Last updated: 2022-02-09
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Knit directory: MelanomaIMC/
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Here, we add more metadata and colData annotations to the SCE object which are needed for downstream analyses.
library(SingleCellExperiment)
library(dplyr)sce <- readRDS(file = "data/data_for_analysis/sce_RNA.rds")
cur_sce <- data.frame(colData(sce))
cur_meta <- data.frame(metadata(sce))cur_sce <- cur_sce %>%
left_join(., cur_meta[,c("ImageNumber", "Location", "Adjuvant", "IHC_T_score", "Mutation",
"Cancer_Stage", "Status_at_3m", "BlockID", "Description", "TissueType",
"MM_location", "Age_range", "Gender", "PatientID", "relapse")])Joining, by = "ImageNumber"colData(sce)$Location <- cur_sce$Location
colData(sce)$Adjuvant <- cur_sce$Adjuvant
colData(sce)$E_I_D <- cur_sce$IHC_T_score
colData(sce)$Mutation <- cur_sce$Mutation
colData(sce)$Cancer_Stage <- cur_sce$Cancer_Stage
colData(sce)$Status_at_3m <- cur_sce$Status_at_3m
colData(sce)$BlockID <- cur_sce$BlockID
colData(sce)$Description <- cur_sce$Description
colData(sce)$TissueType <- cur_sce$TissueType
colData(sce)$MM_location <- cur_sce$MM_location
colData(sce)$Age <- cur_sce$Age_range
colData(sce)$Gender <- cur_sce$Gender
colData(sce)$PatientID <- cur_sce$PatientID
colData(sce)$relapse <- cur_sce$relapse# create simplified location of biopsies
cur_sce$MM_location_simplified <- NA
cur_sce[grep("CTRL", cur_sce$Location), ]$MM_location_simplified <- "control"
cur_sce[grep("LN", cur_sce$MM_location), ]$MM_location_simplified <- "LN"
cur_sce[grep("skin", cur_sce$MM_location), ]$MM_location_simplified <- "skin"
cur_sce[is.na(cur_sce$MM_location_simplified) == TRUE, ]$MM_location_simplified <- "other"
# add to colData
colData(sce)$MM_location_simplified <- cur_sce$MM_location_simplified
# add to metadata
cur_meta <- left_join(cur_meta, distinct(cur_sce, MM_location_simplified, ImageNumber))Joining, by = "ImageNumber"# unique treatments
unique(cur_meta$Last_sys_treatment_before_surgery) [1] NA "untreated" "aPD1"
[4] "aPD1 + aCTLA4" "aCTLA4" "chemotherapy"
[7] "BRAFi + MEKi" "MEKi" "aPD1 + aCTLA4 or aPD1"
[10] "aPD1 + aLAG3" # group treatment types
cur_meta$treatment_group_before_surgery <- NA
cur_meta[cur_meta$MM_location_simplified == "control",]$treatment_group_before_surgery <- "control"
cur_meta[cur_meta$Last_sys_treatment_before_surgery %in% "untreated",]$treatment_group_before_surgery <- "untreated"
cur_meta[cur_meta$Last_sys_treatment_before_surgery %in% c("aPD1", "aPD1 + aCTLA4", "aCTLA4",
"aPD1 + aCTLA4 or aPD1", "aPD1 + aLAG3"),]$treatment_group_before_surgery <- "ICI"
cur_meta[cur_meta$Last_sys_treatment_before_surgery %in% c("BRAFi + MEKi", "MEKi"),]$treatment_group_before_surgery <- "Targeted"
cur_meta[cur_meta$Last_sys_treatment_before_surgery %in% c("chemotherapy"),]$treatment_group_before_surgery <- "Chemotherapy"
cur_meta[is.na(cur_meta$Last_sys_treatment_before_surgery),]$treatment_group_before_surgery <- "unknown"
# add treatment type to cur_sce
cur_sce <- left_join(cur_sce, cur_meta[,c("ImageNumber", "treatment_group_before_surgery")])Joining, by = "ImageNumber"# add relapse to SCE
colData(sce)$treatment_group_before_surgery <- cur_sce$treatment_group_before_surgery# unique treatments
unique(cur_meta$Treatment_after_surgery) [1] NA "untreated" "BRAFi + MEKi"
[4] "aPD1 + aCTLA4" "aPD1" "aCTLA4"
[7] "TVEC" "PC" "chemotherapy"
[10] "aPD1 + aLAG3" "BRAFi" "BRAFi + MEKi +/- aPD1"
[13] "MEKi" # group treatment types
cur_meta$treatment_group_after_surgery <- NA
cur_meta[cur_meta$MM_location_simplified == "control",]$treatment_group_after_surgery <- "control"
cur_meta[cur_meta$Treatment_after_surgery %in% "untreated",]$treatment_group_after_surgery <- "untreated"
cur_meta[cur_meta$Treatment_after_surgery %in% c("aPD1 + aCTLA4", "aPD1", "aCTLA4", "aPD1 + aLAG3"),]$treatment_group_after_surgery <- "ICI"
cur_meta[cur_meta$Treatment_after_surgery %in% c("BRAFi + MEKi", "BRAFi", "MEKi", "BRAFi + MEKi +/- aPD1"),]$treatment_group_after_surgery <- "Targeted"
cur_meta[cur_meta$Treatment_after_surgery %in% c("chemotherapy"),]$treatment_group_after_surgery <- "Chemotherapy"
cur_meta[cur_meta$Treatment_after_surgery %in% c("TVEC"),]$treatment_group_after_surgery <- "TVEC"
cur_meta[cur_meta$Treatment_after_surgery %in% c("PC"),]$treatment_group_after_surgery <- "Palliative"
cur_meta[is.na(cur_meta$Treatment_after_surgery),]$treatment_group_after_surgery <- "unknown"
# add treatment type to cur_sce
cur_sce <- left_join(cur_sce, cur_meta[,c("ImageNumber", "treatment_group_after_surgery")])Joining, by = "ImageNumber"# add relapse to SCE
colData(sce)$treatment_group_after_surgery <- cur_sce$treatment_group_after_surgerycur_meta$treatment_status_before_surgery <- NA
cur_meta[cur_meta$Location == "CTRL",]$treatment_status_before_surgery <- "control"
cur_meta[cur_meta$Location != "CTRL",]$treatment_status_before_surgery <- ifelse(cur_meta[cur_meta$Location != "CTRL",]$Nr_treatments_before_surgery == 0, "naive", "non-naive")
# add treatment type to cur_sce
cur_sce <- left_join(cur_sce, cur_meta[,c("ImageNumber", "treatment_status_before_surgery")])Joining, by = "ImageNumber"# add to SCE
colData(sce)$treatment_status_before_surgery <- cur_sce$treatment_status_before_surgeryDate_death <- metadata(sce)$Date_death
sce$Date_death <- Date_death[sce$ImageNumber]
#binarize death
sce$Death <- "no"
sce[,grepl("20",sce$Date_death)]$Death <- "yes"metadata(sce) <- as.list(cur_meta)saveRDS(sce,file = "data/data_for_analysis/sce_RNA.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] dplyr_1.0.7 SingleCellExperiment_1.16.0
[3] SummarizedExperiment_1.24.0 Biobase_2.54.0
[5] GenomicRanges_1.46.1 GenomeInfoDb_1.30.1
[7] IRanges_2.28.0 S4Vectors_0.32.3
[9] BiocGenerics_0.40.0 MatrixGenerics_1.6.0
[11] matrixStats_0.61.0 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] tidyselect_1.1.1 xfun_0.29 bslib_0.3.1
[4] purrr_0.3.4 lattice_0.20-45 generics_0.1.2
[7] vctrs_0.3.8 htmltools_0.5.2 yaml_2.2.2
[10] utf8_1.2.2 rlang_1.0.0 jquerylib_0.1.4
[13] later_1.3.0 pillar_1.7.0 DBI_1.1.2
[16] glue_1.6.1 GenomeInfoDbData_1.2.7 lifecycle_1.0.1
[19] stringr_1.4.0 zlibbioc_1.40.0 evaluate_0.14
[22] knitr_1.37 callr_3.7.0 fastmap_1.1.0
[25] httpuv_1.6.5 ps_1.6.0 fansi_1.0.2
[28] Rcpp_1.0.8 promises_1.2.0.1 DelayedArray_0.20.0
[31] jsonlite_1.7.3 XVector_0.34.0 fs_1.5.2
[34] digest_0.6.29 stringi_1.7.6 processx_3.5.2
[37] getPass_0.2-2 grid_4.1.2 rprojroot_2.0.2
[40] cli_3.1.1 tools_4.1.2 bitops_1.0-7
[43] magrittr_2.0.2 sass_0.4.0 RCurl_1.98-1.5
[46] tibble_3.1.6 crayon_1.4.2 whisker_0.4
[49] pkgconfig_2.0.3 Matrix_1.4-0 ellipsis_0.3.2
[52] assertthat_0.2.1 rmarkdown_2.11 httr_1.4.2
[55] rstudioapi_0.13 R6_2.5.1 git2r_0.29.0
[58] compiler_4.1.2