Package: unpast
UnPaStUnPaSt is a novel method for identification of differentially expressed biclusters.Requirements:Python:
    fisher==0.1.10
    jenkspy==0.2.0
    matplotlib-venn==0.11.6
    numba==0.55.2
    numpy==1.22.3
    scikit-learn==1.1.0
    scikit-network==0.25.0
    scipy==1.7.3
    statsmodels==0.13.2
    pandas==1.4.2
    python-louvain==0.15
    statsmodels==0.13.2

R:
    WGCNA>=1.70-3
    limma>=3.42.2InstallationUnPaSt can be installed usingpip,poetry, or run usingDocker, or as a script (see examples section). Follow the appropriate instructions below for your preferred method. You need to have R andPython 3.8-3.10installed.Usingpip:To install the project usingpip, first make sure you havepipinstalled on your system. If you haven't installed it already, you can find the installation instructionshere.Oncepipis installed, you can install UnPaSt by running the following command:pipinstallunpastRun it:run_unpast-hDependencies. To use this package, you will need to have R and theWGCNA libraryandlimmainstalled. You can easily install these dependencies by running the following command after installing unpast:python-munpast.install_r_dependencies# or you can install it directlyR-e"install.packages('BiocManager'); BiocManager::install(c('WGCNA', 'limma'))"Installation usingPoetry:To install the package using Poetry, first make sure you have Poetry installed, clone the repo and run:poetryaddunpastRun it:poetryrunrun_unpast-hDependencies. To use this package, you will need to have R and theWGCNA libraryandlimmainstalled. You can easily install these dependencies by running the following command after installing unpast:poetryrunpython-munpast.install_r_dependencies# or you can install it directlyR-e"install.packages('BiocManager'); BiocManager::install(c('WGCNA', 'limma'))"Running withDocker:You can also run the package using Docker. First, pull the Docker image:dockerpullfreddsle/unpast:latestNext, run the UnPaSt:dockerrun-v/your/data/path/:/user_data/freddsle/unpast:latest--exprs/user_data/exprs.tsv--out_dir/user_data/out_dir/ExamplesUnPaSt requires a tab-separated file with features (e.g. genes) in rows, and samples in columns. Feature and sample names must be unique.cd test;
mkdir -p results;

# running UnPaSt with default parameters and example data
python ../run_unpast.py --exprs scenario_B500.exprs.tsv.gz --basename results/scenario_B500

# with different binarization and clustering methods
python ../run_unpast.py --exprs scenario_B500.exprs.tsv.gz --basename results/scenario_B500 --binarization ward --clustering Louvain

# help
python run_unpast.py -hOutputs<basename>.[parameters].biclusters.tsv - a .tsv table with found biclsuters, wherethe first line starts from '#' and stores parameterseach following line represents a biclusterSNR column contains SNR of a biclustercolumns "n_genes" and "n_samples" provide the numbers of genes and samples, respectively"gene","sample" contain gene and sample names respectively"gene_indexes" and  "sample_indexes" - 0-based gene and sample indexes in the input matrix.binarized expressions, background distributions of SNR for each bicluster size and binarization statistics [if clustering is WGCNA,  or  '--save_binary' flag is added]AboutUnPaSt is an unconstrained version of DESMOND method (repository,publication)Major modifications:it does not require the network of feature interactionsUnPaSt clusters individual features instead of pairs of featuresuses 2-means, hierarchicla clustering or GMM for binarization of individual gene expressionsSNR threshold for featuer selection is authomatically determined; it depends on bicluster size in samples and user-defined p-value cutoffLicenseFree for non-for-profit use. For commercial use please contact the developers.