Use the latest version of Circos and read Circos best practices—these list recent important changes and identify sources of common problems.
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Don't know what question to ask? Read Points of View: Visualizing Biological Data by Bang Wong from the Points of View series.
This tutorial shows how to use Circos to create a general information graphic, not related to genome visualization. Or at least, not obviously related.
The image is a series of concentric stacked bar plots. Each ring corresponds to several data points, which have been normalized so that their sum is constant (e.g. 1000). We'll hide the scale and ideograms, so only the data appear.
The axis is a single "ideogram" of length 1000. We'll normalize all
the data sets to add up to 1000. The axis name is
# scale.txt chr - gh gh 0 1000 black
The karyotype is set to this file
# circos.conf ... karyotype = scale.txt chromosomes_units = 1 chromosomes_display_default = yes ...
The data were created by a short script which generated 27 random data sets. Each data set had between 4 and 10 data points and for each data set the values were cumulative.
The data files are named
# species.0.txt gh 0 401 id=gh6 gh 401 468 id=gh8 gh 468 674 id=gh9 gh 674 1000 id=gh10 ... # species.26.txt gh 0 235 id=gh3 gh 235 454 id=gh4 gh 454 534 id=gh7 gh 534 710 id=gh8 gh 710 757 id=gh9 gh 757 1000 id=gh10
Each data value has a unique id (
gh10) which is used to assign a color.
This kind of data set corresponds to a situation where you have
several samples (e.g. species) each composed of one or more components
from a fixed set (
gh10). The components are
displayed as cumulative fractions and therefore each data set has the
same maximum value.
Let's display the first 7 data sets. We'll use automated track placement to do so, so that the same configuration file can be used for each track.
# circos.conf <plots> <<include speciesplot.conf>> <<include speciesplot.conf>> <<include speciesplot.conf>> <<include speciesplot.conf>> <<include speciesplot.conf>> <<include speciesplot.conf>> <<include speciesplot.conf>> </plots>
# speciesplot.conf <plot> type = highlight file = species.counter(plot).txt r0 = eval(sprintf("%fr",conf(track_start) - conf(track_step) * counter(plot) )) r1 = eval(sprintf("%fr",conf(track_start) - conf(track_step) * counter(plot) + conf(track_width) )) fill_color = black stroke_thickness = 10p stroke_color = white #stroke_color = black <<include ghcolorrule.conf>> </plot>
You can futher simplify this configuration by moving the common constant parameters (e.g. fill_color) to the outer <plots> block. Any parameters in this block will be inherited by each of the <plot> blocks.
Data values are colored based on rules, which use the value of the
id parameter. The rules are imported into each track
ghcolorrule.conf which contains a single rule
<rules> <rule> condition = 1 # ghN -> spectral-10-div-N fill_color = eval(sprintf("spectral-10-div-%d",substr(var(id),2))) </rule> </rules>
The condition is always true, so the rule applies to each point. The number from the point's
id field (e.g.
gh7) is extracted and then used to define a color (e.g.
spectral-10-div-7). The color palette used is the 10-color diverging spectral Brewer Palette.
Tracks are placed automatically, controlled by three
# if using 7 data sets # well spaced #track_width = 0.08 #track_step = 0.1 #track_start = 0.9 # abutting track_width = 0.1 track_step = 0.1 track_start = 0.9 # if using 3x7 data sets #track_width = 0.03 #track_step = 0.04 #track_start = 0.95
To add more tracks, add more
lines to the <plot> block in