Interactive dimensionality reduction for large datasets

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Deep Learningblossom
Overview

BlosSOM 🌼

BlosSOM is a graphical environment for running semi-supervised dimensionality reduction with EmbedSOM. You can use it to explore multidimensional datasets, and produce great-looking 2-dimensional visualizations.

WARNING: BlosSOM is still under development, some stuff may not work right, but things will magically improve without notice. Feel free to open an issue if something looks wrong.

screenshot

BlosSOM was developed at the MFF UK Prague, in cooperation with IOCB Prague.

MFF logoIOCB logo

Overview

BlosSOM creates a landmark-based model of the dataset, and dynamically projects all dataset point to your screen (using EmbedSOM). Several other algorithms and tools are provided to manage the landmarks; a quick overview follows:

  • High-dimensional landmark positioning:
    • Self-organizing maps
    • k-Means
  • 2D landmark positioning
    • k-NN graph generation (only adds edges, not vertices)
    • force-based graph layouting
    • dynamic t-SNE
  • Dimensionality reduction
    • EmbedSOM
    • CUDA EmbedSOM (with roughly 500x speedup, enabling smooth display of a few millions of points)
  • Manual landmark position optimization
  • Visualization settings (colors, transparencies, cluster coloring, ...)
  • Dataset transformations and dimension scaling
  • Import from matrix-like data files
    • FCS3.0 (Flow Cytometry Standard files)
    • TSV (Tab-separated CSV)
  • Export of the data for plotting

Compiling and running BlosSOM

You will need cmake build system and SDL2.

For CUDA EmbedSOM to work, you need the NVIDIA CUDA toolkit. Append -DBUILD_CUDA=1 to cmake options to enable the CUDA version.

Windows (Visual Studio 2019)

Dependencies

The project requires SDL2 as an external dependency:

  1. install vcpkg tool and remember your vcpkg directory
  2. install SDL: vcpkg install SDL2:x64-windows

Compilation

git submodule init
git submodule update

mkdir build
cd build

# You need to fix the path to vcpkg in the following command:
cmake .. -G "Visual Studio 16 2019" -A x64 -DCMAKE_BUILD_TYPE="Release" -DCMAKE_INSTALL_PREFIX=./inst -DCMAKE_TOOLCHAIN_FILE=your-vcpkg-clone-directory/scripts/buildsystems/vcpkg.cmake

cmake --build . --config Release
cmake --install . --config Release

Running

Open Visual Studio solution BlosSOM.sln, set blossom as startup project, set configuration to Release and run the project.

Linux (and possibly other unix-like systems)

Dependencies

The project requires SDL2 as an external dependency. Install libsdl2-dev (on Debian-based systems) or SDL2-devel (on Red Hat-based systems), or similar (depending on the Linux distribution). You should be able to install cmake package the same way.

Compilation

git submodule init
git submodule update

mkdir build
cd build
cmake .. -DCMAKE_INSTALL_PREFIX=./inst    # or any other directory
make install                              # use -j option to speed up the build

Running

./inst/bin/blossom

Documentation

Quickstart

  1. Click on the "plus" button on the bottom right side of the window
  2. Choose Open file (the first button from the top) and open a file from the demo_data/ directory
  3. You can now add and delete landmarks using ctrl+mouse click, and drag them around.
  4. Use the tools and settings available under the "plus" button to optimize the landmark positions and get a better visualization.

See the HOWTO for more details and hints.

Performance and CUDA

If you pass -DBUILD_CUDA=1 to the cmake commands, you will get extra executable called blossom_cuda (or blossom_cuda.exe, on Windows).

The 2 versions of BlosSOM executable differ mainly in the performance of EmbedSOM projection, which is more than 100× faster on GPUs than on CPUs. If the dataset gets large, only a fixed-size slice of the dataset gets processed each frame (e.g., at most 1000 points in case of CPU) to keep the framerate in a usable range. The defaults in BlosSOM should work smoothly for many use-cases (defaulting at 1k points per frame on CPU and 50k points per frame on GPU).

If required (e.g., if you have a really fast GPU), you may modify the constants in the corresponding source files, around the call sites of clean_range(), which is the function that manages the round-robin refreshing of the data. Functionality that dynamically chooses the best data-crunching rate is being implemented and should be available soon.

License

BlosSOM is licensed under GPLv3 or later. Several small libraries bundled in the repository are licensed with MIT-style licenses.

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