Last updated: 2021-02-04

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Knit directory: melanoma_publication_old_data/

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Ignored files:
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    Ignored:    data/200323_TMA_256_Hot Cold_Clinical Data_Updated Response Data_For Collaborators_latest updated_Mar_2020_for_Coxph_modeling.csv
    Ignored:    data/colour_vector.rds
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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/06_Protein_Ki67status.Rmd) and HTML (docs/06_Protein_Ki67status.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd 9442cb9 toobiwankenobi 2020-12-22 add all new files
Rmd 7affca0 toobiwankenobi 2020-10-13 clean branch and add suppfigure 2
Rmd d8819f2 toobiwankenobi 2020-10-08 read new data (nuclei expansion) and adapt scripts

Preparations

knitr::opts_chunk$set(echo = TRUE, message= FALSE)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())

Load libraries

sapply(list.files("code/helper_functions", full.names = TRUE), source)
        code/helper_functions/calculateSummary.R
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visible FALSE                                   
        code/helper_functions/censor_dat.R
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        code/helper_functions/detect_mRNA_expression.R
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visible FALSE                                         
        code/helper_functions/DistanceToClusterCenter.R
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visible FALSE                                          
        code/helper_functions/findClusters.R
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visible FALSE                               
        code/helper_functions/findCommunity.R
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visible FALSE                                
        code/helper_functions/getCellCount.R
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visible FALSE                               
        code/helper_functions/getInfoFromString.R
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visible FALSE                                    
        code/helper_functions/getSpotnumber.R
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visible FALSE                                
        code/helper_functions/plotBarFracCluster.R
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visible FALSE                                     
        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/plotCellFractions.R
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        code/helper_functions/plotDist.R
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        code/helper_functions/scatter_function.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(dplyr)
library(ggplot2)
library(reshape2)
library(ggridges)

Load the single cell experiment object and the image metadata

sce_protein <- readRDS(file = "data/sce_protein.rds")
sce_rna <- readRDS(file = "data/sce_RNA.rds")

Ki-67 status on tumor cells

Define Ki67+ cells

y <- c(rep(1:10,16),rep(11,7))

# add the group information to the sce object
sce_protein$groups <- y[colData(sce_protein)$ImageNumber]

# now we use the function written by Nils
plotDist(sce_protein["Ki67",], plot_type = "ridges", 
         colour_by = "groups", split_by = "rows", 
         exprs_values = "asinh") +
  geom_vline(xintercept = 1)

We define the Ki67 treshold at 1 (asinh) count. This corresponds to a mean pixel count of 1.175 per cell. This is robust enough to detect all Ki67+ cells (verified using histoCAT web version). All cells that are visible identifiable as Ki67+ have a mean count of clearly more than 1.

# remove exisiting columns
sce_rna$Ki67_fraction <- NULL
sce_rna$Ki67_status <- NULL
sce_protein$Ki67_fraction <- NULL
sce_protein$Ki67_status <- NULL

sce_protein$Ki67 <- ifelse(assay(sce_protein["Ki67",], "asinh") > 1, "positive", "negative")

# calculate percentage of positive tumor cells over all tumor cells
cur_df <- data.frame(colData(sce_protein)) %>%
  group_by(Description, celltype, Ki67) %>%
  summarise(n = n()) %>%
  filter(celltype == "Tumor") %>%
  reshape2::dcast(Description ~ Ki67, value.var = "n", fill = 0) %>%
  group_by(Description) %>%
  mutate(Ki67_fraction = (positive / (positive + negative)))

# add staging (<6% low, 6-10% intermediate, >10% high)
cur_df$Ki67_status <- ""
cur_df[cur_df$Ki67_fraction < 0.06,]$Ki67_status <- "low"
cur_df[cur_df$Ki67_fraction >= 0.06 & cur_df$Ki67_fraction <= 0.1,]$Ki67_status <- "intermediate"
cur_df[cur_df$Ki67_fraction > 0.1,]$Ki67_status <- "high"

# remove unwanted columns from cur_df
cur_df[,2:3] <- NULL

# add to colData
cur_prot <- data.frame(colData(sce_protein))
cur_prot <- left_join(cur_prot, cur_df, by="Description")

# add to rna set based on protein staging
cur_rna <- data.frame(colData(sce_rna))
cur_rna <- left_join(cur_rna, cur_df, by="Description")

# add new column to SCE
sce_protein$Ki67_status <- cur_prot$Ki67_status
sce_rna$Ki67_status <- cur_rna$Ki67_status
sce_protein$Ki67_fraction <- cur_prot$Ki67_fraction
sce_rna$Ki67_fraction <- cur_rna$Ki67_fraction

# relevel
sce_protein$Ki67_status <- factor(sce_protein$Ki67_status, levels = c("low", "intermediate", "high"))
sce_rna$Ki67_status <- factor(sce_rna$Ki67_status, levels = c("low", "intermediate", "high"))

Save SCE

saveRDS(sce_protein, file = "data/sce_protein.rds")
saveRDS(sce_rna, file = "data/sce_RNA.rds")

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] ggridges_0.5.3              reshape2_1.4.4             
 [3] ggplot2_3.3.3               dplyr_1.0.2                
 [5] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
 [7] Biobase_2.50.0              GenomicRanges_1.42.0       
 [9] GenomeInfoDb_1.26.2         IRanges_2.24.1             
[11] S4Vectors_0.28.1            BiocGenerics_0.36.0        
[13] MatrixGenerics_1.2.0        matrixStats_0.57.0         
[15] workflowr_1.6.2            

loaded via a namespace (and not attached):
 [1] tidyselect_1.1.0       xfun_0.20              purrr_0.3.4           
 [4] lattice_0.20-41        colorspace_2.0-0       vctrs_0.3.6           
 [7] generics_0.1.0         htmltools_0.5.0        yaml_2.2.1            
[10] rlang_0.4.10           later_1.1.0.1          pillar_1.4.7          
[13] withr_2.3.0            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        munsell_0.5.0          gtable_0.3.0          
[22] evaluate_0.14          labeling_0.4.2         knitr_1.30            
[25] httpuv_1.5.4           Rcpp_1.0.5             promises_1.1.1        
[28] scales_1.1.1           DelayedArray_0.16.0    XVector_0.30.0        
[31] farver_2.0.3           fs_1.5.0               digest_0.6.27         
[34] stringi_1.5.3          rprojroot_2.0.2        grid_4.0.3            
[37] tools_4.0.3            bitops_1.0-6           magrittr_2.0.1        
[40] RCurl_1.98-1.2         tibble_3.0.4           crayon_1.3.4          
[43] whisker_0.4            pkgconfig_2.0.3        ellipsis_0.3.1        
[46] Matrix_1.3-2           rmarkdown_2.6          rstudioapi_0.13       
[49] R6_2.5.0               git2r_0.28.0           compiler_4.0.3