Circos > Documentation > Tutorials > Recipes > Cell Cycle Part 1

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9 — Recipes

20. Cell Cycle — Part 1

This tutorial will show you how to use Circos to create a diagram of a typical cell cycle (G1, S, G2, M) and annotate it with links and text.

The tutorial has two parts, which show two ways of organizing the cycle and phase axes. In the first part (this one), the each phase has its own axis. In Cell Cycle — Part 2, there is a single axis, which represents the full cycle and which is cropped into individual ideograms that each correspond to a phase. The two approaches differentiate themselves in how coordinates are defined (relative to phase or cycle).

cycle segments

There are four phases in the cell cycle: gap 1 (G1), synthesis (S), gap 2 (G2) and mitosis (M). There's also a senescence phase (gap 0, G0), into which the cell can pass to/from G1. I'll ignore G0 in this example.

The lengths of the phases vary. I have selected durations that mimic the cell cycle diagram on Wikipedia. These are approximately: G1 45%, S 35%, G2 15% and M 5%.

The first step is to define each of these phases as individual axes. Circos requires integer coordinates, so I've arbitrarily chosen to make each phase axis be 100 units long. The variety in axis lengths in the image will be accomplised by setting chromosomes_scale.

# phases.txt
chr - g1 g1 0 100 greys-4-seq-1
chr - s s 0 100 greys-4-seq-2
chr - g2 g2 0 100 greys-4-seq-3
chr - m m 0 100 greys-4-seq-4

Making each of the phase axis lengths equal greatly facilitates defining coordinates. For example, if you wish to place at 25% into phase G1, the coordiate is g1 25 25.

segment colors

I've used a grayscale palette for the phases. Using the Brewer grey 4-color sequential palette, greys-4-seq, is helpful here. By making the phase segments grey, colors in other parts of the image will stand out better.

If you need to color code phases (e.g. you have other image elements that will inherit a phase's color), consider the spectral Brewer palette. You can always overwrite axis colors using chromosomes_color.

chromosomes_color = g1=spectral-7-seq-2,s=spectral-7-seq-3,g2=spectral-7-seq-6,m=spectral-7-seq-7

A useful shortcut is to store the palette name in one variable, and then use it in chromosomes_color using conf().

palette           = specral-7-seq
chromosomes_color = g1=conf(palette)-2,s=conf(palette)-3,g2=conf(palette)-6,m=conf(palette)-7

You can make the definition clearer by adding a <phase> block in which the color indexes are defined. With this approach, the value of chromosomes_color never needs to be adjusted. Instead, you change the palette name and color indexes in palette parameter and <phases> block, which is less prone to error.

palette  = spectral-7-div
g1 = 2
s  = 3
g2 = 6
m  = 7
chromosomes_color = g1=conf(palette)-conf(phases,g1),

axis organization and tick marks

There are several ways to organize the axis definition for the cell cycle diagram.

one segment per phase

This is the most intuitive approach. Each phase has its own axis, with a unique name and coordinate. This is the way this tutorial is defined.

The benefit of this approach is that generating coordinates for specific positions within each phase is trivial.

# 25% into G1
g1 25 25
# 5% into G2
g2 5 5
# 50% into M
m 50 50

The drawback is that generating coordinates based on position within the cycle is harder. For example, to figure out the coordinate for 60% into the cycle, you have to first calculate which phase it falls into and then the relative position within that phase.

one segment for cycle, divided into phases

Here, you would define a single axis, which corresponds to the full length of the cycle.

# the entire cycle is the axis
chr - cycle cycle 0 100 greys-4-seq-1

This second method is covered in the next tutorial, Cell Cycle — Part 2.

tick marks

I will set up the tick marks so that they run 0-100% in each phase and have labels like 0%, 10%, 20%, etc. I will also use grids to visually divide the phase segments into 5% regions.

Because the spacing will be relative (e.g. every 5% and 10%), we'll need to use the relative tick mark features: spacing_type and rspacing.

spacing_type   = relative
rspacing       = 0.05
rspacing       = 0.10

We also want the tick mark label to be relative, so that the label at the 10% tick mark will show 10% and not the actual position that the 10% corresponds to. Here are the relevant parameters:

label_relative = yes # the label will be pos/axis_length
format         = %d
rmultiplier    = 100 # 0.45 will be shown as 0.45*100=45
suffix         = %
rspacing       = 0.10
show_label     = yes
label_size     = 26p
label_offset   = 5p

By adding a grid to both the 5% and 10% ticks, you can extend the divisions provided by the ticks into other parts of the image. The relevant parameters are

show_grid           = yes


grid           = yes
grid_start     = dims(ideogram,radius_outer) # grid runs from outer ideogram edge
grid_end       = dims(ideogram,radius_inner) # ... to inner ideogram edge
grid_color     = white

rspacing       = 0.05
grid_thickness = 1p

rspacing       = 0.10
grid_thickness = 2p


scaling phases

We've defined the axis sizes for each phase to be the same (100 units). The actual duration of phases is different, so the axes will need to be scaled accordingly.

chromosomes_scale = g1=0.45,s=0.35,g2=0.15,m=0.05

The benefit of this approach over scaling coordinates is that you can change the length of the phases and keep the relative positions of all points intact without changing any of the coordinates in your data.

# 45:35:15:5
chromosomes_scale = g1=0.45,s=0.35,g2=0.15,m=0.05

# shorter G1: 20:60:15:5
chromosomes_scale = g1=0.20,s=0.60,g2=0.15,m=0.05

suppressing phases

To remove a phase from the figure, use the chromosomes parameter.

#don't show phase m
chromosomes = -m          

labeling cell cycle phase positions

Suppose that you have identified some genes (A..J) that are active at certain parts of each phase. Here I define a data file that will be used to position both symbols and text on the figure. I've given each data point a name and type parameter, so that these can be used later to change the text label and color of the symbol.

# genes.txt
# A active at 5% into g1
g1 5 5 0 name=A,type=1
# B active at 25% into g1
g1 25 25 0 name=B,type=1
g1 45 45 0 name=C,type=1
g1 55 55 0 name=D,type=1
g1 75 75 0 name=E,type=1
s 25 25 0 name=F,type=1
s 75 75 0 name=G,type=1
g2 15 15 0 name=H,type=2
g2 35 35 0 name=I,type=2
g2 65 65 0 name=J,type=2

The labels are created using a text track. The small trick here is that the position of the text track is inside the phase segments

r0 = 0.95r

but the text label is outside the phase segments, connected by a label link.

show_links     = yes
link_dims      = 0p,200p,20p,10p,20p
link_thickness = 3
link_color     = black

We also need a rule that changes the label of the text, because the data file has 0 for each position. We'll derive the value of the text data point, which is what the label will be, from the name parameter.

condition = 1
value     = eval(var(name))

marking cell cycle phase positions with symbols

One way to place symbols on the plot is define a zero-height scatter plot. We can use the same data file as for the text labels. Rules will be used to change the color of the symbol based on the type parameter.

type = scatter
file = genes.txt
r0   = 0.95r
r1   = 0.95r

glyph            = circle
glyph_size       = 36
color            = white
stroke_color     = black
stroke_thickness = 2

condition = var(type) == 1
color     = blues-5-seq-4
condition = var(type) == 2
color     = reds-5-seq-4


connecting cell cycle positions

To connect positions in the cycle with curves, the link track is used. Here the input data file defines each link as a coordinate pair, with any optional parameters.

# links.txt
g1 5 5 g1 25 25 type=1
g1 5 5 g1 45 45 type=1
g1 5 5 g1 55 55 type=1
g1 5 5 g1 75 75 type=1
g2 15 15 g2 35 35 type=2
g2 15 15 g2 65 65 type=2

Note that these positions have to be defined again — the coordinates in the genes.txt file used for the text labels and symbol positions cannot be referenced.

file          = links.txt

radius        = 0.95r
bezier_radius = 0r

# shorter links will be drawn closer
# to the edge of the circle
bezier_radius_purity = 0.1
crest                = 1
thickness            = 3

condition = var(type) == 1
color     = red
condition = var(type) == 2
color     = blue


I've used rules to change the color of the link based on the value of the type parameter in the links.txt file.