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g(r)osling

The goal of g(r)osling is to help you build interactive genomics visualizations with Gosling. This package uses reticulate to provide an interface to the Gos Python package.

Example

library(gosling)

data <- gos$bigwig(
  url = "https://s3.amazonaws.com/gosling-lang.org/data/ExcitatoryNeurons-insertions_bin100_RIPnorm.bw",
  column = "position",
  value = "peak"
)

track <- gos$Track(data, height = 100)$mark_point()$encode(
  x = gos$X("position:G"),
  y = gos$Y("peak:Q")
)

track$view()

Gosling visualization

Installation

You can install the development version of gosling like so:

devtools::install_github("gosling-lang/grosling") 

Because of Python, there may be some additional installation steps.

1.) Python must be installed on your system. We recommend conda.

2.) Create a conda environment called "r-reticulate". It is recommended to use this environment name for all packages that use reticulate.

3.) Install Gos into your "r-reticulate" environment using gosling::install_gosling().

You may wish to add a line like this to the .First() function in your .Rprofile:

reticulate::use_condaenv("r-reticulate")

The use_condaenv() function is called to provide a hint to reticulate on which Python environment to use.

Local Data

Gos transparently serves local and in-memory datasets for the Gosling client via a background data server. This feature is enabled automatically whenever a local file path is detected (rather than a URL).

Due to a temporary limitation with reticulate, some additional setup is required to enable this feature in grosling. Hopefully this will be resolved in reticulate soon, but the current workaround is shown below.

library(gosling)

### Setup reticulate to continually yield R <> Python. You only need to run this *once*.
local({
  reticulate::py_run_string("from time import sleep")
  py_yield_and_register_next_yield <- function() {
    reticulate::py_eval("sleep(0.001)")
    later::later(py_yield_and_register_next_yield, .001)
    invisible()
  }
  later::later(py_yield_and_register_next_yield)
})
###

file <- "./ExcitatoryNeurons-insertions_bin100_RIPnorm.bw" # local file
data <- gos$bigwig(file, column = "position", value = "peak")

track <- gos$Track(data, height = 100, width = 750)$mark_bar()$encode(
  x = gos$X("position:G"),
  y = gos$Y("peak:Q"),
  color = gos$Color("peak:Q")
)

track$view()

Gosling visualization using local file