Last updated: 2021-02-10
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Knit directory: melanoma_publication_old_data/
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First, we will load the libraries needed for this part of the analysis.
sapply(list.files("code/helper_functions", full.names = TRUE), source) code/helper_functions/calculateSummary.R
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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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code/helper_functions/findClusters.R
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code/helper_functions/findCommunity.R
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code/helper_functions/getCellCount.R
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code/helper_functions/getInfoFromString.R
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code/helper_functions/getSpotnumber.R
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code/helper_functions/plotBarFracCluster.R
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code/helper_functions/plotCellCounts.R
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code/helper_functions/plotCellFrac.R
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code/helper_functions/plotCellFracGroups.R
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code/helper_functions/plotCellFracGroupsSubset.R
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code/helper_functions/sceChecks.R
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code/helper_functions/validityChecks.R
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visible FALSE library(SingleCellExperiment)
library(reshape2)
library(tidyverse)
library(dplyr)
library(data.table)
library(ggplot2)
library(survminer)
library(survival)sce_rna = readRDS(file = "data/sce_RNA.rds")
sce_prot = readRDS(file = "data/sce_protein.rds")
# meta data
dat_survival = fread(file = "data/protein/clinical_data_protein.csv",stringsAsFactors = FALSE)
time_data = fread(file = "data/survdat_for_modelling.csv",stringsAsFactors = FALSE)
time_data$BlockID <- time_data$Block.ID# Select for BlockID: Pre-Treatment, Mutation, Age, Cancer Stage
cur_dat <- data.frame(colData(sce_prot)) %>%
distinct(BlockID, .keep_all = T) %>%
select(BlockID, Mutation, Age, Cancer_Stage, treatment_status_before_surgery) %>%
filter(treatment_status_before_surgery != "control")
# derive Tcell Score for Block (max score per block)
score <- data.frame(colData(sce_prot)) %>%
distinct(Description, .keep_all = T) %>%
filter(paste(MM_location, Location, sep = "_") != "LN_M") %>% # remove LN margin samples as these are not relevant
group_by(BlockID, Tcell_density_score_image) %>%
summarise(n=n()) %>%
filter(row_number()==n()) %>% # select last row of groups since this will always be the highest ranking
select(BlockID, Tcell_density_score_image)`summarise()` regrouping output by 'BlockID' (override with `.groups` argument)cur_dat <- left_join(cur_dat, score)Joining, by = "BlockID"# change certain levels
cur_dat$Age <- ifelse(cur_dat$Age == "?60", ">60", cur_dat$Age)
cur_dat$Cancer_Stage <- ifelse(cur_dat$Cancer_Stage == "III or IV", "IV/III", cur_dat$Cancer_Stage)
# relevel factors
cur_dat$Age <- factor(cur_dat$Age, levels = c("<45", "45-59", ">60"))
cur_dat$Mutation <- factor(cur_dat$Mutation, levels = c("wt", "BRAF", "NRAS", "unknown"))
cur_dat$Cancer_Stage <- factor(cur_dat$Cancer_Stage, levels = c("III", "IV"))
cur_dat$treatment_status_before_surgery <- factor(cur_dat$treatment_status_before_surgery, levels = c("naive", "non-naive"))
cur_dat$Tcell_density_score_image <- factor(cur_dat$Tcell_density_score_image, levels = c("high", "med", "low", "absent"))
# join with survival dat
surv <- left_join(time_data[,c("BlockID", "Time_to_.progression_or_last_PET", "censoring_progression", "Time_to_death_or_last_PET", "censoring_death")], cur_dat)Joining, by = "BlockID"# exclude GNAQ and KRAS mutation due to too few data points
surv_sub <- surv[!(surv$Mutation %in% c("KRAS", "GNAQ")),]
surv_sub <- surv[!(surv$Cancer_Stage %in% c("III/IV", "unknown")),]
colnames(surv_sub) <- c("BlockID", "Time_progression", "censoring_progression", "Time_death", "censoring_death",
"Mutation", "Age", "Cancer Stage", "Treatment Status", "Max Tcell Score")
model <- coxph(Surv(Time_death, censoring_death) ~
Age + `Cancer Stage` + `Treatment Status` + `Max Tcell Score` + Mutation,
data = surv_sub)
ggforest(model, data=surv_sub)
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] survival_3.2-7 survminer_0.4.8
[3] ggpubr_0.4.0 data.table_1.13.6
[5] forcats_0.5.0 stringr_1.4.0
[7] dplyr_1.0.2 purrr_0.3.4
[9] readr_1.4.0 tidyr_1.1.2
[11] tibble_3.0.4 ggplot2_3.3.3
[13] tidyverse_1.3.0 reshape2_1.4.4
[15] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
[17] Biobase_2.50.0 GenomicRanges_1.42.0
[19] GenomeInfoDb_1.26.2 IRanges_2.24.1
[21] S4Vectors_0.28.1 BiocGenerics_0.36.0
[23] MatrixGenerics_1.2.0 matrixStats_0.57.0
[25] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] bitops_1.0-6 fs_1.5.0 lubridate_1.7.9.2
[4] httr_1.4.2 rprojroot_2.0.2 tools_4.0.3
[7] backports_1.2.1 R6_2.5.0 DBI_1.1.0
[10] colorspace_2.0-0 withr_2.3.0 gridExtra_2.3
[13] tidyselect_1.1.0 curl_4.3 compiler_4.0.3
[16] git2r_0.28.0 cli_2.2.0 rvest_0.3.6
[19] xml2_1.3.2 DelayedArray_0.16.0 labeling_0.4.2
[22] scales_1.1.1 survMisc_0.5.5 digest_0.6.27
[25] foreign_0.8-81 rmarkdown_2.6 rio_0.5.16
[28] XVector_0.30.0 pkgconfig_2.0.3 htmltools_0.5.0
[31] dbplyr_2.0.0 rlang_0.4.10 readxl_1.3.1
[34] rstudioapi_0.13 farver_2.0.3 generics_0.1.0
[37] zoo_1.8-8 jsonlite_1.7.2 zip_2.1.1
[40] car_3.0-10 RCurl_1.98-1.2 magrittr_2.0.1
[43] GenomeInfoDbData_1.2.4 Matrix_1.3-2 Rcpp_1.0.5
[46] munsell_0.5.0 fansi_0.4.1 abind_1.4-5
[49] lifecycle_0.2.0 stringi_1.5.3 whisker_0.4
[52] yaml_2.2.1 carData_3.0-4 zlibbioc_1.36.0
[55] plyr_1.8.6 grid_4.0.3 promises_1.1.1
[58] crayon_1.3.4 lattice_0.20-41 cowplot_1.1.1
[61] splines_4.0.3 haven_2.3.1 hms_0.5.3
[64] knitr_1.30 pillar_1.4.7 ggsignif_0.6.0
[67] reprex_0.3.0 glue_1.4.2 evaluate_0.14
[70] modelr_0.1.8 vctrs_0.3.6 httpuv_1.5.4
[73] cellranger_1.1.0 gtable_0.3.0 km.ci_0.5-2
[76] assertthat_0.2.1 openxlsx_4.2.3 xfun_0.20
[79] xtable_1.8-4 broom_0.7.3 rstatix_0.6.0
[82] later_1.1.0.1 KMsurv_0.1-5 ellipsis_0.3.1