Dandelion Plot

Sometimes, there are as many as hundreds of SNPs invoved in one gene. Dandelion plot can be used to depict such dense SNPs. Please note that the height of the dandelion indicates the desity of the SNPs.

library(trackViewer)
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
library(org.Hs.eg.db)
library(rtracklayer)
methy <- import(system.file("extdata", "methy.bed", package="trackViewer"), "BED")
gr <- GRanges("chr22", IRanges(50968014, 50970514, names="TYMP"))
trs <- geneModelFromTxdb(TxDb.Hsapiens.UCSC.hg19.knownGene,
                         org.Hs.eg.db,
                         gr=gr)
features <- c(range(trs[[1]]$dat), range(trs[[5]]$dat))
names(features) <- c(trs[[1]]$name, trs[[5]]$name)
features$fill <- c("lightblue", "mistyrose")
features$height <- c(.02, .04)
dandelion.plot(methy, features, ranges=gr, type="pin")

methy$color <- 3
methy$border <- "gray"
## Score info is required and the score must be a number in [0, 1]
m <- max(methy$score)
methy$score <- methy$score/m
dandelion.plot(methy, features, ranges=gr, type="fan")

methy$color <- rep(list(c(3, 5)), length(methy))
methy$score2 <- (max(methy$score) - methy$score)/m
legends <- list(list(labels=c("s1", "s2"), fill=c(3, 5)))
dandelion.plot(methy, features, ranges=gr, type="pie", legend=legends)

## Less dandelions
dandelion.plot(methy, features, ranges=gr, type="circle", maxgaps=1/10)

## More dandelions
dandelion.plot(methy, features, ranges=gr, type="circle", maxgaps=1/100)

maxgaps <- tile(gr, n = 10)[[1]]
dandelion.plot(methy, features, ranges=gr, type="circle", maxgaps=maxgaps)

dandelion.plot(methy, features, ranges=gr, type="pie", 
               maxgaps=1/100, yaxis = TRUE, heightMethod = mean,
               ylab='mean of methy scores')

yaxis = c(0, 0.5, 1)
dandelion.plot(methy, features, ranges=gr, type="pie", 
               maxgaps=1/100, yaxis = yaxis, heightMethod = mean,
               ylab='mean of methy scores')

Session Info

sessionInfo()

R version 4.4.2 (2024-10-31) Platform: x86_64-pc-linux-gnu Running under: Ubuntu 24.04.1 LTS

Matrix products: default BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0

locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 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: Etc/UTC tzcode source: system (glibc)

attached base packages: [1] grid stats4 stats graphics grDevices utils datasets [8] methods base

other attached packages: [1] httr_1.4.7
[2] VariantAnnotation_1.53.0
[3] Rsamtools_2.23.1
[4] Biostrings_2.75.2
[5] XVector_0.47.0
[6] SummarizedExperiment_1.37.0
[7] MatrixGenerics_1.19.0
[8] matrixStats_1.4.1
[9] org.Hs.eg.db_3.20.0
[10] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2 [11] GenomicFeatures_1.59.1
[12] AnnotationDbi_1.69.0
[13] Biobase_2.67.0
[14] Gviz_1.51.0
[15] rtracklayer_1.67.0
[16] trackViewer_1.43.5
[17] GenomicRanges_1.59.1
[18] GenomeInfoDb_1.43.2
[19] IRanges_2.41.2
[20] S4Vectors_0.45.2
[21] BiocGenerics_0.53.3
[22] generics_0.1.3

loaded via a namespace (and not attached): [1] strawr_0.0.92 RColorBrewer_1.1-3 sys_3.4.3
[4] rstudioapi_0.17.1 jsonlite_1.8.9 magrittr_2.0.3
[7] rmarkdown_2.29 BiocIO_1.17.1 zlibbioc_1.52.0
[10] vctrs_0.6.5 memoise_2.0.1 RCurl_1.98-1.16
[13] base64enc_0.1-3 htmltools_0.5.8.1 S4Arrays_1.7.1
[16] progress_1.2.3 curl_6.0.1 Rhdf5lib_1.29.0
[19] rhdf5_2.51.1 SparseArray_1.7.2 Formula_1.2-5
[22] sass_0.4.9 bslib_0.8.0 htmlwidgets_1.6.4
[25] httr2_1.0.7 cachem_1.1.0 buildtools_1.0.0
[28] GenomicAlignments_1.43.0 lifecycle_1.0.4 pkgconfig_2.0.3
[31] Matrix_1.7-1 R6_2.5.1 fastmap_1.2.0
[34] GenomeInfoDbData_1.2.13 digest_0.6.37 colorspace_2.1-1
[37] Hmisc_5.2-1 RSQLite_2.3.9 filelock_1.0.3
[40] fansi_1.0.6 abind_1.4-8 compiler_4.4.2
[43] bit64_4.5.2 htmlTable_2.4.3 backports_1.5.0
[46] BiocParallel_1.41.0 DBI_1.2.3 biomaRt_2.63.0
[49] rappdirs_0.3.3 DelayedArray_0.33.3 rjson_0.2.23
[52] tools_4.4.2 foreign_0.8-87 nnet_7.3-19
[55] glue_1.8.0 InteractionSet_1.35.0 restfulr_0.0.15
[58] rhdf5filters_1.19.0 checkmate_2.3.2 cluster_2.1.7
[61] gtable_0.3.6 BSgenome_1.75.0 ensembldb_2.31.0
[64] data.table_1.16.4 hms_1.1.3 xml2_1.3.6
[67] utf8_1.2.4 pillar_1.9.0 stringr_1.5.1
[70] dplyr_1.1.4 BiocFileCache_2.15.0 lattice_0.22-6
[73] bit_4.5.0.1 deldir_2.0-4 biovizBase_1.55.0
[76] tidyselect_1.2.1 maketools_1.3.1 knitr_1.49
[79] gridExtra_2.3 ProtGenerics_1.39.0 xfun_0.49
[82] stringi_1.8.4 UCSC.utils_1.3.0 lazyeval_0.2.2
[85] yaml_2.3.10 evaluate_1.0.1 codetools_0.2-20
[88] interp_1.1-6 tibble_3.2.1 BiocManager_1.30.25
[91] cli_3.6.3 rpart_4.1.23 munsell_0.5.1
[94] jquerylib_0.1.4 dichromat_2.0-0.1 Rcpp_1.0.13-1
[97] grImport_0.9-7 dbplyr_2.5.0 png_0.1-8
[100] XML_3.99-0.17 parallel_4.4.2 ggplot2_3.5.1
[103] blob_1.2.4 prettyunits_1.2.0 latticeExtra_0.6-30
[106] jpeg_0.1-10 AnnotationFilter_1.31.0 bitops_1.0-9
[109] txdbmaker_1.3.1 scales_1.3.0 crayon_1.5.3
[112] BiocStyle_2.35.0 rlang_1.1.4 KEGGREST_1.47.0