Analyses related to Illumina control SNPs and fCPGs, Gabbutt and Duran-Ferrer 2025
10/07/2025
Analyses realted to control probes from Illumina arrays, showing that fCpGs are not SNPs.
1 Load packages and fCpGs information
##
## Load necessary packages and set global options
##
library(data.table)
library(janitor)
library(dplyr)
library(ggpubr)
library(pals)
library(GenomicRanges)
library(openxlsx)
library(Biostrings)
library(IlluminaHumanMethylation450kanno.ilmn12.hg19)
library(patchwork)
library(ComplexHeatmap)
library(circlize)
library(pals)
library(SNPlocs.Hsapiens.dbSNP155.GRCh38)
library(MafH5.gnomAD.v4.0.GRCh38)
options(stringsAsFactors = F,max.print = 10000,error=NULL,
"openxlsx.dateFormat" = "dd/mm/yyyy"
)
## load fCpGs with meth values
fcpgs.betas <-
fread("../Data/FMC/Final_data/beta_filtered_samples_fcpgs_laplacian.tsv",data.table = F) %>%
tibble::column_to_rownames(var = "V1")
fcpgs.betas[1:5,1:5]
dim(fcpgs.betas)
##load all metadata
sheets <- getSheetNames("../Revision/Data/Supplementary_Tables_revision_curated.3.xlsx")
sheets <- setNames(sheets,sheets)
fCpGs.metadata <- lapply(sheets,function(i){
openxlsx::read.xlsx("../Revision/Data/Supplementary_Tables_revision_curated.3.xlsx",sheet = i,detectDates = T)
})2 fCpG hg38
fcpgs.hg19.CG <- minfi::getAnnotation(IlluminaHumanMethylation450kanno.ilmn12.hg19)[rownames(fcpgs.betas),] %>%
as.data.frame()
fcpgs.hg19.CG <-
GRanges(fcpgs.hg19.CG$chr,IRanges(fcpgs.hg19.CG$pos,fcpgs.hg19.CG$pos),strand = fcpgs.hg19.CG$strand,
fcpgs.hg19.CG %>% select(-c(chr,pos,strand))
)
fcpgs.hg19.CG
getSeq(BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19,fcpgs.hg19.CG) %>% as.character() %>% table
getSeq(BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19,fcpgs.hg19.CG[strand(fcpgs.hg19.CG)=="+"]) %>% as.character() %>% table
getSeq(BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19,fcpgs.hg19.CG[strand(fcpgs.hg19.CG)=="-"]) %>% as.character() %>% table
##perform liftover
hg19.to.hg38.chain <- rtracklayer::import.chain(con = "../Revision/Data/Nanopore/hg19ToHg38.over.chain")
fcpgs.hg38.CG <- rtracklayer::liftOver(x = fcpgs.hg19.CG,chain = hg19.to.hg38.chain)
fcpgs.hg38.CG <- unlist(fcpgs.hg38.CG)
length(fcpgs.hg19.CG)
length(fcpgs.hg38.CG)
fcpgs.hg19.CG$Name[which(!fcpgs.hg19.CG$Name %in% fcpgs.hg38.CG$Name)]
fcpgs.hg38.CG
getSeq(BSgenome.Hsapiens.UCSC.hg38::BSgenome.Hsapiens.UCSC.hg38,fcpgs.hg38.CG) %>% as.character() %>% table
getSeq(BSgenome.Hsapiens.UCSC.hg38::BSgenome.Hsapiens.UCSC.hg38,fcpgs.hg38.CG[strand(fcpgs.hg38.CG)=="+"]) %>% as.character() %>% table
getSeq(BSgenome.Hsapiens.UCSC.hg38::BSgenome.Hsapiens.UCSC.hg38,fcpgs.hg38.CG[strand(fcpgs.hg38.CG)=="-"]) %>% as.character() %>% table
fcpgs.hg38.CG[strand(fcpgs.hg38.CG)=="-"] %>% granges()
##liftover do not work on this CpG
fcpgs.hg38.CG[strand(fcpgs.hg38.CG)=="-"][which(getSeq(BSgenome.Hsapiens.UCSC.hg38::BSgenome.Hsapiens.UCSC.hg38,fcpgs.hg38.CG[strand(fcpgs.hg38.CG)=="-"]) %>% as.character() !="G"),]
exclude <- fcpgs.hg38.CG[strand(fcpgs.hg38.CG)=="-"]$Name[which(getSeq(BSgenome.Hsapiens.UCSC.hg38::BSgenome.Hsapiens.UCSC.hg38,fcpgs.hg38.CG[strand(fcpgs.hg38.CG)=="-"]) %>% as.character() !="G")]
exclude
fcpgs.hg38.CG <- fcpgs.hg38.CG[fcpgs.hg38.CG$Name!=exclude]
fcpgs.hg38.CG
#check nucleotides ## OK!
getSeq(BSgenome.Hsapiens.UCSC.hg38::BSgenome.Hsapiens.UCSC.hg38,fcpgs.hg38.CG[strand(fcpgs.hg38.CG)=="+"]) %>% as.character() %>% table
getSeq(BSgenome.Hsapiens.UCSC.hg38::BSgenome.Hsapiens.UCSC.hg38,fcpgs.hg38.CG[strand(fcpgs.hg38.CG)=="-"]) %>% as.character() %>% table3 Control probes
We next load Illumina control SNP probes for each processed batch. We get the methyaltion value of the 59 control probes for all the samples (n=2,204)
data.paths <- list.files("../Data",pattern = "RGset.qs",recursive = T,full.names = T)
data.paths <- setNames(data.paths,
strsplit(data.paths,"/") %>% sapply(.,function(x)x[5]) %>% gsub("QC_|_RGset.qs","",.)
)
data.paths
ctl.snps.betas <- parallel::mclapply(data.paths,function(file.i){
writeLines(file.i)
RGset <- qs::qread(file.i)
getSnpBeta(RGset)
},mc.cores = 8)
sapply(ctl.snps.betas,nrow)
ctl.snps.betas$Bcells_Kulis_2015_450k %>% head
shared.ctl.snps.betas <- do.call(cbind,lapply(ctl.snps.betas,function(x)x[Reduce(intersect,lapply(ctl.snps.betas,rownames)),]))
head(shared.ctl.snps.betas);dim(shared.ctl.snps.betas)
##retain samples used in the study, with same order as emtadata
table(fCpGs.metadata$supplementary_table_1$participant_id_anonymous %in% colnames(shared.ctl.snps.betas))
shared.ctl.snps.betas <- shared.ctl.snps.betas[,fCpGs.metadata$supplementary_table_1$participant_id_anonymous]
head(shared.ctl.snps.betas);dim(shared.ctl.snps.betas)4 SNP annotation
We next use annotation packages to annotate the minor allele frequency and SNP name of all the cntrol probes provided by Illumina.
snps <- SNPlocs.Hsapiens.dbSNP155.GRCh38
mafh5 <- MafH5.gnomAD.v4.0.GRCh38
ctl.snps.hg38 <- snpsById(snps,rownames(shared.ctl.snps.betas),
ifnotfound="warning",
genome=BSgenome.Hsapiens.UCSC.hg38::BSgenome.Hsapiens.UCSC.hg38)
# rs13369115 has merged into rs10155413, change it accordingly to get hg38 coordinates (checks at web dbSNP)
rownames(shared.ctl.snps.betas)[which(rownames(shared.ctl.snps.betas)=="rs13369115")] <- "rs10155413"
ctl.snps.hg38 <- snpsById(snps,rownames(shared.ctl.snps.betas),
ifnotfound="warning",
genome=BSgenome.Hsapiens.UCSC.hg38::BSgenome.Hsapiens.UCSC.hg38)
ctl.snps.hg38
##overlap with rs IDs
ctl.snps.hg38.mAF <- gscores(x = mafh5,ranges = ctl.snps.hg38,pop=populations(mafh5))
ctl.snps.hg38.mAF$AF %>% summary
identical(ctl.snps.hg38.mAF$RefSNP_id,rownames(shared.ctl.snps.betas))5 Heatmap control SNPs
We next plot the methylation of control SNPs as well as the retrieved information
##plot
evoflux.meth <- fread("../Revision/Data/meth_palette_evoflux.tsv")
evoflux.meth
fCpGs.metadata$supplementary_table_1 %>% dplyr::count(cell_type_annot_1,cell_type_annot_3)
# color smaples
cols.paper <- c(
NA_values="#666666",
HPC="#E6E6E6",
pre.Bcell="#D5D5D5",
NBC="#C3C3C3",
GCB="#AEAEAE",
tPC="#969696",
MBC="#787878",
bmPC="#4D4D4D",
Bcell="#C3C3C3",
Tcell="#F0F8FF",
PBMCs="#FFF8DC",
Whole_blood="#CDC8B1",
"Whole blood"="#CDC8B1",
Normal_lymphoid_cell="#D5D5D5",
Normal_myeloid_cell="#E6E6FA",
"T-ALL"="#CD3333",
"T-ALL-remission"="#C7A5A5",
"T-ALL-relapse"="#CD3333",
"B-ALL"="#E69F00",
"B-ALL-remission"="#D8CAAB",
"B-ALL-relapse"="#E69F00",
MCL="#F0E442",
C1="#F5D51B",
cMCL="#F5D51B",
C2="#0072B2",
nnMCL="#0072B2",
"DLBCL-NOS"="#56B4E9",
MBL="#5A9A89",
CLL="#009E73",
RT="#02C590",
unmutated="#e65100",
mutated="#361379",
nCLL="#006E93",
iCLL="#FDC010",
mCLL="#963736",
Disease.Unclassified="#CCCCCC",
MGUS="#BF99AE",
MM="#CC79A7"
)
ht_opt$legend_grid_width <- unit(2,"mm")
ht_opt$legend_grid_height <-unit(2,"mm")
ht_opt$legend_labels_gp <- gpar(fontsize=5)
ht_opt$legend_title_gp <- gpar(fontsize=5)
ht_opt$heatmap_column_title_gp <- gpar(fontsize=5)
ht_opt$heatmap_row_title_gp <- gpar(fontsize=5)
ht_opt$heatmap_row_names_gp <- gpar(fontsize=5)
ht_opt$heatmap_column_names_gp <- gpar(fontsize=5)
ht_opt$HEATMAP_LEGEND_PADDING <- unit(1,"mm")
ht_opt$ANNOTATION_LEGEND_PADDING <- unit(1,"mm")
ht_opt$legend_gap <- unit(c(1,1),"mm")
ht_opt$fast_hclust <- T
h.ctl.snps <- Heatmap(shared.ctl.snps.betas,
col = evoflux.meth$color,
name="DNA methylation",
column_title = paste0("Control SNPs from Illumina 450k/EPIC arrays (N=",nrow(shared.ctl.snps.betas),")"),
row_title = paste0("N=",nrow(shared.ctl.snps.betas)),
heatmap_legend_param = list(direction="horizontal"),
clustering_distance_columns = "euclidean",
clustering_method_columns = "single",
show_column_names = F,
show_row_names = F,
row_dend_gp = gpar(lwd=0.1),
column_dend_gp = gpar(lwd=0.1),
raster_quality = 10,
top_annotation = HeatmapAnnotation(annot2=factor(case_when(fCpGs.metadata$supplementary_table_1$cell_type_annot_1!="PBMCs" &
fCpGs.metadata$supplementary_table_1$cell_type_annot_1!="Whole_blood" &
fCpGs.metadata$supplementary_table_1$cell_type_annot_1!="Normal_lymphoid_cell" &
fCpGs.metadata$supplementary_table_1$cell_type_annot_1!="B-ALL-remission" &
fCpGs.metadata$supplementary_table_1$cell_type_annot_1!="T-ALL-remission" ~ "Lymphoid tumour",
.default = "Healthy sample/Blood from remission")
),
annot=factor(fCpGs.metadata$supplementary_table_1$cell_type_annot_1,
levels = names(cols.paper)[names(cols.paper) %in% fCpGs.metadata$supplementary_table_1$cell_type_annot_1]),
col=list(annot=cols.paper,
annot2=c("Healthy sample/Blood from remission"="grey0","Lymphoid tumour"="grey90")
),
show_annotation_name = F,
annotation_label = "Samples",
simple_anno_size = unit(3,"mm"),
annotation_legend_param = list(direction="horizontal",nrow=3)
),
right_annotation = HeatmapAnnotation(which="row",
mAF=ctl.snps.hg38.mAF$AF,
col=list(mAF=colorRamp2(seq(min(ctl.snps.hg38.mAF$AF,na.rm = T),
max(ctl.snps.hg38.mAF$AF,na.rm = T),
length.out=100
),
colors = brewer.blues(100)
)
),
na_col = "grey45",
show_annotation_name = F,
# annotation_name_side = "left",
show_legend = T,
annotation_label = "mAF reporeted in gnomAD.v4.0",
annotation_name_gp = gpar(fontsize=5),
simple_anno_size = unit(2,"mm"),
annotation_legend_param = list(direction="horizontal")
)
)
draw(h.ctl.snps,
heatmap_legend_side = "bottom",
annotation_legend_side = "bottom"
)
6 Heatmap fCpGs
We next plot the methylation of 59 random fCpGs for comparison, and see a completely distinct scenario.
##random. 59 fCPGs
set.seed(123) # for reproducibility
fCpGs.59 <- sample(x = rownames(fcpgs.betas),size = 59,replace = F)
fCpGs.59
h.fCpGs.59 <- Heatmap(fcpgs.betas[fCpGs.59,],
col = evoflux.meth$color,
name="DNA methylation",
column_title = paste0("59 random Pan-lymphoid fCpGs (N=",length(fCpGs.59),")"),
row_title = paste0("N=",length(fCpGs.59)),
heatmap_legend_param = list(direction="horizontal"),
clustering_distance_columns = "euclidean",
clustering_method_columns = "single",
show_column_names = F,
show_row_names = F,
row_dend_gp = gpar(lwd=0.1),
column_dend_gp = gpar(lwd=0.1),
raster_quality = 10,
top_annotation = HeatmapAnnotation(annot=factor(fCpGs.metadata$supplementary_table_1$cell_type_annot_1,
levels = names(cols.paper)[names(cols.paper) %in% fCpGs.metadata$supplementary_table_1$cell_type_annot_1]),
col=list(annot=cols.paper),
show_annotation_name = F,
annotation_label = "Samples",
simple_anno_size = unit(3,"mm"),
annotation_legend_param = list(direction="horizontal",nrow=3)
)
)
draw(h.fCpGs.59,
heatmap_legend_side = "bottom",
annotation_legend_side = "bottom"
)
7 Histrograms SNPs vs fCpGs
We next plot example histograms in normal lymphoid cells of SNP control probes vs 59 random fCPGs, showing very distinct distributions.
## plot histogram distributions
shared.ctl.snps.betas %>% dim
fCpGs.metadata$supplementary_table_1 %>% dplyr::count(cell_type_annot_1)
identical(colnames(shared.ctl.snps.betas),fCpGs.metadata$supplementary_table_1$participant_id_anonymous)
shared.ctl.snps.betas %>%
as.data.frame() %>%
select( fCpGs.metadata$supplementary_table_1 %>%
filter(cell_type_annot_1=="PBMCs" | cell_type_annot_1=="Whole_blood"
| cell_type_annot_1=="Normal_lymphoid_cell"
) %>%
pull(participant_id_anonymous)
) %>%
reshape2::melt() %>%
mutate(fill=cut(value,breaks = seq(0,1,length.out=101),include.lowest = T,labels = 1:100)) %>%
ggplot(aes(x=value))+
geom_histogram(fill="grey70")+
coord_cartesian(ylim = c(0,15))+
# scale_fill_manual(values = setNames(evoflux.meth$color,1:100))+
facet_wrap(~variable,nrow = 8,ncol = 11,
# scales = "free_y"
)+
ggtitle("Control SNPs (N=59) from Illumina arrays (shared probes 450k & EPIC) in normal lymphoid samples")+
theme_bw()+
theme(text = element_text(size = 4),
panel.grid = element_blank(),
legend.position = "none",
legend.margin = margin(0,0,0,0),
plot.margin = margin(0,0,0,0),
line = element_line(linewidth = 0.1),
strip.placement = "outside",
strip.background = element_blank(),
plot.background = element_blank(),
panel.background = element_blank(),
panel.spacing = unit(0.2,"pt"),
axis.title.y = element_text(size = 6),
axis.text.x = element_text(angle = 90,hjust = 1,vjust = 1,size = 4,margin = margin(0,0,0,0,"pt")),
strip.text.x = element_text(angle = 0,size = 4,vjust = 0.5,hjust = 0.5),
legend.key.size = unit(5,"pt")
) |
fcpgs.betas[fCpGs.59,] %>%
as.data.frame() %>%
select( fCpGs.metadata$supplementary_table_1 %>%
filter(cell_type_annot_1=="PBMCs" | cell_type_annot_1=="Whole_blood"
| cell_type_annot_1=="Normal_lymphoid_cell"
) %>%
pull(participant_id_anonymous)
) %>%
reshape2::melt() %>%
mutate(fill=cut(value,breaks = seq(0,1,length.out=101),include.lowest = T,labels = 1:100)) %>%
ggplot(aes(x=value))+
geom_histogram(fill="grey70")+
coord_cartesian(ylim = c(0,15))+
# scale_fill_manual(values = setNames(evoflux.meth$color,1:100))+
facet_wrap(~variable,nrow = 8,ncol = 11,
# scales = "free_y"
)+
theme_bw()+
ggtitle("Random fCpGs (N=59) in normal lymphoid samples")+
theme(text = element_text(size = 4),
panel.grid = element_blank(),
legend.position = "none",
legend.margin = margin(0,0,0,0),
plot.margin = margin(0,0,0,0),
line = element_line(linewidth = 0.1),
strip.placement = "outside",
strip.background = element_blank(),
plot.background = element_blank(),
panel.background = element_blank(),
panel.spacing = unit(0.2,"pt"),
axis.title.y = element_text(size = 6),
axis.text.x = element_text(angle = 90,hjust = 1,vjust = 1,size = 4,margin = margin(0,0,0,0,"pt")),
strip.text.x = element_text(angle = 0,size = 4,vjust = 0.5,hjust = 0.5),
legend.key.size = unit(5,"pt")
)
8 Intermediate values
We see how SNPs and fCpGs have a completely distinc distribution of intermediate methylation values.
##
##Plot percentage of intermediate meth values
##
identical(colnames(shared.ctl.snps.betas),fCpGs.metadata$supplementary_table_1$participant_id_anonymous)
fCpGs.metadata$supplementary_table_1 %>% dplyr::count(cell_type_annot_1,cell_type_annot_3)
sample.indx <-
split(seq_along(fCpGs.metadata$supplementary_table_1$participant_id_anonymous),
fCpGs.metadata$supplementary_table_1$cell_type_annot_3
)
snps.pct.intermediate <-
lapply(sample.indx,function(indx){
mat <- apply(shared.ctl.snps.betas[,indx],2,function(x){
(x>0.2 & x <0.4) | (x>0.6 & x <0.8)
})
res <- rowMeans(mat) %>% sort()*100
return(res)
})
identical(colnames(fcpgs.betas),fCpGs.metadata$supplementary_table_1$participant_id_anonymous)
fCpGs.pct.intermediate <-
lapply(sample.indx,function(indx){
mat <- apply(fcpgs.betas[,indx],2,function(x){
(x>0.2 & x <0.4) | (x>0.6 & x <0.8)
})
res <- rowMeans(mat) %>% sort()*100
return(res)
})
##
bind_rows(snps.pct.intermediate %>% reshape2::melt() %>% mutate(CpGs="Control SNPs"),
fCpGs.pct.intermediate %>% reshape2::melt() %>% mutate(CpGs="fCpGs")
) %>%
rename(L1="samples",
value="pct"
) %>%
mutate(samples=factor(samples,
levels=names(cols.paper)[names(cols.paper) %in% fCpGs.metadata$supplementary_table_1$cell_type_annot_3])
) %>%
ggplot(aes(x = CpGs,
y=pct
))+
geom_boxplot(aes(fill=samples),lwd=0.2,outlier.size = 0.5)+
facet_grid(~samples)+
stat_compare_means(method = "t.test",size=1)+
xlab(NULL)+
ylab("Percentange (%) of CpGs in intermediate peaks (x>0.2 & x <0.4) | (x>0.6 & x <0.8)")+
scale_fill_manual(values = cols.paper)+
theme_classic()+
theme(text = element_text(colour = "grey0",size = 5),
panel.grid.major = element_line(linewidth = 0.075),
# panel.grid.minor.x = element_line(linewidth = 0.025),
strip.placement = "out",
strip.background = element_blank(),
strip.text = element_text(angle = 0),
plot.margin = unit(c(0,0,0,0),"pt"),
line=element_line(linewidth = 0.1),
legend.position = "none",
legend.box.margin=margin(-5,-5,-5,-5),
# legend.justification="top",
axis.text.x = element_text(angle = 90,vjust = 1,hjust = 1),
legend.text = element_text(angle = 90,vjust = 0.5,hjust = 0.5),
plot.background =element_blank(),
legend.key.size = unit(5,"pt")
)
9 Longitudinal samples
We next follow our analsyes plotting the methylation dynamics of SNPs vs fCpGs in longitudinal samples experiencing (sub)clonal evolution in ALL and CLL, thorugh diagnosis, relapse or Richter transformation (in CLL). While we see the same methylation over time in control SNPs, we observe how fCpG methylation values vary hugely over time during (sub)clonal evolution, even with only 59 fCpGs.
9.1 CLL
9.1.1 Control SNPs
## CLL
rt.meta <-
fCpGs.metadata$supplementary_table_1 %>%
arrange(same_sample_id_new,sample_timepoints,factor(platform,levels=c("Illumina-EPIC","Illumina-450k"))) %>%
filter(grepl("SCLL-012|SCLL-019",participant_id_new),
)
rt.meta
rt.meta %>% filter(duplicated(same_sample_id_new) | duplicated(same_sample_id_new,fromLast=T)) %>%
select(sample_id,sample_id_new,sample_timepoints,platform,sampling_time_disease_state)
rt.meta <-
rt.meta %>%
filter(!(sample_id_new=="012-02-1TD" & platform=="Illumina-450k"),
!(sample_id_new=="019-0047-01TD" & platform=="Illumina-450k")
) %>%
mutate(case=gsub("SCLL-|0","",participant_id_new))
rt.meta %>%
select(sample_id_new,case,sampling_time_disease_state,sample_timepoints,platform)
rt.meta %>% select(case,sample_timepoints)
comparisons <-
paste0(rt.meta$case[-nrow(rt.meta)],"_",
rt.meta$sample_timepoints[-nrow(rt.meta)],"_",
rt.meta$sample_timepoints[-1],"_",
gsub("Illumina-","",rt.meta$platform[-nrow(rt.meta)]),
"_",
gsub("Illumina-","",rt.meta$platform[-1])
)
comparisons
comparisons <- comparisons[comparisons!="12_T4_T1_EPIC_450k"]
comparisons <- setNames(comparisons,comparisons)
comparisons
##plot ctl SNPs
rt.ctl.snps.betas <-
shared.ctl.snps.betas[,rt.meta$participant_id_anonymous]
dim(rt.ctl.snps.betas)
df <- lapply(comparisons, function(comp.i){
case.i <- strsplit(comp.i,"_")[[1]][1]
timepoint.1 <- strsplit(comp.i,"_")[[1]][2]
timepoint.2 <- strsplit(comp.i,"_")[[1]][3]
sample.t1 <-
rt.meta %>%
filter(case==case.i,sample_timepoints==timepoint.1) %>%
pull(participant_id_anonymous)
sample.t2 <-
rt.meta %>%
filter(case==case.i,sample_timepoints==timepoint.2) %>%
pull(participant_id_anonymous)
dat <- data.frame(case=case.i,
comparison=comp.i,
samples=paste0("sample.t1:",sample.t1,", sample.t2:",sample.t2),
# cpgs_diff_direction=rt.ctl.snps.betas[,sample.t2]-rt.ctl.snps.betas[,sample.t1],
cpgs_diff_abs=abs((rt.ctl.snps.betas[,sample.t2]-rt.ctl.snps.betas[,sample.t1])),
rt.ctl.snps.betas[,c(sample.t1,sample.t2)]
)
colnames(dat)[(ncol(dat)-1):ncol(dat)] <- c("meth.T1","meth.T2")
return(dat)
})
df <- do.call(rbind,df)
head(df)
## Loop over each case
for(case.i in unique(df$case)){
p <-
df %>%
filter(case==case.i) %>%
mutate(cpgs_diff_abs=case_when(cpgs_diff_abs>= 0.25 ~ 0.25,
.default = cpgs_diff_abs
)
) %>%
ggplot(aes(x=meth.T2,
y=meth.T1,
fill=cpgs_diff_abs
)
)+
geom_point(pch=21,stroke=0.1,size=0.6)+
scale_fill_gradientn("Absolute methylation\ndifference",limits=c(0,0.25),
colours = viridis(100) %>% rev()
)+
stat_cor(size=1)+
facet_grid(~comparison)+
theme_classic()+
theme(text = element_text(colour = "grey0",size = 5),
panel.grid.major = element_line(linewidth = 0.075),
# panel.grid.minor.x = element_line(linewidth = 0.025),
strip.placement = "out",
strip.background = element_blank(),
strip.text = element_text(angle = 0),
plot.margin = unit(c(0,0,0,0),"pt"),
line=element_line(linewidth = 0.1),
legend.position = "bottom",
legend.box.margin=margin(-5,-5,-5,-5),
# legend.justification="top",
legend.text = element_text(angle = 90,vjust = 0.5,hjust = 0.5),
plot.background =element_blank(),
legend.key.size = unit(5,"pt")
)
print(p)
}
9.1.2 Random fCpGs
##plot random 59 fCPGs
##plot ctl SNPs
set.seed(123)
fCpGs.59 <- sample(x = rownames(fcpgs.betas),size = 59,replace = F)
fCpGs.59
random.59.fCpGs.betas <-
fcpgs.betas[fCpGs.59,rt.meta$participant_id_anonymous]
dim(random.59.fCpGs.betas)
df <- lapply(comparisons, function(comp.i){
case.i <- strsplit(comp.i,"_")[[1]][1]
timepoint.1 <- strsplit(comp.i,"_")[[1]][2]
timepoint.2 <- strsplit(comp.i,"_")[[1]][3]
sample.t1 <-
rt.meta %>%
filter(case==case.i,sample_timepoints==timepoint.1) %>%
pull(participant_id_anonymous)
sample.t2 <-
rt.meta %>%
filter(case==case.i,sample_timepoints==timepoint.2) %>%
pull(participant_id_anonymous)
dat <- data.frame(case=case.i,
comparison=comp.i,
samples=paste0("sample.t1:",sample.t1,", sample.t2:",sample.t2),
cpgs_diff_abs=abs((random.59.fCpGs.betas[,sample.t2]-random.59.fCpGs.betas[,sample.t1])),
random.59.fCpGs.betas[,c(sample.t1,sample.t2)]
)
colnames(dat)[(ncol(dat)-1):ncol(dat)] <- c("meth.T1","meth.T2")
return(dat)
})
df <- do.call(rbind,df)
head(df)
## loop over each case
for(case.i in unique(df$case)){
p <-
df %>%
filter(case==case.i) %>%
mutate(cpgs_diff_abs=case_when(cpgs_diff_abs>= 0.25 ~ 0.25,
.default = cpgs_diff_abs
)
) %>%
ggplot(aes(x=meth.T2,
y=meth.T1,
fill=cpgs_diff_abs
)
)+
geom_point(pch=21,stroke=0.1,size=0.6)+
scale_fill_gradientn("Absolute methylation\ndifference",limits=c(0,0.25),
colours = viridis(100) %>% rev()
)+
stat_cor(size=1)+
facet_grid(~comparison)+
theme_classic()+
theme(text = element_text(colour = "grey0",size = 5),
panel.grid.major = element_line(linewidth = 0.075),
# panel.grid.minor.x = element_line(linewidth = 0.025),
strip.placement = "out",
strip.background = element_blank(),
strip.text = element_text(angle = 0),
plot.margin = unit(c(0,0,0,0),"pt"),
line=element_line(linewidth = 0.1),
legend.position = "bottom",
legend.box.margin=margin(-5,-5,-5,-5),
# legend.justification="top",
legend.text = element_text(angle = 90,vjust = 0.5,hjust = 0.5),
plot.background =element_blank(),
legend.key.size = unit(5,"pt")
)
print(p)
}
9.2 ALL, 2 timepoints
9.2.1 Control SNPs
fCpGs.metadata$supplementary_table_1 %>% head
fCpGs.metadata$supplementary_table_1 %>% dplyr::count(cell_type_annot_3)
ALL.summarized <-
fCpGs.metadata$supplementary_table_1 %>%
filter(cell_type_annot_3=="B-ALL" | cell_type_annot_3=="B-ALL-remission" | cell_type_annot_3=="B-ALL-relapse",
!is.na(same_sample_id_new)
) %>%
group_by(same_sample_id_new) %>%
summarise(n=n(),
timepoints=list((sample_timepoints)),
unique.timepoints=list(unique(sample_timepoints))
) %>%
filter(n>=2) %>%
arrange(desc(n))
ALL.summarized
ALL.summarized$unique.timepoints[7]
ALL.summarized %>% tail
table(sapply(ALL.summarized$unique.timepoints,length)==ALL.summarized$n)
ALL.meta <-
fCpGs.metadata$supplementary_table_1 %>%
filter(same_sample_id_new %in% ALL.summarized$same_sample_id_new) %>%
arrange(same_sample_id_new,sample_timepoints) %>%
mutate(samples.factors=factor(same_sample_id_new,levels=ALL.summarized$same_sample_id_new))
ALL.meta %>% head
ALL.meta %>% dim
comparisons <- lapply(split(seq_along(ALL.meta$samples.factors),ALL.meta$samples.factors),function(indx){
dat <- ALL.meta[indx,]
res <- paste0(dat$same_sample_anonymous,"_",
dat$sample_timepoints[-nrow(dat)],"_",
dat$sample_timepoints[-1]
)
res <- setNames(res,res)
})
comparisons <- unlist(comparisons,use.names = F)
comparisons <- setNames(comparisons,comparisons)
comparisons
##plot ctl SNPs
ALL.ctl.snps.betas <-
shared.ctl.snps.betas[,ALL.meta$participant_id_anonymous]
dim(ALL.ctl.snps.betas)
df <- lapply(comparisons, function(comp.i){
case.i <- strsplit(comp.i,"_T")[[1]][1]
timepoint.1 <- paste0("T",strsplit(comp.i,"_T")[[1]][2])
timepoint.2 <- paste0("T",strsplit(comp.i,"_T")[[1]][3])
sample.t1 <-
ALL.meta %>%
filter(same_sample_anonymous==case.i,sample_timepoints==timepoint.1) %>%
pull(participant_id_anonymous)
sample.t2 <-
ALL.meta %>%
filter(same_sample_anonymous==case.i,sample_timepoints==timepoint.2) %>%
pull(participant_id_anonymous)
dat <- data.frame(case=case.i,
comparison=comp.i,
samples=paste0("sample.t1:",sample.t1,", sample.t2:",sample.t2),
# cpgs_diff_direction=ALL.ctl.snps.betas[,sample.t2]-ALL.ctl.snps.betas[,sample.t1],
cpgs_diff_abs=abs((ALL.ctl.snps.betas[,sample.t2]-ALL.ctl.snps.betas[,sample.t1])),
ALL.ctl.snps.betas[,c(sample.t1,sample.t2)]
)
colnames(dat)[(ncol(dat)-1):ncol(dat)] <- c("meth.T1","meth.T2")
return(dat)
})
df <- do.call(rbind,df)
head(df)
df %>% dplyr::count(case)
##plot
for(case.i in unique(df$case)){
p <-
df %>%
filter(case==case.i) %>%
mutate(cpgs_diff_abs=case_when(cpgs_diff_abs>= 0.25 ~ 0.25,
.default = cpgs_diff_abs
)
) %>%
ggplot(aes(x=meth.T2,
y=meth.T1,
fill=cpgs_diff_abs
)
)+
geom_point(pch=21,stroke=0.1,size=0.6)+
scale_fill_gradientn("Absolute methylation\ndifference",limits=c(0,0.25),
colours = viridis(100) %>% rev()
)+
stat_cor(size=1)+
facet_grid(~comparison)+
theme_classic()+
theme(text = element_text(colour = "grey0",size = 5),
panel.grid.major = element_line(linewidth = 0.075),
# panel.grid.minor.x = element_line(linewidth = 0.025),
strip.placement = "out",
strip.background = element_blank(),
strip.text = element_text(angle = 0,size=4),
plot.margin = unit(c(0,0,0,0),"pt"),
line=element_line(linewidth = 0.1),
legend.position = "bottom",
legend.box.margin=margin(-5,-5,-5,-5),
# legend.justification="top",
legend.text = element_text(angle = 90,vjust = 0.5,hjust = 0.5),
plot.background =element_blank(),
legend.key.size = unit(5,"pt")
)
print(p)
}
9.2.2 Random fCpGs
#plot 59 random fCpGs
##plot ctl SNPs
set.seed(123)
fCpGs.59 <- sample(x = rownames(fcpgs.betas),size = 59,replace = F)
fCpGs.59
random.59.fCpGs.betas <-
fcpgs.betas[fCpGs.59,ALL.meta$participant_id_anonymous]
dim(random.59.fCpGs.betas)
df <- lapply(comparisons, function(comp.i){
case.i <- strsplit(comp.i,"_T")[[1]][1]
timepoint.1 <- paste0("T",strsplit(comp.i,"_T")[[1]][2])
timepoint.2 <- paste0("T",strsplit(comp.i,"_T")[[1]][3])
sample.t1 <-
ALL.meta %>%
filter(same_sample_anonymous==case.i,sample_timepoints==timepoint.1) %>%
pull(participant_id_anonymous)
sample.t2 <-
ALL.meta %>%
filter(same_sample_anonymous==case.i,sample_timepoints==timepoint.2) %>%
pull(participant_id_anonymous)
dat <- data.frame(case=case.i,
comparison=comp.i,
samples=paste0("sample.t1:",sample.t1,", sample.t2:",sample.t2),
# cpgs_diff_direction=random.59.fCpGs.betas[,sample.t2]-random.59.fCpGs.betas[,sample.t1],
cpgs_diff_abs=abs((random.59.fCpGs.betas[,sample.t2]-random.59.fCpGs.betas[,sample.t1])),
random.59.fCpGs.betas[,c(sample.t1,sample.t2)]
)
colnames(dat)[(ncol(dat)-1):ncol(dat)] <- c("meth.T1","meth.T2")
return(dat)
})
df <- do.call(rbind,df)
head(df)
df %>% dplyr::count(case)
##plot
for(case.i in unique(df$case)){
p <-
df %>%
filter(case==case.i) %>%
mutate(cpgs_diff_abs=case_when(cpgs_diff_abs>= 0.25 ~ 0.25,
.default = cpgs_diff_abs
)
) %>%
ggplot(aes(x=meth.T2,
y=meth.T1,
fill=cpgs_diff_abs
)
)+
geom_point(pch=21,stroke=0.1,size=0.6)+
scale_fill_gradientn("Absolute methylation\ndifference",limits=c(0,0.25),
colours = viridis(100) %>% rev()
)+
stat_cor(size=1)+
facet_grid(~comparison)+
theme_classic()+
theme(text = element_text(colour = "grey0",size = 5),
panel.grid.major = element_line(linewidth = 0.075),
# panel.grid.minor.x = element_line(linewidth = 0.025),
strip.placement = "out",
strip.background = element_blank(),
strip.text = element_text(angle = 0,size=4),
plot.margin = unit(c(0,0,0,0),"pt"),
line=element_line(linewidth = 0.1),
legend.position = "bottom",
legend.box.margin=margin(-5,-5,-5,-5),
# legend.justification="top",
legend.text = element_text(angle = 90,vjust = 0.5,hjust = 0.5),
plot.background =element_blank(),
legend.key.size = unit(5,"pt")
)
print(p)
}
9.3 ALL, 3 timepoints
9.3.1 Control SNPs
fCpGs.metadata$supplementary_table_1 %>% head
fCpGs.metadata$supplementary_table_1 %>% dplyr::count(cell_type_annot_3)
ALL.summarized <-
fCpGs.metadata$supplementary_table_1 %>%
filter(cell_type_annot_3=="B-ALL" | cell_type_annot_3=="B-ALL-remission" | cell_type_annot_3=="B-ALL-relapse",
!is.na(same_sample_id_new)
) %>%
group_by(same_sample_id_new) %>%
summarise(n=n(),
timepoints=list((sample_timepoints)),
unique.timepoints=list(unique(sample_timepoints))
) %>%
filter(n>=2) %>%
arrange(desc(n))
ALL.summarized
ALL.summarized$unique.timepoints[7]
ALL.summarized %>% tail
table(sapply(ALL.summarized$unique.timepoints,length)==ALL.summarized$n)
ALL.meta <-
fCpGs.metadata$supplementary_table_1 %>%
filter(same_sample_id_new %in% ALL.summarized$same_sample_id_new) %>%
arrange(same_sample_id_new,sample_timepoints) %>%
mutate(samples.factors=factor(same_sample_id_new,levels=ALL.summarized$same_sample_id_new))
ALL.meta %>% head
ALL.meta %>% dim
comparisons <- lapply(split(seq_along(ALL.meta$samples.factors),ALL.meta$samples.factors),function(indx){
dat <- ALL.meta[indx,]
res <- paste0(dat$same_sample_id_new,"_",
dat$sample_timepoints[-nrow(dat)],"_",
dat$sample_timepoints[-1]
)
res <- setNames(res,res)
})
comparisons <- unlist(comparisons,use.names = F)
comparisons <- setNames(comparisons,comparisons)
comparisons
##plot ctl SNPs
ALL.ctl.snps.betas <-
shared.ctl.snps.betas[,ALL.meta$participant_id_anonymous]
dim(ALL.ctl.snps.betas)
df <- lapply(comparisons, function(comp.i){
case.i <- strsplit(comp.i,"_T")[[1]][1]
timepoint.1 <- paste0("T",strsplit(comp.i,"_T")[[1]][2])
timepoint.2 <- paste0("T",strsplit(comp.i,"_T")[[1]][3])
sample.t1 <-
ALL.meta %>%
filter(same_sample_id_new==case.i,sample_timepoints==timepoint.1) %>%
pull(participant_id_anonymous)
sample.t2 <-
ALL.meta %>%
filter(same_sample_id_new==case.i,sample_timepoints==timepoint.2) %>%
pull(participant_id_anonymous)
dat <- data.frame(case=case.i,
comparison=comp.i,
samples=paste0("sample.t1:",sample.t1,", sample.t2:",sample.t2),
# cpgs_diff_direction=ALL.ctl.snps.betas[,sample.t2]-ALL.ctl.snps.betas[,sample.t1],
cpgs_diff_abs=abs((ALL.ctl.snps.betas[,sample.t2]-ALL.ctl.snps.betas[,sample.t1])),
ALL.ctl.snps.betas[,c(sample.t1,sample.t2)]
)
colnames(dat)[(ncol(dat)-1):ncol(dat)] <- c("meth.T1","meth.T2")
return(dat)
})
df <- do.call(rbind,df)
head(df)
df %>% dplyr::count(case)
##plot
for(case.i in unique(df$case)){
p <-
df %>%
filter(case==case.i) %>%
mutate(cpgs_diff_abs=case_when(cpgs_diff_abs>= 0.25 ~ 0.25,
.default = cpgs_diff_abs
)
) %>%
ggplot(aes(x=meth.T2,
y=meth.T1,
fill=cpgs_diff_abs
)
)+
geom_point(pch=21,stroke=0.1,size=0.6)+
scale_fill_gradientn("Absolute methylation\ndifference",limits=c(0,0.25),
colours = viridis(100) %>% rev()
)+
stat_cor(size=1)+
facet_grid(~comparison)+
theme_classic()+
theme(text = element_text(colour = "grey0",size = 5),
panel.grid.major = element_line(linewidth = 0.075),
# panel.grid.minor.x = element_line(linewidth = 0.025),
strip.placement = "out",
strip.background = element_blank(),
strip.text = element_text(angle = 0,size=4),
plot.margin = unit(c(0,0,0,0),"pt"),
line=element_line(linewidth = 0.1),
legend.position = "bottom",
legend.box.margin=margin(-5,-5,-5,-5),
# legend.justification="top",
legend.text = element_text(angle = 90,vjust = 0.5,hjust = 0.5),
plot.background =element_blank(),
legend.key.size = unit(5,"pt")
)
print(p)
}
9.3.2 Random fCpGs
##plot 59 random fCpGs
##plot ctl SNPs
set.seed(123)
fCpGs.59 <- sample(x = rownames(fcpgs.betas),size = 59,replace = F)
fCpGs.59
random.59.fCpGs.betas <-
fcpgs.betas[fCpGs.59,ALL.meta$participant_id_anonymous]
dim(random.59.fCpGs.betas)
df <- lapply(comparisons, function(comp.i){
case.i <- strsplit(comp.i,"_T")[[1]][1]
timepoint.1 <- paste0("T",strsplit(comp.i,"_T")[[1]][2])
timepoint.2 <- paste0("T",strsplit(comp.i,"_T")[[1]][3])
sample.t1 <-
ALL.meta %>%
filter(same_sample_id_new==case.i,sample_timepoints==timepoint.1) %>%
pull(participant_id_anonymous)
sample.t2 <-
ALL.meta %>%
filter(same_sample_id_new==case.i,sample_timepoints==timepoint.2) %>%
pull(participant_id_anonymous)
dat <- data.frame(case=case.i,
comparison=comp.i,
samples=paste0("sample.t1:",sample.t1,", sample.t2:",sample.t2),
# cpgs_diff_direction=random.59.fCpGs.betas[,sample.t2]-random.59.fCpGs.betas[,sample.t1],
cpgs_diff_abs=abs((random.59.fCpGs.betas[,sample.t2]-random.59.fCpGs.betas[,sample.t1])),
random.59.fCpGs.betas[,c(sample.t1,sample.t2)]
)
colnames(dat)[(ncol(dat)-1):ncol(dat)] <- c("meth.T1","meth.T2")
return(dat)
})
df <- do.call(rbind,df)
head(df)
df %>% dplyr::count(case)
##plot
for(case.i in unique(df$case)){
p <-
df %>%
filter(case==case.i) %>%
mutate(cpgs_diff_abs=case_when(cpgs_diff_abs>= 0.25 ~ 0.25,
.default = cpgs_diff_abs
)
) %>%
ggplot(aes(x=meth.T2,
y=meth.T1,
fill=cpgs_diff_abs
)
)+
geom_point(pch=21,stroke=0.1,size=0.6)+
scale_fill_gradientn("Absolute methylation\ndifference",limits=c(0,0.25),
colours = viridis(100) %>% rev()
)+
stat_cor(size=1)+
facet_grid(~comparison)+
theme_classic()+
theme(text = element_text(colour = "grey0",size = 5),
panel.grid.major = element_line(linewidth = 0.075),
# panel.grid.minor.x = element_line(linewidth = 0.025),
strip.placement = "out",
strip.background = element_blank(),
strip.text = element_text(angle = 0,size=4),
plot.margin = unit(c(0,0,0,0),"pt"),
line=element_line(linewidth = 0.1),
legend.position = "bottom",
legend.box.margin=margin(-5,-5,-5,-5),
# legend.justification="top",
legend.text = element_text(angle = 90,vjust = 0.5,hjust = 0.5),
plot.background =element_blank(),
legend.key.size = unit(5,"pt")
)
print(p)
}
10 Session info
print(sessionInfo())## R version 4.5.0 (2025-04-11)
## Platform: aarch64-apple-darwin20
## Running under: macOS Ventura 13.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: Europe/Madrid
## tzcode source: internal
##
## attached base packages:
## [1] grid parallel stats4 stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] MafH5.gnomAD.v4.0.GRCh38_3.19.0
## [2] GenomicScores_2.20.2
## [3] SNPlocs.Hsapiens.dbSNP155.GRCh38_0.99.24
## [4] BSgenome_1.76.0
## [5] rtracklayer_1.68.0
## [6] BiocIO_1.18.0
## [7] circlize_0.4.16
## [8] ComplexHeatmap_2.24.1
## [9] patchwork_1.3.2
## [10] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.1
## [11] minfi_1.54.1
## [12] bumphunter_1.50.0
## [13] locfit_1.5-9.12
## [14] iterators_1.0.14
## [15] foreach_1.5.2
## [16] SummarizedExperiment_1.38.1
## [17] Biobase_2.68.0
## [18] MatrixGenerics_1.20.0
## [19] matrixStats_1.5.0
## [20] Biostrings_2.76.0
## [21] XVector_0.48.0
## [22] openxlsx_4.2.8
## [23] GenomicRanges_1.60.0
## [24] GenomeInfoDb_1.44.2
## [25] IRanges_2.42.0
## [26] S4Vectors_0.46.0
## [27] BiocGenerics_0.54.0
## [28] generics_0.1.4
## [29] pals_1.10
## [30] ggpubr_0.6.1
## [31] ggplot2_3.5.2
## [32] dplyr_1.1.4
## [33] janitor_2.2.1
## [34] data.table_1.17.8
## [35] BiocStyle_2.36.0
##
## loaded via a namespace (and not attached):
## [1] splines_4.5.0 filelock_1.0.3
## [3] bitops_1.0-9 tibble_3.3.0
## [5] preprocessCore_1.70.0 XML_3.99-0.19
## [7] lifecycle_1.0.4 rstatix_0.7.2.999
## [9] fastcluster_1.3.0 doParallel_1.0.17
## [11] lattice_0.22-7 MASS_7.3-65
## [13] base64_2.0.2 scrime_1.3.5
## [15] backports_1.5.0 magrittr_2.0.3
## [17] limma_3.64.3 sass_0.4.10
## [19] rmarkdown_2.29 BSgenome.Hsapiens.UCSC.hg38_1.4.5
## [21] jquerylib_0.1.4 yaml_2.3.10
## [23] doRNG_1.8.6.2 zip_2.3.3
## [25] askpass_1.2.1 mapproj_1.2.12
## [27] DBI_1.2.3 RColorBrewer_1.1-3
## [29] lubridate_1.9.4 maps_3.4.3
## [31] abind_1.4-8 quadprog_1.5-8
## [33] purrr_1.1.0 RCurl_1.98-1.17
## [35] rappdirs_0.3.3 GenomeInfoDbData_1.2.14
## [37] rentrez_1.2.4 genefilter_1.90.0
## [39] annotate_1.86.1 DelayedMatrixStats_1.30.0
## [41] codetools_0.2-20 DelayedArray_0.34.1
## [43] xml2_1.4.0 shape_1.4.6.1
## [45] tidyselect_1.2.1 UCSC.utils_1.4.0
## [47] farver_2.1.2 beanplot_1.3.1
## [49] BiocFileCache_2.16.1 illuminaio_0.50.0
## [51] GenomicAlignments_1.44.0 jsonlite_2.0.0
## [53] GetoptLong_1.0.5 multtest_2.64.0
## [55] Formula_1.2-5 survival_3.8-3
## [57] tools_4.5.0 Rcpp_1.1.0
## [59] glue_1.8.0 SparseArray_1.8.1
## [61] xfun_0.53 HDF5Array_1.36.0
## [63] withr_3.0.2 BiocManager_1.30.26
## [65] fastmap_1.2.0 rhdf5filters_1.20.0
## [67] openssl_2.3.3 digest_0.6.37
## [69] timechange_0.3.0 R6_2.6.1
## [71] colorspace_2.1-1 Cairo_1.6-5
## [73] dichromat_2.0-0.1 RSQLite_2.4.3
## [75] h5mread_1.0.1 utf8_1.2.6
## [77] tidyr_1.3.1 httr_1.4.7
## [79] S4Arrays_1.8.1 pkgconfig_2.0.3
## [81] gtable_0.3.6 blob_1.2.4
## [83] siggenes_1.82.0 htmltools_0.5.8.1
## [85] carData_3.0-5 bookdown_0.44
## [87] clue_0.3-66 scales_1.4.0
## [89] png_0.1-8 snakecase_0.11.1
## [91] knitr_1.50 rstudioapi_0.17.1
## [93] reshape2_1.4.4 tzdb_0.5.0
## [95] rjson_0.2.23 nlme_3.1-168
## [97] curl_7.0.0 GlobalOptions_0.1.2
## [99] cachem_1.1.0 rhdf5_2.52.1
## [101] stringr_1.5.1 BiocVersion_3.21.1
## [103] AnnotationDbi_1.70.0 restfulr_0.0.16
## [105] GEOquery_2.76.0 pillar_1.11.0
## [107] reshape_0.8.10 vctrs_0.6.5
## [109] car_3.1-3 dbplyr_2.5.0
## [111] cluster_2.1.8.1 xtable_1.8-4
## [113] evaluate_1.0.5 tinytex_0.57
## [115] magick_2.8.7 readr_2.1.5
## [117] GenomicFeatures_1.60.0 cli_3.6.5
## [119] compiler_4.5.0 Rsamtools_2.24.0
## [121] rlang_1.1.6 crayon_1.5.3
## [123] rngtools_1.5.2 ggsignif_0.6.4
## [125] labeling_0.4.3 nor1mix_1.3-3
## [127] mclust_6.1.1 plyr_1.8.9
## [129] stringi_1.8.7 BiocParallel_1.42.1
## [131] Matrix_1.7-4 BSgenome.Hsapiens.UCSC.hg19_1.4.3
## [133] hms_1.1.3 sparseMatrixStats_1.20.0
## [135] bit64_4.6.0-1 Rhdf5lib_1.30.0
## [137] KEGGREST_1.48.1 statmod_1.5.0
## [139] AnnotationHub_3.16.1 broom_1.0.9
## [141] memoise_2.0.1 bslib_0.9.0
## [143] bit_4.6.0