{"id":2493,"date":"2018-11-27T19:50:54","date_gmt":"2018-11-27T19:50:54","guid":{"rendered":"http:\/\/pcool.dyndns.org:8080\/statsbook\/?page_id=2493"},"modified":"2025-06-30T09:34:36","modified_gmt":"2025-06-30T08:34:36","slug":"circos-plots-circlize","status":"publish","type":"page","link":"https:\/\/pcool.dyndns.org\/index.php\/circos-plots-circlize\/","title":{"rendered":"Circos Plots – circlize"},"content":{"rendered":"\n

circlize1<\/a><\/sup>\u00a0can be used to make chromosome ideograms.\u00a0The example below uses the hg19 assembly (GRCh37) as reference. However, the newer (hg38 \/ GRCh38) can also be downloaded, manipulated and used instead (see below). It is important to use the appropriate reference assembly when working with genomic data.<\/p>\n\n\n\n

For the examples below to work, the dplyr2<\/a><\/sup> and stringr3<\/a><\/sup> packages should also be installed and loaded for a default plot with the hg19 assembly:<\/p>\n\n\n\n

BiocManager::install('circlize')<\/mark><\/em>\n\nlibrary(circlize)<\/span>\nlibrary(dplyr)<\/span>\nlibrary(stringr)<\/span>\n\n# https:\/\/jokergoo.github.io\/circlize_book\/book\/initialize-genomic-plot.html<\/span>\n# https:\/\/cran.r-project.org\/web\/packages\/circlize\/circlize.pdf<\/span>\n\n# create an ideogram<\/span>\ncircos.initializeWithIdeogram()<\/span>\n# the plot names the chromosome numeric, but the internal names have 'chr' in front of them<\/span>\n# for index use chr1 etc<\/span>\n\n# add text:<\/span>\ntext(0, 0, \"Chromosome Ideogram\", cex = 1)<\/span>\n# info:<\/span>\ncircos.info()<\/span>\nAll your sectors:<\/span>\n [1] \"chr1\" \"chr2\" \"chr3\" \"chr4\" \"chr5\" \"chr6\" \"chr7\" \"chr8\" \"chr9\" \"chr10\" \"chr11\"<\/span>\n[12] \"chr12\" \"chr13\" \"chr14\" \"chr15\" \"chr16\" \"chr17\" \"chr18\" \"chr19\" \"chr20\" \"chr21\" \"chr22\"<\/span>\n[23] \"chrX\" \"chrY\"<\/span>\n\nAll your tracks:<\/span>\n[1] 1 2<\/span>\n\nYour current sector.index is chrY<\/span>\nYour current track.index is 2<\/span>\n\n# clear the plotting<\/span>\ncircos.clear()<\/span><\/code><\/pre>\n\n\n\n
\"\"<\/figure>\n\n\n\n
The default ideogram is hg19, but this can be changed with the species argument. However,\u00a0<\/span>circos.initializeWithIdeogram(species = “hg38”) doesn’t work as there are too many unmapped regions. T<\/span>he hg38 cytoband can be downloaded from: <\/span>
http:\/\/hgdownload.cse.ucsc.edu\/goldenpath\/hg38\/database\/<\/a><\/span>
(file = ‘cytoband.txt.gz’). Put it on the desktop, extract the file and read it in:<\/span><\/p>\n\n\n\n
cytoband.df.1 <- read.table(\"~\/Desktop\/cytoBand.txt\", colClasses = c(\"character\", \"numeric\", \"numeric\", \"character\", \"character\"), sep = \"\\t\")<\/em><\/span>\nhead(cytoband.df.1)<\/mark>\n    V1       V2       V3     V4     V5\n1 chr1        0  2300000 p36.33   gneg\n2 chr1  2300000  5300000 p36.32 gpos25\n3 chr1  5300000  7100000 p36.31   gneg\n4 chr1  7100000  9100000 p36.23 gpos25\n5 chr1  9100000 12500000 p36.22   gneg\n6 chr1 12500000 15900000 p36.21 gpos50<\/mark><\/em>\n\n# Remove the unmapped areas: remove chromosome names <\/em><\/span>\n# that end in _alt, _random, _fix or start with chrUn:<\/em><\/span>\ncytoband.df.2 <- cytoband.df.1<\/em><\/span> %>% dplyr::filter(!str_detect(V1, \"chrUn\")) %>%<\/em><\/span>\ndplyr::filter(!str_detect(V1, \"_alt\")) %>%<\/em><\/span>\ndplyr::filter(!str_detect(V1, \"_random\"))<\/em><\/span> %>%\ndplyr::filter(!str_detect(V1, \"_fix\"))<\/mark><\/em>\nhead(cytoband.df.2)<\/em><\/mark>\n    V1       V2       V3     V4     V5\n1 chr1        0  2300000 p36.33   gneg\n2 chr1  2300000  5300000 p36.32 gpos25\n3 chr1  5300000  7100000 p36.31   gneg\n4 chr1  7100000  9100000 p36.23 gpos25\n5 chr1  9100000 12500000 p36.22   gneg\n6 chr1 12500000 15900000 p36.21 gpos50<\/em><\/mark>\ncircos.initializeWithIdeogram(cytoband.df.2)<\/em><\/span>\ncircos.clear()<\/em><\/span><\/code><\/pre>\n\n\n\n
\"\"<\/figure>\n\n\n\n
By default, all chromosomes are displayed, but this can be changed with the index:<\/p>\n\n\n\n
circos.initializeWithIdeogram(cytoband.df.2, chromosome.index = paste0(\"chr\", c(3,5,2,8)))<\/em><\/span>\ntext(0, 0, \"subset of chromosomes\", cex = 1)<\/em><\/span>\ncircos.clear()<\/em><\/span><\/code><\/pre>\n\n\n\n
\"\"<\/figure>\n\n\n\n
Where to start can be set with circos.par:<\/p>\n\n\n\n
circos.par(\"start.degree\" = 90)<\/em><\/span>\ncircos.initializeWithIdeogram(cytoband.df.2)<\/em><\/span>\ncircos.clear()<\/em><\/span>\ntext(0, 0, \"'start.degree' = 90\", cex = 1)<\/em><\/span><\/code><\/pre>\n\n\n\n
\"\"<\/figure>\n\n\n\n
To plot different genes:<\/p>\n\n\n\n
df = data.frame(<\/span><\/em>name = c(\"TP53\", \"TP63\", \"TP73\"), <\/span><\/em>start = c(7565097, 189349205, 3569084), <\/span><\/em>end = c(7590856, 189615068, 3652765))<\/span><\/em>\ncircos.genomicInitialize(df)<\/span><\/em>\ncircos.clear()<\/span><\/em><\/code><\/pre>\n\n\n\n
\"\"<\/figure>\n\n\n\n
Custom plot:<\/p>\n\n\n\n
tp_family = readRDS(system.file(package = \"circlize\", \"extdata\", \"tp_family_df.rds\"))<\/span><\/em>\nhead(tp_family)<\/span><\/em>\n  gene   start     end        transcript exon\n1 TP53 7565097 7565332 ENST00000413465.2    7\n2 TP53 7577499 7577608 ENST00000413465.2    6\n3 TP53 7578177 7578289 ENST00000413465.2    5\n4 TP53 7578371 7578554 ENST00000413465.2    4\n5 TP53 7579312 7579590 ENST00000413465.2    3\n6 TP53 7579700 7579721 ENST00000413465.2    2<\/mark><\/em>\n\n# build track<\/span><\/em>\ncircos.genomicInitialize(tp_family)<\/span><\/em>\n# give colours to different genes:<\/span><\/em>\ncircos.track(ylim = c(0, 1), bg.col = c(\"#FF000040\", \"#00FF0040\", \"#0000FF40\"), bg.border = NA, track.height = 0.05)<\/span><\/em>\n# add transcripts<\/span><\/em>\nn = max(tapply(tp_family$transcript, tp_family$gene, function(x) length(unique(x))))<\/span><\/em>\n\ncircos.genomicTrack(tp_family, ylim = c(0.5, n + 0.5), panel.fun = function(region, value, ...) {<\/span><\/em>\n  all_tx = unique(value$transcript)<\/span><\/em>\n    for(i in seq_along(all_tx)) {<\/span><\/em>\n      l = value$transcript == all_tx[i]<\/span><\/em>\n      # for each transcript<\/span><\/em>\n      current_tx_start = min(region[l, 1])<\/span><\/em>\n      current_tx_end = max(region[l, 2])<\/span><\/em>\n      circos.lines(c(current_tx_start, current_tx_end), c(n - i + 1, n - i + 1), col = \"#CCCCCC\")<\/span><\/em>\n      circos.genomicRect(region[l, , drop = FALSE], ytop = n - i + 1 + 0.4, ybottom = n - i + 1 - 0.4, col = \"orange\", border = NA)<\/span><\/em>\n    }<\/span><\/em>\n  }, bg.border = NA, track.height = 0.4)<\/span><\/em>\ncircos.clear()<\/span><\/em><\/code><\/pre>\n\n\n\n
\"\"<\/figure>\n\n\n\n
Custom zoomed plot:<\/p>\n\n\n\n
# zooming into chromosomes<\/em><\/span>\nextend_chromosomes = function(bed, chromosome, prefix = \"zoom_\") {<\/em><\/span>\n  zoom_bed = bed[bed[[1]] %in% chromosome, , drop = FALSE]<\/em><\/span>\n  zoom_bed[[1]] = paste0(prefix, zoom_bed[[1]])<\/em><\/span>\n  rbind(bed, zoom_bed)<\/em><\/span>\n}<\/em><\/span>\n# use the downloaded hg38 assembly from above:<\/em><\/span>\nhead(cytoband.df.2)<\/em><\/span>\n    V1       V2       V3     V4     V5\n1 chr1        0  2300000 p36.33   gneg\n2 chr1  2300000  5300000 p36.32 gpos25\n3 chr1  5300000  7100000 p36.31   gneg\n4 chr1  7100000  9100000 p36.23 gpos25\n5 chr1  9100000 12500000 p36.22   gneg\n6 chr1 12500000 15900000 p36.21 gpos50<\/mark><\/em>\n# chromosome names<\/em><\/span>\nchromosome <- unique(cytoband.df.2$V1)<\/em><\/span>\nchromosome<\/em><\/span>\n [1] \"chr1\" \"chr10\" \"chr11\" \"chr12\" \"chr13\" \"chr14\" \"chr15\" \"chr16\" \"chr17\" \"chr18\" \"chr19\"<\/em><\/span>\n[12] \"chr2\" \"chr20\" \"chr21\" \"chr22\" \"chr3\" \"chr4\" \"chr5\" \"chr6\" \"chr7\" \"chr8\" \"chr9\" <\/em><\/span>\n[23] \"chrM\" \"chrX\" \"chrY\"<\/span> <\/em><\/span>\n# chromosome length<\/em><\/span>\nchromosome.length <- cytoband.df.2 %>% dplyr::group_by(V1) %>% summarise(max = max(V3))<\/em><\/span>\nchromosome.length<\/em><\/span>\n# A tibble: 25 \u00d7 2\n   V1          max\n   <chr>     <dbl>\n 1 chr1  248956422\n 2 chr10 133797422\n 3 chr11 135086622\n 4 chr12 133275309\n 5 chr13 114364328\n 6 chr14 107043718\n 7 chr15 101991189\n 8 chr16  90338345\n 9 chr17  83257441\n10 chr18  80373285\n# \u2139 15 more rows<\/mark><\/em>\n\n# length of 1st and second chromosome<\/em><\/span>\nchromosome.1.length <- as.numeric(chromosome.length[1,2])<\/em><\/span>\nchromosome.2.length <- as.numeric(chromosome.length[12,2])<\/em><\/span>\n\n# add all chromosome lengths to a vector and add again chr1 and chr2; # used for zooming<\/em><\/span>\nx_range <- c(chromosome.length$max, chromosome.1.length, chromosome.2.length)<\/em><\/span>\nnames(x_range) <- c(chromosome.length$V1, \"chr1\", \"chr2\")<\/em><\/span>\n# calculate sector width<\/em><\/span>\nnormal_chr_index = 1:25<\/em><\/span>\nzoomed_chr_index = 26:27<\/em><\/span>\n# normalize in normal chromsomes and zoomed chromosomes separately<\/em><\/span>\nsector.width = c(x_range[normal_chr_index]\/sum(x_range[normal_chr_index]),x_range[zoomed_chr_index]\/sum(x_range[zoomed_chr_index])) <\/em><\/span>\n\n### create the plot<\/em><\/span>\ncircos.par(start.degree = 90)<\/em><\/span>\ncircos.initializeWithIdeogram(extend_chromosomes(cytoband.df.2,c(\"chr1\", \"chr2\")), sector.width = sector.width)<\/em><\/span>\n# add a new track:<\/em><\/span>\nbed = generateRandomBed(500)<\/em><\/span>\ncircos.genomicTrack(extend_chromosomes(bed, c(\"chr1\", \"chr2\")),<\/em><\/span>\npanel.fun = function(region, value, ...) {<\/em><\/span>\n  circos.genomicPoints(region, value, pch = 16, cex = 0.3)<\/em><\/span>\n})<\/em><\/span>\n\n# Add a link from original chromosome to the zoomed chromosome <\/em><\/span>\n# (Figure\u00a08.7).<\/em><\/span>\ncircos.link(\"chr1\", get.cell.meta.data(\"cell.xlim\", sector.index = \"chr1\"),\"zoom_chr1\", get.cell.meta.data(\"cell.xlim\", sector.index = \"zoom_chr1\"),col = \"#00000020\", border = NA)<\/em><\/span><\/em><\/span>\ncircos.clear()<\/em><\/span><\/code><\/pre>\n\n\n\n
\"\"<\/figure>\n\n\n\n
High level genomic functions<\/span><\/p>\n\n\n\n
# hg19 (default)<\/span><\/em>\ncircos.initializeWithIdeogram(plotType = c(\"labels\", \"axis\"))<\/span><\/em>\ncircos.track(ylim = c(0, 1)) # this puts a blank track in<\/span><\/em>\ncircos.genomicIdeogram() # put ideogram as the third track<\/span><\/em>\ncircos.genomicIdeogram(track.height = 0.1)<\/span><\/em>\ncircos.clear()<\/span><\/em><\/code><\/pre>\n\n\n\n
\"\"<\/figure>\n\n\n\n
Heatmap<\/p>\n\n\n\n
circos.initializeWithIdeogram()<\/span><\/em>\nbed = generateRandomBed(nr = 100, nc = 4)<\/span><\/em>\ncol_fun = colorRamp2(c(-1, 0, 1), c(\"green\", \"black\", \"red\"))<\/span><\/em>\ncircos.genomicHeatmap(bed, col = col_fun, side = \"inside\", border = \"white\")<\/span><\/em>\ncircos.clear()<\/span><\/em><\/code><\/pre>\n\n\n\n
\"\"<\/figure>\n\n\n\n
Labels<\/p>\n\n\n\n
circos.initializeWithIdeogram()<\/span><\/em>\nbed = generateRandomBed(nr = 50, fun = function(k) sample(letters, k, replace = TRUE))<\/span><\/em>\nbed[1, 4] = \"aaaaa\"<\/span><\/em>\ncircos.genomicLabels(bed, labels.column = 4, side = \"inside\")<\/span><\/em><\/code><\/pre>\n\n\n\n
\"\"<\/figure>\n\n\n\n
Density and rainfall plots<\/span><\/p>\n\n\n\n
load(system.file(package = \"circlize\", \"extdata\", \"DMR.RData\"))<\/span><\/em>\ncircos.initializeWithIdeogram(chromosome.index = paste0(\"chr\", 1:22))<\/span><\/em>\nbed_list = list(DMR_hyper, DMR_hypo)<\/span><\/em>\ncircos.genomicRainfall(bed_list, pch = 16, cex = 0.4, col = c(\"#FF000080\", \"#0000FF80\"))<\/span><\/em>\ncircos.genomicDensity(DMR_hyper, col = c(\"#FF000080\"), track.height = 0.1)<\/span><\/em>\ncircos.genomicDensity(DMR_hypo, col = c(\"#0000FF80\"), track.height = 0.1)<\/span><\/em>\n<\/code><\/pre>\n\n\n\n
\"\"<\/figure>\n","protected":false},"excerpt":{"rendered":"
circlize\u00a0can be used to make chromosome ideograms.\u00a0The example below uses the hg19 assembly (GRCh37) as reference. However, the newer (hg38 \/ GRCh38) can also be downloaded, manipulated and used instead (see below). It is important to use the appropriate reference assembly when working with genomic data. For the examples below to work, the dplyr and […]<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"inline_featured_image":false,"footnotes":""},"class_list":["post-2493","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/pcool.dyndns.org\/index.php\/wp-json\/wp\/v2\/pages\/2493","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pcool.dyndns.org\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/pcool.dyndns.org\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/pcool.dyndns.org\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/pcool.dyndns.org\/index.php\/wp-json\/wp\/v2\/comments?post=2493"}],"version-history":[{"count":2,"href":"https:\/\/pcool.dyndns.org\/index.php\/wp-json\/wp\/v2\/pages\/2493\/revisions"}],"predecessor-version":[{"id":4585,"href":"https:\/\/pcool.dyndns.org\/index.php\/wp-json\/wp\/v2\/pages\/2493\/revisions\/4585"}],"wp:attachment":[{"href":"https:\/\/pcool.dyndns.org\/index.php\/wp-json\/wp\/v2\/media?parent=2493"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}