0 assertthat_0.2.1
[45] promises_1.2.0.1 scales_1.1.1
[47] beeswarm_0.4.0 gtable_0.3.0
[49] beachmat_2.10.0 globals_0.14.0
[51] processx_3.5.2 goftest_1.2-3
[53] rlang_1.0.0 GlobalOptions_0.1.2
[55] splines_4.1.2 rstatix_0.7.0
[57] lazyeval_0.2.2 broom_0.7.12
[59] spatstat.geom_2.3-1 yaml_2.2.2
[61] reshape2_1.4.4 abind_1.4-5
[63] backports_1.4.1 httpuv_1.6.5
[65] tools_4.1.2 ellipsis_0.3.2
[67] spatstat.core_2.3-2 jquerylib_0.1.4
[69] RColorBrewer_1.1-2 ggridges_0.5.3
[71] Rcpp_1.0.8 plyr_1.8.6
[73] sparseMatrixStats_1.6.0 zlibbioc_1.40.0
[75] RCurl_1.98-1.5 ps_1.6.0
[77] rpart_4.1.16 deldir_1.0-6
[79] GetoptLong_1.0.5 pbapply_1.5-0
[81] viridis_0.6.2 zoo_1.8-9
[83] ggrepel_0.9.1 cluster_2.1.2
[85] fs_1.5.2 magrittr_2.0.2
[87] scattermore_0.7 circlize_0.4.13
[89] lmtest_0.9-39 RANN_2.6.1
[91] whisker_0.4 fitdistrplus_1.1-6
[93] patchwork_1.1.1 mime_0.12
[95] evaluate_0.14 xtable_1.8-4
[97] shape_1.4.6 gridExtra_2.3
[99] compiler_4.1.2 tibble_3.1.6
[101] KernSmooth_2.23-20 crayon_1.4.2
[103] htmltools_0.5.2 mgcv_1.8-38
[105] later_1.3.0 DBI_1.1.2
[107] MASS_7.3-55 car_3.0-12
[109] Matrix_1.4-0 cli_3.1.1
[111] parallel_4.1.2 metapod_1.2.0
[113] igraph_1.2.11 pkgconfig_2.0.3
[115] getPass_0.2-2 plotly_4.10.0
[117] spatstat.sparse_2.1-0 foreach_1.5.2
[119] vipor_0.4.5 bslib_0.3.1
[121] dqrng_0.3.0 XVector_0.34.0
[123] stringr_1.4.0 callr_3.7.0
[125] digest_0.6.29 sctransform_0.3.3
[127] RcppAnnoy_0.0.19 spatstat.data_2.1-2
[129] rmarkdown_2.11 leiden_0.3.9
[131] uwot_0.1.11 edgeR_3.36.0
[133] DelayedMatrixStats_1.16.0 shiny_1.7.1
[135] rjson_0.2.21 lifecycle_1.0.1
[137] nlme_3.1-155 jsonlite_1.7.3
[139] carData_3.0-5 BiocNeighbors_1.12.0
[141] viridisLite_0.4.0 limma_3.50.0
[143] fansi_1.0.2 pillar_1.7.0
[145] lattice_0.20-45 fastmap_1.1.0
[147] httr_1.4.2 survival_3.2-13
[149] glue_1.6.1 iterators_1.0.13
[151] png_0.1-7 bluster_1.4.0
[153] bit_4.0.4 stringi_1.7.6
[155] sass_0.4.0 BiocSingular_1.10.0
[157] irlba_2.3.5 future.apply_1.8.1