Consensus clustering analysis based on drug responses
Junyan Lu
31 March 2023
Last updated: 2023-03-31
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Knit directory: EMBL2016/analysis/
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Load libraries and datasets
Perform consensus clustering to identify CLL subgroups with different drug response pattern
Pre-processing
Remove drugs with self-lumination
screenData <- filter(screenData, !problemDrug)Select CLL samples and use AUC under lines
#Prepare data
viabMat <- screenData %>%
filter(diagnosis %in% "CLL") %>% #only CLL
distinct(patientID, Drug, .keep_all = TRUE) %>%
select(patientID, Drug, viab.auc) %>%
#group_by(patientID, Drug) %>% summarise(viab = mean(viab.auc, na.rm=TRUE)) %>%
spread(key = patientID, value = "viab.auc") %>% data.frame() %>%
column_to_rownames("Drug") %>% as.matrix()Estimate missing value percentage
missDrug <- rowSums(is.na(viabMat))
missPat <- colSums(is.na(viabMat))Original dimension
dim(viabMat)[1] 391 132Keep drug that have non-NA values in at least 80% of samples (in this version, no drugs will be removed at this step)
viabMatFilt <- viabMat[missDrug/ncol(viabMat) <= 0.2, ]Remove drugs do not show variations (not used)
#sds <- genefilter::rowSds(viabMatFilt, na.rm=TRUE)
#viabMatFilt <- viabMatFilt[sds > genefilter::shorth(sds),]Number of filtered dimensions
dim(viabMatFilt)[1] 391 132Run clustering with ConcsensusClustterPlus
# Impute missing values using missForest, as missing values are not allowed for consensus clustering
set.seed(2021)
impForest <- missForest(t(viabMatFilt))
viabMatImp <- t(impForest$ximp)#Center each feature by median
d <- sweep(viabMatImp,1, apply(viabMatImp,1, median, na.rm=T))
#consensus clustering
resConsClust <- ConsensusClusterPlus(d, maxK=20, reps=1000 , pItem=0.8, pFeature=1, title = "noFit_CLL",
clusterAlg="hc",distance="pearson",seed=2021, plot="png")
#plot clustering result
icl = calcICL(resConsClust,title="noFit_CLL",plot="png")
#save results for later use
save(viabMatImp, resConsClust, file = "../output/resConsClust_noFit.RData")Based on delta curve, three clusters would be most appropriate
Load saved data
load("../output/resConsClust_noFit.RData")Post-processing consensus clustering results
Select samples with clustering consensus over 80%
k=3
conMat <- resConsClust[[k]]$consensusMatrix
conClust <- resConsClust[[k]]$consensusClass
colnames(conMat) <- colnames(viabMatImp)Visualization
clusterTab <- tibble(patientID = colnames(conMat),
cluster = paste0("C",conClust),
IGHV.status = patMeta[match(names(conClust),patMeta$Patient.ID),]$IGHV.status,
Mclust = patMeta[match(names(conClust),patMeta$Patient.ID),]$Methylation_Cluster,
trisomy12 = patMeta[match(names(conClust),patMeta$Patient.ID),]$trisomy12,
sampleID = screenData[match(patientID, screenData$patientID),]$sampleID
)
#add sample year information
yearTab <- distinct(screenData, patientID, sampleID) %>%
filter(str_detect(sampleID, "PB")) %>%
mutate(year = str_sub(sampleID ,1, 2))
clusterTab <- clusterTab %>%
mutate(sampleYear = yearTab[match(patientID, yearTab$patientID),]$year)
colAnno <- select(clusterTab,-sampleID) %>% data.frame() %>% column_to_rownames("patientID")
pheatmap(conMat, annotation_col = colAnno, method = "average", clustering_distance_rows = "correlation", clustering_distance_cols = "correlation", show_colnames = FALSE)
write_csv2(clusterTab, "../output/consClust_EMBL2016.csv")Based on the heatmap, C2 is primarily U-CLL samples while C1 and C3 are primarily M-CLL samples pdf file
Confounding between year and cluster
table(colAnno$cluster, colAnno$sampleYear)
11 12 13 14 15 16
C1 2 4 10 7 9 0
C2 1 10 13 17 6 3
C3 0 11 11 15 11 2Visualization with viability matrix (all drugs)
colAnnoAlt <- data.frame(row.names = colnames(conMat),
cluster = paste0("C",conClust),
IGHV.status = patMeta[match(names(conClust),patMeta$Patient.ID),]$IGHV.status)
annoCol <- list(IGHV.status = c(M = "#E41A1C", U = "#377EB8"),
cluster = c(C1 = "#4DAF4A", C2 = "#984EA3", C3 = "#FF7F00"))
# add genomic annotations
geneMat <- patMeta %>% filter(Patient.ID %in% colnames(conMat)) %>%
select(Patient.ID, del10p:inv_9) %>%
mutate(across(where(is.factor), as.character)) %>%
column_to_rownames("Patient.ID") %>%
data.frame()
geneCount <- geneMat %>% as_tibble(rownames = "patID") %>%
pivot_longer(-patID) %>%
group_by(name) %>%
summarise(nNA = sum(is.na(value)),
nMut = sum(value %in% "1"),
nAll = length(patID)) %>%
mutate(mutFrac = nMut/nAll,
naFrac = nNA/nAll) %>%
filter(mutFrac >=0.02, naFrac < 0.3)
geneMat <- geneMat[,geneCount$name]
colAnnoAlt <- cbind(colAnnoAlt, geneMat)
geneAnnoCol <- lapply(colnames(geneMat), function(x) c(`1`="black",`0`="white"))
names(geneAnnoCol) <- colnames(geneMat)
annoCol <- c(annoCol, geneAnnoCol)
viabMatScale <- jyluMisc::mscale(viabMatFilt, censor =5)
viabMatScale <- viabMatScale[,arrange(clusterTab,cluster)$patientID]
pheatmap(viabMatScale, scale="none", clustering_method = "ward.D2", clustering_distance_cols = "correlation",
cluster_cols = FALSE,
annotation_col = colAnnoAlt,
annotation_colors = annoCol,
color = colorRampPalette(c("red","white","blue"))(100),
show_colnames = FALSE)
Visualization (for abstract)
#pdf("consensus_clusters.pdf", height = 4, width = 5)
pheatmap(conMat, annotation_col = colAnnoAlt, method = "average",
clustering_distance_rows = "correlation", clustering_distance_cols = "correlation",
color = blues9, treeheight_row = 0, treeheight_col = 1,
annotation_colors = annoCol,
border_color = NA, show_colnames = FALSE)
#dev.off()C1 and C3 groups are predominately M-CLL samples
table(clusterTab$cluster, clusterTab$IGHV.status)
M U
C1 30 2
C2 8 42
C3 31 17plotTab <- clusterTab %>%
filter(!is.na(IGHV.status)) %>%
group_by(cluster, IGHV.status) %>%
summarise(n=length(patientID))
ggplot(plotTab, aes(x=cluster,y=n, fill = IGHV.status)) +
geom_bar(stat="identity", postion = "stack") +
xlab("number of samples") +
scale_fill_manual(values = c(M = "#E41A1C", U = "#377EB8")) +
theme_my +
theme(legend.position = "bottom")
C1, C3 and C4 groups are predominately M-CLL samples
table(clusterTab$cluster, clusterTab$Mclust)
HP IP LP
C1 21 8 2
C2 8 2 39
C3 20 10 14plotTab <- clusterTab %>%
filter(!is.na(Mclust)) %>%
group_by(cluster, Mclust) %>%
summarise(n=length(patientID))
ggplot(plotTab, aes(x=cluster,y=n, fill = Mclust)) +
geom_bar(stat="identity", postion = "stack") +
xlab("number of samples") +
#scale_fill_manual(values = c(M = "#E41A1C", U = "#377EB8")) +
theme_my +
theme(legend.position = "bottom")
PCA visualization
T-SNE visualization
Association with clinical outcomes (TTT and OS)
(Only M-CLL samples clustered as C1 and C3 groups are included)
load("../../var/survival_190516.RData")
testTab <- clusterTab %>% left_join(survT, by = "sampleID") %>%
filter(!cluster%in% "C2", IGHV.status %in% "M")Function for cox regression
com <- function(response, time, endpoint, scale =FALSE) {
if (scale) {
#calculate z-score
response <- (response - mean(response, na.rm = TRUE))/sd(response, na.rm=TRUE)
}
surv <- coxph(Surv(time, endpoint) ~ response)
tibble(p = summary(surv)[[7]][,5],
HR = summary(surv)[[7]][,2],
lower = summary(surv)[[8]][,3],
higher = summary(surv)[[8]][,4])
}Cox regression results
TTT
com(factor(testTab$cluster), testTab$TTT, testTab$treatedAfter)# A tibble: 1 × 4
p HR lower higher
<dbl> <dbl> <dbl> <dbl>
1 0.217 0.534 0.197 1.45OS
com(factor(testTab$cluster), testTab$OS, testTab$died)# A tibble: 1 × 4
p HR lower higher
<dbl> <dbl> <dbl> <dbl>
1 0.686 1.45 0.241 8.66Kaplan-Meier plots
Function for KM plot
formatNum <- function(i, limit = 0.01, digits =1, format="e") {
r <- sapply(i, function(n) {
if (n < limit) {
formatC(n, digits = digits, format = format)
} else {
format(n, digits = digits)
}
})
return(r)
}
theme_half <- ggplot2::theme_bw() + ggplot2::theme(axis.text = ggplot2::element_text(size=15),
axis.title = ggplot2::element_text(size=16),
axis.line = ggplot2::element_line(size=0.8),
panel.border = ggplot2::element_blank(),
axis.ticks = ggplot2::element_line(size=1.5),
plot.title = ggplot2::element_text(size = 16, hjust =0.5, face="bold"),
panel.grid.major = ggplot2::element_blank(),
panel.grid.minor = ggplot2::element_blank())
km <- function(response, time, endpoint, titlePlot = "KM plot", pval = NULL,
stat = "median", maxTime =NULL, showP = TRUE, showTable = FALSE,
ylab = "Fraction", xlab = "Time (years)",
table_ratio = c(0.7,0.3), yLabelAdjust = 0) {
colList <- c("#BC3C29FF","#0072B5FF","#E18727FF","#20854EFF","#7876B1FF","#6F99ADFF","#FFDC91FF","#EE4C97FF")
#function for km plot
survS <- tibble(time = time,
endpoint = endpoint)
if (!is.null(maxTime))
survS <- mutate(survS, endpoint = ifelse(time > maxTime, FALSE, endpoint),
time = ifelse(time > maxTime, maxTime, time))
if (stat == "maxstat") {
ms <- maxstat.test(Surv(time, endpoint) ~ response,
data = survS,
smethod = "LogRank",
minprop = 0.2,
maxprop = 0.8,
alpha = NULL)
survS$group <- factor(ifelse(response >= ms$estimate, "high", "low"))
p <- com(survS$group, survS$time, survS$endpoint)$p
} else if (stat == "median") {
med <- median(response, na.rm = TRUE)
survS$group <- factor(ifelse(response >= med, "high", "low"))
p <- com(survS$group, survS$time, survS$endpoint)$p
} else if (stat == "binary") {
survS$group <- factor(response)
if (nlevels(survS$group) > 2) {
sdf <- survdiff(Surv(survS$time,survS$endpoint) ~ survS$group)
p <- 1 - pchisq(sdf$chisq, length(sdf$n) - 1)
} else {
p <- com(survS$group, survS$time, survS$endpoint)$p
}
}
if (is.null(pval)) {
if(p< 1e-16) {
pAnno <- bquote(italic("P")~"< 1e-16")
} else {
pval <- formatNum(p, digits = 1)
pAnno <- bquote(italic("P")~"="~.(pval))
}
} else {
pval <- formatNum(pval, digits = 1)
pAnno <- bquote(italic("P")~"="~.(pval))
}
if (!showP) pAnno <- ""
colListNew <- colList[-4] #remove green
colorPal <- colListNew[1:length(unique(survS$group))]
p <- ggsurvplot(survfit(Surv(time, endpoint) ~ group, data = survS),
data = survS, pval = FALSE, conf.int = FALSE, palette = colorPal,
legend = ifelse(showTable, "none","top"),
ylab = "Fraction", xlab = "Time (years)", title = titlePlot,
pval.coord = c(0,0.1), risk.table = showTable, legend.labs = sort(unique(survS$group)),
ggtheme = theme_half + theme(plot.title = element_text(hjust =0.5),
panel.border = element_blank(),
axis.title.y = element_text(vjust =yLabelAdjust)))
if (!showTable) {
p <- p$plot + annotate("text",label=pAnno, x = 0.1, y=0.1, hjust =0, size =5)
return(p)
} else {
#construct a gtable
pp <- p$plot + annotate("text",label=pAnno, x = 0.1, y=0.1, hjust =0, size=5)
pt <- p$table + ylab("") + xlab("") + theme(plot.title = element_text(hjust=0, size =10))
p <- plot_grid(pp,pt, rel_heights = table_ratio, nrow =2, align = "v")
return(p)
}
}TTT
km(testTab$cluster, testTab$TTT, testTab$treatedAfter, "cluster VS TTT", stat = "binary", showTable = TRUE)
OS
km(testTab$cluster, testTab$OS, testTab$died, "cluster VS OS", stat = "binary", showTable = TRUE)
Baseline ATP levels after 48 hours
Baseline ATP is the ATP level in the control wells after 48 hours of culture. It can be regarded as a baseline viability of the cells.
load("~/CLLproject_jlu/var/newEMBL_20210129.RData")
basalATP <- emblNew %>% filter(type == "neg") %>%
group_by(patID) %>% summarise(ATPcount = median(val, na.rm=TRUE))All samples
testTab <- clusterTab %>%
left_join(basalATP, by = c(patientID = "patID"))
#t.test(log(ATPcount) ~ cluster, testTab, var.equal=TRUE)
ggplot(testTab, aes(x=cluster, y=log(ATPcount))) +
geom_boxplot(outlier.shape = NA, width=0.3) +
ggbeeswarm::geom_quasirandom(aes(col = cluster)) +
scale_color_manual(values = annoCol$cluster) +
ylab("baseline ATP") +
theme_my + theme(legend.position = "none")
Stratified by IGHV
testTab <- clusterTab %>%
left_join(basalATP, by = c(patientID = "patID")) %>%
filter(!is.na(IGHV.status))
#t.test(log(ATPcount) ~ cluster, testTab, var.equal=TRUE)
ggplot(testTab, aes(x=cluster, y=log(ATPcount))) +
geom_boxplot(outlier.shape = NA, width=0.3) +
ggbeeswarm::geom_quasirandom(aes(col = cluster)) +
scale_color_manual(values = annoCol$cluster) +
ylab("baseline ATP") +
facet_wrap(~IGHV.status) +
theme_my + theme(legend.position = "none")
Whether it correlates with CLL-PD?
load("~/CLLproject_jlu/analysis/CLLsubgroup/facTab_CPSatLeast3New.RData")
testTab <- clusterTab %>%
mutate(CLLPD = facTab[match(patientID, facTab$patID),]$factor)
#t.test(CLLPD ~ cluster, testTab, var.equal=TRUE)
ggplot(testTab, aes(x=cluster, y=CLLPD)) +
geom_boxplot(outlier.shape = NA, width=0.3) +
ggbeeswarm::geom_quasirandom(aes(col = cluster)) +
scale_color_manual(values = annoCol$cluster) +
ylab("CLL-PD") +
theme_my + theme(legend.position = "none")
Stratified by IGHV status
testTab <- clusterTab %>%
mutate(CLLPD = facTab[match(patientID, facTab$patID),]$factor) %>%
filter(!is.na(IGHV.status))
#t.test(CLLPD ~ cluster, testTab, var.equal=TRUE)
ggplot(testTab, aes(x=cluster, y=CLLPD)) +
geom_boxplot(outlier.shape = NA, width=0.3) +
ggbeeswarm::geom_quasirandom(aes(col = cluster)) +
scale_color_manual(values = annoCol$cluster) +
ylab("CLL-PD") +
facet_wrap(~IGHV.status) +
theme_my + theme(legend.position = "none")
Identify drug that associate with the 4 groups (independent of their IGHV status)
ANOVA test
testTabAll <- screenData %>%
mutate(IGHV.status = patMeta[match(patientID, patMeta$Patient.ID),]$IGHV.status) %>%
filter(diagnosis %in% "CLL", !is.na(IGHV.status)) %>% #only CLL
distinct(patientID, Drug, .keep_all = TRUE) %>%
select(patientID, Drug, viab.auc) %>%
dplyr::rename(viab = viab.auc) %>%
left_join(clusterTab, by = "patientID")
resTab <- testTabAll %>% group_by(Drug) %>% nest() %>%
mutate(m=map(data, ~car::Anova(lm(viab~ IGHV.status + cluster, .,)))) %>%
mutate(res = map(m, broom::tidy)) %>%
unnest(res) %>% ungroup() %>%
filter(term == "cluster") %>%
select(Drug, p.value) %>%
mutate(p.adj = p.adjust(p.value, method = "BH")) %>%
arrange(p.value)
resTab.sig <- filter(resTab, p.adj < 0.1)Boxplot of top 18 associated drugs
drugList <- resTab$Drug[1:18]
plotTabBox <- filter(testTabAll, Drug %in% drugList) %>%
mutate(pathway = drugAnno[match(Drug, drugAnno$drugName),]$pathway) %>%
mutate(drugPath = sprintf("%s\n(%s)", Drug, pathway))
ggplot(plotTabBox, aes(x=cluster, y = viab)) +
geom_boxplot(outlier.shape = NA, aes(fill = cluster)) +
ggbeeswarm::geom_quasirandom(aes(col=IGHV.status)) +
facet_wrap(~drugPath, scale ="free_x", ncol=3) +
scale_fill_manual(values = annoCol$cluster) +
scale_color_manual(values = annoCol$IGHV.status) +
theme_my
Heatmap visualization of drug passed 10% FDR
viabMatScale <- jyluMisc::mscale(viabMatImp[rownames(viabMatImp) %in% resTab.sig$Drug,], censor =5)
viabMatScale <- viabMatScale[,arrange(clusterTab,cluster)$patientID]
pheatmap(viabMatScale, scale="none", clustering_method = "ward.D2", clustering_distance_cols = "correlation",
cluster_cols = FALSE,
annotation_col = colAnnoAlt,
annotation_colors = annoCol,
color = colorRampPalette(c("red","white","blue"))(100), show_colnames = FALSE)
Characterize the drug response phenotypes of C1 and C3 subgroup within M-CLL samples
In this part, I want to answer the question how C1 and C3 subgroups are different in terms of drug response profile. As samples in C1 and C3 group are primarily M-CLL samples, in the analysis below, only M-CLL samples will be considered.
Identify drugs that show differential responses between C1 and C3 in M-CLL samples
testTabAll <- screenData %>%
mutate(IGHV.status = patMeta[match(patientID, patMeta$Patient.ID),]$IGHV.status) %>%
filter(diagnosis %in% "CLL", !is.na(IGHV.status)) %>% #only CLL
distinct(patientID, Drug, .keep_all = TRUE) %>%
select(patientID, Drug, viab.auc) %>%
dplyr::rename(viab = viab.auc) %>%
left_join(clusterTab, by = "patientID") %>%
filter(cluster != "C4")
testTab <- testTabAll %>%
filter(cluster %in% c("C1","C3"),
IGHV.status %in% "M",
!is.na(viab)) %>%
mutate(cluster =factor(cluster, levels = c("C1","C3")))
#at least five samples if each cluster for each drug, this is because for some drugs the AUC could not be fitted
drugFilt <- group_by(testTab, cluster, Drug) %>%
summarise(n = length(!is.na(viab))) %>%
pivot_wider(names_from = cluster, values_from = n) %>%
filter(C1>=5 & C3>=5)
testTab <- filter(testTab, Drug %in% drugFilt$Drug)resTab <- testTab %>% group_by(Drug) %>% nest() %>%
mutate(m=map(data, ~t.test(viab~cluster, ., var.equal=TRUE))) %>%
mutate(res = map(m, broom::tidy)) %>%
unnest(res) %>% ungroup() %>%
select(Drug, estimate, p.value, estimate1, estimate2) %>%
mutate(p.adj = p.adjust(p.value, method = "BH"), log2FC = log2(estimate2/estimate1)) %>%
arrange(p.value) %>%
mutate(pathway = drugAnno[match(Drug, drugAnno$drugName),]$pathway) %>%
mutate(drugPath = sprintf("%s (%s)", Drug, pathway))
resTab.sig <- resTab %>% filter(p.adj < 0.1)Volcano plot
plotTabVol <- resTab %>%
mutate(direction = case_when(p.adj > 0.01 ~ "n.s.",
p.adj < 0.01 & log2FC <0 ~ "sensitive in C3",
p.adj < 0.01 & log2FC >0 ~ "resistent in C3"))
#label top 20 drugs judged by pvalue
topDrug <- arrange(resTab, p.value)$Drug[1:20]
plotTabVol <- mutate(plotTabVol, drugLabel = ifelse(Drug %in% topDrug, as.character(drugPath), ""))
ggplot(plotTabVol, aes(y=-log10(p.adj), x= log2FC)) +
geom_point(aes(col = direction)) +
geom_hline(yintercept = 2, linetype ="dashed") +
ggrepel::geom_text_repel(aes(label = drugLabel),max.overlaps=100) +
scale_color_manual(values = c(n.s. = "grey50", `sensitive in C3` = "blue", `resistent in C3` = "red")) +
#xlim(-0.5,0.5) +
ggtitle("Drug sensitivity between C1 and C3\n(within M-CLL samples)") +
theme_my +
theme(plot.title = element_text(hjust=0.5, size=15, face ="bold"))
ggsave("volcano.png", height = 5, width = 6)Drug with 1% FDR and abs(log2FC) > 0.5 are labeled
A list of all drugs associated with C1/C3 subgroups
10% FDR cut-off is used
resTab %>% filter(p.adj < 0.1) %>%
mutate_if(is.numeric, formatC, digits=2) %>%
select(-drugPath) %>%
DT::datatable()Visualization with viability matrix of associated drugs
viabMatScale <- jyluMisc::mscale(viabMatImp[rownames(viabMatImp) %in% resTab.sig$Drug, colnames(viabMatImp) %in% testTab$patientID], censor =5)
viabMatScale <- viabMatScale[,unique(arrange(testTab,cluster)$patientID)]
pheatmap(viabMatScale, scale="none", clustering_method = "ward.D2", clustering_distance_cols = "correlation",
cluster_cols = FALSE,
annotation_col = colAnnoAlt,
annotation_colors = annoCol,
color = colorRampPalette(c("red","white","blue"))(100), show_colnames = FALSE)
Boxplots
Only M-CLL samples that belong to C1 and C3 group
drugList <- filter(plotTabVol, drugLabel != "")$Drug
plotTabBox <- filter(testTab, Drug %in% drugList) %>%
mutate(pathway = drugAnno[match(Drug, drugAnno$drugName),]$pathway) %>%
mutate(drugPath = sprintf("%s\n(%s)", Drug, pathway))
ggplot(plotTabBox, aes(x=cluster, y = viab)) +
geom_boxplot(outlier.shape = NA, aes(fill = cluster)) + ggbeeswarm::geom_quasirandom() +
facet_wrap(~drugPath) +
theme_my
All samples and colored by their IGHV status
drugList <- filter(plotTabVol, drugLabel != "")$Drug
plotTabBox <- filter(testTabAll, Drug %in% drugList, !is.na(IGHV.status)) %>%
mutate(pathway = drugAnno[match(Drug, drugAnno$drugName),]$pathway) %>%
mutate(drugPath = sprintf("%s\n(%s)", Drug, pathway))
ggplot(plotTabBox, aes(x=cluster, y = viab)) +
geom_boxplot(outlier.shape = NA) +
ggbeeswarm::geom_quasirandom(aes(col= IGHV.status)) +
scale_color_manual(values = c(M = "#E41A1C", U = "#377EB8")) +
facet_wrap(~drugPath, ncol=4) +
ylab("Viability (AUC)") + xlab("Clusters") +
theme_my
#ggsave("boxplot_AUC.png", height = 6, width = 12)It can be seen that for many drugs, the difference between C1 and C3 groups are even larger than between C2 (U-CLL) and C1 or C2 and C3.
Distribution of target (all drugs passed 1% FDR will be considered)
More resistant
Over-representation test
classAnno <- distinct(screenData, Drug, class)
targetAnno <- select(drugAnno, drugName, target,pathway) %>%
left_join(classAnno, by = c(drugName = "Drug"))
resTabSig <- resTab %>%
filter(p.adj <0.01, log2FC > 0) %>%
left_join(targetAnno, by = c(Drug = "drugName")) %>%
mutate(pathway=pathway.x)drugAll <- filter(targetAnno, drugName %in% resTab$Drug) %>%
mutate(ifSig = drugName %in% resTabSig$Drug)
enrichTab <- lapply(unique(resTabSig$class), function(n) {
drugTest <- filter(drugAll, !is.na(class)) %>%
mutate(ifGroup = class %in% n)
tt <- table(drugTest$ifGroup, drugTest$ifSig)
res <- fisher.test(tt, alternative = "greater")
data.frame(class = n, p = res$p.value)
}) %>% bind_rows() %>% arrange(p)
head(enrichTab) class p
1 Differentiating /Epigenetic modifier 0.1263263
2 Hedgehog inhibitor 0.2525528
3 Immunomodulatory 0.4094913
4 Other 0.5539799
5 Metabolic modifier 0.6760808
6 Kinase inhibitor 0.7108863enrichTab <- lapply(na.omit(unique(resTabSig$pathway)), function(n) {
drugTest <- filter(drugAll, !is.na(pathway)) %>%
mutate(ifGroup = pathway %in% n)
tt <- table(drugTest$ifGroup, drugTest$ifSig)
res <- fisher.test(tt, alternative = "greater")
data.frame(class = n, p = res$p.value)
}) %>% bind_rows() %>% arrange(p)
head(enrichTab) class p
1 DNA damage response 0.02357063
2 MEN1 0.10816387
3 TAM 0.15797386
4 Notch 0.15797386
5 Cytokine receptor 0.20512733
6 Hedgehog 0.24975922More sensitive
Over-representation test
classAnno <- distinct(screenData, Drug, class)
targetAnno <- select(drugAnno, drugName, target, pathway) %>%
left_join(classAnno, by = c(drugName = "Drug"))
resTabSig <- resTab %>%
filter(p.adj <0.01, log2FC < 0 ) %>%
left_join(targetAnno, by = c(Drug = "drugName")) %>%
mutate(pathway = pathway.x)drugAll <- filter(targetAnno, drugName %in% resTab$Drug) %>%
mutate(ifSig = drugName %in% resTabSig$Drug)
enrichTab <- lapply(unique(resTabSig$class), function(n) {
drugTest <- filter(drugAll, !is.na(class)) %>%
mutate(ifGroup = class %in% n)
tt <- table(drugTest$ifGroup, drugTest$ifSig)
res <- fisher.test(tt, alternative = "greater")
data.frame(class = n, p = res$p.value)
}) %>% bind_rows() %>% arrange(p)
head(enrichTab) class p
1 ROS 0.005741289
2 Apoptotic modulator 0.131103238
3 Protease/Proteosome inhibitor 0.337665079
4 Conventional Chemo 0.437261739
5 Immunomodulatory 0.451706812
6 Differentiating /Epigenetic modifier 0.582142339enrichTab <- lapply(na.omit(unique(resTabSig$pathway)), function(n) {
drugTest <- filter(drugAll, !is.na(pathway)) %>%
mutate(ifGroup = pathway %in% n)
tt <- table(drugTest$ifGroup, drugTest$ifSig)
res <- fisher.test(tt, alternative = "greater")
data.frame(class = n, p = res$p.value)
}) %>% bind_rows() %>%
arrange(p)
head(enrichTab) class p
1 ROS 0.005266872
2 Cell adhesion 0.069470759
3 JAK/STAT 0.131238159
4 Apoptosis 0.177914772
5 bromodomain 0.191555042
6 Vitamin 0.264550265General toxicity and group
meanViabTab <-screenData %>%
group_by(Drug) %>% summarise(meanViab = mean(viab.auc, na.rm=TRUE)) %>%
left_join(resTab, by = "Drug") %>%
mutate(dir = case_when(p.adj <0.01 & log2FC >0 ~ "resistant in C3",
p.adj <0.01 & log2FC < 0 ~ "sensitive in C3",
TRUE~ "not associated"))
car::Anova(lm(meanViab ~ dir, meanViabTab))Anova Table (Type II tests)
Response: meanViab
Sum Sq Df F value Pr(>F)
dir 0.0750 2 3.8476 0.02215 *
Residuals 3.7807 388
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ggplot(meanViabTab, aes(x=dir, y=meanViab)) +
geom_boxplot() + geom_point() +
theme_my +
ylab("mean viability among all samples") + xlab("")
ggsave("toxivity_box.png", width = 5, height = 4)Multi-omics characterization of C1 and C3 groups
In this part, I want to answer the questions that why samples in C1 and C3 groups response differently to those above drugs. In order to explain this, I will look at some of the omics data we have.
Does it correlate with previous identified mTOR group?
All CLLs
drugGroup <- read_csv("~/CLLproject_jlu/data/expressionAnalysis/selNEW.csv") %>%
select(`...1`, group) %>% dplyr::rename(patID = `...1`) %>%
mutate(cluster = clusterTab[match(patID, clusterTab$patientID),]$cluster) %>%
filter(!is.na(cluster))
table(drugGroup$group, drugGroup$cluster)
C1 C2 C3
BTK 0 14 1
MEK 2 4 2
mTOR 2 1 6
none 10 7 10Within M-CLLs
drugGroup <- mutate(drugGroup, IGHV = patMeta[match(patID, patMeta$Patient.ID),]$IGHV.status) %>%
filter(cluster %in% c("C1","C3"), IGHV %in% "M")
table(drugGroup$group, drugGroup$cluster)
C1 C3
MEK 2 1
mTOR 2 5
none 9 4The C1 and C3 groups identified from EMBL2016 screen are not the same as drug sensitivity groups previously identified. Although the C1 group maybe related to the non-responder group. But C3 is not the mTOR group
plotEve <- filter(screenData, Drug %in% c("Everolimus","Rapamycin")) %>%
group_by(Drug, patientID) %>% summarise(viab = mean(viab.auc)) %>%
mutate(cluster = clusterTab[match(patientID, clusterTab$patientID),]$cluster,
IGHV = patMeta[match(patientID, patMeta$Patient.ID),]$IGHV.status) %>%
filter(cluster %in% c("C1","C3"), IGHV %in% "M")
ggplot(plotEve, aes(x=cluster, y = viab)) +
geom_boxplot(width=0.3) +
ggbeeswarm::geom_quasirandom(aes(col = cluster)) +
scale_color_manual(values = annoCol$cluster) +
facet_wrap(~Drug) +
ylab("Viability (AUC)") +
theme_my
Correlations between genomics/demographics and C1/C3 groups
clusterAnno <- filter(clusterTab, cluster %in% c("C1","C3"), IGHV.status == "M") %>%
mutate(pretreat = treatmentTab[match(sampleID, treatmentTab$sampleID),]$pretreat)
geneTab <- select(patMeta, Patient.ID, gender, Methylation_Cluster, del10p:U1) %>%
dplyr::rename(sex = gender)
testTab <- select(clusterAnno, patientID, cluster, pretreat) %>%
left_join(geneTab, by = c(patientID = "Patient.ID")) %>%
mutate_all(as.character) %>%
pivot_longer(!c(patientID, cluster))
sumTab <- group_by(testTab, name) %>%
summarise(noNA = sum(!is.na(value)), numMut = sum(value %in% c("1","m","HP", "M"))) %>%
filter(noNA > 40, numMut >=5)
testTab <- filter(testTab, name %in% sumTab$name)resTab <- group_by(testTab, name) %>% nest() %>%
mutate(m=map(data, ~chisq.test(.$cluster, .$value))) %>%
mutate(res = map(m, broom::tidy)) %>%
unnest(res) %>%
select(name, p.value) %>%
arrange(p.value)
resTab# A tibble: 7 × 2
# Groups: name [7]
name p.value
<chr> <dbl>
1 trisomy19 0.0617
2 sex 0.370
3 trisomy12 0.504
4 TP53 0.969
5 del13q 1.00
6 pretreat 1.00
7 Methylation_Cluster 1 No significant associations can be identified, indicating the C1/C3 group is not driven by genomic, demographic or treatment. It can potentially be a new functional group
Transcriptomic characterization (baseline expression).
Differential gene expression between C1 and C3 groups
load("../../var/ddsrna_180717.RData")
dds$cluster <- factor(clusterAnno[match(dds$PatID, clusterAnno$patientID),]$cluster)
dds$CLLPD <- facTab[match(dds$PatID, facTab$patID),]$factor
dds$IGHV <- factor(patMeta[match(dds$PatID, patMeta$Patient.ID),]$IGHV.status)
ddsSub <- dds[,!is.na(dds$cluster)]ddsSub <- ddsSub[rowMedians(counts(ddsSub, normalized = TRUE)) > 10,]
ddsSub <- ddsSub[rowData(ddsSub)$biotype %in% "protein_coding",]
ddsSub <- ddsSub[!rowData(ddsSub)$symbol %in% c("", NA)]
table(ddsSub$cluster)
C1 C3
30 28 library(DESeq2)
design(ddsSub) <- ~cluster
deRes <- DESeq(ddsSub)Table of differentially expressed genes (10% FDR)
resTab <- results(deRes, tidy = TRUE, name = "cluster_C3_vs_C1") %>%
mutate(symbol = rowData(ddsSub[row,])$symbol) %>%
arrange(pvalue)
resTab.sig <- filter(resTab, padj < 0.1) %>%
mutate(symbol = factor(symbol, levels = symbol))
DT::datatable(resTab.sig %>% select(symbol, row, stat, pvalue, padj) %>%
mutate_if(is.numeric, formatC, digits=2))P-value histogram
hist(resTab$pvalue)
Boxplots of top 9 candidates based on p-value
plotTab <- counts(ddsSub, normalized = TRUE)[resTab.sig$row[1:9],] %>%
as_tibble(rownames = "id") %>% pivot_longer(-id) %>%
mutate(cluster = clusterAnno[match(name, clusterAnno$patientID),]$cluster) %>%
left_join(resTab.sig, by = c(id = "row"))
ggplot(plotTab, aes(x=cluster, y=log10(value))) +
geom_boxplot(outlier.shape = NA, width=0.3) +
ggbeeswarm::geom_quasirandom(aes(col=cluster)) +
facet_wrap(~symbol, scale ="free") +
scale_color_manual(values = annoCol$cluster) +
ylab("RNAseq count") +
theme_my + theme(legend.position = "none")
Pathway enrichment analysis
exprMat <- counts(ddsSub)
exprMat <- limma::voom(exprMat, lib.size = ddsSub$sizeFactor)$E
designMat <- model.matrix(~ddsSub$cluster)Hallmark gene sets
(Raw p values < 0.05, no sets passed 10% FDR)
gmts <- list(H = "~/CLLproject_jlu/data/commonFiles/h.all.v6.2.symbols.gmt",
KEGG = "~/CLLproject_jlu/data/commonFiles/c2.cp.kegg.v5.1.symbols.gmt")
enHallmark <- jyluMisc::runCamera(exprMat, designMat, gmts$H, id = rowData(ddsSub)$symbol,ifFDR = TRUE, pCut =0.25, plotTitle = "Cancer Hallmarks")
enHallmark$enrichPlot
KEGG gene set
(Raw p values < 0.05, no sets passed 10% FDR)
gmts <- list(H = "~/CLLproject_jlu/data/commonFiles/h.all.v6.2.symbols.gmt",
KEGG = "~/CLLproject_jlu/data/commonFiles/c2.cp.kegg.v5.1.symbols.gmt")
enHallmark <- jyluMisc::runCamera(exprMat, designMat, gmts$KEGG, id = rowData(ddsSub)$symbol,ifFDR = TRUE, pCut =0.1, plotTitle = "KEGG gene sets")[1] "No sets passed the criteria"enHallmark$enrichPlotNULLTranscriptomic characterization (expression after cultured in DMSO for 48h).
Differential gene expression between C1 and C3 groups
load("~/CLLproject_jlu/analysis/drugSeq/ddsAll.RData")
dds <- ddsAll[,ddsAll$treatment =="DMSO"]
dds$cluster <- factor(clusterAnno[match(dds$patID, clusterAnno$patientID),]$cluster)
dds$CLLPD <- facTab[match(dds$patID, facTab$patID),]$factor
dds$IGHV <- factor(patMeta[match(dds$patID, patMeta$Patient.ID),]$IGHV.status)
colnames(dds) <- dds$patID
ddsSub <- dds[,!is.na(dds$cluster)]
table(ddsSub$cluster)
C1 C3
10 9 ddsSub <- ddsSub[rowMedians(counts(ddsSub, normalized = TRUE)) > 10,]
ddsSub <- ddsSub[rowData(ddsSub)$biotype %in% "protein_coding",]
ddsSub <- ddsSub[!rowData(ddsSub)$symbol %in% c("", NA)]
sds <- genefilter::rowSds(counts(ddsSub, normalized = TRUE))
ddsSub <- ddsSub[sds > genefilter::shorth(sds),]library(DESeq2)
design(ddsSub) <- ~cluster
deRes <- DESeq(ddsSub, betaPrior = TRUE)Table of differentially expressed genes (10% FDR)
resTab <- results(deRes, tidy = TRUE) %>%
mutate(symbol = rowData(ddsSub[row,])$symbol) %>%
arrange(pvalue)
resTab.sig <- filter(resTab, pvalue < 0.01) %>%
mutate(symbol = factor(symbol, levels = symbol))
DT::datatable(resTab.sig %>% select(symbol, row, stat, pvalue, padj) %>%
mutate_if(is.numeric, formatC, digits=2))P-value histogram
hist(resTab$pvalue)
Boxplots of top 9 candidates based on p-value
plotTab <- counts(ddsSub, normalized = TRUE)[resTab.sig$row[1:9],] %>%
as_tibble(rownames = "id") %>% pivot_longer(-id) %>%
mutate(cluster = clusterAnno[match(name, clusterAnno$patientID),]$cluster) %>%
left_join(resTab.sig, by = c(id = "row"))
ggplot(plotTab, aes(x=cluster, y=log10(value))) +
geom_boxplot(outlier.shape = NA, width=0.3) +
ggbeeswarm::geom_quasirandom(aes(col=cluster)) +
facet_wrap(~symbol, scale ="free") +
scale_color_manual(values = annoCol$cluster) +
ylab("RNAseq count") +
theme_my + theme(legend.position = "none")
Pathway enrichment analysis
exprMat <- counts(ddsSub)
exprMat <- limma::voom(exprMat, lib.size = ddsSub$sizeFactor)$E
designMat <- model.matrix(~ddsSub$cluster)Hallmark gene sets
(Raw p values < 0.05, no sets passed 10% FDR)
gmts <- list(H = "~/CLLproject_jlu/data/commonFiles/h.all.v6.2.symbols.gmt",
KEGG = "~/CLLproject_jlu/data/commonFiles/c2.cp.kegg.v5.1.symbols.gmt")
enHallmark <- jyluMisc::runCamera(exprMat, designMat, gmts$H, id = rowData(ddsSub)$symbol,ifFDR = TRUE, pCut =0.25, plotTitle = "Cancer Hallmarks")
enHallmark$enrichPlot
KEGG gene set
(Raw p values < 0.05, no sets passed 10% FDR)
gmts <- list(H = "~/CLLproject_jlu/data/commonFiles/h.all.v6.2.symbols.gmt",
KEGG = "~/CLLproject_jlu/data/commonFiles/c2.cp.kegg.v5.1.symbols.gmt")
enHallmark <- jyluMisc::runCamera(exprMat, designMat, gmts$KEGG, id = rowData(ddsSub)$symbol,ifFDR = TRUE, pCut =0.1, plotTitle = "KEGG gene sets")
enHallmark$enrichPlot
Differential expression at protein levels
load("../../var/proteomic_LUMOS_batch13.RData")
protCLL$patID <- colnames(protCLL)
protCLL$cluster <- factor(clusterAnno[match(protCLL$patID, clusterAnno$patientID),]$cluster)
protCLL$CLLPD <- facTab[match(protCLL$patID, facTab$patID),]$factor
protCLL$IGHV <- factor(patMeta[match(protCLL$patID, patMeta$Patient.ID),]$IGHV.status)
protSub <- protCLL[,!is.na(protCLL$cluster)]
table(protCLL$cluster)
C1 C3
10 16 Differential expression
library(proDA)
designMat <- data.frame(row.names = protSub$patID,
cluster = protSub$cluster,
batch = protSub$batch)
protMat <- assays(protSub)[["count"]]
fit <- proDA(protMat, design = ~ .,
col_data = designMat)
resTab <- test_diff(fit, "clusterC3") %>%
dplyr::rename(id = name, logFC = diff, t=t_statistic,
pvalue = pval, padj = adj_pval) %>%
mutate(symbol = rowData(protCLL[id,])$hgnc_symbol) %>%
select(symbol, id, logFC, t, pvalue, padj, n_obs) %>%
arrange(pvalue) %>%
as_tibble()Tables of candiates with P value < 0.01
(none passed 10% FDR)
resTab.sig <- filter(resTab, pvalue < 0.01) %>%
mutate(symbol = factor(symbol, levels = symbol))
DT::datatable(resTab.sig %>% select(symbol, logFC, pvalue, padj) %>%
mutate_if(is.numeric, formatC, digits=2))P-value histogram
hist(resTab$pvalue)
Boxplots of top 9 candidates
plotTab <- assays(protSub)[["log2Norm_combat"]][resTab.sig$id[1:9],] %>%
as_tibble(rownames = "id") %>% pivot_longer(-id) %>%
mutate(cluster = clusterAnno[match(name, clusterAnno$patientID),]$cluster) %>%
left_join(resTab.sig, by = "id")
ggplot(plotTab, aes(x=cluster, y=log10(value))) +
geom_boxplot(outlier.shape = NA, width=0.3) +
ggbeeswarm::geom_quasirandom(aes(col = cluster)) +
scale_color_manual(values = annoCol$cluster) +
facet_wrap(~symbol, scale ="free") +
theme_my + theme(legend.position = "none")
Association with BH3 profiling data
load("../../BH3profiling/output/dynamicBH3.RData")
dataBH3 <- dynamicBH3 %>% filter(drug == "DMSO", peptide != "DMSO") %>%
distinct(patID, peptide, .keep_all = TRUE) %>%
mutate(feature = peptide, value = AUC, concIndex =1) %>%
mutate(cluster = clusterAnno[match(patID, clusterAnno$patientID),]$cluster) %>%
filter(!is.na(cluster))
tRes <- group_by(dataBH3, feature) %>% nest() %>%
mutate(m = map(data, ~t.test(value ~ cluster,., var.equal=TRUE))) %>%
mutate(res = map(m, broom::tidy)) %>%
unnest(res) %>%
arrange(p.value)
head(tRes)# A tibble: 6 × 13
# Groups: feature [6]
feature data m estimate estimate1 estimate2 statis…¹ p.value param…²
<chr> <list> <list> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 BIM <tibble> <htest> -13.5 47.4 60.9 -2.08 0.0511 19
2 PUMA <tibble> <htest> -16.1 44.8 60.9 -2.07 0.0527 19
3 BAD <tibble> <htest> -12.3 61.1 73.5 -1.82 0.0840 19
4 FS1 <tibble> <htest> -10.5 18.0 28.5 -1.80 0.0881 19
5 HRKy <tibble> <htest> -3.80 -0.126 3.67 -1.61 0.123 19
6 MS1 <tibble> <htest> -12.9 29.5 42.4 -1.46 0.159 19
# … with 4 more variables: conf.low <dbl>, conf.high <dbl>, method <chr>,
# alternative <chr>, and abbreviated variable names ¹statistic, ²parameter
# ℹ Use `colnames()` to see all variable namesplotTab <- dataBH3 %>% filter(feature %in% c("FS1","MS1", "BIM"))
ggplot(plotTab, aes(x=cluster ,y = value)) +
geom_boxplot(width=0.5) +
geom_point(aes(col = cluster)) +
scale_color_manual(values = annoCol$cluster) +
facet_wrap(~feature) +
ylab("mitochondrial priming") + xlab("") +
theme_my + theme(legend.position = "none")
Association with treatment history
Only samples with BR therapy are enough for the test
load("../../var/inVivoEffect.RData")
testTab <- inVivoEffect %>% pivot_longer(-c(patientID, item)) %>%
left_join(clusterTab, by = "patientID") %>%
filter(cluster %in% c("C1","C3"), item == "BR", IGHV.status == "M")
table(testTab$name, testTab$cluster)
C1 C3
dropRate 2 3
lymDrop 2 3testRes <- group_by(testTab, name) %>% nest() %>%
mutate(m=map(data, ~t.test(value~cluster, ., var.equal = FALSE))) %>%
mutate(res = map(m, broom::tidy)) %>%
unnest(res)
testRes# A tibble: 2 × 13
# Groups: name [2]
name data m estimate estimate1 estimate2 stati…¹ p.value param…²
<chr> <list> <list> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 lymDrop <tibble> <htest> -0.354 1.37 1.72 -1.01 0.418 2.04
2 dropRate <tibble> <htest> -0.00180 0.00649 0.00829 -1.60 0.225 2.48
# … with 4 more variables: conf.low <dbl>, conf.high <dbl>, method <chr>,
# alternative <chr>, and abbreviated variable names ¹statistic, ²parameter
# ℹ Use `colnames()` to see all variable namesggplot(testTab, aes(x=cluster, y = value, col = cluster)) +
#geom_boxplot(outlier.shape = NA, width =0.3) +
geom_point(size=3) +
scale_color_manual(values = annoCol$cluster) +
facet_wrap(~name, scale = "free") +
theme_my
Compare the consistency of clustering in the three datasets (EMBL2016, CPS1000)
All patients
All samples
library(ggsankey)
cEMBL <- read_csv2("../output/consClust_EMBL2016.csv") %>%
select(patientID, cluster) %>% dplyr::rename(EMBL2016 = cluster)
cCPS <- read_csv2("../output/consClust_CPS.csv") %>%
select(patientID, cluster) %>% dplyr::rename(CPS1000 = cluster)
#cIC50 <- read_csv2("../output/consClust_IC50.csv") %>%
# select(patientID, cluster) %>% dplyr::rename(IC50 = cluster)
comTab <- full_join(cEMBL, cCPS, by = "patientID") %>%
#full_join(cIC50, by = "patientID") %>%
filter(!is.na(EMBL2016),!is.na(CPS1000)) %>%
make_long(EMBL2016, CPS1000)
ggplot(comTab, aes(x = x,
next_x = next_x,
node = node,
next_node = next_node,
fill = node)) +
geom_sankey() +
theme_sankey(base_size = 16) +
xlab("")
M-CLL samples
library(ggsankey)
cEMBL <- read_csv2("../output/consClust_EMBL2016.csv") %>%
filter(IGHV.status == "M") %>%
select(patientID, cluster) %>% dplyr::rename(EMBL2016 = cluster)
cCPS <- read_csv2("../output/consClust_CPS.csv") %>%
select(patientID, cluster) %>% dplyr::rename(CPS1000 = cluster)
#cIC50 <- read_csv2("../output/consClust_IC50.csv") %>%
# select(patientID, cluster) %>% dplyr::rename(IC50 = cluster)
comTab <- full_join(cEMBL, cCPS, by = "patientID") %>%
#full_join(cIC50, by = "patientID") %>%
filter(!is.na(EMBL2016),!is.na(CPS1000)) %>%
make_long(EMBL2016, CPS1000)
ggplot(comTab, aes(x = x,
next_x = next_x,
node = node,
next_node = next_node,
fill = node)) +
geom_sankey() +
theme_sankey(base_size = 16) +
xlab("")
U-CLL samples
library(ggsankey)
cEMBL <- read_csv2("../output/consClust_EMBL2016.csv") %>%
filter(IGHV.status == "U") %>%
select(patientID, cluster) %>% dplyr::rename(EMBL2016 = cluster)
cCPS <- read_csv2("../output/consClust_CPS.csv") %>%
select(patientID, cluster) %>% dplyr::rename(CPS1000 = cluster)
#cIC50 <- read_csv2("../output/consClust_IC50.csv") %>%
# select(patientID, cluster) %>% dplyr::rename(IC50 = cluster)
comTab <- full_join(cEMBL, cCPS, by = "patientID") %>%
#full_join(cIC50, by = "patientID") %>%
filter(!is.na(EMBL2016),!is.na(CPS1000)) %>%
make_long(EMBL2016, CPS1000)
ggplot(comTab, aes(x = x,
next_x = next_x,
node = node,
next_node = next_node,
fill = node)) +
geom_sankey() +
theme_sankey(base_size = 16) +
xlab("")
Only patients with the same samples used for both screens
Get the patient list with same samples used for both screens
load("~/CLLproject_jlu/var/CPS1000_mainAnalysis.RData")
cpsScreen <- pheno1000_main
load("../output/screenData.RData")
emblScreen <- screenData
overSample <- intersect(cpsScreen$sampleID, emblScreen$sampleID)
overPat <- filter(emblScreen, sampleID %in% overSample)$patientIDCompare all CLL samples
All samples
library(ggsankey)
cEMBL <- read_csv2("../output/consClust_EMBL2016.csv") %>%
select(patientID, cluster) %>% dplyr::rename(EMBL2016 = cluster)
cCPS <- read_csv2("../output/consClust_CPS.csv") %>%
select(patientID, cluster) %>% dplyr::rename(CPS1000 = cluster)
#cIC50 <- read_csv2("../output/consClust_IC50.csv") %>%
# select(patientID, cluster) %>% dplyr::rename(IC50 = cluster)
comTab <- full_join(cEMBL, cCPS, by = "patientID") %>%
filter(patientID %in% overPat) %>%
#full_join(cIC50, by = "patientID") %>%
filter(!is.na(EMBL2016),!is.na(CPS1000)) %>%
make_long(EMBL2016, CPS1000)
ggplot(comTab, aes(x = x,
next_x = next_x,
node = node,
next_node = next_node,
fill = node)) +
geom_sankey() +
theme_sankey(base_size = 16) +
xlab("")
M-CLL samples
library(ggsankey)
cEMBL <- read_csv2("../output/consClust_EMBL2016.csv") %>%
filter(IGHV.status == "M") %>%
select(patientID, cluster) %>% dplyr::rename(EMBL2016 = cluster)
cCPS <- read_csv2("../output/consClust_CPS.csv") %>%
select(patientID, cluster) %>% dplyr::rename(CPS1000 = cluster)
#cIC50 <- read_csv2("../output/consClust_IC50.csv") %>%
# select(patientID, cluster) %>% dplyr::rename(IC50 = cluster)
comTab <- full_join(cEMBL, cCPS, by = "patientID") %>%
filter(patientID %in% overPat) %>%
#full_join(cIC50, by = "patientID") %>%
filter(!is.na(EMBL2016),!is.na(CPS1000)) %>%
make_long(EMBL2016, CPS1000)
ggplot(comTab, aes(x = x,
next_x = next_x,
node = node,
next_node = next_node,
fill = node)) +
geom_sankey() +
theme_sankey(base_size = 16) +
xlab("")
U-CLL samples
library(ggsankey)
cEMBL <- read_csv2("../output/consClust_EMBL2016.csv") %>%
filter(IGHV.status == "U") %>%
select(patientID, cluster) %>% dplyr::rename(EMBL2016 = cluster)
cCPS <- read_csv2("../output/consClust_CPS.csv") %>%
select(patientID, cluster) %>% dplyr::rename(CPS1000 = cluster)
#cIC50 <- read_csv2("../output/consClust_IC50.csv") %>%
# select(patientID, cluster) %>% dplyr::rename(IC50 = cluster)
comTab <- full_join(cEMBL, cCPS, by = "patientID") %>%
filter(patientID %in% overPat) %>%
#full_join(cIC50, by = "patientID") %>%
filter(!is.na(EMBL2016),!is.na(CPS1000)) %>%
make_long(EMBL2016, CPS1000)
ggplot(comTab, aes(x = x,
next_x = next_x,
node = node,
next_node = next_node,
fill = node)) +
geom_sankey() +
theme_sankey(base_size = 16) +
xlab("")
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] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] ggsankey_0.0.99999 forcats_0.5.1
[3] stringr_1.4.1 dplyr_1.0.9
[5] purrr_0.3.4 readr_2.1.2
[7] tidyr_1.2.0 tibble_3.1.8
[9] tidyverse_1.3.2 missForest_1.5
[11] Rtsne_0.16 pheatmap_1.0.12
[13] proDA_1.10.0 DESeq2_1.36.0
[15] SummarizedExperiment_1.26.1 Biobase_2.56.0
[17] MatrixGenerics_1.8.1 matrixStats_0.62.0
[19] GenomicRanges_1.48.0 GenomeInfoDb_1.32.2
[21] IRanges_2.30.0 S4Vectors_0.34.0
[23] BiocGenerics_0.42.0 survminer_0.4.9
[25] ggpubr_0.4.0 ggplot2_3.4.1
[27] survival_3.4-0 cowplot_1.1.1
[29] ConsensusClusterPlus_1.60.0
loaded via a namespace (and not attached):
[1] shinydashboard_0.7.2 utf8_1.2.2 tidyselect_1.1.2
[4] htmlwidgets_1.5.4 RSQLite_2.2.15 AnnotationDbi_1.58.0
[7] grid_4.2.0 BiocParallel_1.30.3 maxstat_0.7-25
[10] munsell_0.5.0 ragg_1.2.2 codetools_0.2-18
[13] DT_0.23 withr_2.5.0 colorspace_2.0-3
[16] highr_0.9 knitr_1.39 rstudioapi_0.13
[19] ggsignif_0.6.3 labeling_0.4.2 git2r_0.30.1
[22] slam_0.1-50 GenomeInfoDbData_1.2.8 KMsurv_0.1-5
[25] bit64_4.0.5 farver_2.1.1 rprojroot_2.0.3
[28] vctrs_0.5.2 generics_0.1.3 TH.data_1.1-1
[31] xfun_0.31 itertools_0.1-3 randomForest_4.7-1.1
[34] sets_1.0-21 markdown_1.1 R6_2.5.1
[37] ggbeeswarm_0.6.0 locfit_1.5-9.6 fgsea_1.22.0
[40] bitops_1.0-7 cachem_1.0.6 DelayedArray_0.22.0
[43] assertthat_0.2.1 promises_1.2.0.1 scales_1.2.0
[46] vroom_1.5.7 multcomp_1.4-19 googlesheets4_1.0.0
[49] beeswarm_0.4.0 gtable_0.3.0 extraDistr_1.9.1
[52] sandwich_3.0-2 workflowr_1.7.0 rlang_1.0.6
[55] genefilter_1.78.0 systemfonts_1.0.4 splines_4.2.0
[58] rstatix_0.7.0 gargle_1.2.0 broom_1.0.0
[61] BiocManager_1.30.18 yaml_2.3.5 abind_1.4-5
[64] modelr_0.1.8 crosstalk_1.2.0 backports_1.4.1
[67] httpuv_1.6.6 gridtext_0.1.4 tools_4.2.0
[70] relations_0.6-12 ellipsis_0.3.2 gplots_3.1.3
[73] jquerylib_0.1.4 RColorBrewer_1.1-3 Rcpp_1.0.9
[76] visNetwork_2.1.0 zlibbioc_1.42.0 RCurl_1.98-1.7
[79] zoo_1.8-10 ggrepel_0.9.1 haven_2.5.0
[82] cluster_2.1.3 exactRankTests_0.8-35 fs_1.5.2
[85] magrittr_2.0.3 data.table_1.14.2 reprex_2.0.1
[88] googledrive_2.0.0 mvtnorm_1.1-3 shinyjs_2.1.0
[91] hms_1.1.1 mime_0.12 evaluate_0.15
[94] xtable_1.8-4 XML_3.99-0.10 readxl_1.4.0
[97] gridExtra_2.3 compiler_4.2.0 KernSmooth_2.23-20
[100] crayon_1.5.2 htmltools_0.5.4 later_1.3.0
[103] tzdb_0.3.0 ggtext_0.1.1 geneplotter_1.74.0
[106] lubridate_1.8.0 DBI_1.1.3 dbplyr_2.2.1
[109] MASS_7.3-58 jyluMisc_0.1.5 BiocStyle_2.24.0
[112] Matrix_1.4-1 car_3.1-0 cli_3.4.1
[115] marray_1.74.0 parallel_4.2.0 igraph_1.3.4
[118] pkgconfig_2.0.3 km.ci_0.5-6 piano_2.12.0
[121] xml2_1.3.3 foreach_1.5.2 annotate_1.74.0
[124] vipor_0.4.5 bslib_0.4.1 rngtools_1.5.2
[127] XVector_0.36.0 drc_3.0-1 rvest_1.0.2
[130] doRNG_1.8.2 digest_0.6.30 Biostrings_2.64.0
[133] fastmatch_1.1-3 rmarkdown_2.14 cellranger_1.1.0
[136] survMisc_0.5.6 shiny_1.7.4 gtools_3.9.3
[139] lifecycle_1.0.3 jsonlite_1.8.3 carData_3.0-5
[142] limma_3.52.2 fansi_1.0.3 pillar_1.8.0
[145] lattice_0.20-45 KEGGREST_1.36.3 fastmap_1.1.0
[148] httr_1.4.3 plotrix_3.8-2 glue_1.6.2
[151] png_0.1-7 iterators_1.0.14 bit_4.0.4
[154] stringi_1.7.8 sass_0.4.2 blob_1.2.3
[157] textshaping_0.3.6 caTools_1.18.2 memoise_2.0.1