Identify disease specific drug responses
Junyan Lu
6 May 2022
Last updated: 2022-05-06
Checks: 6 1
Knit directory: EMBL2016/analysis/
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Load libraries and dataset
Load datasets
Disease stratification by drug response phenotypes
T-sne plot
Get disease types with at least 3 cases
diagTab <- screenData %>% distinct(patientID, diagnosis) %>%
group_by(diagnosis) %>% summarise(n=length(patientID)) %>%
arrange(n)
filter(diagTab, n>=3)# A tibble: 6 × 2
diagnosis n
<chr> <int>
1 B-NHL 3
2 B-PLL 3
3 T-PLL 8
4 MCL 10
5 AML 14
6 CLL 132
UMAP (alternative for T-SNE)
#Calculate UMAP layout, which can better retain global structure
plotTab <- smallvis(t(viabMat), method = "umap", perplexity = 30,
eta = 0.01, epoch_callback = FALSE, verbose = FALSE)
colnames(plotTab) <- c("x","y")
plotTab <- plotTab %>% as.tibble() %>% mutate(Patient.ID = colnames(viabMat)) %>%
left_join(select(patMeta, Patient.ID, diagnosis, IGHV.status, project), by = "Patient.ID") %>%
mutate(diagnosis = as.character(diagnosis), project = as.character(project)) %>%
mutate(diagnosis = ifelse(diagnosis == "CLL" & IGHV.status == "U", "U-CLL",diagnosis)) %>%
filter(!is.na(diagnosis))UMAP plot (better retain global structure) 
Hierarchical clustering & heatmap
All samples
NA values will cause problem of hierarchical clustering
Evaluate the completeness of data points
plotTab <- filter(screenData, ! Drug %in% c("DMSO","PBS")) %>%
#filter(diagnosis %in% c("CLL","MCL","T-PLL","MZL","B-NHL","B-PLL","Sezary")) %>% #only disease with more than 2 cases
group_by(patientID, Drug) %>% summarise(viab = mean(viab.auc, na.rm=TRUE)) %>% ungroup()
sumTab <- group_by(plotTab, Drug) %>% summarise(noNA = sum(!is.na(viab)), fracNoNA = sum(!is.na(viab))/length(patientID)) %>%
arrange(desc(fracNoNA)) %>% ungroup() %>% mutate(Drug = factor(Drug, levels = Drug))
ggplot(sumTab, aes(x=Drug, y = fracNoNA)) + geom_point() +
ylab("Fraction of successfully fitted values") +
theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5))
In this part, only disease that have at least 2 cases will be included. There are 145 out of 166 samples passed the criteria.
Only include samples that have at least 50% non-missing vlaues
sumPatTab <- group_by(plotTab, patientID) %>%
summarise(noNA = sum(!is.na(viab)), fracNoNA = sum(!is.na(viab))/length(patientID)) %>%
arrange(desc(fracNoNA)) %>% ungroup()
usePat <- filter(sumPatTab, fracNoNA >= 0.5)$patientIDHow many samples will be included?
length(usePat)[1] 186How many drugs will be included?
length(filter(sumTab, fracNoNA > 0.2)$Drug)[1] 408Prepare viability matrix
plotMat <- plotTab %>% filter(Drug %in% filter(sumTab, fracNoNA > 0.2)$Drug, patientID %in% usePat) %>%
spread(key = patientID, value = "viab") %>% data.frame() %>%
column_to_rownames("Drug") %>% as.matrix()
#Prepare data
#plotMat.complete <- plotMat[complete.cases(plotMat),]
#center by mean and scale by MAD
#plotMat <- jyluMisc::mscale(plotMat, censor = 4, useMad = TRUE)Heatmap (row centered by median and scaled by MAD) 
PDF version: heatmap_all-1.pdf
Only plot the tree for identifying clusters 
PDF version: tree_all-1.pdf
Boxplot with the same order of rows in the heatmap above
dOrder <- p$tree_row$labels[p$tree_row$order]
plotTabBox <- plotMat %>% as_tibble(rownames = "Drug") %>%
pivot_longer(-Drug, names_to = "patID", values_to = "auc") %>%
mutate(Drug = factor(Drug, levels = dOrder))
ggplot(plotTabBox, aes(x=Drug, y=auc)) +
geom_boxplot() +
coord_flip() +
theme_bw() +
theme(axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
panel.grid = element_blank()) +
xlab("") +
ylab("AUC") 
PDF version: sideBox_all-1.pdf
Only CLL
Evaluate the completeness of data points
plotTab <- filter(screenData, ! Drug %in% c("DMSO","PBS")) %>%
filter(diagnosis %in% c("CLL")) %>%
group_by(patientID, Drug) %>% summarise(viab = mean(viab.auc, na.rm=TRUE)) %>% ungroup()
sumTab <- group_by(plotTab, Drug) %>% summarise(noNA = sum(!is.na(viab)), fracNoNA = sum(!is.na(viab))/length(patientID)) %>%
arrange(desc(fracNoNA)) %>% ungroup() %>% mutate(Drug = factor(Drug, levels = Drug))
ggplot(sumTab, aes(x=Drug, y = fracNoNA)) + geom_point() +
ylab("Fraction of successfully fitted values") +
theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5))
How many drugs will be included?
length(filter(sumTab, fracNoNA > 0.2)$Drug)[1] 408Prepare viability matrix
plotMat <- plotTab %>% filter(Drug %in% filter(sumTab, fracNoNA > 0.2)$Drug) %>%
spread(key = patientID, value = "viab") %>% data.frame() %>%
column_to_rownames("Drug") %>% as.matrix()
#Prepare data
#plotMat.complete <- plotMat[complete.cases(plotMat),]
#center by mean and scale by MAD
#plotMat <- jyluMisc::mscale(plotMat, censor = 4, useMad = TRUE)Heatmap plot (row centered by median and scaled by MAD) 
PDF version: heatmap_CLL-1.pdf
Only plot the tree for identifying clusters 
PDF version: tree_CLL-1.pdf
Boxplot with the same order of rows in the heatmap above
dOrder <- p$tree_row$labels[p$tree_row$order]
plotTabBox <- plotMat %>% as_tibble(rownames = "Drug") %>%
pivot_longer(-Drug, names_to = "patID", values_to = "auc") %>%
mutate(Drug = factor(Drug, levels = dOrder))
ggplot(plotTabBox, aes(x=Drug, y=auc)) +
geom_boxplot() +
coord_flip() +
theme_bw() +
theme(axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
panel.grid = element_blank()) +
xlab("") +
ylab("AUC") 
PDF version: sideBox_cll-1.pdf
Disease-specific drug effect
[1] "AML"
[1] "MCL"
[1] "T-PLL"
[1] "B-NHL"
[1] "B-PLL"p value heatmap 
PDF version
Blue means increased sensitivity compared to CLL, while red means increased resistance. (only drugs that have associations in at least 3 concentrations are shown, FDR = 10%)
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
[4] purrr_0.3.4 readr_2.1.1 tidyr_1.1.4
[7] tibble_3.1.6 tidyverse_1.3.1 limma_3.50.0
[10] readxl_1.3.1 gtable_0.3.0 glmnet_4.1-3
[13] Matrix_1.4-0 ggbeeswarm_0.6.0 jyluMisc_0.1.5
[16] colorspace_2.0-2 RColorBrewer_1.1-2 ggrepel_0.9.1
[19] ggplot2_3.3.5 smallvis_0.0.0.9000 Rtsne_0.15
[22] cowplot_1.1.1 genefilter_1.76.0 pheatmap_1.0.12
[25] reshape2_1.4.4 gridExtra_2.3 Biobase_2.54.0
[28] BiocGenerics_0.40.0
loaded via a namespace (and not attached):
[1] utf8_1.2.2 shinydashboard_0.7.2
[3] tidyselect_1.1.1 RSQLite_2.2.9
[5] AnnotationDbi_1.56.2 htmlwidgets_1.5.4
[7] grid_4.1.2 BiocParallel_1.28.3
[9] maxstat_0.7-25 munsell_0.5.0
[11] codetools_0.2-18 DT_0.20
[13] withr_2.4.3 highr_0.9
[15] knitr_1.37 rstudioapi_0.13
[17] stats4_4.1.2 ggsignif_0.6.3
[19] MatrixGenerics_1.6.0 labeling_0.4.2
[21] git2r_0.29.0 slam_0.1-50
[23] GenomeInfoDbData_1.2.7 KMsurv_0.1-5
[25] farver_2.1.0 bit64_4.0.5
[27] rprojroot_2.0.2 vctrs_0.3.8
[29] generics_0.1.1 TH.data_1.1-0
[31] xfun_0.29 sets_1.0-20
[33] R6_2.5.1 GenomeInfoDb_1.30.0
[35] bitops_1.0-7 cachem_1.0.6
[37] fgsea_1.20.0 DelayedArray_0.20.0
[39] assertthat_0.2.1 promises_1.2.0.1
[41] scales_1.1.1 multcomp_1.4-18
[43] beeswarm_0.4.0 sandwich_3.0-1
[45] workflowr_1.7.0 rlang_0.4.12
[47] splines_4.1.2 rstatix_0.7.0
[49] broom_0.7.11 BiocManager_1.30.16
[51] yaml_2.2.1 abind_1.4-5
[53] modelr_0.1.8 backports_1.4.1
[55] httpuv_1.6.5 tools_4.1.2
[57] relations_0.6-10 ellipsis_0.3.2
[59] gplots_3.1.1 jquerylib_0.1.4
[61] Rcpp_1.0.8 plyr_1.8.6
[63] visNetwork_2.1.0 zlibbioc_1.40.0
[65] RCurl_1.98-1.5 ggpubr_0.4.0
[67] S4Vectors_0.32.3 zoo_1.8-9
[69] SummarizedExperiment_1.24.0 haven_2.4.3
[71] cluster_2.1.2 exactRankTests_0.8-34
[73] fs_1.5.2 magrittr_2.0.1
[75] data.table_1.14.2 reprex_2.0.1
[77] survminer_0.4.9 mvtnorm_1.1-3
[79] matrixStats_0.61.0 hms_1.1.1
[81] shinyjs_2.1.0 mime_0.12
[83] evaluate_0.14 xtable_1.8-4
[85] XML_3.99-0.8 IRanges_2.28.0
[87] shape_1.4.6 compiler_4.1.2
[89] KernSmooth_2.23-20 crayon_1.4.2
[91] htmltools_0.5.2 later_1.3.0
[93] tzdb_0.2.0 lubridate_1.8.0
[95] DBI_1.1.2 dbplyr_2.1.1
[97] MASS_7.3-55 BiocStyle_2.22.0
[99] car_3.0-12 cli_3.1.1
[101] marray_1.72.0 parallel_4.1.2
[103] igraph_1.2.11 GenomicRanges_1.46.1
[105] pkgconfig_2.0.3 km.ci_0.5-2
[107] piano_2.10.0 xml2_1.3.3
[109] foreach_1.5.1 annotate_1.72.0
[111] vipor_0.4.5 bslib_0.3.1
[113] XVector_0.34.0 drc_3.0-1
[115] rvest_1.0.2 digest_0.6.29
[117] Biostrings_2.62.0 rmarkdown_2.11
[119] cellranger_1.1.0 fastmatch_1.1-3
[121] survMisc_0.5.5 shiny_1.7.1
[123] gtools_3.9.2 lifecycle_1.0.1
[125] jsonlite_1.7.3 carData_3.0-5
[127] fansi_1.0.2 pillar_1.6.5
[129] lattice_0.20-45 KEGGREST_1.34.0
[131] fastmap_1.1.0 httr_1.4.2
[133] plotrix_3.8-2 survival_3.2-13
[135] glue_1.6.1 FNN_1.1.3
[137] png_0.1-7 iterators_1.0.13
[139] bit_4.0.4 stringi_1.7.6
[141] sass_0.4.0 blob_1.2.2
[143] caTools_1.18.2 memoise_2.0.1