Last updated: 2021-02-12
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Knit directory: melanoma_publication_old_data/
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In this script, we load the clinical data and clean and prepare the data for further analysis. Furthermore, we create two .csv files containing the clinical data for RNA and Protein TMA separately in order to attach them to the SingleCellExperiment in a later step.
knitr::opts_chunk$set(echo = TRUE, message= FALSE)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())library(dplyr)# load clinical data and TMA data to link images with patients
clinical_mat <- read.csv(file = "data/data_for_analysis/200323_TMA_256_clinical_data_March2020.csv",
sep=",",stringsAsFactors = FALSE)
clinical_mat <- clinical_mat[is.na(clinical_mat$SpotNr) == FALSE,]
spot_mat <- read.csv(file = "data/data_for_analysis/191119_TMA_spotmatrix.csv", sep= ";",stringsAsFactors = FALSE)
# remove missing value rows from spot_mat
spot_mat <- spot_mat[is.na(spot_mat$SpotNr) == FALSE,]
# in the spot_mat BlockID "B2017.47224" has a space incorporated: "B2017.47224 ". We will rename this BlockID here.
spot_mat[which(spot_mat$BlockID == "B2017.47224 "),]$BlockID <- "B2017.47224"
# load image metadata as output from CellProfiler
image_mat_prot <- read.csv(file = "data/data_for_analysis/protein/Image.csv", stringsAsFactors = FALSE)
image_mat_rna <- read.csv(file = "data/data_for_analysis/rna/Image.csv", stringsAsFactors = FALSE)colnames(clinical_mat) [1] "SpotNr"
[2] "Internal.Pat..ID"
[3] "Block.ID"
[4] "IHC.T.cell.scoring"
[5] "Age.range"
[6] "Gender"
[7] "MM.location"
[8] "melanoma.subtype..site.of.primary.tumor."
[9] "Cancer.Stage"
[10] "Mutation"
[11] "Number.of.treatments.before.surgery"
[12] "Name.of.last.drug.used"
[13] "Last.systemic.treatment.before.surgery"
[14] "date..last.prev.therapy"
[15] "Date.of.surgery"
[16] "Drug.used.after.surgery"
[17] "Next.systemic.treatment.after.surgery..PC.Paliative.Care."
[18] "Adjuvant"
[19] "start.of.treatment"
[20] "end.of.treatment"
[21] "Reason.to.stop.treatment..EOT..end.of.treatment..PD..progression..SE..side.effects..TC..therapy.change..NA..not.applicable."
[22] "Date.of.progression"
[23] "Response.at..3m..R.or.NR.or.NA..for.MR.or.no.treatment..or.adjuvant.at.3m."
[24] "X.at.3m..PD.progressive.disease..SD.stable.disease..PR.partial.response..CR.complete.response..MR.mixed.response..RFS.relapse.free.survival..adjuvant.patients."
[25] "Time.of.response.assesment..3m.for.all.except.for.adjuvant.12m."
[26] "Response.at..6m..PD.progressive.disease..SD.stable.disease..PR.partial.response..CR.complete.response..MR.mixed.response..RFS.relapse.free.survival..adjuvant.patients...TC..therapy.change"
[27] "Response.at..12m..PD.progressive.disease..SD.stable.disease..PR.partial.response..CR.complete.response..MR.mixed.response..RFS.relapse.free.survival..adjuvant...NY..not.yet"
[28] "Last.PET"
[29] "Death.Date" clean_names <- c("SpotNr","PatientID","BlockID","IHC_T_score","Age_range","Gender",
"MM_location","Primary_melanoma_type","Cancer_Stage","Mutation",
"Nr_treatments_before_surgery","Name_last_drug_used","Last_sys_treatment_before_surgery",
"Last_prev_therapy","Date_surgery","Drug_after_surgery","Treatment_after_surgery","Adjuvant",
"Start_treatment","End_treatment","Reason_to_stop_treatment","Date_progression",
"Status_at_3m","Response_at_3m","Time_of_response_assesment","Response_at_6m",
"Response_at_12m","Last_PET","Date_death")
colnames(clinical_mat) <- clean_names
# empty but non NA date_progression is equal to no progression
clinical_mat <- data.frame(clinical_mat)
clinical_mat$relapse <- NA
clinical_mat[is.na(clinical_mat$Date_progression) & is.na(clinical_mat$Nr_treatments_before_surgery) == FALSE,]$relapse <- "untreated/lost"
clinical_mat[clinical_mat$Date_progression %in% c("") & is.na(clinical_mat$Nr_treatments_before_surgery) == FALSE,]$relapse <- "no relapse"
clinical_mat[is.na(clinical_mat$relapse) & is.na(clinical_mat$Nr_treatments_before_surgery) == FALSE,]$relapse <- "relapse"
clinical_mat[is.na(clinical_mat$Nr_treatments_before_surgery),]$relapse <- "control"Starting point is the beginning of the surgery, end point is death/progression or last PET
clinical_mat$Date_surgery <- as.Date(clinical_mat$Date_surgery, format = "%d.%m.%Y")
clinical_mat$Date_death <- as.Date(clinical_mat$Date_death, format = "%d.%m.%Y")
clinical_mat$Date_progression <- as.Date(clinical_mat$Date_progression, format = "%d.%m.%Y")
clinical_mat$Last_PET <- as.Date(clinical_mat$Last_PET, format = "%d.%m.%Y")
clinical_mat$Start_treatment <- as.Date(clinical_mat$Start_treatment, format = "%d.%m.%Y")
clinical_mat$End_treatment <- as.Date(clinical_mat$End_treatment, format = "%d.%m.%Y")clinical_mat$Time_to_death_or_last_PET <- ifelse(is.na(clinical_mat$Date_death) == TRUE, # if no death, then
clinical_mat$Last_PET - clinical_mat$Start_treatment, # time to last PET
clinical_mat$Date_death - clinical_mat$Start_treatment) # else: time to progression
clinical_mat$censoring_death <- ifelse(is.na(clinical_mat$Date_death) == TRUE, 0, 1) # 0: no Death, 1: Deathclinical_mat$Time_to_progression_or_last_PET <- ifelse(is.na(clinical_mat$Date_progression) == TRUE, # if no progression, then
clinical_mat$Last_PET - clinical_mat$Start_treatment, # time to last PET
clinical_mat$Date_progression - clinical_mat$Start_treatment) # else: time to progression
clinical_mat$censoring_progression <- ifelse(is.na(clinical_mat$Date_progression) == TRUE, 0, 1) # 0: no Relapse, 1: Relapse# SpotNr and Description in one mat
full_mat_rna <- spot_mat[,c("SpotNr", "BlockID", "Description", "TissueType", "Location")]
# add respective ImageNumber
image_mat_rna$Description <- image_mat_rna$Metadata_Description
full_mat_rna <- left_join(full_mat_rna, image_mat_rna[,c("ImageNumber", "Description")], by = "Description")
# join with clinical_mat
full_mat_rna <- left_join(full_mat_rna, clinical_mat[,-1], by="BlockID")
# remove Images that were not acquired or removed after initial processing (missing on TMA or too bad quality)
full_mat_rna <- full_mat_rna[is.na(full_mat_rna$ImageNumber) == FALSE,]# SpotNr and Description in one mat
full_mat_prot <- spot_mat[,c("SpotNr", "BlockID", "Description", "TissueType", "Location")]
image_mat_prot$Description <- image_mat_prot$Metadata_Description
# rename "G1 - split" row in image_mat (this core was acquired in two measurements because the machine stopped due to an error)
image_mat_prot$Description <- ifelse(image_mat_prot$Description == "G1 - split", "G1", image_mat_prot$Description)
# add respective ImageNumber
full_mat_prot <- left_join(full_mat_prot, image_mat_prot[,c("ImageNumber", "Description")], by = "Description")
# join with clinical_mat
full_mat_prot <- left_join(full_mat_prot, clinical_mat[,-1], by = "BlockID")
# remove Images that were not acquired or removed after initial processing (missing on TMA or too bad quality)
full_mat_prot <- full_mat_prot[is.na(full_mat_prot$ImageNumber) == FALSE,]# check if the BlockID/PatientID of Description is the same in both data sets
rna_sub <- full_mat_rna[,c("Description", "BlockID", "PatientID")]
prot_sub <- full_mat_prot[,c("Description", "BlockID", "PatientID")]
compare <- left_join(prot_sub, rna_sub, by = "Description")
all(compare$BlockID.x == compare$BlockID.y)[1] TRUEunique(compare$PatientID.x == compare$PatientID.y)[1] TRUE NAwrite.csv(x = full_mat_prot, file = "data/data_for_analysis/protein/clinical_data_protein.csv", row.names = F)
write.csv(x = full_mat_rna, file = "data/data_for_analysis/rna/clinical_data_RNA.csv", row.names = F)
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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] dplyr_1.0.2 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 rstudioapi_0.13 knitr_1.30 magrittr_2.0.1
[5] tidyselect_1.1.0 R6_2.5.0 rlang_0.4.10 stringr_1.4.0
[9] tools_4.0.3 xfun_0.20 git2r_0.28.0 htmltools_0.5.0
[13] ellipsis_0.3.1 rprojroot_2.0.2 yaml_2.2.1 digest_0.6.27
[17] tibble_3.0.4 lifecycle_0.2.0 crayon_1.3.4 purrr_0.3.4
[21] later_1.1.0.1 vctrs_0.3.6 promises_1.1.1 fs_1.5.0
[25] glue_1.4.2 evaluate_0.14 rmarkdown_2.6 stringi_1.5.3
[29] compiler_4.0.3 pillar_1.4.7 generics_0.1.0 httpuv_1.5.4
[33] pkgconfig_2.0.3