Last updated: 2021-02-10
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Knit directory: melanoma_publication_old_data/
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This script gives an overview over the clinical data and generates statistics used in the manuscript.
sapply(list.files("code/helper_functions", full.names = TRUE), source) code/helper_functions/calculateSummary.R
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code/helper_functions/sceChecks.R
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code/helper_functions/validityChecks.R
value ?
visible FALSE library(data.table)
library(dplyr)
library(SingleCellExperiment)
library(readr)# SCE object
sce_prot = readRDS(file = "data/sce_protein.rds")
sce_rna = readRDS(file = "data/sce_RNA.rds")
# image
image_mat_rna <- read.csv("data/rna/Image.csv")
image_mat_prot <- read.csv("data/protein/Image.csv")# Number of Patients
data.frame(colData(sce_prot)) %>%
filter(Location != "CTRL") %>%
distinct(PatientID) %>%
summarise(n=n()) n
1 69# Number of Samples without control samples
data.frame(colData(sce_prot)) %>%
filter(Location != "CTRL") %>%
distinct(Description) %>%
summarise(n=n()) n
1 159# Number of Samples - Mutation
data.frame(colData(sce_prot)) %>%
filter(Location != "CTRL") %>%
distinct(Description, .keep_all = T) %>%
group_by(Mutation) %>%
summarise(n=n()) %>%
mutate(percentage = round(n/sum(n) *100,0))# A tibble: 6 x 3
Mutation n percentage
<chr> <int> <dbl>
1 BRAF 71 45
2 GNAQ 2 1
3 KRAS 1 1
4 NRAS 50 31
5 unknown 7 4
6 wt 28 18# Number of Samples - Location
data.frame(colData(sce_prot)) %>%
filter(Location != "CTRL") %>%
distinct(Description, .keep_all = T) %>%
group_by(MM_location_simplified) %>%
summarise(n=n()) %>%
mutate(percentage = round(n/sum(n) *100,0))# A tibble: 3 x 3
MM_location_simplified n percentage
<chr> <int> <dbl>
1 LN 54 34
2 other 29 18
3 skin 76 48# Number of Patients
data.frame(colData(sce_prot)) %>%
filter(Location != "CTRL") %>%
distinct(Description, .keep_all = T) %>%
group_by(PatientID) %>%
summarise(n=n()) %>%
group_by(n) %>%
summarise(group_sizes = n())# A tibble: 7 x 2
n group_sizes
<int> <int>
1 1 14
2 2 39
3 3 7
4 4 4
5 5 1
6 6 3
7 7 1# Number Controls
data.frame(colData(sce_prot)) %>%
filter(Location != "CTRL") %>%
distinct(Description, .keep_all = T) %>%
group_by(MM_location) %>%
summarise(n=n())# A tibble: 11 x 2
MM_location n
<chr> <int>
1 brain 7
2 LN 49
3 LN or soft tissue 3
4 mucosal 4
5 parotis or LN 2
6 skin 4
7 skin_cutaneous 10
8 skin_subcutaneous 52
9 skin_undefine 10
10 soft tissue 9
11 visceral 9# Number Controls
data.frame(colData(sce_prot)) %>%
filter(Location == "CTRL") %>%
distinct(Description, .keep_all = T) %>%
group_by(TissueType) %>%
summarise(n=n())# A tibble: 3 x 2
TissueType n
<chr> <int>
1 Lymphnode 2
2 PSO 2
3 Skin 3ncol(sce_rna)[1] 864263ncol(sce_prot)[1] 989404# diff in ablated area
area_prot <- data.frame(image_area = image_mat_prot$Height_FullStack * image_mat_prot$Width_FullStack)
area_prot$Description <- image_mat_prot$Metadata_Description
area_prot$data_set <- "Protein"
area_rna <- data.frame(image_area = image_mat_rna$Height_FullStack * image_mat_rna$Width_FullStack)
area_rna$Description <- image_mat_rna$Metadata_Description
area_rna$data_set <- "RNA"
image_area <- rbind(area_rna, area_prot)
# Ratio #Cells Protein/RNA
round(ncol(sce_prot) / ncol(sce_rna),2)[1] 1.14area <- image_area %>%
group_by(data_set) %>%
summarise(area_sum = sum(image_area))
# Ratio Area Protein/RNA
round(area[area$data_set == "Protein", ]$area_sum / area[area$data_set == "RNA", ]$area_sum,2)[1] 1.07# Protein data
data.frame(colData(sce_prot)) %>%
filter(Location != "CTRL") %>%
group_by(celltype) %>%
summarise(n=n()) %>%
mutate(percentage=round(n/sum(n)*100,1))# A tibble: 11 x 3
celltype n percentage
<chr> <int> <dbl>
1 Bcell 39604 4.1
2 BnTcell 25921 2.7
3 Macrophage 62049 6.5
4 Neutrophil 10900 1.1
5 pDC 6850 0.7
6 Stroma 59246 6.2
7 Tcytotoxic 52567 5.5
8 Thelper 54306 5.6
9 Tregulatory 9226 1
10 Tumor 622114 64.7
11 unknown 18567 1.9# RNA data
data.frame(colData(sce_rna)) %>%
filter(Location != "CTRL") %>%
group_by(celltype) %>%
summarise(n=n()) %>%
mutate(percentage=round(n/sum(n)*100,1))# A tibble: 10 x 3
celltype n percentage
<chr> <int> <dbl>
1 CD38 10931 1.3
2 HLA-DR 29557 3.5
3 Macrophage 64405 7.7
4 Neutrophil 5018 0.6
5 Stroma 24574 2.9
6 Tcell 87858 10.5
7 Tcytotoxic 44630 5.3
8 Tumor 549153 65.3
9 unknown 2972 0.4
10 Vasculature 21297 2.5# Percentage of Chemokine Producing Cells
data.frame(colData(sce_rna)) %>%
filter(Location != "CTRL") %>%
group_by(chemokine) %>%
summarise(n=n()) %>%
mutate(percentage = round((n / sum(n)) *100,1))# A tibble: 2 x 3
chemokine n percentage
<lgl> <int> <dbl>
1 FALSE 782417 93.1
2 TRUE 57978 6.9# Chemokines by Celltype
data.frame(colData(sce_rna)) %>%
filter(Location != "CTRL") %>%
filter(chemokine == 1) %>%
group_by(celltype) %>%
summarise(n=n()) %>%
mutate(percentage = round((n / sum(n)) *100,1))# A tibble: 10 x 3
celltype n percentage
<chr> <int> <dbl>
1 CD38 920 1.6
2 HLA-DR 3202 5.5
3 Macrophage 12675 21.9
4 Neutrophil 1378 2.4
5 Stroma 3913 6.7
6 Tcell 12179 21
7 Tcytotoxic 9428 16.3
8 Tumor 12965 22.4
9 unknown 45 0.1
10 Vasculature 1273 2.2# Sum of major expressing cell types
data.frame(colData(sce_rna)) %>%
filter(Location != "CTRL") %>%
filter(chemokine == 1) %>%
group_by(celltype) %>%
summarise(n=n()) %>%
mutate(percentage = round((n / sum(n)) *100,1)) %>%
filter(percentage > 15) %>%
summarise(sum <- sum(percentage))# A tibble: 1 x 1
`sum <- sum(percentage)`
<dbl>
1 81.6# CXCL13 experssion by Tcells
# Chemokines by Celltype
data.frame(colData(sce_rna)) %>%
filter(Location != "CTRL") %>%
filter(expressor == "CXCL13") %>%
group_by(celltype) %>%
summarise(n=n()) %>%
mutate(percentage = round((n / sum(n)) *100,1)) %>%
filter(percentage > 30) %>%
summarise(sum <- sum(percentage))# A tibble: 1 x 1
`sum <- sum(percentage)`
<dbl>
1 71.1data.frame(colData(sce_rna)) %>%
rowwise() %>%
mutate(number_of_chemokines = length(strsplit(expressor, "_")[[1]])) %>%
filter(Location != "CTRL") %>%
filter(chemokine == 1) %>%
group_by(number_of_chemokines) %>%
summarise(n=n()) %>%
mutate(percentage = round((n / sum(n)) *100,1))# A tibble: 8 x 3
number_of_chemokines n percentage
<int> <int> <dbl>
1 1 45687 78.8
2 2 9479 16.3
3 3 2152 3.7
4 4 518 0.9
5 5 109 0.2
6 6 22 0
7 7 8 0
8 8 3 0 data.frame(colData(sce_rna)) %>%
filter(Location != "CTRL") %>%
filter(CCL8 == 1) %>%
summarise(n=n()) n
1 862# Percentage of Tumor Cells that Express a Chemokine
data.frame(colData(sce_rna)) %>%
filter(Location != "CTRL") %>%
filter(celltype == "Tumor") %>%
group_by(chemokine) %>%
summarise(n=n()) %>%
mutate(percentage = round((n / sum(n)) *100,1))# A tibble: 2 x 3
chemokine n percentage
<lgl> <int> <dbl>
1 FALSE 536188 97.6
2 TRUE 12965 2.4# CXCL10 in Tumor cells
data.frame(colData(sce_rna)) %>%
filter(Location != "CTRL") %>%
filter(CXCL10 == 1) %>%
group_by(celltype) %>%
summarise(n=n()) %>%
mutate(percentage = round((n / sum(n)) *100,1))# A tibble: 10 x 3
celltype n percentage
<chr> <int> <dbl>
1 CD38 71 0.6
2 HLA-DR 332 2.8
3 Macrophage 3624 30.3
4 Neutrophil 166 1.4
5 Stroma 212 1.8
6 Tcell 1275 10.7
7 Tcytotoxic 1364 11.4
8 Tumor 4778 40
9 unknown 3 0
10 Vasculature 117 1 data.frame(colData(sce_rna)) %>%
filter(Location != "CTRL") %>%
filter(celltype == "Tumor") %>%
group_by(CXCL10) %>%
summarise(n=n()) %>%
mutate(percentage = round((n / sum(n)) *100,1))# A tibble: 2 x 3
CXCL10 n percentage
<dbl> <int> <dbl>
1 0 544375 99.1
2 1 4778 0.9# CCL2 in Tumor cells
data.frame(colData(sce_rna)) %>%
filter(Location != "CTRL") %>%
filter(CCL2 == 1) %>%
group_by(celltype) %>%
summarise(n=n()) %>%
mutate(percentage = round((n / sum(n)) *100,1))# A tibble: 10 x 3
celltype n percentage
<chr> <int> <dbl>
1 CD38 270 2.7
2 HLA-DR 327 3.3
3 Macrophage 3041 30.3
4 Neutrophil 89 0.9
5 Stroma 1182 11.8
6 Tcell 1198 11.9
7 Tcytotoxic 768 7.7
8 Tumor 2872 28.6
9 unknown 7 0.1
10 Vasculature 282 2.8data.frame(colData(sce_rna)) %>%
filter(Location != "CTRL") %>%
filter(celltype == "Tumor") %>%
group_by(CCL2) %>%
summarise(n=n()) %>%
mutate(percentage = round((n / sum(n)) *100,1))# A tibble: 2 x 3
CCL2 n percentage
<dbl> <int> <dbl>
1 0 546281 99.5
2 1 2872 0.5# CXCL8 in Tumor cells
data.frame(colData(sce_rna)) %>%
filter(Location != "CTRL") %>%
filter(CXCL8 == 1) %>%
group_by(celltype) %>%
summarise(n=n()) %>%
mutate(percentage = round((n / sum(n)) *100,1))# A tibble: 10 x 3
celltype n percentage
<chr> <int> <dbl>
1 CD38 18 0.4
2 HLA-DR 53 1.2
3 Macrophage 641 14.8
4 Neutrophil 1058 24.4
5 Stroma 69 1.6
6 Tcell 133 3.1
7 Tcytotoxic 95 2.2
8 Tumor 2214 51
9 unknown 13 0.3
10 Vasculature 46 1.1data.frame(colData(sce_rna)) %>%
filter(Location != "CTRL") %>%
filter(celltype == "Tumor") %>%
group_by(CXCL8) %>%
summarise(n=n()) %>%
mutate(percentage = round((n / sum(n)) *100,1))# A tibble: 2 x 3
CXCL8 n percentage
<dbl> <int> <dbl>
1 0 546939 99.6
2 1 2214 0.4# CXCL12 in Tumor cells
data.frame(colData(sce_rna)) %>%
filter(Location != "CTRL") %>%
filter(CXCL12 == 1) %>%
group_by(celltype) %>%
summarise(n=n()) %>%
mutate(percentage = round((n / sum(n)) *100,1))# A tibble: 10 x 3
celltype n percentage
<chr> <int> <dbl>
1 CD38 340 4.3
2 HLA-DR 255 3.2
3 Macrophage 1287 16.3
4 Neutrophil 14 0.2
5 Stroma 2373 30
6 Tcell 1445 18.3
7 Tcytotoxic 681 8.6
8 Tumor 1060 13.4
9 unknown 3 0
10 Vasculature 453 5.7data.frame(colData(sce_rna)) %>%
filter(Location != "CTRL") %>%
filter(celltype == "Tumor") %>%
group_by(CXCL12) %>%
summarise(n=n()) %>%
mutate(percentage = round((n / sum(n)) *100,1))# A tibble: 2 x 3
CXCL12 n percentage
<dbl> <int> <dbl>
1 0 548093 99.8
2 1 1060 0.2# overall chemokine expression
data.frame(colData(sce_rna)) %>%
filter(Location == "CTRL") %>%
group_by(chemokine, TissueType) %>%
summarise(n=n()) %>%
reshape2::dcast(TissueType ~ chemokine, value.var = "n", fill = 0) %>%
mutate(percentage_expressing = round(`TRUE` / (`FALSE`+`TRUE`) * 100,1)) TissueType FALSE TRUE percentage_expressing
1 Lymphnode 16949 1001 5.6
2 PSO 2524 264 9.5
3 Skin 3097 33 1.1# CXCL13 expression in control samples
data.frame(colData(sce_rna)) %>%
filter(Location == "CTRL") %>%
group_by(CXCL13, TissueType) %>%
summarise(n=n()) %>%
reshape2::dcast(TissueType ~ CXCL13, value.var = "n", fill = 0) %>%
mutate(percentage_expressing = round(`1` / (`0`+`1`) * 100,1)) TissueType 0 1 percentage_expressing
1 Lymphnode 17316 634 3.5
2 PSO 2787 1 0.0
3 Skin 3130 0 0.0# CXCL13 expression in tumor samples
data.frame(colData(sce_rna)) %>%
filter(Location != "CTRL") %>%
group_by(CXCL13, MM_location) %>%
summarise(n=n()) %>%
reshape2::dcast(MM_location ~ CXCL13, value.var = "n", fill = 0) %>%
mutate(percentage_expressing = round(`1` / (`0`+`1`) * 100,1)) MM_location 0 1 percentage_expressing
1 brain 28923 922 3.1
2 LN 288382 5157 1.8
3 LN or soft tissue 21060 148 0.7
4 mucosal 20428 34 0.2
5 parotis or LN 12698 23 0.2
6 skin 9656 72 0.7
7 skin_cutaneous 64397 74 0.1
8 skin_subcutaneous 268021 1582 0.6
9 skin_undefine 38163 508 1.3
10 soft tissue 38483 159 0.4
11 visceral 41479 26 0.1# CXCL10 expression in control samples
data.frame(colData(sce_rna)) %>%
filter(Location == "CTRL") %>%
group_by(CXCL10, TissueType) %>%
summarise(n=n()) %>%
reshape2::dcast(TissueType ~ CXCL10, value.var = "n", fill = 0) %>%
mutate(percentage_expressing = round(`1` / (`0`+`1`) * 100,1)) TissueType 0 1 percentage_expressing
1 Lymphnode 17916 34 0.2
2 PSO 2782 6 0.2
3 Skin 3130 0 0.0# CXCL10 expression in tumor samples
data.frame(colData(sce_rna)) %>%
filter(Location != "CTRL") %>%
group_by(CXCL10, MM_location) %>%
summarise(n=n()) %>%
reshape2::dcast(MM_location ~ CXCL10, value.var = "n", fill = 0) %>%
mutate(percentage_expressing = round(`1` / (`0`+`1`) * 100,1)) MM_location 0 1 percentage_expressing
1 brain 29336 509 1.7
2 LN 289761 3778 1.3
3 LN or soft tissue 20929 279 1.3
4 mucosal 20405 57 0.3
5 parotis or LN 12714 7 0.1
6 skin 9681 47 0.5
7 skin_cutaneous 64227 244 0.4
8 skin_subcutaneous 264588 5015 1.9
9 skin_undefine 36915 1756 4.5
10 soft tissue 38399 243 0.6
11 visceral 41498 7 0.0cont_table <- data.frame(colData(sce_prot)) %>%
distinct(Description,.keep_all = T) %>%
filter(is.na(dysfunction_score) == FALSE) %>%
group_by(bcell_patch_score, dysfunction_score) %>%
summarise(n=n()) %>%
filter(bcell_patch_score %in% c("small B cell patches", "TLS")) %>%
reshape2::dcast(bcell_patch_score ~ dysfunction_score, value.var = "n", fill = 0)
fisher.test(cont_table[,-1])
Fisher's Exact Test for Count Data
data: cont_table[, -1]
p-value = 0.04056
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
1.087594 1088.437009
sample estimates:
odds ratio
16.19942
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04 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=C
[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] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] readr_1.4.0 SingleCellExperiment_1.12.0
[3] SummarizedExperiment_1.20.0 Biobase_2.50.0
[5] GenomicRanges_1.42.0 GenomeInfoDb_1.26.2
[7] IRanges_2.24.1 S4Vectors_0.28.1
[9] BiocGenerics_0.36.0 MatrixGenerics_1.2.0
[11] matrixStats_0.57.0 dplyr_1.0.2
[13] data.table_1.13.6 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] tidyselect_1.1.0 xfun_0.20 reshape2_1.4.4
[4] purrr_0.3.4 lattice_0.20-41 vctrs_0.3.6
[7] generics_0.1.0 htmltools_0.5.0 yaml_2.2.1
[10] utf8_1.1.4 rlang_0.4.10 later_1.1.0.1
[13] pillar_1.4.7 glue_1.4.2 plyr_1.8.6
[16] GenomeInfoDbData_1.2.4 lifecycle_0.2.0 stringr_1.4.0
[19] zlibbioc_1.36.0 evaluate_0.14 knitr_1.30
[22] httpuv_1.5.4 fansi_0.4.1 Rcpp_1.0.5
[25] promises_1.1.1 DelayedArray_0.16.0 XVector_0.30.0
[28] fs_1.5.0 hms_0.5.3 digest_0.6.27
[31] stringi_1.5.3 rprojroot_2.0.2 grid_4.0.3
[34] cli_2.2.0 tools_4.0.3 bitops_1.0-6
[37] magrittr_2.0.1 RCurl_1.98-1.2 tibble_3.0.4
[40] crayon_1.3.4 whisker_0.4 pkgconfig_2.0.3
[43] ellipsis_0.3.1 Matrix_1.3-2 assertthat_0.2.1
[46] rmarkdown_2.6 rstudioapi_0.13 R6_2.5.0
[49] git2r_0.28.0 compiler_4.0.3