Metadata-Version: 2.1
Name: python-sumo
Version: 0.2.4
Summary: **sumo** is a command-line tool to identify molecular subtypes in multi-omics datasets. It implements a novel nonnegative matrix factorization (NMF) algorithm to identify groups of samples that share molecular signatures, and provides tools to evaluate such assignments.
Home-page: https://github.com/ratan-lab/sumo
Author: Karolina Sienkiewicz
Author-email: sienkiewicz2k@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/x-rst
Requires-Dist: seaborn
Requires-Dist: scikit-learn
Requires-Dist: scipy
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: lightgbm
Requires-Dist: hyperopt
Requires-Dist: shap



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**sumo** is a command-line tool to identify molecular subtypes in multi-omics datasets. It implements a novel nonnegative matrix factorization (NMF) algorithm to identify groups of samples that share molecular signatures, and provides tools to evaluate such assignments.

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Installation
------------
You can install **sumo** from PyPI, by executing command below. Please note that we require **python 3.6+**.

.. code:: sh

    pip install python-sumo

Documentation
-------------
The official documentation is available at https://python-sumo.readthedocs.io

License
-------

`MIT <LICENSE>`__

Usage
-----

Typical workflow includes running *prepare* mode for preparation of similarity
matrices from feature matrices, followed by factorization of produced multiplex network (mode *run*).
Third mode *evaluate* can be used for comparison of created cluster labels against biologically significant labels.

prepare
^^^^^^^
Generates similarity matrices for samples based on biological data and saves them into multiplex network files.

::

    usage: sumo prepare [-h] [-method METHOD] [-k K] [-alpha ALPHA]
                        [-missing MISSING] [-atol ATOL] [-sn SN] [-fn FN] [-df DF]
                        [-ds DS] [-logfile LOGFILE] [-log {DEBUG,INFO,WARNING}]
                        [-plot PLOT]
                        infile1,infile2,... outfile.npz

    positional arguments:
      infile1,infile2,...   comma-delimited list of paths to input files,
                            containing standardized feature matrices, with samples
                            in columns and features in rows (supported types of
                            files: ['.txt', '.txt.gz', '.txt.bz2', '.tsv',
                            '.tsv.gz', '.tsv.bz2'])
      outfile.npz           path to output .npz file

    optional arguments:
      -h, --help            show this help message and exit
      -method METHOD        either one method of sample-sample similarity
                            calculation, or comma-separated list of methods for
                            every layer (available methods: ['euclidean',
                            'cosine', 'pearson', 'spearman'], default of
                            euclidean)
      -k K                  fraction of nearest neighbours to use for sample
                            similarity calculation using Euclidean distance
                            similarity (default of 0.1)
      -alpha ALPHA          hypherparameter of RBF similarity kernel, for
                            Euclidean distance similarity (default of 0.5)
      -missing MISSING      acceptable fraction of available values for assessment
                            of distance/similarity between pairs of samples -
                            either one value or comma-delimited list for every
                            layer (default of [0.1])
      -atol ATOL            if input files have continuous values, sumo checks if
                            data is standardized feature-wise, meaning all
                            features should have mean close to zero, with standard
                            deviation around one; use this parameter to set
                            tolerance of standardization checks (default of 0.01)
      -sn SN                index of row with sample names for input files
                            (default of 0)
      -fn FN                index of column with feature names for input files
                            (default of 0)
      -df DF                if percentage of missing values for feature exceeds
                            this value, remove feature (default of 0.1)
      -ds DS                if percentage of missing values for sample (that
                            remains after feature dropping) exceeds this value,
                            remove sample (default of 0.1)
      -logfile LOGFILE      path to save log file, by default stdout is used
      -log {DEBUG,INFO,WARNING}
                            sets the logging level (default of INFO)
      -plot PLOT            path to save adjacency matrix heatmap(s), by default
                            plots are displayed on screen

**Example**

.. code:: sh

    sumo prepare -plot plot.png methylation.txt,expression.txt prepared.data.npz

run
^^^
Cluster multiplex network using non-negative matrix tri-factorization to identify molecular subtypes.

::

    usage: sumo run [-h] [-sparsity SPARSITY] [-n N]
                    [-method {max_value,spectral}] [-max_iter MAX_ITER] [-tol TOL]
                    [-calc_cost CALC_COST] [-logfile LOGFILE]
                    [-log {DEBUG,INFO,WARNING}] [-h_init H_INIT] [-t T]
                    infile.npz k outdir

    positional arguments:
      infile.npz            input .npz file containing adjacency matrices for
                            every network layer and sample names (file created by
                            running program with mode "run") - consecutive
                            adjacency arrays in file are indexed in following way:
                            "0", "1" ... and index of sample name vector is
                            "samples"
      k                     either one value describing number of clusters or
                            coma-delimited range of values to check (sumo will
                            suggest cluster structure based on cophenetic
                            correlation coefficient)
      outdir                path to save output files

    optional arguments:
      -h, --help            show this help message and exit
      -sparsity SPARSITY    either one value or coma-delimited list of sparsity
                            penalty values for H matrix (sumo will try different
                            values and select the best results; default of [0.1])
      -n N                  number of repetitions (default of 50)
      -method {max_value,spectral}
                            method of cluster extraction (default of "max_value")
      -max_iter MAX_ITER    maximum number of iterations for factorization
                            (default of 500)
      -tol TOL              if objective cost function value fluctuation (|Δℒ|) is
                            smaller than this value, stop iterations before
                            reaching max_iter (default of 1e-05)
      -calc_cost CALC_COST  number of steps between every calculation of objective
                            cost function (default of 20)
      -logfile LOGFILE      path to save log file (by default printed to stdout)
      -log {DEBUG,INFO,WARNING}
                            set the logging level (default of INFO)
      -h_init H_INIT        index of adjacency matrix to use for H matrix
                            initialization (by default using average adjacency)
      -t T                  number of threads (default of 1)

**Example**

.. code:: sh

    sumo run -t 10 prepared.data.npz 2,5 results_dir

evaluate
^^^^^^^^
Evaluate clustering results, given set of labels.

::

    usage: sumo evaluate [-h] [-metric {NMI,purity,ARI}] [-logfile LOGFILE]
                         infile.tsv labels

    positional arguments:
      infile.tsv            input .tsv file containing sample names in 'sample'
                            and clustering labels in 'label' column (clusters.tsv
                            file created by running sumo with mode 'run')
      labels                .tsv of the same structure as input file

    optional arguments:
      -h, --help            show this help message and exit
      -metric {NMI,purity,ARI}
                            metric for accuracy evaluation (by default all metrics
                            are calculated)
      -logfile LOGFILE      path to save log file (by default printed to stdout)
      -log {DEBUG,INFO,WARNING}
                        sets the logging level (default of INFO)

**Example**

.. code:: sh

    sumo evaluate results_dir/k3/clusters.tsv labels.tsv

interpret
^^^^^^^^^
Find features that support clusters separation.

::

    usage: sumo interpret [-h] [-logfile LOGFILE] [-log {DEBUG,INFO,WARNING}]
                          [-hits HITS] [-max_iter MAX_ITER] [-n_folds N_FOLDS]
                          [-t T] [-seed SEED] [-sn SN] [-fn FN] [-df DF] [-ds DS]
                          sumo_results.npz infile1,infile2,... output_prefix

    positional arguments:
      sumo_results.npz      path to sumo_results.npz (created by running program
                            with mode "run")
      infile1,infile2,...   comma-delimited list of paths to input files,
                            containing standardized feature matrices, with samples
                            in columns and features in rows(supported types of
                            files: ['.txt', '.txt.gz', '.txt.bz2', '.tsv',
                            '.tsv.gz', '.tsv.bz2'])
      output_prefix         prefix of output files - sumo will create two output
                            files (1) .tsv file containing matrix (features x
                            clusters), where the value in each cell is the
                            importance of the feature in that cluster; (2)
                            .hits.tsv file containing features of most importance

    optional arguments:
      -h, --help            show this help message and exit
      -logfile LOGFILE      path to save log file (by default printed to stdout)
      -log {DEBUG,INFO,WARNING}
                            sets the logging level (default of INFO)
      -hits HITS            sets number of most important features for every
                            cluster, that are logged in .hits.tsv file
      -max_iter MAX_ITER    maximum number of iterations, while searching through
                            hyperparameter space
      -n_folds N_FOLDS      number of folds for model cross validation (default of
                            5)
      -t T                  number of threads (default of 1)
      -seed SEED            random state (default of 1)
      -sn SN                index of row with sample names for input files
                            (default of 0)
      -fn FN                index of column with feature names for input files
                            (default of 0)
      -df DF                if percentage of missing values for feature exceeds
                            this value, remove feature (default of 0.1)
      -ds DS                if percentage of missing values for sample (that
                            remains after feature dropping) exceeds this value,
                            remove sample (default of 0.1)

**Example**

.. code:: sh

    sumo interpret results_dir/k3/sumo_results.npz methylation.txt,expression.txt interpret_results

.. inclusion-end-marker-do-not-remove

Please refer to documentation for `example usage cases and suggestions for data preprocessing <https://python-sumo.readthedocs.io/en/latest/example.html>`_.

.. 

