Jellyfisher is an R package for visualizing tumor evolution and subclonal compositions using Jellyfish plots, which display both spatial and temporal dimensions in a single unified figure.
The package is based on the Jellyfish visualization tool, bringing its functionality to R users. Jellyfisher supports both ClonEvol results and plain data frames, making it compatible with various tools and workflows.
install.packages("jellyfisher")
::install_github("HautaniemiLab/jellyfisher", build_vignettes = TRUE) devtools
Jellyfisher is designed to work with data frames or ClonEvol results.
The input data should follow specific structures for
samples, phylogeny, and subclonal
compositions, which are described in the jellyfisher
function’s documentation.
library(jellyfisher)
# Plot the bundled example data
jellyfisher(jellyfisher_example_tables)
Jellyfisher provides a straightforward way to visualize ClonEvol results using the
extract_tables_from_clonevol
function. See the reference page for details.
library(clonevol)
library(jellyfisher)
# Run ClonEvol. Check the ClonEvol documentation for details.
<- infer.clonal.models(...)
y <- convert.consensus.tree.clone.to.branch(y)
y
# Plot the results
extract_tables_from_clonevol(y, model = 1) |>
jellyfisher()
By default, all samples that have no explicit parent are children of
the inferred root sample. You can customize the parent-child
relationships by modifying the parent
column in the
samples
data frame before plotting.
You can also modify the relationships with ease using the set_parents
function.
For example, if you have three samples, A, B, and C, they will have the following relationships by default:
Root
/ | \
A B C
With the explicit parents, you can customize the relationships:
|>
tables set_parents(list(
# The parent of C is B
"C" = "B"
|>
) jellyfisher()
Root
/ \
A B
\
C
Jellyfisher is a thin wrapper for the Jellyfish
visualization tool. Jellyfish is included as a git submodule in the tools/
directory.
To build the Jellyfish JavaScript dependency, run the update-and-build.sh
script in the tools/
directory. Most of the R code is autogenerated from the Jellyfish
JavaScript code using the generate-R-code.mjs
script, which should be run after building the Jellyfish dependency.
Copyright (c) 2025 Kari Lavikka. MIT license, see LICENSE.md for details.
Jellyfisher is developed in The Systems Biology of Drug Resistance in Cancer group at the University of Helsinki.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements No. 965193 (DECIDER) and No. 847912 (RESCUER).