Nanopore data and methylation haplotype blocks, Gabbutt and Duran-Ferrer 2025
10/07/2025
fCpG methylation represents a previously unrecognized type of CpG methylation and is different from any other reported epigenetic clock or cell-type specific CpG, repreenting neutral and cell lineage markers.
1 Load packages and data
library(ggpubr)
library(patchwork)
library(ComplexHeatmap)
library(circlize)
library(pals)
library(dplyr)
# color pals# color smaples
cols.paper <- c(
NA_values="#666666",
HPC="#E6E6E6",
pre.Bcell="#D5D5D5",
NBC="#C3C3C3",
GCB="#AEAEAE",
tPC="#969696",
MBC="#787878",
bmPC="#4D4D4D",
Tcell="#F0F8FF",
PBMCs="#FFF8DC",
Whole_blood="#CDC8B1",
Normal_lymphoid_cell="#D5D5D5",
Normal_myeloid_cell="#E6E6FA",
"T-ALL"="#CD3333",
"T-ALL-remission"="#C7A5A5",
"B-ALL"="#E69F00",
"B-ALL-remission"="#D8CAAB",
MCL="#F0E442",
C1.or.cMCL="#F5D51B",
C2.or.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"
)
# plot heatmap sorted by age and with colors
evoflux.meth <- data.table::fread("../Revision//Data/meth_palette_evoflux.tsv")
evoflux.meth
## load reads and metadata
metadata <- data.table::fread("../Revision/Results/fcpgs_Nanopore/Fig1_haplotypes/readinfo_chr7_25854120-25856220.csv",data.table = F)
colnames(metadata)[grep("V1",colnames(metadata))] <- "read_id"
metadata %>% head
metadata %>% select(patient,sample_name)
metadata <-
metadata %>%
mutate(patient=case_when(patient=="12" ~as.character(patient),
patient=="19" ~as.character(patient),
.default = sample_name
)
)
metadata$patient
## set order factors in metadat for the heatmap
metadata <-
metadata %>%
mutate(patient=factor(patient,levels=c("12","19",
"22_10_NBC","22_4_NBC","22_5_NBC",
"22_4_MBC","22_5_MBC","22_9_MBC"
)),
sample_group=factor(sample_group,levels=c("NBC","MBC","CLL","RT"))
)
##load reads
reads <- data.table::fread("../Revision/Results/fcpgs_Nanopore/Fig1_haplotypes/combined_reads_haplotype_chr7_25854120-25856220.csv",header = T,data.table = F)
colnames(reads)[grep("V1",colnames(reads))] <- "read_id"
reads %>% head
identical(metadata$read_id,reads$read_id)2 Chromosome region chr7_25854120-25856220
We draw the chromosome region to plot
## draw chromosome and genomic coordinates on top
library(karyoploteR)
kp <- plotKaryotype(genome = "hg38",
plot.type = 7,
chromosomes = "chr7",
main = NULL,#"chr7:25854120-25856220 (hg38)",
srt=0,
cex=0.35,
lwd=0.2
)
kpRect(kp, chr="chr7", x0=25854120, x1=25956220, y0=0, y1=1, col="red3", data.panel="ideogram", border=NA)
3 Heatmap
We next plot heatmap with haplotypes information for the region chr7_25854120-25856220, which contains reads covering 4 fCpGs. We can see that 1) there is higher methylation similarity between haplotypes, and 2) that CLL and RT (cancer samples) show more homogenous values comapred to normal cells, consistent with fCpGs functioning as cell-sepcific barcodes.
##plot
ht_opt$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$fast_hclust <- T
h.reads <-
Heatmap(reads %>% select(-read_id),
column_title ="chr7:25854120-25856220 (hg38)",
column_title_gp = gpar(fontsize=7),
col=colorRamp2(breaks = c(0,1),colors = c("grey93","grey0")),
heatmap_legend_param = list(title="Long-read fCpG methylation",
color_bar="discrete",
at=c(1,0),
labels=c("Methylated","Unmethylated")
),
row_split = metadata %>% select(patient,sample_group,haplotype),
cluster_row_slices = F,
cluster_rows = T,
cluster_columns = F,
row_title_rot = 0,
border=T,
show_row_dend = F,
left_annotation = HeatmapAnnotation(which="row",
df = metadata %>% select(sample_group,patient,haplotype),
col=list(patient=setNames(cols25(length(metadata$patient %>% unique())),
metadata$patient %>% unique()
),
sample_group=cols.paper,
haplotype=setNames(brewer.set2(3),c(1,2,3))
),
annotation_legend_param = list(labels_gp=gpar(fontsize=4),
color_bar=list(haplotype="discrete")
),
annotation_label = list(sample_group="Sample group",
patient="Patient",
haplotype="Haplotype"
),
show_annotation_name = T,
na_col = "grey45",
show_legend = T,
annotation_name_gp = gpar(fontsize=4),
simple_anno_size = unit(1.5,"mm")
),
)
draw(h.reads)
4 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] stats4 grid stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] karyoploteR_1.34.2 regioneR_1.40.1 GenomicRanges_1.60.0
## [4] GenomeInfoDb_1.44.2 IRanges_2.42.0 S4Vectors_0.46.0
## [7] BiocGenerics_0.54.0 generics_0.1.4 dplyr_1.1.4
## [10] pals_1.10 circlize_0.4.16 ComplexHeatmap_2.24.1
## [13] patchwork_1.3.2 ggpubr_0.6.1 ggplot2_3.5.2
## [16] BiocStyle_2.36.0
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 rstudioapi_0.17.1
## [3] jsonlite_2.0.0 shape_1.4.6.1
## [5] magrittr_2.0.3 magick_2.8.7
## [7] GenomicFeatures_1.60.0 farver_2.1.2
## [9] rmarkdown_2.29 GlobalOptions_0.1.2
## [11] BiocIO_1.18.0 zlibbioc_1.54.0
## [13] vctrs_0.6.5 Cairo_1.6-5
## [15] memoise_2.0.1 Rsamtools_2.24.0
## [17] RCurl_1.98-1.17 base64enc_0.1-3
## [19] tinytex_0.57 rstatix_0.7.2.999
## [21] htmltools_0.5.8.1 S4Arrays_1.8.1
## [23] curl_7.0.0 broom_1.0.9
## [25] SparseArray_1.8.1 Formula_1.2-5
## [27] sass_0.4.10 bslib_0.9.0
## [29] htmlwidgets_1.6.4 cachem_1.1.0
## [31] GenomicAlignments_1.44.0 lifecycle_1.0.4
## [33] iterators_1.0.14 pkgconfig_2.0.3
## [35] Matrix_1.7-4 R6_2.6.1
## [37] fastmap_1.2.0 GenomeInfoDbData_1.2.14
## [39] MatrixGenerics_1.20.0 clue_0.3-66
## [41] digest_0.6.37 colorspace_2.1-1
## [43] AnnotationDbi_1.70.0 bezier_1.1.2
## [45] Hmisc_5.2-3 RSQLite_2.4.3
## [47] httr_1.4.7 abind_1.4-8
## [49] compiler_4.5.0 bit64_4.6.0-1
## [51] withr_3.0.2 doParallel_1.0.17
## [53] htmlTable_2.4.3 backports_1.5.0
## [55] BiocParallel_1.42.1 carData_3.0-5
## [57] DBI_1.2.3 maps_3.4.3
## [59] ggsignif_0.6.4 DelayedArray_0.34.1
## [61] rjson_0.2.23 tools_4.5.0
## [63] foreign_0.8-90 nnet_7.3-20
## [65] glue_1.8.0 restfulr_0.0.16
## [67] checkmate_2.3.3 cluster_2.1.8.1
## [69] gtable_0.3.6 BSgenome_1.76.0
## [71] tidyr_1.3.1 ensembldb_2.32.0
## [73] data.table_1.17.8 car_3.1-3
## [75] XVector_0.48.0 foreach_1.5.2
## [77] pillar_1.11.0 stringr_1.5.1
## [79] lattice_0.22-7 rtracklayer_1.68.0
## [81] bit_4.6.0 biovizBase_1.56.0
## [83] tidyselect_1.2.1 Biostrings_2.76.0
## [85] knitr_1.50 gridExtra_2.3
## [87] bookdown_0.44 ProtGenerics_1.40.0
## [89] SummarizedExperiment_1.38.1 xfun_0.53
## [91] Biobase_2.68.0 matrixStats_1.5.0
## [93] stringi_1.8.7 UCSC.utils_1.4.0
## [95] lazyeval_0.2.2 yaml_2.3.10
## [97] evaluate_1.0.5 codetools_0.2-20
## [99] tibble_3.3.0 BiocManager_1.30.26
## [101] cli_3.6.5 rpart_4.1.24
## [103] jquerylib_0.1.4 dichromat_2.0-0.1
## [105] Rcpp_1.1.0 mapproj_1.2.12
## [107] png_0.1-8 fastcluster_1.3.0
## [109] XML_3.99-0.19 parallel_4.5.0
## [111] blob_1.2.4 AnnotationFilter_1.32.0
## [113] bitops_1.0-9 VariantAnnotation_1.54.1
## [115] scales_1.4.0 purrr_1.1.0
## [117] crayon_1.5.3 bamsignals_1.40.0
## [119] GetoptLong_1.0.5 rlang_1.1.6
## [121] KEGGREST_1.48.1