EBIC.jl

EBIC - a biclustering algorithm in Julia
Author EpistasisLab
Github
Popularity
7 Stars
Updated Last
1 Year Ago
Started In
April 2021

EBIC.jl

EBIC is a next-generation biclustering algorithm based on artificial intelligence. EBIC is probably the first algorithm capable of discovering the most challenging patterns (i.e. row-constant, column-constant, shift, scale, shift-scale and trend-preserving) in complex and noisy data with average accuracy of over 90%. It is also one of the very few parallel biclustering algorithms that use at least one graphics processing unit (GPU) and is ready for big-data challenges.

The repository contains the new version of EBIC rewritten and improved.

Requirements

  • Julia 1.6 or higher
  • CUDA-capable GPU with compute capability 5.0 (Maxwell) or higher

Setting up project

  1. Clone the project.

  2. Enter the project root directory:

  3. Install dependencies

julia --project -E "using Pkg; Pkg.instantiate()"
  1. Start quick test on data/example_input.json (running a Julia program takes siginificant amount of time as source code has to be compiled each time, better use Julia REPL).
julia --project=. src/EBIC.jl -v -i data/example_input.csv

Getting test data

We provide three processed test datasets which can be fetched from remote DVC repository on Google Drive:

  • Unibic
  • RecBic Maintext
  • RecBic Sup (much larger the others)

In order to aquire the datasets, the DVC application is requried installed on your system.

Run inside the repository dvc pull if you want to fetch all three datasets, whereas to download a particular one use dvc pull <dataset_name> where dataset_name is one of the following:

  • unibic
  • recbic_maintext
  • recbic_sup

When using DVC for the first time for a repository, one must authenticate with their Google account following instructions given by DVC.

Usage

Julia REPL

This is the recommanded way of testing Julia applications.

Running the algorithm

The algorithm is run using run_ebic(). The function shares the same API as command line version described below. Example run with extended results (best_bclrs_stats):

julia> include("src/EBIC.jl"); res = Ebic.run_ebic(; input_path = "data/example_input.csv", best_bclrs_stats = true)
Progress: 100%|████████████████████| Time: 0:00:41
Dict{String,Any} with 7 entries:
  "data_load_time"      => 0.0338261
  "best_bclrs_iter"     => 815
  "biclusters"          => [Dict("rows"=>[16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, …
  "last_iter_tabu_hits" => 333
  "best_bclrs_time"     => 37.6397
  "algorithm_time"      => 41.164
  "performed_iters"     => 894

Running tests on provided datasets

To test all three datasets at once run the following:

julia> include("test/ebic_synth_test.jl"); synthtest.main()
results/Ebic.jl/unibic
####################################
Starting test case: 'narrow_100_10'
####################################
##################################
Testing: 'narrow_100_10_data1.txt'
Ground truth: 'narrow_100_10_data1_hiddenBics.txt'

The above is the same as running the three functions one after another:

synthtest.test_unibic()
synthtest.test_recbic_maintext()
synthtest.test_recbic_sup()

The tests' results are save in output folder in the repository root directory.

Command line

usage: EBIC.jl [-i INPUT_PATH] [-n MAX_ITERATIONS] [-b MAX_BICLUSTERS]
               [-x OVERLAP_THRESHOLD] [-t] [-g GPUS_NUM]
               [-a APPROX_TRENDS_RATIO] [-v] [-s] [-o] [-h]

EBIC is a next-generation biclustering algorithm based on artificial
intelligence (AI). EBIC is probably the first algorithm capable of
discovering the most challenging patterns (i.e. row-constant,
column-constant, shift, scale, shift-scale and trend-preserving) in
complex and noisy data with average accuracy of over 90%. It is also
one of the very few parallel biclustering algorithms that use at least
one graphics processing unit (GPU) and is ready for big-data
challenges.

optional arguments:
  -i, --input INPUT_PATH
                        The path to the input file. (default:
                        "data/example_input.csv")
  -n, --max_iterations MAX_ITERATIONS
                        The maximum number of iterations of the
                        algorithm. (type: Int64, default: 2000)
  -b, --biclusters_num MAX_BICLUSTERS
                        The number of biclusters that will be returned
                        in the end. (type: Int64, default: 3)
  -x, --overlap_threshold OVERLAP_THRESHOLD
                        The maximum similarity level of each two
                        chromosomes held in top rank list. (type:
                        Float64, default: 0.75)
  -t, --negative_trends
                        Enable negative trends.
  -g, --gpus_num GPUS_NUM
                        The number of gpus the algorithm should run
                        on. (type: Int64, default: 1)
  -a, --approx_trends APPROX_TRENDS_RATIO
                        (type: Float32, default: 0.85)
  -v, --verbose         Turn on the progress bar.
  -s, --best_bclrs_stats
                        Evaluate resulting biclusters finding
                        iteration and time. Enabled, it slightly
                        worsens overall algorithm performance.
  -o, --output          Save biclusters to a file in the JSON format.
                        The output file name is a concatenation of the
                        input file name and '-res.json' suffix.
  -h, --help            show this help message and exit

Cite us

@inproceedings{10.1145/3449726.3463197,
    author = {Renc, Pawe\l{} and Orzechowski, Patryk and Byrski, Aleksander and W\u{a}s, Jaros\l{}aw and Moore, Jason H.},
    title = {EBIC.JL: An Efficient Implementation of Evolutionary Biclustering Algorithm in Julia},
    year = {2021},
    isbn = {9781450383516},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3449726.3463197},
    doi = {10.1145/3449726.3463197},
    booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
    pages = {1540–1548},
    numpages = {9},
    keywords = {data mining, biclustering, parallel algorithms, evolutionary computation, machine learning},
    location = {Lille, France},
    series = {GECCO '21}
}

@inproceedings{10.1145/3449726.3462739,
    author = {Renc, Pawe\l{} and Orzechowski, Patryk and Byrski, Aleksander and W\k{a}s, Jaros\l{}aw and Moore, Jason H.},
    title = {Rapid Prototyping of Evolution-Driven Biclustering Methods in Julia},
    year = {2021},
    isbn = {9781450383516},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3449726.3462739},
    doi = {10.1145/3449726.3462739},
    booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
    pages = {61–62},
    numpages = {2},
    keywords = {biclustering, evolutionary computation, parallel algorithms, data mining, machine learning},
    location = {Lille, France},
    series = {GECCO '21}
}

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