Last updated: 2022-02-22
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library(SingleCellExperiment)Loading required package: SummarizedExperimentLoading required package: MatrixGenericsLoading required package: matrixStats
Attaching package: 'MatrixGenerics'The following objects are masked from 'package:matrixStats':
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expand.grid, I, unnameLoading required package: IRangesLoading required package: GenomeInfoDbLoading required package: BiobaseWelcome to Bioconductor
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'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
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anyMissing, rowMedianslibrary(dplyr)
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date, intersect, setdiff, unionlibrary(stringr)clinical_data_protein <- read.csv2("data/data_for_analysis/protein/clinical_data_protein.csv", sep = ",")
sce_prot = readRDS(file = "data/data_for_analysis/sce_protein.rds")
sce_prot <- sce_prot[,sce_prot$Location != "CTRL"]info <- as.data.frame(colData(sce_prot)) %>%
distinct(BlockID, .keep_all = TRUE) %>%
select(BlockID, MM_location_simplified)
clinical_data_protein <- left_join(clinical_data_protein, info)Joining, by = "BlockID"block_level <- clinical_data_protein %>%
filter(Location != "CTRL") %>%
distinct(BlockID, .keep_all = TRUE) %>%
select(PatientID, BlockID, Age_range, Gender, Primary_melanoma_type, MM_location, Cancer_Stage, Mutation,
Last_sys_treatment_before_surgery, Treatment_after_surgery, MM_location_simplified, Status_at_3m)
# total blocks
length(unique(block_level$BlockID))[1] 86# Sex
block_level %>%
group_by(Gender) %>%
summarise(n=n()) %>%
mutate(percentage = round(n / sum(n) *100,2)) %>%
as.data.frame() Gender n percentage
1 f 36 41.86
2 m 50 58.14# Age
block_level %>%
group_by(Age_range) %>%
summarise(n=n()) %>%
mutate(percentage = round(n / sum(n) *100,2)) %>%
as.data.frame() Age_range n percentage
1 <45 9 10.47
2 ≥60 55 63.95
3 45-59 22 25.58# Primary Melanoma Type
block_level %>%
group_by(Primary_melanoma_type) %>%
summarise(n=n()) %>%
mutate(percentage = n / sum(n) *100) %>%
mutate(Primary_melanoma_type = ifelse(percentage > 5 | Primary_melanoma_type == "unknown", Primary_melanoma_type, "other")) %>%
group_by(Primary_melanoma_type) %>%
summarise(n=sum(n), percentage=round(sum(percentage),2)) %>%
as.data.frame() Primary_melanoma_type n percentage
1 acral lentiginous 9 10.47
2 cutaneous 67 77.91
3 other 6 6.98
4 unknown 4 4.65# Met Location
block_level %>%
group_by(MM_location) %>%
summarise(n=n()) %>%
mutate(percentage = n / sum(n) *100) %>%
mutate(MM_location = ifelse(percentage > 5, MM_location, "other")) %>%
group_by(MM_location) %>%
summarise(n=sum(n), percentage=round(sum(percentage),2)) %>%
as.data.frame() MM_location n percentage
1 brain 6 6.98
2 LN 27 31.40
3 other 16 18.60
4 skin_subcutaneous 26 30.23
5 skin_undefine 6 6.98
6 soft tissue 5 5.81# Cancer Stage
block_level %>%
group_by(Cancer_Stage) %>%
summarise(n=n()) %>%
mutate(percentage = round(n / sum(n) *100,2)) %>%
as.data.frame() Cancer_Stage n percentage
1 III 31 36.05
2 III or IV 2 2.33
3 IV 50 58.14
4 unknown 3 3.49# Mutation
block_level %>%
group_by(Mutation) %>%
summarise(n=n()) %>%
mutate(percentage = n / sum(n) *100) %>%
mutate(Mutation = ifelse(percentage > 5 | Mutation == "unknown", Mutation, "other")) %>%
group_by(Mutation) %>%
summarise(n=sum(n), percentage=sum(percentage))# A tibble: 5 × 3
Mutation n percentage
<chr> <int> <dbl>
1 BRAF 38 44.2
2 NRAS 26 30.2
3 other 3 3.49
4 unknown 4 4.65
5 wt 15 17.4 # Systemic Treatment Before
block_level %>%
group_by(Last_sys_treatment_before_surgery) %>%
summarise(n=n()) %>%
mutate(percentage = n / sum(n) *100) %>%
mutate(Last_sys_treatment_before_surgery = ifelse(percentage > 5 | is.na(Last_sys_treatment_before_surgery) == T, Last_sys_treatment_before_surgery, "other")) %>%
group_by(Last_sys_treatment_before_surgery) %>%
summarise(n=sum(n)) %>%
mutate(percentage = round(n / sum(n) *100,2)) %>%
as.data.frame() Last_sys_treatment_before_surgery n percentage
1 aPD1 17 19.77
2 other 12 13.95
3 untreated 56 65.12
4 <NA> 1 1.16# Systemic Treatment After
block_level %>%
mutate(Last_sys_treatment_before_surgery = ifelse(is.na(Last_sys_treatment_before_surgery),
"unknown", Last_sys_treatment_before_surgery)) %>%
mutate(Treatment_after_surgery = ifelse(is.na(Treatment_after_surgery),
"unknown", Treatment_after_surgery)) %>%
mutate(treatmentGroup_before = ifelse(Last_sys_treatment_before_surgery == "untreated",
"Naive", "Pre-Treated")) %>%
mutate(treatmentGroup_before = ifelse(Last_sys_treatment_before_surgery == "unknown",
"Unknown", treatmentGroup_before)) %>%
mutate(treatmentGroups = paste(treatmentGroup_before, Treatment_after_surgery, sep = "_")) %>%
group_by(treatmentGroup_before, treatmentGroups) %>%
summarise(n=n()) %>%
ungroup() %>%
mutate(percentage = n / sum(n) *100) %>%
mutate(treatmentGroups = ifelse(percentage > 5, treatmentGroups, paste(treatmentGroup_before, "other", sep = "_"))) %>%
group_by(treatmentGroups) %>%
summarise(n=sum(n), percentage=round(sum(percentage),2)) %>%
as.data.frame()`summarise()` has grouped output by 'treatmentGroup_before'. You can override
using the `.groups` argument. treatmentGroups n percentage
1 Naive_aPD1 22 25.58
2 Naive_BRAFi + MEKi 6 6.98
3 Naive_other 18 20.93
4 Naive_untreated 10 11.63
5 Pre-Treated_aPD1 9 10.47
6 Pre-Treated_BRAFi + MEKi 8 9.30
7 Pre-Treated_other 12 13.95
8 Unknown_other 1 1.16# Response Information 3M after
block_level %>%
group_by(Status_at_3m) %>%
summarise(n=n()) %>%
mutate(percentage = n / sum(n) *100) %>%
mutate(Status_at_3m = ifelse(percentage > 5, Status_at_3m, "other")) %>%
group_by(Status_at_3m) %>%
summarise(n=sum(n), percentage=round(sum(percentage),2)) %>%
as.data.frame() Status_at_3m n percentage
1 NR 30 34.88
2 R 16 18.60
3 <NA> 40 46.51unique_variables <- block_level %>%
reshape2::melt(id.vars="PatientID", variable.name="Variable", value.name="Value") %>%
group_by(PatientID, Variable) %>%
distinct(Value, .keep_all = T) %>%
summarise(n=n())`summarise()` has grouped output by 'PatientID'. You can override using the
`.groups` argument.multiple_levels <- unique(as.character(unique_variables[unique_variables$n > 1,]$Variable))
all_variables <- unique(as.character(unique_variables$Variable))
unique_variables <- setdiff(all_variables, multiple_levels)patient_level <- block_level %>%
distinct(PatientID, Age_range, Gender, Primary_melanoma_type, Cancer_Stage)
# total blocks
length(unique(patient_level$PatientID))[1] 69# Sex
patient_level %>%
group_by(Gender) %>%
summarise(n=n()) %>%
mutate(percentage = round(n / sum(n) *100,2)) %>%
as.data.frame() Gender n percentage
1 f 28 40.58
2 m 41 59.42# Age
patient_level %>%
group_by(Age_range) %>%
summarise(n=n()) %>%
mutate(percentage = round(n / sum(n) *100,2)) %>%
as.data.frame() Age_range n percentage
1 <45 6 8.70
2 ≥60 47 68.12
3 45-59 16 23.19# Primary Melanoma Type
patient_level %>%
group_by(Primary_melanoma_type) %>%
summarise(n=n()) %>%
mutate(percentage = n / sum(n) *100) %>%
mutate(Primary_melanoma_type = ifelse(percentage > 5, Primary_melanoma_type, "other")) %>%
group_by(Primary_melanoma_type) %>%
summarise(n=sum(n), percentage=round(sum(percentage),2)) %>%
as.data.frame() Primary_melanoma_type n percentage
1 acral lentiginous 4 5.80
2 cutaneous 57 82.61
3 other 4 5.80
4 unknown 4 5.80# Cancer Stage
patient_level %>%
group_by(Cancer_Stage) %>%
summarise(n=n()) %>%
mutate(percentage = round(n / sum(n) *100,2)) %>%
as.data.frame() Cancer_Stage n percentage
1 III 29 42.03
2 III or IV 2 2.90
3 IV 35 50.72
4 unknown 3 4.35# join with T cell Score, B cell Score, Dysfunction Score
scores <- as.data.frame(colData(sce_prot)) %>%
distinct(Description, .keep_all = T) %>%
select(Description, Tcell_density_score_image, bcell_patch_score, dysfunction_score)
clinicalDat <- left_join(scores, clinical_data_protein)Joining, by = "Description"clinicalDat$IHC_T_score <- NULL
clinicalDat$Age_range <- ifelse(clinicalDat$Age_range =="≥60", ">=60", clinicalDat$Age_range)
# correct clinicalDat$Last_prev_therapy
clinicalDat$Last_prev_therapy <- str_replace_all(clinicalDat$Last_prev_therapy, "-", ".")
clinicalDat$Last_prev_therapy <- format(as.Date(my(clinicalDat$Last_prev_therapy)),"01-%b-20%y")
write.csv2(clinicalDat, "data/data_for_analysis/Table_S4.csv", row.names = FALSE)
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] stringr_1.4.0 lubridate_1.8.0
[3] dplyr_1.0.7 SingleCellExperiment_1.16.0
[5] SummarizedExperiment_1.24.0 Biobase_2.54.0
[7] GenomicRanges_1.46.1 GenomeInfoDb_1.30.1
[9] IRanges_2.28.0 S4Vectors_0.32.3
[11] BiocGenerics_0.40.0 MatrixGenerics_1.6.0
[13] matrixStats_0.61.0 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.8 lattice_0.20-45 getPass_0.2-2
[4] ps_1.6.0 assertthat_0.2.1 rprojroot_2.0.2
[7] digest_0.6.29 utf8_1.2.2 plyr_1.8.6
[10] R6_2.5.1 evaluate_0.14 httr_1.4.2
[13] pillar_1.7.0 zlibbioc_1.40.0 rlang_1.0.0
[16] rstudioapi_0.13 whisker_0.4 callr_3.7.0
[19] jquerylib_0.1.4 Matrix_1.4-0 rmarkdown_2.11
[22] RCurl_1.98-1.5 DelayedArray_0.20.0 compiler_4.1.2
[25] httpuv_1.6.5 xfun_0.29 pkgconfig_2.0.3
[28] htmltools_0.5.2 tidyselect_1.1.1 tibble_3.1.6
[31] GenomeInfoDbData_1.2.7 fansi_1.0.2 crayon_1.4.2
[34] later_1.3.0 bitops_1.0-7 grid_4.1.2
[37] jsonlite_1.7.3 lifecycle_1.0.1 DBI_1.1.2
[40] git2r_0.29.0 magrittr_2.0.2 cli_3.1.1
[43] stringi_1.7.6 XVector_0.34.0 reshape2_1.4.4
[46] fs_1.5.2 promises_1.2.0.1 bslib_0.3.1
[49] ellipsis_0.3.2 vctrs_0.3.8 generics_0.1.2
[52] tools_4.1.2 glue_1.6.1 purrr_0.3.4
[55] processx_3.5.2 fastmap_1.1.0 yaml_2.2.2
[58] knitr_1.37 sass_0.4.0