GigaSOM is a Julia toolkit for clustering and visualisation of really large cytometry data. Most generally, it can load FCS files, perform transformation and cleaning operations in their contents, run FlowSOM-style clustering, and visualize and export the results. GigaSOM is distributed and parallel in nature, which makes processing huge datasets a breeze -- a hundred of millions of cells with a few dozen parameters can be clustered and visualized in a few minutes.

Documentation	Test Coverage	CI	SciCrunch

If you use GigaSOM.jl and want to refer to it in your work, use the following citation format (also available as BibTeX in gigasom.bib):

Miroslav Kratochvíl, Oliver Hunewald, Laurent Heirendt, Vasco Verissimo, Jiří Vondrášek, Venkata P Satagopam, Reinhard Schneider, Christophe Trefois, Markus Ollert. GigaSOM.jl: High-performance clustering and visualization of huge cytometry datasets. GigaScience, Volume 9, Issue 11, November 2020, giaa127, https://doi.org/10.1093/gigascience/giaa127

• Operating system: Use Linux (Debian, Ubuntu or centOS), MacOS, or Windows 10 as your operating system. GigaSOM has been tested on these systems.
• Julia language: In order to use GigaSOM, you need to install Julia 1.0 or higher. You can find the download and installation instructions for Julia here.
• Hardware requirements: GigaSOM runs on any hardware that can run Julia, and can easily use resources from multiple computers interconnected by network. For processing large datasets, you require to ensure that the total amount of available RAM on all involved computers is larger than the data size.

💡 If you are new to Julia, it is adviseable to familiarize youself with the environment first. Use the full Julia documentation to solve various possible language-related problems, and the Julia package manager docs to solve installation-related difficulties.

Using the Julia package manager to install GigaSOM is easy -- after starting Julia, type:

import Pkg; Pkg.add("GigaSOM");

All these commands should be run from Julia at the julia> prompt.

Then you can load the GigaSOM package and start using it:

using GigaSOM

The first loading of the GigaSOM package may take several minutes to complete due to precompilation of the sources, especially on a fresh Julia install.

If you run a non-standard platform (e.g. a customized operating systems), or if you added any modifications to GigaSOM source code, you may want to run the test suite to ensure that everything works as expected:

import Pkg; Pkg.test("GigaSOM");

For debugging, it is sometimes very useful to enable the @debug messages from the source, as such:

using Logging
global_logger(ConsoleLogger(stderr, Logging.Debug))

A comprehensive documentation is available online; several introductory tutorials of increasing complexity are also included.

A very basic dataset (Levine13 from FR-FCM-ZZPH) can be loaded, clustered and visualized as such:

using GigaSOM

params, fcsmatrix = loadFCS("Levine_13dim.fcs")  # load the FCS file

exprs = fcsmatrix[:,1:13]  # extract only the data columns with expression values

som = initGigaSOM(exprs, 20, 20)    # random initialization of the SOM codebook
som = trainGigaSOM(som, exprs)      # SOM training
clusters = mapToGigaSOM(som, exprs) # extraction of per-cell cluster IDs
e = embedGigaSOM(som, exprs)        # EmbedSOM projection to 2D

The example loads the data, runs the SOM training (as in FlowSOM) and computes a 2D projection of the dataset (using EmbedSOM); the total computation time (excluding the possible precompilation of the libraries) should be around 15 seconds.

The results can be visualized e.g. with GigaScatter which we developed for this purpose, or by exporting the data and plotting them with any other programming language. For example, to save an embedding with highlighted expression of CD4, you can install and use GigaScatter as such:

import Pkg; Pkg.add("GigaScatter")
using GigaScatter

savePNG("Levine13-CD4.png",
  solidBackground(rasterize((500,500),        # bitmap size
    Matrix{Float64}(e'),                      # the embedding coordinates
    expressionColors(
      scaleNorm(Array{Float64}(exprs[:,5])),  # 5th column contains CD4 expressions
      expressionPalette(100, alpha=0.5)))))   # colors for plotting (based on RdYlBu)

The output may look like this (blue is negative expresison, red is positive):

Please follow the contributing guide when you have questions, want to raise issues, or just want to leave us some feedback!