Metadata-Version: 2.1
Name: genewalk
Version: 1.2.2
Summary: Determine gene function based on network embeddings.
Home-page: https://github.com/churchmanlab/genewalk
Author: Robert Ietswaart
Author-email: robert_ietswaart@hms.harvard.edu
License: UNKNOWN
Description: # GeneWalk
        
        [![License](https://img.shields.io/badge/License-BSD%202--Clause-orange.svg)](https://opensource.org/licenses/BSD-2-Clause)
        [![Documentation](https://readthedocs.org/projects/genewalk/badge/?version=latest)](https://genewalk.readthedocs.io/en/latest/?badge=latest)
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        GeneWalk determines for individual genes the functions that are relevant in a
        particular biological context and experimental condition. GeneWalk quantifies
        the similarity between vector representations of a gene and annotated GO terms
        through representation learning with random walks on a condition-specific gene
        regulatory network. Similarity significance is determined through comparison
        with node similarities from randomized networks.
        
        ## Install GeneWalk
        To install the latest release of GeneWalk (preferred):
        ```
        pip install genewalk
        ```
        To install the latest code from Github (typically ahead of releases):
        ```
        pip install git+https://github.com/churchmanlab/genewalk.git
        ```
        
        ## Using GeneWalk
        
        ### Gene list file
        GeneWalk always requires as input a text file containing a list with genes of interest
        relevant to the biological context. For example, differentially expressed genes
        from a sequencing experiment that compares an experimental versus control condition.
        GeneWalk supports gene list files containing HGNC human gene symbols,
        HGNC IDs, human Ensembl gene IDs, MGI mouse gene IDs, or human or mouse entrez IDs. 
        Each line in the file contains a gene identifier of one of these types.
        
        ### GeneWalk command line interface
        Once installed, GeneWalk can be run from the command line as `genewalk`, with
        a set of required and optional arguments. The required arguments include the
        project name, a path to a text file containing a list of genes, and an argument
        specifying the types of genes in the file.
        
        Example
        ```bash
        genewalk --project context1 --genes gene_list.txt --id_type hgnc_symbol
        ```
        
        Below is the full documentation of the command line interface:
        
        ```
        genewalk [-h] [--version] --project PROJECT --genes GENES --id_type
                      {hgnc_symbol,hgnc_id,mgi_id,ensembl_id}
                      [--stage {all,node_vectors,null_distribution,statistics}]
                      [--base_folder BASE_FOLDER]
                      [--network_source {pc,indra,edge_list,sif}]
                      [--network_file NETWORK_FILE] [--nproc NPROC] [--nreps NREPS]
                      [--alpha_fdr ALPHA_FDR] [--save_dw SAVE_DW]
                      [--random_seed RANDOM_SEED]
        
        
        required arguments:
          --version             Print the version of GeneWalk and exit.
          --project PROJECT     A name for the project which determines the folder
                                within the base folder in which the intermediate and
                                final results are written. Must contain only
                                characters that are valid in folder names.
          --genes GENES         Path to a text file with a list of differentially
                                expressed genes. Thetype of gene identifiers used in
                                the text file are provided in the id_type argument.
          --id_type {hgnc_symbol,hgnc_id,ensembl_id,mgi_id,entrez_human,entrez_mouse}
                                The type of gene IDs provided in the text file in the
                                genes argument. Possible values are: hgnc_symbol,
                                hgnc_id, ensembl_id, mgi_id, entrez_human and
                                entrez_mouse.
        
        optional arguments:
          --stage {all,node_vectors,null_distribution,statistics}
                                The stage of processing to run. Default: all
          --base_folder BASE_FOLDER
                                The base folder used to store GeneWalk temporary and
                                result files for a given project. Default:
                                ~/genewalk
          --network_source {pc,indra,edge_list,sif}
                                The source of the network to be used.Possible values
                                are: pc, indra, edge_list, and sif. In case of indra,
                                edge_list, and sif, the network_file argument must be
                                specified. Default: pc
          --network_file NETWORK_FILE
                                If network_source is indra, this argument points to a
                                Python pickle file in which a list of INDRA Statements
                                constituting the network is contained. In case
                                network_source is edge_list or sif, the network_file
                                argument points to a text file representing the
                                network.
          --nproc NPROC         The number of processors to use in a multiprocessing
                                environment. Default: 1
          --nreps_graph NREPS_GRAPH
                                The number of repeats to run when calculating node
                                vectors on the GeneWalk graph. Default: 3
          --nreps_null NREPS_NULL
                                The number of repeats to run when calculating node
                                vectors on the random network graphs for constructing
                                the null distribution. Default: 3
          --alpha_fdr ALPHA_FDR
                                The false discovery rate to use when outputting the
                                final statistics table. If 1 (default), all
                                similarities are output, otherwise only the ones whose
                                false discovery rate are below this parameter are
                                included. Default: 1
          --save_dw SAVE_DW     If True, the full DeepWalk object for each repeat is
                                saved in the project folder. This can be useful for
                                debugging but the files are typically very large.
                                Default: False
          --random_seed RANDOM_SEED
                                If provided, the random number generator is seeded
                                with the given value. This should only be used if the
                                goal is to deterministically reproduce a prior result
                                obtained with the same random seed.
        
        ```
        
        
        ### Output files
        GeneWalk automatically creates a `genewalk` folder in the user's home folder
        (or the user specified base_folder).
        When running GeneWalk, one of the required inputs is a project name.
        A sub-folder is created for the given project name where all intermediate and
        final results are stored. The files stored in the project folder are:
        - **`genewalk_results.csv`** - The main results table, a comma-separated values text file. See below for detailed description.
        - `genes.pkl` - A processed representation of the given gene list, in Python pickle (.pkl) binary file format.
        - `multi_graph.pkl` - A networkx MultiGraph resembling the GeneWalk network which was assembled based on the
        given list of genes, an interaction network, GO annotations, and the GO ontology.
        - `deepwalk_node_vectors_*.pkl` - A set of learned node vectors for each analysis repeat for the graph.
        - `deepwalk_node_vectors_rand_*.pkl` - A set of learned node vectors for each analysis repeat for a random graph.
        - `genewalk_rand_simdists.pkl` - Distributions constructed from repeats.
        - `deepwalk_*.pkl` - A DeepWalk object for each analysis repeat on the graph
        (only present if save_dw argument is set to True).
        - `deepwalk_rand_*.pkl` - A DeepWalk object for each analysis repeat on a random graph
        (only present if save_dw argument is set to True).
        
        
        ### GeneWalk results file description
        `genewalk_results.csv` is the main GeneWalk output table, a comma-separated values text file
        with the following column headers:
        - hgnc_id - human gene HGNC identifier.
        - **hgnc_symbol** - human gene symbol.
        - **go_name** - GO term name.
        - go_id - GO term identifier.
        - go_domain - Ontology domain that GO term belongs to
        (biological process, cellular component or molecular function).
        - ncon_gene - number of connection to gene in GeneWalk network.
        - ncon_go - number of connections to GO term in GeneWalk network.
        - **mean_padj** - mean false discovery rate (FDR) adjusted p-value of the similarity between gene and GO term.
        This is the key statistic indicating how relevant the GO term (function) is to the gene in the
        particular biological context or tested condition. GeneWalk determines an adjusted p-value with
        Benjamini Hochberg FDR correction for multiple tested of all connected GO term for each
        nreps_graph repeat analysis. The value presented here is the average over all p-adjust values
        from each repeat analysis. 
        - cilow_padj - lower bound of 95% confidence interval on mean_padj estimate from the nreps_graph repeat analyses.
        - ciupp_padj - upper bound of 95% confidence interval on mean_padj estimate.
        - mean_pval - mean p-values of gene - GO term similarities, not FDR corrected for multiple testing.
        - cilow_pval - lower bound of 95% confidence interval on mean_pval estimate.
        - ciupp_pval - upper bound of 95% confidence interval on mean_pval estimate.
        - mean_sim - mean of gene - GO term similarities.
        - sem_sim - standard error on mean_sim estimate.
        - mgi_id, ensembl_id, mgi_id, entrez_human or entrez_mouse - in case one of
          these gene identifiers were provided as input, the GeneWalk results table
          starts with an additional column to indicate the gene identifiers. In the
          case of mouse genes, the corresponding hgnc_id and hgnc_symbol resemble its
          human ortholog gene used for the GeneWalk analysis.
        
        
        ### Run time and stages of GeneWalk algorithm
        Recommended number of processors (optional argument: nproc) for a short (1-2h)
        run time is 4:
        ```bash
        genewalk --project context1 --genes gene_list.txt --id_type hgnc_symbol --nproc 4
        ```
        By default GeneWalk will run with 1 processor, resulting in a longer overall
        run time: 6-12h.
        Given a list of genes, GeneWalk runs three stages of analysis:
        1. Assembling a GeneWalk network and learning node vector representations
        by running DeepWalk on this network, for a specified number of repeats.
        Typical run time: one to a few hours.
        2. Learning random node vector representations by running DeepWalk on a set of
        randomized versions of the GeneWalk network, for a specified number of
        repeats. Typical run time: one to a few hours.
        3. Calculating statistics of similarities between genes and GO terms, and
        outputting  the GeneWalk results in a table. Typical run time: a few minutes.
        
        GeneWalk can either be run once to complete all these stages (default), or
        called separately for each stage (optional argument: stage).  Recommended
        memory availability on your operating system: 16Gb or 32Gb RAM.  GeneWalk
        outputs the uncertainty (95% confidence intervals) of the similarity
        significance (mean p-adjust). Depending on the context-specific network
        topology, this uncertainty can be large for individual gene - function
        associations. However, if overall the uncertainties turn out very large, one
        can set the optional arguments nreps_graph to 10 (or more) and nreps_null to 10
        to increase the algorithm's precision. This comes at the cost of an increased
        run time.
        
        
        ### Further documentation
        For a tutorial and more general information see the
        [GeneWalk website](http://churchman.med.harvard.edu/genewalk).  
        For further code documentation see our [readthedocs page](https://genewalk.readthedocs.io).
        
        
        ### Citation
        Robert Ietswaart, Benjamin M. Gyori, John A. Bachman, Peter K. Sorger, and
        L. Stirling Churchman
        *GeneWalk identifies relevant gene functions for a biological context using network
        representation learning* (2019), [BioRxiv; 755579](https://www.biorxiv.org/content/10.1101/755579v2).
        
        
        ### Funding
        This work was supported by National Institutes of Health grant 5R01HG007173-07
        (L.S.C.), EMBO fellowship ALTF 2016-422 (R.I.), and DARPA grants W911NF-15-1-0544
        and W911NF018-1-0124 (P.K.S.).
        
Keywords: gene function,network,embedding
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Description-Content-Type: text/markdown
