Last updated: 2022-04-28

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Knit directory: combiDLBCL/analysis/

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Load libraries and datasets

Preprocessing

Get base drug only data

screenSub <- filter(screenData, Drug_A == "DMSO", Drug_B.Conc !=0)

Get the effect of CHP single agent, Pola single agent and CHP+Pola

comTab <- filter(screenSub, Drug_B == "CHP_Pola") %>%
  select(Name, Drug_B, normVal, Drug_B.ConcStep) %>%
  dplyr::rename(viabObs = normVal) %>%
  mutate(Drug_A = "CHP", Drug_B = "Polatuzumab", 
         Drug_A.ConcStep = Drug_B.ConcStep)

drugATab <- filter(screenSub, Drug_B == "CHP") %>%
  select(Name, Drug_B, normVal, Drug_B.ConcStep, Drug_B.Conc) %>%
  dplyr::rename(viabA = normVal) %>%
  dplyr::rename(Drug_A = Drug_B, Drug_A.ConcStep = Drug_B.ConcStep,
                Drug_A.Conc = Drug_B.Conc)

drugBTab <- filter(screenSub, Drug_B == "Polatuzumab") %>%
  select(Name, Drug_B, Drug_B.Conc, normVal, Drug_B.ConcStep, Drug_B.Conc) %>%
  dplyr::rename(viabB = normVal)

Calculate CI for the combination of CHP and Pola

synTab <- comTab %>% left_join(drugATab, by =c("Name","Drug_A","Drug_A.ConcStep")) %>%
  left_join(drugBTab, by = c("Name","Drug_B","Drug_B.ConcStep")) %>%
  mutate(viabExp = viabA*viabB) %>%
  mutate(CI = viabObs-viabExp,
         logCI = log10(viabObs/viabExp))

Visualize CI for cell line and concentration

orders <- hcOrder(synTab$Name, synTab$Drug_B.ConcStep, synTab$CI)
plotTab <- synTab %>% mutate(Name = factor(Name, levels = orders$row))
ggplot(plotTab, aes(x=Drug_A.ConcStep, y=Name, fill = CI)) +
  geom_tile() +
  scale_fill_gradient2(low ="red",high="blue",mid="white")

Very few combinatorial effect. Mostly antagonistic

Summarise CI

Calculate synergistic and antagonistic effect separately, using a similar way as bayesyngergy package

sumSyn <- function(viabExp, viabObs) {
  tab <- tibble(viabExp=viabExp, viabObs = viabObs) %>%
    mutate(syn = min(0, viabObs - viabExp),
           anta = max(0, viabObs - viabExp))
  return(tibble(syn = sum(tab$syn), anta = sum(tab$anta)))
}

ciTabSum <- group_by(synTab, Name, Drug_A) %>% nest() %>%
  mutate(res = map(data, ~sumSyn(.$viabExp, .$viabObs))) %>%
  unnest(res) %>% select(-data) %>% arrange(syn) %>%
  mutate(cellName = ifelse(syn<0 | anta > 3,Name,""))
ggplot(ciTabSum, aes(x=syn,y=anta, label = cellName)) +
  geom_point() + ggrepel::geom_text_repel() +
  xlab("Synergistic score") + ylab("Antagonistic score")

Plot combination curve for each drug, drug are ranked based on the sigificance correlation, cell lines are ranked based on CI

pList <- lapply(unique(ciTabSum$Name), function(eachCell) {
  plotTab <- filter(synTab, Name == eachCell) %>%
    select(Name, Drug_A, Drug_A.ConcStep, viabA, viabB, viabExp, viabObs) %>%
    pivot_longer(-c("Name","Drug_A","Drug_A.ConcStep"), names_to = "type", values_to = "viab") %>%
    mutate(lineType = ifelse(type %in% c("viabA","viabB"), "single","combine"))
  
  ggplot(plotTab, aes(x=Drug_A.ConcStep, y=viab, group=type)) +
    geom_line(aes(col = type, linetype = lineType)) +
    scale_linetype_manual(values = c(combine = "solid", `single` = "dotted"), name = "combination", guide = "none") +
    scale_color_manual(values =c(viabA = "orange", viabB="darkgreen",
                                 viabExp = "red", viabObs="blue"),
                       labels=c("CHP only ","Pola only","expected effect","observed effect"),
                       name = "treatment") + 
    ggtitle(eachCell) +
    ylab("Viability") + xlab("Concentration step")
})
cowplot::plot_grid(plotlist= pList, ncol=3)


sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] forcats_0.5.1   stringr_1.4.0   dplyr_1.0.7     purrr_0.3.4    
[5] readr_2.1.1     tidyr_1.1.4     tibble_3.1.6    ggplot2_3.3.5  
[9] tidyverse_1.3.1

loaded via a namespace (and not attached):
 [1] ggrepel_0.9.1    Rcpp_1.0.8       lubridate_1.8.0  assertthat_0.2.1
 [5] rprojroot_2.0.2  digest_0.6.29    utf8_1.2.2       R6_2.5.1        
 [9] cellranger_1.1.0 backports_1.4.1  reprex_2.0.1     evaluate_0.14   
[13] highr_0.9        httr_1.4.2       pillar_1.6.5     rlang_0.4.12    
[17] readxl_1.3.1     rstudioapi_0.13  jquerylib_0.1.4  rmarkdown_2.11  
[21] labeling_0.4.2   munsell_0.5.0    broom_0.7.11     compiler_4.1.2  
[25] httpuv_1.6.5     modelr_0.1.8     xfun_0.29        pkgconfig_2.0.3 
[29] htmltools_0.5.2  tidyselect_1.1.1 workflowr_1.7.0  fansi_1.0.2     
[33] crayon_1.4.2     tzdb_0.2.0       dbplyr_2.1.1     withr_2.4.3     
[37] later_1.3.0      grid_4.1.2       jsonlite_1.7.3   gtable_0.3.0    
[41] lifecycle_1.0.1  DBI_1.1.2        git2r_0.29.0     magrittr_2.0.1  
[45] scales_1.1.1     cli_3.1.1        stringi_1.7.6    farver_2.1.0    
[49] fs_1.5.2         promises_1.2.0.1 xml2_1.3.3       bslib_0.3.1     
[53] ellipsis_0.3.2   generics_0.1.1   vctrs_0.3.8      cowplot_1.1.1   
[57] tools_4.1.2      glue_1.6.1       hms_1.1.1        fastmap_1.1.0   
[61] yaml_2.2.1       colorspace_2.0-2 rvest_1.0.2      knitr_1.37      
[65] haven_2.4.3      sass_0.4.0