Use the latest version of Circos and read Circos best practices—these list recent important changes and identify sources of common problems.
If you are having trouble, post your issue to the Circos Google Group and include all files and detailed error logs. Please do not email me directly unless it is urgent—you are much more likely to receive a timely reply from the group.
Don't know what question to ask? Read Points of View: Visualizing Biological Data by Bang Wong, myself and invited authors from the Points of View series.
9 — Recipes
16. Automating Heatmaps
circos.conf
<<include etc/colors_fonts_patterns.conf>> <<include ideogram.conf>> <<include ticks.conf>> <image> <<include etc/image.conf>> </image> karyotype = data/karyotype/karyotype.human.txt chromosomes_units = 1000000 chromosomes_display_default = no chromosomes = hs1;hs2;hs3 track_width = 0.007 track_start = 0.991 track_step = 0.01 <plots> type = heatmap min = 0 max = 1 <<include 10plot.conf>> <<include 10plot.conf>> <<include 10plot.conf>> <<include 10plot.conf>> <<include 10plot.conf>> <<include 10plot.conf>> <<include 10plot.conf>> <<include 10plot.conf>> <<include 10plot.conf>> <<include 10plot.conf>> </plots> <<include etc/housekeeping.conf>>
10plot.conf
<<include plot.conf>> <<include plot.conf>> <<include plot.conf>> <<include plot.conf>> <<include plot.conf>> <<include plot.conf>> <<include plot.conf>> <<include plot.conf>> <<include plot.conf>> <<include plot.conf>>
color.conf
# The track counter will run 0-99, and we want to map this into range 3..11 # to smoothly vary the spectral color map #color = spectral-11-div color = eval(sprintf("spectral-%d-div",remap_round(counter(plot),0,99,11,3))) # The track counter will run 0-99, and we want to map this into range 3..9 # to smoothly vary the red color map # color = eval(sprintf("reds-%d-seq",remap_round(counter(plot),0,99,9,3))) # Combine two color maps #color = eval(sprintf("blues-%d-seq-rev,oranges-%d-seq-rev",remap_round(counter(plot),0,99,9,3),remap_round(counter(plot),0,99,9,3)))
file.conf
file = data/8/17/genes.counter(plot).txt
ideogram.conf
<ideogram> show = no <spacing> default = 0u </spacing> # thickness (px) of chromosome ideogram thickness = 20p stroke_thickness = 2 # ideogram border color stroke_color = black fill = yes # the default chromosome color is set here and any value # defined in the karyotype file overrides it fill_color = black # fractional radius position of chromosome ideogram within image radius = 1r show_label = no label_with_tag = yes label_font = default label_radius = dims(ideogram,radius) + 0.075r label_size = 60p # cytogenetic bands band_stroke_thickness = 2 # show_bands determines whether the outline of cytogenetic bands # will be seen show_bands = yes # in order to fill the bands with the color defined in the karyotype # file you must set fill_bands fill_bands = yes </ideogram>
plot.conf
<plot> <<include file.conf>> <<include r0r1.conf>> <<include color.conf>> <<include rules.conf>> scale_log_base = eval(0.05*(100-counter(plot))) </plot>
r0r1.conf
r0 = eval(sprintf("%fr",conf(track_start)-counter(plot)*conf(track_step)))
r1 = eval(sprintf("%fr",conf(track_start)+conf(track_width)-counter(plot)*conf(track_step)))
orientation = eval( counter(plot) % 2 ? "in" : "out" )
rules.conf
<rules> # If you wish, you can generate the random # values using this rule. Don't forget to set # flow=continue so that rule testing # doesn't short-circuit. #<rule> #condition = 1 #value = eval(rand()) #flow = continue #</rule> <rule> importance = 100 condition = var(value) < 0.005 #fill_color = white show = no </rule> </rules>
ticks.conf
show_ticks = yes show_tick_labels = yes <ticks> radius = dims(ideogram,radius_outer) multiplier = 1e-6 <tick> spacing = 5u size = 10p thickness = 3p color = black show_label = yes label_size = 24pp label_offset = 5p format = %d </tick> <tick> spacing = 20u size = 16p thickness = 3p color = black show_label = yes label_size = 30p label_offset = 5p format = %d </tick> </ticks>