hicVennDiagram Vignette: overview
Jianhong Ou
29 October 2024
Introduction
When comparing samples, it is common to perform the task of
identifying overlapping loops among two or more sets of genomic
interactions. Traditionally, this is achieved through the use of
visualizations such as vennDiagram or UpSet
plots. However, it is frequently observed that the total count displayed
in these plots does not match the original counts for each individual
list. The reason behind this discrepancy is that a single overlap may
encompass multiple interactions for one or more samples. This issue is
extensively discussed in the realm of overlapping caller for ChIP-Seq
peaks.
The hicVennDiagram aims to provide a easy to use tool for overlapping interactions calculation and proper visualization methods. The hicVennDiagram generates plots specifically crafted to eliminate the deceptive visual representation caused by the counts method.
Quick start
Here is an example using hicVennDiagram with 3 files in
BEDPE format.
Installation
First, install hicVennDiagram and other packages required to run the examples.
library(BiocManager)
BiocManager::install("hicVennDiagram")Load library
library(hicVennDiagram)
library(ggplot2)# list the BEDPE files
file_folder <- system.file("extdata",
package = "hicVennDiagram",
mustWork = TRUE)
file_list <- dir(file_folder, pattern = ".bedpe", full.names = TRUE)
names(file_list) <- sub(".bedpe", "", basename(file_list))
basename(file_list)## [1] "group1.bedpe" "group2.bedpe" "group3.bedpe"venn <- vennCount(file_list)
## upset plot
## temp fix for https://github.com/krassowski/complex-upset/issues/195
upset_themes_fix <- lapply(ComplexUpset::upset_themes, function(.ele){
lapply(.ele, function(.e){
do.call(theme, .e[names(.e) %in% names(formals(theme))])
})
})
upsetPlot(venn,
themes = upset_themes_fix)
## venn plot
vennPlot(venn)
## use browser to adjust the text position, and shape colors.
browseVenn(vennPlot(venn))Details about vennCount
The vennCount function borrows the power of
InteractionSet:findOverlaps to calculate the overlaps and
then summarizes the results for each category. Users may want to try
different combinations of maxgap and
minoverlap parameters to calculate the overlapping
loops.
venn <- vennCount(file_list, maxgap=50000, FUN = max) # by default FUN = min
upsetPlot(venn, label_all=list(
na.rm = TRUE,
color = 'black',
alpha = .9,
label.padding = unit(0.1, "lines")
),
themes = upset_themes_fix)
Plot for overlapping peaks output by ChIPpeakAnno
library(ChIPpeakAnno)
bed <- system.file("extdata", "MACS_output.bed", package="ChIPpeakAnno")
gr1 <- toGRanges(bed, format="BED", header=FALSE)
gff <- system.file("extdata", "GFF_peaks.gff", package="ChIPpeakAnno")
gr2 <- toGRanges(gff, format="GFF", header=FALSE, skip=3)
ol <- findOverlapsOfPeaks(gr1, gr2)
overlappingPeaksToVennTable <- function(.ele){
.venn <- .ele$venn_cnt
k <- which(colnames(.venn)=="Counts")
rownames(.venn) <- apply(.venn[, seq.int(k-1)], 1, paste, collapse="")
colnames(.venn) <- sub("count.", "", colnames(.venn))
vennTable(combinations=.venn[, seq.int(k-1)],
counts=.venn[, k],
vennCounts=.venn[, seq.int(ncol(.venn))[-seq.int(k)]])
}
venn <- overlappingPeaksToVennTable(ol)
vennPlot(venn)
## or you can simply try vennPlot(vennCount(c(bed, gff)))
upsetPlot(venn, themes = upset_themes_fix)
## change the font size of labels and numbers
updated_theme <- ComplexUpset::upset_modify_themes(
## get help by vignette('Examples_R', package = 'ComplexUpset')
list('intersections_matrix'=
ggplot2::theme(
## font size of label: gr1/gr2
axis.text.y=ggplot2::element_text(size=24),
## font size of label `group`
axis.title.x=ggplot2::element_text(size=24)),
'overall_sizes'=
ggplot2::theme(
## font size of x-axis 0-200
axis.text=ggplot2::element_text(size=12),
## font size of x-label `Set size`
axis.title=ggplot2::element_text(size=18)),
'Intersection size'=
ggplot2::theme(
## font size of y-axis 0-150
axis.text=ggplot2::element_text(size=20),
## font size of y-label `Intersection size`
axis.title=ggplot2::element_text(size=16)
),
'default'=ggplot2::theme_minimal())
)
updated_theme <- lapply(updated_theme, function(.ele){
lapply(.ele, function(.e){
do.call(theme, .e[names(.e) %in% names(formals(theme))])
})
})
upsetPlot(venn,
label_all=list(na.rm = TRUE, color = 'gray30', alpha = .7,
label.padding = unit(0.1, "lines"),
size = 8 #control the font size of the individual num
),
base_annotations=list('Intersection size'=
ComplexUpset::intersection_size(
## font size of counts in the bar-plot
text = list(size=6)
)),
themes = updated_theme
)
Session Info
sessionInfo()R Under development (unstable) (2024-10-21 r87258) Platform: x86_64-pc-linux-gnu Running under: Ubuntu 24.04.1 LTS
Matrix products: default BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_GB LC_COLLATE=C
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: America/New_York tzcode source: system (glibc)
attached base packages: [1] stats4 stats graphics grDevices utils
datasets methods
[8] base
other attached packages: [1] ChIPpeakAnno_3.41.0 ggplot2_3.5.1
GenomicRanges_1.59.0 [4] GenomeInfoDb_1.43.0 IRanges_2.41.0
S4Vectors_0.45.0
[7] BiocGenerics_0.53.0 hicVennDiagram_1.5.0
loaded via a namespace (and not attached): [1] eulerr_7.0.2
jsonlite_1.8.9
[3] magrittr_2.0.3 GenomicFeatures_1.59.0
[5] farver_2.1.2 rmarkdown_2.28
[7] BiocIO_1.17.0 zlibbioc_1.53.0
[9] ragg_1.3.3 vctrs_0.6.5
[11] multtest_2.63.0 memoise_2.0.1
[13] Rsamtools_2.23.0 RCurl_1.98-1.16
[15] htmltools_0.5.8.1 S4Arrays_1.7.0
[17] progress_1.2.3 lambda.r_1.2.4
[19] curl_5.2.3 ComplexUpset_1.3.3
[21] SparseArray_1.7.0 sass_0.4.9
[23] bslib_0.8.0 htmlwidgets_1.6.4
[25] plyr_1.8.9 httr2_1.0.5
[27] futile.options_1.0.1 cachem_1.1.0
[29] GenomicAlignments_1.43.0 lifecycle_1.0.4
[31] pkgconfig_2.0.3 Matrix_1.7-1
[33] R6_2.5.1 fastmap_1.2.0
[35] GenomeInfoDbData_1.2.13 MatrixGenerics_1.19.0
[37] digest_0.6.37 colorspace_2.1-1
[39] patchwork_1.3.0 AnnotationDbi_1.69.0
[41] regioneR_1.39.0 textshaping_0.4.0
[43] RSQLite_2.3.7 filelock_1.0.3
[45] labeling_0.4.3 fansi_1.0.6
[47] httr_1.4.7 polyclip_1.10-7
[49] abind_1.4-8 compiler_4.5.0
[51] bit64_4.5.2 withr_3.0.2
[53] BiocParallel_1.41.0 DBI_1.2.3
[55] highr_0.11 biomaRt_2.63.0
[57] MASS_7.3-61 rappdirs_0.3.3
[59] DelayedArray_0.33.0 rjson_0.2.23
[61] tools_4.5.0 glue_1.8.0
[63] VennDiagram_1.7.3 restfulr_0.0.15
[65] InteractionSet_1.35.0 grid_4.5.0
[67] polylabelr_0.2.0 reshape2_1.4.4
[69] generics_0.1.3 BSgenome_1.75.0
[71] gtable_0.3.6 tidyr_1.3.1
[73] ensembldb_2.31.0 data.table_1.16.2
[75] hms_1.1.3 xml2_1.3.6
[77] utf8_1.2.4 XVector_0.47.0
[79] pillar_1.9.0 stringr_1.5.1
[81] splines_4.5.0 dplyr_1.1.4
[83] BiocFileCache_2.15.0 lattice_0.22-6
[85] survival_3.7-0 rtracklayer_1.67.0
[87] bit_4.5.0 universalmotif_1.25.0
[89] tidyselect_1.2.1 RBGL_1.83.0
[91] Biostrings_2.75.0 knitr_1.48
[93] ProtGenerics_1.39.0 SummarizedExperiment_1.37.0 [95] svglite_2.1.3
futile.logger_1.4.3
[97] xfun_0.48 Biobase_2.67.0
[99] matrixStats_1.4.1 stringi_1.8.4
[101] UCSC.utils_1.3.0 lazyeval_0.2.2
[103] yaml_2.3.10 evaluate_1.0.1
[105] codetools_0.2-20 tibble_3.2.1
[107] BiocManager_1.30.25 graph_1.85.0
[109] cli_3.6.3 systemfonts_1.1.0
[111] munsell_0.5.1 jquerylib_0.1.4
[113] Rcpp_1.0.13 dbplyr_2.5.0
[115] png_0.1-8 XML_3.99-0.17
[117] parallel_4.5.0 blob_1.2.4
[119] prettyunits_1.2.0 AnnotationFilter_1.31.0
[121] bitops_1.0-9 pwalign_1.3.0
[123] scales_1.3.0 purrr_1.0.2
[125] crayon_1.5.3 BiocStyle_2.35.0
[127] rlang_1.1.4 KEGGREST_1.47.0
[129] formatR_1.14