Exploratory analysis the AML CART-T drug screen project
Junyan Lu
Last updated: 2023-06-06
Checks: 5 1
Knit directory: combiCART/analysis/
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Load packages and dataset
library(readxl)
library(tidyverse)
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
source("../code/helper.R")
load("../output/screenData.RData")Compare the effect of Control, CAR-T only and NT only treatment on viability
plotTab <- filter(screenData, Drug == "DMSO")
ggplot(plotTab, aes(x=donorConstruct, y=normVal,dodge = Tcell)) +
geom_boxplot(position = position_dodge(width = 0.8)) +
geom_point(aes(col=Tcell), position = position_dodge(width = 0.8)) +
facet_wrap(~cellTime, scale = "free",ncol=2) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
ylab("Viability")
Visualize difference among T cell donors
Compare the trend of cell line with different donors
**Do cells cultured with T-cells from different donors show consistent changes?
donorTab <- filter(screenData, Drug == "DMSO", Tcell != "NEG") %>%
mutate(cellTimeConst = paste0(cellTime,"_",construct)) %>%
group_by(Tcell, cellTimeConst, donor) %>%
summarise(medVal = median(normVal))
ggplot(donorTab, aes(x=cellTimeConst, y=medVal, color = donor)) +
geom_point() +
geom_line(aes(group = donor)) +
facet_wrap(~Tcell, ncol=1) + theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
ylab("Median viability") + xlab("Cell")
PCA
**Do cells cultured with T-cells from different donors show consistent changes?
viabMat <- filter(screenData, Tcell!="NEG", Drug != "DMSO") %>%
mutate(nameConc = paste0(name,"_",conc)) %>%
group_by(nameConc, plateID) %>% summarise(val = mean(normVal)) %>%
pivot_wider(names_from = nameConc, values_from = val) %>%
column_to_rownames("plateID") %>% as.matrix()
pcTab <- prcomp(viabMat)$x %>%
as_tibble(rownames= "plateID") %>%
left_join(distinct(screenData, plateID, donor, cell, time, construct)) %>%
mutate(celLTimeConst = paste0(cell,time,construct))
ggplot(pcTab, aes(x=PC1, y=PC2)) +
geom_point(aes(col = cell, shape = construct, size = time)) +
geom_line(aes(group = celLTimeConst), linetype = "dashed")
Dotted lines connect the cell models (cellline + time +
construct) with the same T-cell donor. It can be seen that the cell
models with the same donors are loosely grouped together. Indicating
there’s some similarity in their response profile
Visualize Drug effect
Normalized by tumor only wells
Dose-response curves
plotTab <- filter(screenData, Drug != "DMSO", Tcell == "NT")
pList <- lapply(unique(sort(plotTab$cellTime)),function(nn) {
eachTab <- filter(plotTab, cellTime == nn)
ggplot(eachTab, aes(x=conc, y=normVal)) +
geom_point() +
geom_line(aes(group = donorConstruct, color = donorConstruct)) +
facet_wrap(~Drug, scale = "free_x", ncol=4) +
scale_x_log10() +
xlab("Concentration") + ylab("Viability") +
ggtitle(nn)
})
jyluMisc::makepdf(pList, "../docs/drugNT_doseResponse.pdf", ncol=1, nrow=1, height = 15, width = 10)Heatmap of the summarised AUC of drug effect
aucTab <- filter(screenData, Drug != "DMSO", Tcell == "NT") %>%
group_by(cell, time, donor, construct, Drug, plateID) %>%
summarise(auc = calcAUC(normVal, conc)) %>% ungroup()
colAnno <- distinct(aucTab, plateID, cell, time, donor, construct) %>%
column_to_rownames("plateID") %>% data.frame()
viabMat <- aucTab %>% select(plateID, auc, Drug) %>%
pivot_wider(names_from = plateID, values_from = auc) %>%
column_to_rownames("Drug") %>% as.matrix()Without row normalization, colores indicate viability
pheatmap::pheatmap(viabMat, annotation_col = colAnno, show_colnames = TRUE, clustering_method = "ward.D2")
With row normalization, colores indicate row-wise z-scores
pheatmap::pheatmap(viabMat, annotation_col = colAnno, show_colnames = TRUE, clustering_method = "ward.D2", scale = "row")
Normalized by NT only wells
Dose-response curves
plotTab <- filter(screenData, Drug != "DMSO", Tcell == "NT")
pList <- lapply(unique(sort(plotTab$cellTime)),function(nn) {
eachTab <- filter(plotTab, cellTime == nn)
ggplot(eachTab, aes(x=conc, y=normVal.NT)) +
geom_point() +
geom_line(aes(group = donorConstruct, color = donorConstruct)) +
facet_wrap(~Drug, scale = "free_x", ncol=4) +
scale_x_log10() +
xlab("Concentration") + ylab("Viability") +
ggtitle(nn)
})
jyluMisc::makepdf(pList, "../docs/drugNT_doseResponse_NTnorm.pdf", ncol=1, nrow=1, height = 15, width = 10)drugNT_doseResponse_NTnorm.pdf
It seems normalization by NT only wells decreased variation among samples.
Heatmap of the summarised AUC of drug effect
aucTab <- filter(screenData, Drug != "DMSO", Tcell == "NT") %>%
group_by(cell, time, donor, construct, Drug, plateID) %>%
summarise(auc = calcAUC(normVal.NT, conc)) %>% ungroup()
colAnno <- distinct(aucTab, plateID, cell, time, donor, construct) %>%
column_to_rownames("plateID") %>% data.frame()
viabMat <- aucTab %>% select(plateID, auc, Drug) %>%
pivot_wider(names_from = plateID, values_from = auc) %>%
column_to_rownames("Drug") %>% as.matrix()Without row normalization, colores indicate viability
pheatmap::pheatmap(viabMat, annotation_col = colAnno, show_colnames = TRUE, clustering_method = "ward.D2")
With row normalization, colores indicate row-wise z-scores
pheatmap::pheatmap(viabMat, annotation_col = colAnno, show_colnames = TRUE, clustering_method = "ward.D2", scale = "row")
Visualize Drug + CAR effect
Dose-response curves
plotTab <- filter(screenData, Drug != "DMSO", Tcell == "CAR")
pList <- lapply(unique(sort(plotTab$cellTime)),function(nn) {
eachTab <- filter(plotTab, cellTime == nn)
ggplot(eachTab, aes(x=conc, y=normVal)) +
geom_point() +
geom_line(aes(group = donorConstruct, color = donorConstruct)) +
facet_wrap(~Drug, scale = "free_x", ncol=4) +
scale_x_log10() +
xlab("Concentration") + ylab("Viability") +
ggtitle(nn)
})
jyluMisc::makepdf(pList, "../docs/drugCAR_doseResponse.pdf", ncol=1, nrow=1, height = 15, width = 10)Heatmap of AUC of drug effect
aucTab <- filter(screenData, Drug != "DMSO", Tcell == "CAR") %>%
group_by(cell, time, donor, construct, Drug, plateID) %>%
summarise(auc = calcAUC(normVal, conc)) %>% ungroup()
colAnno <- distinct(aucTab, plateID, cell, time, donor, construct) %>%
column_to_rownames("plateID") %>% data.frame()
viabMat <- aucTab %>% select(plateID, auc, Drug) %>%
pivot_wider(names_from = plateID, values_from = auc) %>%
column_to_rownames("Drug") %>% as.matrix()Without row normalization, colores indicate viability
pheatmap::pheatmap(viabMat, annotation_col = colAnno, show_colnames = TRUE, clustering_method = "ward.D2")
With row normalization, colores indicate row-wise z-scores
pheatmap::pheatmap(viabMat, annotation_col = colAnno, show_colnames = TRUE, clustering_method = "ward.D2", scale = "row")
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 haven_2.5.0
[27] xfun_0.31 crayon_1.5.2
[29] RCurl_1.98-1.7 jsonlite_1.8.3
[31] survival_3.4-0 zoo_1.8-10
[33] glue_1.6.2 survminer_0.4.9
[35] gtable_0.3.0 gargle_1.2.0
[37] zlibbioc_1.42.0 XVector_0.36.0
[39] DelayedArray_0.22.0 car_3.1-0
[41] BiocGenerics_0.42.0 abind_1.4-5
[43] scales_1.2.0 pheatmap_1.0.12
[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 RColorBrewer_1.1-3
[59] gplots_3.1.3 ellipsis_0.3.2
[61] pkgconfig_2.0.3 farver_2.1.1
[63] sass_0.4.2 dbplyr_2.2.1
[65] utf8_1.2.2 tidyselect_1.1.2
[67] labeling_0.4.2 rlang_1.0.6
[69] later_1.3.0 munsell_0.5.0
[71] cellranger_1.1.0 tools_4.2.0
[73] visNetwork_2.1.0 cachem_1.0.6
[75] cli_3.4.1 generics_0.1.3
[77] broom_1.0.0 evaluate_0.15
[79] fastmap_1.1.0 yaml_2.3.5
[81] knitr_1.39 fs_1.5.2
[83] survMisc_0.5.6 caTools_1.18.2
[85] mime_0.12 slam_0.1-50
[87] xml2_1.3.3 compiler_4.2.0
[89] rstudioapi_0.13 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 IRanges_2.30.0
[113] codetools_0.2-18 MASS_7.3-58
[115] gtools_3.9.3 exactRankTests_0.8-35
[117] assertthat_0.2.1 SummarizedExperiment_1.26.1
[119] rprojroot_2.0.3 withr_2.5.0
[121] multcomp_1.4-19 S4Vectors_0.34.0
[123] GenomeInfoDbData_1.2.8 parallel_4.2.0
[125] hms_1.1.1 grid_4.2.0
[127] rmarkdown_2.14 MatrixGenerics_1.8.1
[129] carData_3.0-5 googledrive_2.0.0
[131] ggpubr_0.4.0 git2r_0.30.1
[133] maxstat_0.7-25 sets_1.0-21
[135] Biobase_2.56.0 shiny_1.7.4
[137] lubridate_1.8.0