python-sumo 0.2.3

**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.

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.

Installation

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

pip install python-sumo

Documentation

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

License

MIT

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

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

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

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

interpret

Find features that support clusters separation.

usage: sumo interpret [-h] [-logfile LOGFILE] [-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,... outfile.tsv

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'])
  outfile.tsv          output file from this analysis, containing matrix
                       (features x clusters), where the value in each cell is
                       the importance of the feature in that cluster

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)
  -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

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

Please refer to documentation for example usage cases and suggestions for data preprocessing.