Last updated: 2022-05-11
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library(cowplot)
library(tidyverse)
── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
✓ ggplot2 3.3.5 ✓ purrr 0.3.4
✓ tibble 3.1.6 ✓ dplyr 1.0.7
✓ tidyr 1.1.4 ✓ stringr 1.4.0
✓ readr 2.1.1 ✓ forcats 0.5.1
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()
load("../data/patMeta.RData")
# Defien a color scheme, based on ggsci_NEJM panel, for the paper
colList <- c("#BC3C29FF","#0072B5FF","#E18727FF","#20854EFF","#7876B1FF","#6F99ADFF","#FFDC91FF","#EE4C97FF")
Load filtered drug screenData
load("../output/screenData.RData")
includePat <- unique(screenData$patientID)
Load drug screen data
#load("./screenData.RData") #preprocessed
load("../data/newEMBL_20210806.RData") #full data set
screenData <- emblNew
screenData$patientID <- screenData$patID
screenData <- filter(screenData, patID!="ATP",
!str_detect(patID,"-2"))
allPat <- unique(c(as.character(screenData$patientID), c("P0045","P0069")))
geneMat <- patMeta[match(allPat, patMeta$Patient.ID),] %>%
select(Patient.ID, IGHV.status, del10p:inv_9) %>%
mutate_if(is.factor, as.character) %>%
mutate(IGHV.status = ifelse(!is.na(IGHV.status), ifelse(IGHV.status == "M",1,0),NA)) %>%
mutate_at(vars(-Patient.ID), as.numeric) %>% #assign a few unknown mutated cases to wildtype
data.frame() %>% column_to_rownames("Patient.ID")
#remove genes with all NA values
geneMat <- geneMat[,colSums(!is.na(geneMat)) >0]
dim(geneMat)
[1] 210 154
geneTab <- geneMat %>% as_tibble(rownames = "patID") %>%
mutate(diagnosis = patMeta[match(patID, patMeta$Patient.ID),]$diagnosis) %>%
mutate(includeFinal = patID %in% includePat) %>%
select(patID, includeFinal ,diagnosis, colnames(geneMat))
write_csv2(geneTab,"../docs/genomic_table_allPats.csv")
Mutations that will be tested
geneMat <- geneMat[,apply(geneMat,2, function(x) sum(x %in% 1, na.rm = TRUE))>=5]
#Remove some dubious annotations
geneMat <- geneMat[,!colnames(geneMat) %in% c("del5IgH","gain2p","IgH_break")]
colnames(geneMat)
[1] "IGHV.status" "del11q" "del12q" "del13q" "del14q"
[6] "del15q" "del17p" "del17q" "del1q" "del2q"
[11] "del3p" "del3q" "del6q" "del8p" "del9p"
[16] "del9q" "gain17q" "gain18q" "gain3q" "gain8q"
[21] "trisomy12" "trisomy19" "NOTCH1" "ATM" "BRAF"
[26] "CHD2" "CSMD3" "DDX3X" "EGR2" "FAT4"
[31] "FBXW7" "HIST1H1E" "IKZF3" "KRAS" "MED12"
[36] "NFKBIE" "RYR2" "SF3B1" "TP53" "U1"
[41] "FLT3_ITD"
Separate CNV table and mutation table
cnvCol <- colnames(geneMat)[grepl("del|trisomy|IGHV",colnames(geneMat))]
cnvMat <- geneMat[,cnvCol]
mutMat <- geneMat[,!colnames(geneMat) %in% cnvCol]
cnvMat <- cnvMat[,names(sort(colSums(cnvMat == 1,na.rm=TRUE)))]
mutMat <- mutMat[,names(sort(colSums(mutMat == 1, na.rm=TRUE)))]
geneMat <- cbind(mutMat,cnvMat)
geneMat[is.na(geneMat)] <- -1
#put IGHV the last col
allGene <- colnames(geneMat)
geneMat <- geneMat[,c(allGene[allGene != "IGHV.status"],"IGHV.status")]
Sort patient based on CNVs
sortTab <- function(sumTab) {
i <- ncol(sumTab)
#print(i)
if (i == 1) {
return(rownames(sumTab)[order(sumTab[,i])])
}
allLevel <- sort(unique(sumTab[,i]))
orderRow <- lapply(allLevel, function(n) {
sortTab(sumTab[sumTab[,i] %in% n, seq(1,i-1), drop = FALSE])
}) %>% unlist() %>% c()
return(orderRow)
}
sortedPat <- rev(sortTab(geneMat))
Prepare table for plot
geneMat <- geneMat[,colnames(geneMat)!="IGHV.status"]
plotTab <- geneMat %>% as_tibble(rownames="patID") %>% mutate_all(as.character) %>%
pivot_longer(-patID, names_to = "var", values_to = "value") %>%
filter(var != "IGHV.status") %>%
mutate(status = case_when(
value == -1 ~ "NA",
value == 0 ~ "WT",
value == 1 & var %in% cnvCol ~ "CNV",
value == 1 & !var %in% cnvCol ~ "mutation"
)) %>%
mutate(var = factor(var, levels = colnames(geneMat)),
patID = factor(patID, levels = sortedPat),
status = factor(status, levels =c("WT","CNV","mutation","NA")))
formatedName <- lapply(levels(plotTab$var), function(n) {
if(n %in% cnvCol) {
n
} else {
bquote(italic(.(n)))
}
})
Plot mutation matrix
pMain <- ggplot(plotTab, aes(x=patID, y = var, fill = status)) +
geom_tile(color = "grey80") +
theme_minimal() +
scale_fill_manual(values = c("mutation" = colList[5],
"CNV"= colList[4],
"WT" ="white",
"NA" = "grey80"),
name = "aberrations") +
scale_y_discrete(labels = formatedName) +
theme(axis.text.x = element_blank(),
axis.text.y = element_text(size=12),
panel.grid = element_blank(), legend.position = "right") +
ylab("") + xlab("")
#pMain
length(unique(plotTab$patID))
[1] 210
Diagnosis
diagTab <- select(patMeta, Patient.ID, diagnosis) %>%
mutate(patID = Patient.ID, status = diagnosis, type = "diagnosis") %>%
filter(patID %in% sortedPat) %>%
mutate(patID = factor(patID, levels = sortedPat)) %>%
select(patID, type, status) %>%
mutate(status = ifelse(status %in% c("CLL","T-PLL","AML","MCL"), status, "other")) %>%
mutate(status = factor(status, levels = c("CLL","T-PLL","AML","MCL","other")))
pDiag <- ggplot(diagTab, aes(x=patID, y = type, fill = status)) +
geom_tile(color = NA) +
theme_void() + xlab("") + ylab("") +
coord_cartesian(expand = FALSE) +
scale_fill_brewer(palette = "Set2", name = "diagnosis", type = "qualitative") +
theme(axis.text.y = element_text(face = "bold", size=11),
axis.ticks.length.y = unit(0.05,"npc"))
#pDiag
IGHV status
ighvTab <- select(patMeta, Patient.ID, IGHV.status) %>%
mutate(patID = Patient.ID, status = IGHV.status, type = "IGHV") %>%
filter(patID %in% sortedPat) %>%
mutate(patID = factor(patID, levels = sortedPat)) %>%
select(patID, type, status) %>%
mutate(status = replace_na(status, "NA"))
pIGHV <- ggplot(ighvTab, aes(x=patID, y = type, fill = status)) +
geom_tile(color = NA) +
theme_void() + xlab("") + ylab("") +
coord_cartesian(expand = FALSE) +
scale_fill_manual(values = c(M="black",U="white","NA" = "grey80"), name = "IGHV") +
theme(axis.text.y = element_text(face = "bold", size=11),
axis.ticks.length.y = unit(0.05,"npc"))
#pIGHV
Sex
sexTab <- select(patMeta, Patient.ID, gender) %>%
mutate(patID = Patient.ID, status = as.character(gender), type = "sex") %>%
filter(patID %in% sortedPat) %>%
mutate(patID = factor(patID, levels = sortedPat),
status = case_when(status %in% "m" ~ "male",
status %in% "f" ~ "female")) %>%
select(patID, type, status)
pSex <- ggplot(sexTab, aes(x=patID, y = type, fill = status)) +
geom_tile(color = NA) +
theme_void() + xlab("") + ylab("") +
coord_cartesian(expand = FALSE) +
scale_fill_manual(values = c(male=colList[7],female=colList[5]), name = "sex") +
theme(axis.text.y = element_text(face = "bold",size=11),
axis.ticks.length.y = unit(0.05,"npc"))
#pSex
Pretreatment
treatTab <- treatmentTab %>% filter(sampleID %in% unique(screenData$sampleID)) %>%
select(Patient.ID, pretreat) %>%
mutate(treatment = case_when(pretreat %in% 1 ~ "yes",
pretreat %in% 0 ~ "no",
is.na(pretreat) ~ "NA")) %>%
mutate(patID = Patient.ID, status = as.character(treatment), type = "treatment") %>%
filter(patID %in% sortedPat) %>%
mutate(patID = factor(patID, levels = sortedPat)) %>%
select(patID, type, status)
pTreat <- ggplot(treatTab, aes(x=patID, y = type, fill = status)) +
geom_tile(color = NA) +
theme_void() + xlab("") + ylab("") +
coord_cartesian(expand = FALSE) +
scale_fill_manual(values = c(yes = "black", no = "white","NA" = "grey80"), name = "treatment") +
theme(axis.text.y = element_text(face = "bold",size=11),
axis.ticks.length.y = unit(0.05,"npc"))
#pTreat
age
agePlotTab <- ageTab %>% filter(sampleID %in% unique(screenData$sampleID)) %>%
select(patientID, age) %>%
mutate(patID = patientID, status = age, type = "age") %>%
filter(patID %in% sortedPat) %>%
mutate(patID = factor(patID, levels = sortedPat)) %>%
select(patID, type, status)
pAge <- ggplot(agePlotTab, aes(x=patID, y = type, fill = status)) +
geom_tile(color = NA) +
theme_void() + xlab("") + ylab("") +
coord_cartesian(expand = FALSE) +
scale_fill_viridis_b(name = "age") +
theme(axis.text.y = element_text(face = "bold",size=11),
axis.ticks.length.y = unit(0.05,"npc"))
#pAge
Combine all plots
lMain <- get_legend(pMain + geom_tile(color = "black") )
lAge <- get_legend(pAge+ geom_tile(color = "black") )
lSex <- get_legend(pSex+ geom_tile(color = "black") )
lDiag <- get_legend(pDiag + geom_tile(color = "black"))
lIGHV <- get_legend(pIGHV+ geom_tile(color = "black") )
lTreat <- get_legend(pTreat+ geom_tile(color = "black") )
noLegend <- theme(legend.position = "none")
mainPlot <- plot_grid(pDiag + noLegend, pAge + noLegend, pSex + noLegend,
pTreat + noLegend,pIGHV + noLegend,
pMain + noLegend, ncol=1, align = "v",
rel_heights = c(rep(1,5),25))
legendPlot <- plot_grid(lAge, lSex, lIGHV, lTreat, lMain, lDiag ,ncol=2, align = "hv")
plot_grid(mainPlot, NULL, plot_grid(legendPlot,NULL, ncol=1, rel_heights = c(1,0.3)), ncol=3, rel_widths = c(1,0.05, 0.2))
ggsave("../docs/cohortComposition_allSamples.pdf", height=7, width=13)
PDF version: cohortComposition_allSamples.pdf
screenData <- filter(screenData, diagnosis %in% "CLL")
geneMat <- patMeta[match(unique(screenData$patientID), patMeta$Patient.ID),] %>%
select(Patient.ID, IGHV.status, del11p:U1) %>%
mutate_if(is.factor, as.character) %>%
mutate(IGHV.status = ifelse(!is.na(IGHV.status), ifelse(IGHV.status == "M",1,0),NA)) %>%
mutate_at(vars(-Patient.ID), as.numeric) %>% #assign a few unknown mutated cases to wildtype
data.frame() %>% column_to_rownames("Patient.ID")
geneMat <- geneMat[,apply(geneMat,2, function(x) sum(x %in% 1, na.rm = TRUE))>=5]
Mutations that will be tested
#Remove some dubious annotations
geneMat <- geneMat[,!colnames(geneMat) %in% c("del5IgH","gain2p","IgH_break")]
colnames(geneMat)
[1] "IGHV.status" "del11q" "del13q" "del14q" "del15q"
[6] "del17p" "del6q" "del8p" "del9q" "gain8q"
[11] "trisomy12" "trisomy19" "NOTCH1" "ATM" "BRAF"
[16] "DDX3X" "EGR2" "FAT4" "FBXW7" "HIST1H1E"
[21] "IKZF3" "KRAS" "MED12" "NFKBIE" "RYR2"
[26] "SF3B1" "TP53" "U1"
Separate CNV table and mutation table
cnvCol <- colnames(geneMat)[grepl("del|trisomy|IGHV",colnames(geneMat))]
cnvMat <- geneMat[,cnvCol]
mutMat <- geneMat[,!colnames(geneMat) %in% cnvCol]
cnvMat <- cnvMat[,names(sort(colSums(cnvMat == 1,na.rm=TRUE)))]
mutMat <- mutMat[,names(sort(colSums(mutMat == 1, na.rm=TRUE)))]
geneMat <- cbind(mutMat,cnvMat)
geneMat[is.na(geneMat)] <- -1
#put IGHV the last col
allGene <- colnames(geneMat)
geneMat <- geneMat[,c(allGene[allGene != "IGHV.status"],"IGHV.status")]
Sort patient based on CNVs
sortTab <- function(sumTab) {
i <- ncol(sumTab)
#print(i)
if (i == 1) {
return(rownames(sumTab)[order(sumTab[,i])])
}
allLevel <- sort(unique(sumTab[,i]))
orderRow <- lapply(allLevel, function(n) {
sortTab(sumTab[sumTab[,i] %in% n, seq(1,i-1), drop = FALSE])
}) %>% unlist() %>% c()
return(orderRow)
}
sortedPat <- rev(sortTab(geneMat))
Prepare table for plot
geneMat <- geneMat[,colnames(geneMat)!="IGHV.status"]
plotTab <- geneMat %>% as_tibble(rownames="patID") %>% mutate_all(as.character) %>%
pivot_longer(-patID, names_to = "var", values_to = "value") %>%
filter(var != "IGHV.status") %>%
mutate(status = case_when(
value == -1 ~ "NA",
value == 0 ~ "WT",
value == 1 & var %in% cnvCol ~ "CNV",
value == 1 & !var %in% cnvCol ~ "mutation"
)) %>%
mutate(var = factor(var, levels = colnames(geneMat)),
patID = factor(patID, levels = sortedPat),
status = factor(status, levels =c("WT","CNV","mutation","NA")))
formatedName <- lapply(levels(plotTab$var), function(n) {
if(n %in% cnvCol) {
n
} else {
bquote(italic(.(n)))
}
})
length(unique(plotTab$patID))
[1] 146
Plot mutation matrix
pMain <- ggplot(plotTab, aes(x=patID, y = var, fill = status)) +
geom_tile(color = "grey80") +
theme_minimal() +
scale_fill_manual(values = c("mutation" = colList[5],
"CNV"= colList[4],
"WT" ="white",
"NA" = "grey80"),
name = "aberrations") +
scale_y_discrete(labels = formatedName) +
theme(axis.text.x = element_blank(),
axis.text.y = element_text(size=12),
panel.grid = element_blank(), legend.position = "right") +
ylab("") + xlab("")
#pMain
IGHV status
ighvTab <- select(patMeta, Patient.ID, IGHV.status) %>%
mutate(patID = Patient.ID, status = IGHV.status, type = "IGHV") %>%
filter(patID %in% sortedPat) %>%
mutate(patID = factor(patID, levels = sortedPat)) %>%
select(patID, type, status) %>%
mutate(status=replace_na(status, "NA"))
pIGHV <- ggplot(ighvTab, aes(x=patID, y = type, fill = status)) +
geom_tile(color = NA) +
theme_void() + xlab("") + ylab("") +
coord_cartesian(expand = FALSE) +
scale_fill_manual(values = c(M="black",U="white","NA" = "grey80"), name = "IGHV") +
theme(axis.text.y = element_text(face = "bold", size=11),
axis.ticks.length.y = unit(0.05,"npc"))
Sex
sexTab <- select(patMeta, Patient.ID, gender) %>%
mutate(patID = Patient.ID, status = as.character(gender), type = "sex") %>%
filter(patID %in% sortedPat) %>%
mutate(patID = factor(patID, levels = sortedPat),
status = case_when(status %in% "m" ~ "male",
status %in% "f" ~ "female")) %>%
select(patID, type, status)
pSex <- ggplot(sexTab, aes(x=patID, y = type, fill = status)) +
geom_tile(color = NA) +
theme_void() + xlab("") + ylab("") +
coord_cartesian(expand = FALSE) +
scale_fill_manual(values = c(male=colList[7],female=colList[5]), name = "sex") +
theme(axis.text.y = element_text(face = "bold",size=11),
axis.ticks.length.y = unit(0.05,"npc"))
#pSex
Pretreatment
treatTab <- treatmentTab %>% filter(sampleID %in% unique(screenData$sampleID)) %>%
select(Patient.ID, pretreat) %>%
mutate(treatment = case_when(pretreat %in% 1 ~ "yes",
pretreat %in% 0 ~ "no",
is.na(pretreat) ~ "NA")) %>%
mutate(patID = Patient.ID, status = as.character(treatment), type = "treatment") %>%
filter(patID %in% sortedPat) %>%
mutate(patID = factor(patID, levels = sortedPat)) %>%
select(patID, type, status)
pTreat <- ggplot(treatTab, aes(x=patID, y = type, fill = status)) +
geom_tile(color = NA) +
theme_void() + xlab("") + ylab("") +
coord_cartesian(expand = FALSE) +
scale_fill_manual(values = c(yes = "black", no = "white","NA" = "grey80"), name = "treatment") +
theme(axis.text.y = element_text(face = "bold",size=11),
axis.ticks.length.y = unit(0.05,"npc"))
#pTreat
age
agePlotTab <- ageTab %>% filter(sampleID %in% unique(screenData$sampleID)) %>%
select(patientID, age) %>%
mutate(patID = patientID, status = age, type = "age") %>%
filter(patID %in% sortedPat) %>%
mutate(patID = factor(patID, levels = sortedPat)) %>%
select(patID, type, status)
pAge <- ggplot(agePlotTab, aes(x=patID, y = type, fill = status)) +
geom_tile(color = NA) +
theme_void() + xlab("") + ylab("") +
coord_cartesian(expand = FALSE) +
scale_fill_viridis_b(name = "age") +
theme(axis.text.y = element_text(face = "bold",size=11),
axis.ticks.length.y = unit(0.05,"npc"))
#pAge
Combine all plots
lMain <- get_legend(pMain + geom_tile(color = "black") )
lAge <- get_legend(pAge+ geom_tile(color = "black") )
lSex <- get_legend(pSex+ geom_tile(color = "black") )
lIGHV <- get_legend(pIGHV+ geom_tile(color = "black") )
lTreat <- get_legend(pTreat+ geom_tile(color = "black") )
noLegend <- theme(legend.position = "none")
mainPlot <- plot_grid(pAge + noLegend, pSex + noLegend,
pTreat + noLegend,pIGHV + noLegend,
pMain + noLegend, ncol=1, align = "v",
rel_heights = c(rep(1,4),20))
legendPlot <- plot_grid(lAge, lSex, lIGHV, lTreat, lMain,ncol=2, align = "hv")
plot_grid(mainPlot, NULL, plot_grid(legendPlot,NULL, ncol=1, rel_heights = c(1,0.3)), ncol=3, rel_widths = c(1,0.05, 0.2))
ggsave("../docs/cohortComposition_CLLsamples.pdf", height=8, width=10)
PDF version: cohortComposition_CLLsamples.pdf
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 cowplot_1.1.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.8 lubridate_1.8.0 assertthat_0.2.1 rprojroot_2.0.2
[5] digest_0.6.29 utf8_1.2.2 R6_2.5.1 cellranger_1.1.0
[9] backports_1.4.1 reprex_2.0.1 evaluate_0.14 highr_0.9
[13] httr_1.4.2 pillar_1.6.5 rlang_0.4.12 readxl_1.3.1
[17] rstudioapi_0.13 jquerylib_0.1.4 rmarkdown_2.11 labeling_0.4.2
[21] bit_4.0.4 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 viridisLite_0.4.0
[33] fansi_1.0.2 crayon_1.4.2 tzdb_0.2.0 dbplyr_2.1.1
[37] withr_2.4.3 later_1.3.0 grid_4.1.2 jsonlite_1.7.3
[41] gtable_0.3.0 lifecycle_1.0.1 DBI_1.1.2 git2r_0.29.0
[45] magrittr_2.0.1 scales_1.1.1 cli_3.1.1 stringi_1.7.6
[49] vroom_1.5.7 farver_2.1.0 fs_1.5.2 promises_1.2.0.1
[53] xml2_1.3.3 bslib_0.3.1 ellipsis_0.3.2 generics_0.1.1
[57] vctrs_0.3.8 RColorBrewer_1.1-2 tools_4.1.2 bit64_4.0.5
[61] glue_1.6.1 hms_1.1.1 parallel_4.1.2 fastmap_1.1.0
[65] yaml_2.2.1 colorspace_2.0-2 rvest_1.0.2 knitr_1.37
[69] haven_2.4.3 sass_0.4.0