Last updated: 2023-06-14

Checks: 5 1

Knit directory: combiCART/analysis/

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Here, synergy means only if the observed combination effect is stronger than expected additive effect of drug (+NT) and CAR-T, i.e one plus one larger than two, they will be considered as candidates

Load packages and dataset

library(readxl)
library(tidyverse)
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
source("../code/helper.R")
load("../output/screenData.RData")

Use drug effect normalized by NT only wells for the down-stream analysis

screenData <- mutate(screenData, normVal = normVal.NT)

Calculate combination index using Bliss independence model

Get combination effect

comTab <- filter(screenData, Drug !="DMSO", Tcell == "CAR") %>%
  select(plateID, Drug, conc, normVal) %>%
  dplyr::rename(viabObs = normVal)

drugTab <- filter(screenData, Drug != "DMSO", Tcell == "NT") %>%
  select(plateID, Drug, conc, normVal) %>%
  dplyr::rename(viabDrug = normVal)

carTab <- filter(screenData, Drug == "DMSO", Tcell == "CAR") %>%
  select(plateID, normVal) %>% group_by(plateID) %>%
  summarise(viabCar = median(normVal)) 
synTab <- comTab %>% left_join(drugTab, by =c("plateID","Drug","conc")) %>%
  left_join(carTab, by = "plateID") %>%
  mutate(viabExp = viabDrug*viabCar) %>%
  mutate(CI = viabObs-viabExp,
         logCI = log10(viabObs/viabExp))

Remove combination if single agent effect (either drug or CAR) is already very strong (viability < 0.2) if all three donors

excludeTab <- synTab %>% mutate(toxic = (viabDrug < 0.2 | viabCar < 0.2)) %>%
  left_join(distinct(screenData, plateID, construct, cellTime), by = "plateID") %>%
  group_by(cellTime, construct, Drug, conc) %>% summarise(n=sum(toxic)) %>% ungroup()
synTab <- left_join(synTab, distinct(screenData, plateID, construct, cellTime), by = "plateID") %>%
  left_join(excludeTab, by =c("cellTime","construct","Drug","conc")) %>%
  filter(n<3) %>%
  dplyr::select(-cellTime, -construct, -n)

Visualize synergistic index per concentration in a heatmap

24 hours

plotTab <- left_join(synTab, distinct(screenData, plateID, time, construct)) %>%
  filter(time == "24h") %>%
  arrange(construct, plateID) %>%
  mutate(plateID = factor(plateID, levels = unique(plateID)))

ggplot(plotTab, aes(x=factor(conc), y=plateID, fill = CI)) +
  geom_tile() +
  facet_wrap(~Drug, ncol=4, scale = "free_x") +
  scale_fill_gradient2(low ="red",high="blue",mid="white")

**Positive CI indicates antagonistic effect and negative CI indicates synergistic effect)

48 hours

plotTab <- left_join(synTab, distinct(screenData, plateID, time, construct)) %>%
  filter(time == "48h") %>%
  arrange(construct, plateID) %>%
  mutate(plateID = factor(plateID, levels = unique(plateID)))

ggplot(plotTab, aes(x=factor(conc), y=plateID, fill = CI)) +
  geom_tile() +
  facet_wrap(~Drug, ncol=4, scale = "free_x") +
  scale_fill_gradient2(low ="red",high="blue",mid="white")

**Positive CI indicates antagonistic effect and negative CI indicates synergistic effect)

Rank drugs based on their median combination index (for every concentration)

Only drugs with median CI < 0 (synergistic or additive) are shown

24 hours

plotTab <- left_join(synTab, distinct(screenData, plateID, time, construct)) %>%
  left_join(distinct(screenData, Drug, conc, concStep), by = c("Drug","conc")) %>%
  filter(time == "24h") %>%
  mutate(drugConc = paste0(Drug,"_", conc)) %>%
  group_by(drugConc) %>% mutate(medVal = median(CI)) %>% arrange(medVal) %>%
  ungroup()%>%
  mutate(drugConc = factor(drugConc, levels = unique(drugConc))) %>%
  filter(medVal < 0)

ggplot(plotTab, aes(x=drugConc, y=CI, fill = concStep)) +
  ggbeeswarm::geom_quasirandom(shape = 21, alpha =0.5) +
  stat_summary(fun.y = median, fun.ymin = median, fun.ymax = median, color = "darkred",
          geom = "crossbar", width = 0.5) + 
  coord_cartesian(ylim=c(-0.5,0.5)) + 
  geom_hline(yintercept = 0, linetype = "dashed") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

48 hours

plotTab <- left_join(synTab, distinct(screenData, plateID, time, construct)) %>%
  left_join(distinct(screenData, Drug, conc, concStep), by = c("Drug","conc")) %>%
  filter(time == "48h") %>%
  mutate(drugConc = paste0(Drug,"_", conc)) %>%
  group_by(drugConc) %>% mutate(medVal = median(CI)) %>% arrange(medVal) %>%
  ungroup()%>%
  mutate(drugConc = factor(drugConc, levels = unique(drugConc))) %>%
  filter(medVal < 0)

ggplot(plotTab, aes(x=drugConc, y=CI, fill = concStep)) +
  ggbeeswarm::geom_quasirandom(shape = 21, alpha =0.5) +
  stat_summary(fun.y = median, fun.ymin = median, fun.ymax = median, color = "darkred",
          geom = "crossbar", width = 0.5) + 
  coord_cartesian(ylim=c(-0.5,0.5)) + 
  geom_hline(yintercept = 0, linetype = "dashed") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

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$syn,na.rm = TRUE), anta = sum(tab$anta,na.rm = TRUE)))
}

ciTabSum <- group_by(synTab, plateID, Drug) %>% nest() %>%
  mutate(res = map(data, ~sumSyn(.$viabExp, .$viabObs))) %>%
  unnest(res) %>% select(-data)

Visualization of synergistic and antagoistic effect

plotSynScatter <- function(plotTab, labelPercent = 0.05) {
  
  synCut <-  quantile(plotTab$syn, labelPercent)
  antaCut <- quantile(plotTab$anta, 1-labelPercent)
  
  plotTabSyn <- plotTab %>% mutate(labelText = ifelse(syn < synCut, plateID,""), type = "Synergistic effect") %>%
    mutate(score = syn)
  plotTabAnta <- plotTab %>% mutate(labelText = ifelse(anta > antaCut, plateID,""), type = "Antagonistic effect") %>%
    mutate(score = anta)
  plotComb <- bind_rows(plotTabSyn, plotTabAnta)
  
  p <- ggplot(plotComb, aes(x=Drug, y=score, label = labelText)) +
    geom_point(aes(col= type), alpha=0.5) + ggrepel::geom_text_repel(max.overlaps = Inf) +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 90, hjust=1, vjust = 0.5)) +
    scale_color_manual(values = c("Synergistic effect" = "red", "Antagonistic effect" = "blue")) +
    #facet_wrap(~type, ncol=2)
    ylab("Combination Score") + xlab("") +
    theme(legend.position = "bottom")
  return(p)
}

Scatter plot

Top 1% synergistics or antagonistic effect are labelled.

plotSynScatter(ciTabSum,0.01)

Matrix visualization

plotSynMatrix <- function(ciTabSum, cut1 = 0.1, cut2 = 0.2) {
  synCut1 <-  quantile(ciTabSum$syn, cut1)
  antaCut1 <- quantile(ciTabSum$anta, 1-cut1)
  synCut2 <-  quantile(ciTabSum$syn, cut2)
  antaCut2 <- quantile(ciTabSum$anta, 1-cut2)

  synOrder <- hcOrder(ciTabSum$Drug, ciTabSum$plateID, ciTabSum$syn)
  antaOrder <- hcOrder(ciTabSum$Drug, ciTabSum$plateID, ciTabSum$anta)

  plotTabSyn <- ciTabSum %>% mutate(degree = case_when(
    syn <= synCut1 ~ "**",
    syn <= synCut2 ~ "*",
    TRUE ~ ""
  )) %>% mutate(Drug = factor(Drug, levels = synOrder$row),
                plateID = factor(plateID, levels = synOrder$col))

  plotTabAnta <- ciTabSum %>% mutate(degree = case_when(
    anta >= antaCut1 ~ "**",
    anta >= antaCut2 ~ "*",
    TRUE ~ ""
  )) %>% mutate(Drug = factor(Drug, levels = antaOrder$row),
                plateID = factor(plateID, levels = antaOrder$col))

  p1 <- ggplot(plotTabSyn, aes(x=Drug, y=plateID, label = degree)) +
    geom_tile(aes(fill = syn)) +
    scale_fill_gradient(low="red", high="white", name = "syngergy") +
    geom_text() + ggtitle("Synergistic effect") +
    theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5))

  p2 <- ggplot(plotTabAnta, aes(x=Drug, y=plateID, label = degree)) +
    geom_tile(aes(fill = anta)) +
    scale_fill_gradient(low="white", high="blue", name = "antagonism") +
    geom_text() + ggtitle("Antagonistic effect") +
    theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5))

  p <- cowplot::plot_grid(p2, p1, ncol=2)
  return(p)
}
plotSynMatrix(ciTabSum, 0.01, 0.05)

one star indicates top 5% synergy or antagonism, two stars indicate top 1% synergy or antagonism

Plot combination curve for each drug, witin each drug, cell models are ranked based on CI

drugs <- sort(unique(synTab$Drug))

pList <- lapply(drugs, function(eachDrug) {
  ciRank <- filter(ciTabSum, Drug==eachDrug) %>%
    arrange(syn)
  
  plotTab <- filter(synTab, Drug == eachDrug) %>%
    select(plateID, Drug, conc, viabDrug, viabCar, viabExp, viabObs) %>%
    pivot_longer(-c("plateID","Drug","conc"), names_to = "type", values_to = "viab") %>%
    mutate(plateID = factor(plateID, levels = ciRank$plateID)) %>%
    mutate(type = factor(type, levels = c("viabDrug","viabCar","viabExp","viabObs")))
  
  ggplot(plotTab, aes(x=factor(conc), y=viab, group=type)) +
    geom_line(aes(col = type)) +
    facet_wrap(~plateID,ncol=5) +
    #scale_linetype_manual(values = c(combine = "solid", `single` = "dotted"), name = "combination") +
    scale_color_manual(values =c(viabDrug = "orange", viabCar="darkgreen",
                                 viabExp = "red", viabObs="blue"),
                       labels=c("drug only ","Car only","expected effect","observed effect"),
                       name = "treatment") + 
    ggtitle(eachDrug) +
    ylab("Viability") + xlab("Concentration")
})
jyluMisc::makepdf(pList, "../docs/combo_effect_noToxic.pdf",nrow = 1, ncol = 1, height = 18, width = 12)

combo_effect_noToxic.pdf

Testing for significant synergies using paired t-test and consider donors as replicates

Test For each individual concentration

testTab <- synTab %>%
  left_join(distinct(screenData, plateID, cell, time, construct, donor), by = "plateID") %>%
  mutate(cellTimeConst = paste0(cell,"_",time,"_",construct)) 

resTab <- group_by(testTab, cell, time, construct, Drug, conc, cellTimeConst) %>% nest() %>%
  mutate(m = map(data, ~t.test(.$viabObs, .$viabExp, paired=TRUE))) %>%
  mutate(res = map(m, broom::tidy)) %>%
  unnest(res) %>%
  select(Drug, conc, cell, time, construct, cellTimeConst, estimate, p.value) %>%
  arrange(p.value) %>% ungroup() %>%
  mutate(p.adj = p.adjust(p.value, method ="BH"))

P-value heatmap for summarising the results

24 hours

Heatmaps
resSub <- filter(resTab, time == "24h") 

pList <- lapply(unique(resSub$Drug), function(dd) {
  plotTab <- filter(resSub, Drug == dd) %>% 
    mutate(effect = ifelse(estimate >0, "antagonisim","synergy"),
           ifSig = case_when(p.value <= 0.01 & p.adj > 0.1 ~ "*",
                             p.adj <= 0.1 ~ "**",
                             p.value > 0.01 ~ "")) %>%
    arrange(construct,cellTimeConst) %>%
    mutate(cellTimeConst = factor(cellTimeConst, levels = unique(cellTimeConst)))
  
  pHeat <- ggplot(plotTab, aes(x=factor(conc),y=cellTimeConst, fill = estimate)) +
    geom_tile() + 
    geom_text(aes(label = ifSig), vjust =0.5) +
    scale_x_discrete(expand = c(0,0)) + scale_y_discrete(expand = c(0,0)) +
    scale_fill_gradient2(low ="red",high="blue",mid="white", midpoint = 0, name = "CI") +
    theme_minimal() +
    ggtitle(dd) +theme(plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
                       axis.text.x = element_blank(), panel.grid = element_blank(),
                       axis.title.x = element_blank()) +
    ylab("")

  
  pBox <- ggplot(plotTab, aes(x=factor(conc), y=estimate)) +
    geom_point(shape =1,size=2) +
    stat_summary(fun.y = median, fun.ymin = median, fun.ymax = median, color = "darkred",
                 geom = "crossbar", width = 0.5) +
    geom_hline(yintercept = 0, linetype = "dashed") +
    ylab("Average CI") + xlab("Concentration")+
    theme_bw()
  
  cowplot::plot_grid(pHeat, pBox, align = "v", axis ="lr",ncol=1, rel_heights = c(1,0.6))
})

cowplot::plot_grid(plotlist = pList, ncol=4)

one star indicates P value < 0.01, two starts indicate FDR < 10%
negative CI indicates synergy, positive CI indicates antagonism
The plot below the heatmap shows the median CI for each concentration

48 hours

Heatmaps
resSub <- filter(resTab, time == "48h") 

pList <- lapply(unique(resSub$Drug), function(dd) {
  plotTab <- filter(resSub, Drug == dd) %>% 
    mutate(effect = ifelse(estimate >0, "antagonisim","synergy"),
           ifSig = case_when(p.value <= 0.01 & p.adj > 0.1 ~ "*",
                             p.adj <= 0.1 ~ "**",
                             p.value > 0.01 ~ "")) %>%
    arrange(construct,cellTimeConst) %>%
    mutate(cellTimeConst = factor(cellTimeConst, levels = unique(cellTimeConst)))
  
  pHeat <- ggplot(plotTab, aes(x=factor(conc),y=cellTimeConst, fill = estimate)) +
    geom_tile() + 
    geom_text(aes(label = ifSig), vjust =0.5) +
    scale_x_discrete(expand = c(0,0)) + scale_y_discrete(expand = c(0,0)) +
    scale_fill_gradient2(low ="red",high="blue",mid="white", midpoint = 0, name = "CI") +
    theme_minimal() +
    ggtitle(dd) +theme(plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
                       axis.text.x = element_blank(), panel.grid = element_blank(),
                       axis.title.x = element_blank()) +
    ylab("")

  
  pBox <- ggplot(plotTab, aes(x=factor(conc), y=estimate)) +
    geom_point(shape =1,size=2) +
    stat_summary(fun.y = median, fun.ymin = median, fun.ymax = median, color = "darkred",
                 geom = "crossbar", width = 0.5) +
    geom_hline(yintercept = 0, linetype = "dashed") +
    ylab("Average CI") + xlab("Concentration")+
    theme_bw()
  
  cowplot::plot_grid(pHeat, pBox, align = "v", axis ="lr",ncol=1, rel_heights = c(1,0.6))
})

cowplot::plot_grid(plotlist = pList, ncol=4)

Test for summarised effect across concentrations (AUC)

testTab <- synTab %>%
  left_join(distinct(screenData, plateID, cell, time, construct, donor), by = "plateID") %>%
  mutate(cellTimeConst = paste0(cell,"_",time,"_",construct)) %>%
  group_by(Drug, cellTimeConst, donor) %>%
  summarise(viabObs = calcAUC(viabObs, conc),
            viabExp = calcAUC(viabExp, conc))

resTab <- group_by(testTab, Drug, cellTimeConst) %>% nest() %>%
  mutate(m = map(data, ~t.test(.$viabObs, .$viabExp, paired=TRUE))) %>%
  mutate(res = map(m, broom::tidy)) %>%
  unnest(res) %>%
  select(Drug, cellTimeConst, estimate, p.value) %>%
  arrange(p.value) %>% ungroup() %>%
  mutate(p.adj = p.adjust(p.value, method ="BH"))

P-value heatmap for summarising the results

plotTab <- resTab %>% 
  mutate(effect = ifelse(estimate >0, "antagonism","synergy"),
         ifSig = case_when(p.value <= 0.01 & p.adj > 0.1 ~ "*",
                           p.adj <= 0.1 ~ "**",
                           p.value > 0.01 ~ ""))

ggplot(plotTab, aes(x=Drug ,y=cellTimeConst, fill = estimate)) +
  geom_tile() + 
  geom_text(aes(label = ifSig), vjust =0.5) +
  scale_fill_gradient2(low ="red",high="blue",mid="white", midpoint = 0, name = "effect size") +
  theme(axis.text.x = element_text(angle = 90, hjust=1, vjust = 0.5))

one star indicates P value < 0.01, two starts indicate FDR < 10%
negative CI indicates synergy, positive CI indicates antagonism

Volcano plot

plotTab <- resTab %>% 
  mutate(effect = ifelse(p.value <= 0.05,
                         ifelse(estimate > 0, "antagonism","synergy"),
                         "n.s.")) %>%
  mutate(labText = cellTimeConst)

ggplot(plotTab, aes(x=estimate ,y=-log10(p.value), color = effect)) +
  geom_point() +
  ggrepel::geom_text_repel(data = filter(plotTab, p.value <=0.05, estimate <0), aes(label = labText)) +
  scale_color_manual(values = c(synergy = "red", antagonism = "blue", "n.s." = "grey50")) +
  theme_bw() +
  xlab("average CI") +
  facet_wrap(~Drug)

Synergy with P-value < 0.05 are colored

Line plots for significant pairs (P-value < 0.01)

resTab.sig <- filter(plotTab, estimate < 0, p.value < 0.01)

pList <- lapply(seq(nrow(resTab.sig)), function(i) {
  rec <- resTab.sig[i,]
  eachTab <- filter(testTab, Drug == rec$Drug, cellTimeConst == rec$cellTimeConst) %>%
    pivot_longer(c("viabObs","viabExp"), names_to = "type", values_to = "value") %>%
    mutate(type = ifelse(type == "viabObs", "observed","expected"))
  ggplot(eachTab, aes(x=type, y=value, col = donor)) + 
    geom_point() + 
    geom_line(aes(group =donor, col = donor)) +
    xlab("") + ylab("Viability") +
    ggtitle(sprintf("%s in %s", rec$Drug, rec$cellTimeConst)) +
    theme(legend.position = "bottom")
})

cowplot::plot_grid(plotlist = pList, ncol=3)


sessionInfo()
R version 4.2.0 (2022-04-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur/Monterey 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/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] gridExtra_2.3   forcats_0.5.1   stringr_1.4.1   dplyr_1.0.9    
 [5] purrr_0.3.4     readr_2.1.2     tidyr_1.2.0     tibble_3.1.8   
 [9] ggplot2_3.4.1   tidyverse_1.3.2 readxl_1.4.0   

loaded via a namespace (and not attached):
  [1] backports_1.4.1             fastmatch_1.1-3            
  [3] drc_3.0-1                   jyluMisc_0.1.5             
  [5] workflowr_1.7.0             igraph_1.3.4               
  [7] shinydashboard_0.7.2        splines_4.2.0              
  [9] BiocParallel_1.30.3         GenomeInfoDb_1.32.2        
 [11] TH.data_1.1-1               digest_0.6.30              
 [13] htmltools_0.5.4             fansi_1.0.3                
 [15] magrittr_2.0.3              googlesheets4_1.0.0        
 [17] cluster_2.1.3               tzdb_0.3.0                 
 [19] limma_3.52.2                modelr_0.1.8               
 [21] matrixStats_0.62.0          sandwich_3.0-2             
 [23] piano_2.12.0                colorspace_2.0-3           
 [25] rvest_1.0.2                 ggrepel_0.9.1              
 [27] haven_2.5.0                 xfun_0.31                  
 [29] crayon_1.5.2                RCurl_1.98-1.7             
 [31] jsonlite_1.8.3              survival_3.4-0             
 [33] zoo_1.8-10                  glue_1.6.2                 
 [35] survminer_0.4.9             gtable_0.3.0               
 [37] gargle_1.2.0                zlibbioc_1.42.0            
 [39] XVector_0.36.0              DelayedArray_0.22.0        
 [41] car_3.1-0                   BiocGenerics_0.42.0        
 [43] abind_1.4-5                 scales_1.2.0               
 [45] mvtnorm_1.1-3               DBI_1.1.3                  
 [47] relations_0.6-12            rstatix_0.7.0              
 [49] Rcpp_1.0.9                  plotrix_3.8-2              
 [51] xtable_1.8-4                km.ci_0.5-6                
 [53] stats4_4.2.0                DT_0.23                    
 [55] htmlwidgets_1.5.4           httr_1.4.3                 
 [57] fgsea_1.22.0                gplots_3.1.3               
 [59] ellipsis_0.3.2              pkgconfig_2.0.3            
 [61] farver_2.1.1                sass_0.4.2                 
 [63] dbplyr_2.2.1                utf8_1.2.2                 
 [65] tidyselect_1.1.2            labeling_0.4.2             
 [67] rlang_1.0.6                 later_1.3.0                
 [69] visNetwork_2.1.0            munsell_0.5.0              
 [71] cellranger_1.1.0            tools_4.2.0                
 [73] cachem_1.0.6                cli_3.4.1                  
 [75] generics_0.1.3              broom_1.0.0                
 [77] evaluate_0.15               fastmap_1.1.0              
 [79] yaml_2.3.5                  knitr_1.39                 
 [81] fs_1.5.2                    survMisc_0.5.6             
 [83] caTools_1.18.2              mime_0.12                  
 [85] slam_0.1-50                 xml2_1.3.3                 
 [87] compiler_4.2.0              rstudioapi_0.13            
 [89] beeswarm_0.4.0              ggsignif_0.6.3             
 [91] marray_1.74.0               reprex_2.0.1               
 [93] bslib_0.4.1                 stringi_1.7.8              
 [95] highr_0.9                   lattice_0.20-45            
 [97] Matrix_1.5-4                KMsurv_0.1-5               
 [99] shinyjs_2.1.0               vctrs_0.5.2                
[101] pillar_1.8.0                lifecycle_1.0.3            
[103] jquerylib_0.1.4             data.table_1.14.8          
[105] cowplot_1.1.1               bitops_1.0-7               
[107] httpuv_1.6.6                GenomicRanges_1.48.0       
[109] R6_2.5.1                    promises_1.2.0.1           
[111] KernSmooth_2.23-20          vipor_0.4.5                
[113] IRanges_2.30.0              codetools_0.2-18           
[115] MASS_7.3-58                 gtools_3.9.3               
[117] exactRankTests_0.8-35       assertthat_0.2.1           
[119] SummarizedExperiment_1.26.1 rprojroot_2.0.3            
[121] withr_2.5.0                 multcomp_1.4-19            
[123] S4Vectors_0.34.0            GenomeInfoDbData_1.2.8     
[125] parallel_4.2.0              hms_1.1.1                  
[127] grid_4.2.0                  rmarkdown_2.14             
[129] MatrixGenerics_1.8.1        carData_3.0-5              
[131] googledrive_2.0.0           ggpubr_0.4.0               
[133] git2r_0.30.1                maxstat_0.7-25             
[135] sets_1.0-21                 Biobase_2.56.0             
[137] shiny_1.7.4                 lubridate_1.8.0            
[139] ggbeeswarm_0.6.0