Calculate and add genomic density track
Calculate and add genomic density track
circos.genomicDensity( data, ylim.force = FALSE, window.size = NULL, overlap = TRUE, count_by = c("percent", "number"), col = ifelse(area, "grey", "black"), lwd = par("lwd"), lty = par("lty"), type = "l", area = TRUE, area.baseline = NULL, baseline = 0, border = NA, ...)
data |
A bed-file-like data frame or a list of data frames. If the input is a list of data frames. there will be multiple density plot in one same track. |
ylim.force |
Whether to force upper bound of |
window.size |
Pass to |
overlap |
Pass to |
count_by |
Pass to |
col |
Colors. It should be length of one. If |
lwd |
Width of lines, the same setting as |
lty |
Style of lines, the same setting as |
type |
Type of lines, see |
area |
See |
area.baseline |
Deprecated, use |
baseline |
See |
border |
See |
... |
Pass to |
This function is a high-level graphical function, and it will create a new track.
If you have multiple sets of genomic regions, you should make sure the density ranges
for all sets are similar, or I suggest you should put them into different tracks. One example
can be found in the "Examples" Section where the density range for bed_list[[2]]
is too high
compared to the range for bed_list[[1]]
, thus, it is better to put the two sets of
regions into two separate tracks.
load(system.file(package = "circlize", "extdata", "DMR.RData")) # rainfall circos.initializeWithIdeogram(plotType = c("axis", "labels")) bed_list = list(DMR_hyper, DMR_hypo) circos.genomicRainfall(bed_list, pch = 16, cex = 0.4, col = c("#FF000080", "#0000FF80")) circos.genomicDensity(bed_list[[1]], col = c("#FF000080"), track.height = 0.1) circos.genomicDensity(bed_list[[2]], col = c("#0000FF80"), track.height = 0.1) circos.clear() ############ draw the two densities in one track ############# circos.initializeWithIdeogram(plotType = c("axis", "labels")) circos.genomicDensity(bed_list, col = c("#FF000080", "#0000FF80"), track.height = 0.2) circos.clear()
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