Metadata-Version: 2.1
Name: goatools
Version: 1.0.6
Summary: Python scripts to find enrichment of GO terms
Home-page: http://github.com/tanghaibao/goatools
Author: Haibao Tang, DV Klopfenstein
Author-email: tanghaibao@gmail.com
License: BSD
Description: # Tools for Gene Ontology
        
        [![DIO](/doc/images/DOI.svg)](https://www.nature.com/articles/s41598-018-28948-z)
        [![Latest PyPI version](https://img.shields.io/pypi/v/goatools.svg)](https://pypi.python.org/pypi/goatools)
        [![bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/goatools/README.html?highlight=goatools)
        [![Github Actions](https://github.com/tanghaibao/goatools/workflows/build/badge.svg)](https://github.com/tanghaibao/goatools/actions)
        
        |         |                                                                       |
        | ------- | --------------------------------------------------------------------- |
        | Authors | Haibao Tang ([tanghaibao](http://github.com/tanghaibao))              |
        |         | DV Klopfenstein ([dvklopfenstein](https://github.com/dvklopfenstein)) |
        |         | Brent Pedersen ([brentp](http://github.com/brentp))                   |
        |         | Fidel Ramirez ([fidelram](https://github.com/fidelram))               |
        |         | Aurelien Naldi ([aurelien-naldi](http://github.com/aurelien-naldi))   |
        |         | Patrick Flick ([patflick](http://github.com/patflick))                |
        |         | Jeff Yunes ([yunesj](http://github.com/yunesj))                       |
        |         | Kenta Sato ([bicycle1885](http://github.com/bicycle1885))             |
        |         | Chris Mungall ([cmungall](https://github.com/cmungall))               |
        |         | Greg Stupp ([stuppie](https://github.com/stuppie))                    |
        |         | David DeTomaso ([deto](https://github.com/deto))                      |
        |         | Olga Botvinnik ([olgabot](https://github.com/olgabot))                |
        | Email   | <tanghaibao@gmail.com>                                                |
        | License | BSD                                                                   |
        
        ## Description
        
        This package contains a Python library to
        
        - Process over- and under-representation of certain GO terms, based on
          Fisher's exact test. With numerous multiple correction routines
          including locally implemented routines for Bonferroni, Sidak, Holm,
          and false discovery rate. Also included are multiple test
          corrections from
          [statsmodels](http://www.statsmodels.org/stable/index.html): FDR
          Benjamini/Hochberg, FDR Benjamini/Yekutieli, Holm-Sidak,
          Simes-Hochberg, Hommel, FDR 2-stage Benjamini-Hochberg, FDR 2-stage
          Benjamini-Krieger-Yekutieli, FDR adaptive Gavrilov-Benjamini-Sarkar,
          Bonferroni, Sidak, and Holm.
        
        - Process the obo-formatted file from [Gene Ontology
          website](http://geneontology.org). The data structure is a directed
          acyclic graph (DAG) that allows easy traversal from leaf to root.
        
        - Read [GO Association files](http://geneontology.org/page/go-annotation-file-formats):
        
          - GAF ([GO Annotation File](http://geneontology.org/page/go-annotation-file-gaf-format-21))
          - GPAD ([Gene Product Association Data](https://geneontology.github.io/docs/gene-product-association-data-gpad-format/))
          - NCBI's gene2go file
          - id2gos format. See [example](https://raw.githubusercontent.com/tanghaibao/goatools/master/data/association)
        
        - [Print **_decendants count_** and/or **_information content_**](/notebooks/dcnt_and_tinfo.ipynb)
          for a list of GO terms
        
        - [Get parents or ancestors for a GO term with or without optional relationships](https://nbviewer.jupyter.org/github/tanghaibao/goatools/blob/master/notebooks/parents_and_ancestors.ipynb)
        
          - [Print details about a GO ID's parents](https://github.com/tanghaibao/goatools/blob/master/notebooks/parent_go_terms.ipynb)
        
        - Compare two or more lists of GO IDs using _scripts/compare_gos.py_
        - [Plot GO hierarchies](https://github.com/tanghaibao/goatools#plot-go-lineage)
        - [Write GO hierarchies to an ASCII text file](https://github.com/tanghaibao/goatools#write-go-hierarchy)
        - Group GO terms for easier viewing
        
        - Map GO terms (or protein products with multiple associations to
          GO terms) to GOslim terms (analog to the map2slim.pl script supplied
          by geneontology.org)
        
        ## To Cite
        
        _Please cite the following research paper if you use GOATOOLS in your research_:
        
        Klopfenstein DV, Zhang L, Pedersen BS, ... Tang H [GOATOOLS: A Python library for Gene Ontology analyses](https://www.nature.com/articles/s41598-018-28948-z)
        _Scientific reports_ | (2018) 8:10872 | DOI:10.1038/s41598-018-28948-z
        
        - **GO Grouping**:
          Visualize the major findings in a gene ontology enrichment analysis (GEOA) more easily with grouping.
          A detailed description of GOATOOLS GO grouping is found in the
          [manuscript](https://www.nature.com/articles/s41598-018-28948-z).
        - **Compare GO lists**:
          Compare [two](https://github.com/tanghaibao/goatools/issues/162) or more lists of GO IDs using _scripts/compare_gos.py_.
          This script can be used with or without grouping.
        - **Stochastic GOEA simulations**:
          One of the findings resulting from our simulations is:
          [Larger study sizes result in higher GOEA sensitivity](https://github.com/dvklopfenstein/goatools_simulation#manuscript-figures),
          meaning fewer truly significant observations go unreported.
          The code for the stochastic GOEA simulations
          described in the paper is found [here](https://github.com/dvklopfenstein/goatools_simulation)
        
        ## Installation
        
        Make sure your Python version >= 2.7, install the latest stable
        version via PyPI:
        
        ```bash
        pip install goatools
        ```
        
        To install the development version:
        
        ```bash
        pip install git+git://github.com/tanghaibao/goatools.git
        ```
        
        `.obo` file for the most current
        [GO](http://geneontology.org/page/download-ontology):
        
        ```bash
        wget http://geneontology.org/ontology/go-basic.obo
        ```
        
        `.obo` file for the most current [GO
        Slim](http://geneontology.org/page/go-slim-and-subset-guide) terms (e.g.
        generic GOslim) :
        
        ```bash
        wget http://www.geneontology.org/ontology/subsets/goslim_generic.obo
        ```
        
        ## Dependencies
        
        - Simplest is to install via bioconda. See details
          [here](http://bioconda.github.io/recipes/goatools/README.html?highlight=goatools).
        
        - To calculate the uncorrected p-values, there are currently twooptions:
        
          - [fisher](http://pypi.python.org/pypi/fisher/) for calculating Fisher's exact test:
        
          ```bash
          pip install fisher
          ```
        
          - [fisher](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.fisher_exact.html)
            from [SciPy's](https://docs.scipy.org/doc/scipy/reference/)
            [stats](https://docs.scipy.org/doc/scipy/reference/tutorial/stats.html) package
        
          - `statsmodels` (optional) for access to a variety of statistical tests for GOEA:
        
          ```bash
          pip install statsmodels
          ```
        
        - To plot the ontology lineage, install one of these two options:
        
          - Graphviz
        
            - [Graphviz](http://www.graphviz.org/), for graph visualization.
            - [pygraphviz](http://networkx.lanl.gov/pygraphviz/), Python binding for communicating with Graphviz:
        
            ```bash
            pip install pygraphviz
            ```
        
          - [pydot](https://code.google.com/p/pydot/), a Python interface to Graphviz's Dot language.
            - [pyparsing](http://pyparsing.wikispaces.com/) is a prerequisite for `pydot`
            - Images can be viewed using either:
              - [ImageMagick](http://www.imagemagick.org/)'s _display_
              - [Graphviz](http://www.graphviz.org/)
        
        ## Cookbook
        
        `run.sh` contains example cases, which calls the utility scripts in the
        `scripts` folder.
        
        ### Find GO enrichment of genes under study
        
        See examples in [find_enrichment examples](/doc/md/README_find_enrichment.md)
        
        See `find_enrichment.py` for usage. It takes as arguments files
        containing:
        
        - gene names in a study
        - gene names in population (or other study if `--compare` is specified)
        - an association file that maps a gene name to a GO category.
        
        Please look at `tests/data/` folder to see examples on how to make these
        files. when ready, the command looks like:
        
        ```bash
        python scripts/find_enrichment.py --pval=0.05 --indent data/study \
                                          data/population data/association
        ```
        
        and can filter on the significance of (e)nrichment or (p)urification. it
        can report various multiple testing corrected p-values as well as the
        false discovery rate.
        
        The "e" in the "Enrichment" column means "enriched" - the concentration
        of GO term in the study group is significantly _higher_ than those in
        the population. The "p" stands for "purified" - significantly _lower_
        concentration of the GO term in the study group than in the population.
        
        **Important note**: by default, `find_enrichment.py` propagates counts
        to all the parents of a GO term. As a result, users may find terms in
        the output that are not present in their `association` file. Use
        `--no_propagate_counts` to disable this behavior.
        
        ### Write GO hierarchy
        
        - [scripts/wr_hier.py](doc/md/README_wr_hier.md): Given a GO ID, write the hierarchy below (default)
          or above (--up) the given GO.
        
        ### Plot GO lineage
        
        - [scripts/go_plot.py](doc/md/README_go_plot.md):
          - Plots user-specified GO term(s) up to root
          - Multiple user-specified GOs
          - User-defined colors
          - Plot relationships (-r)
          - Optionally plot children of user-specfied GO terms
        - [scripts/plot_go_term.py](plot_go_term-py)
        
        #### plot_go_term.py
        
        See `plot_go_term.py` for usage. `plot_go_term.py` can plot the lineage
        of a certain GO term, by:
        
        ```bash
        python scripts/plot_go_term.py --term=GO:0008135
        ```
        
        This command will plot the following image.
        
        ![GO term lineage](https://www.dropbox.com/s/4zbqx8sqcls3mge/gograph.png?raw=1)
        
        Sometimes people like to stylize the graph themselves, use option
        `--gml` to generate a GML output which can then be used in an external
        graph editing software like [Cytoscape](http://www.cytoscape.org/). The
        following image is produced by importing the GML file into Cytoscape
        using yFile orthogonal layout and solid VizMapping. Note that the [GML
        reader plugin](https://code.google.com/p/graphmlreader/) may need to be
        downloaded and installed in the `plugins` folder of Cytoscape:
        
        ```bash
        python scripts/plot_go_term.py --term=GO:0008135 --gml
        ```
        
        ![GO term lineage (Cytoscape)](https://www.dropbox.com/s/ueov2ioxl063q8h/gograph-gml.png?raw=1)
        
        ### Map GO terms to GOslim terms
        
        See `map_to_slim.py` for usage. As arguments it takes the gene ontology
        files:
        
        - the current gene ontology file `go-basic.obo`
        - the GOslim file to be used (e.g. `goslim_generic.obo` or any other GOslim file)
        
        The script either maps one GO term to its GOslim terms, or protein
        products with multiple associations to all its GOslim terms.
        
        To determine the GOslim terms for a single GO term, you can use the
        following command:
        
        ```bash
        python scripts/map_to_slim.py --term=GO:0008135 go-basic.obo goslim_generic.obo
        ```
        
        To determine the GOslim terms for protein products with multiple
        associations:
        
        ```bash
        python scripts/map_to_slim.py --association_file=data/association go-basic.obo goslim_generic.obo
        ```
        
        Where the `association` file has the same format as used for
        `find_enrichment.py`.
        
        The implemented algorithm is described in more detail at the go-perl
        documentation of
        [map2slim](http://search.cpan.org/~cmungall/go-perl/scripts/map2slim).
        
        ## Technical notes
        
        ### Available statistical tests for calculating uncorrected p-values
        
        There are currently two fisher tests available for calculating uncorrected
        p-values. Both fisher options from the fisher package and SciPy's stats package
        calculate the same pvalues, but provide the user an option in installing
        packages.
        
        - `fisher`, [fisher](http://pypi.python.org/pypi/fisher/) package's `fisher.pvalue_population`
        - `fisher_scipy_stats`:[SciPy](https://docs.scipy.org/doc/scipy/reference/)
          [stats](https://docs.scipy.org/doc/scipy/reference/tutorial/stats.html) package
          [fisher_exact](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.fisher_exact.html)
        
        ### Available multiple test corrections
        
        We have implemented several significance tests:
        
        - `bonferroni`, bonferroni correction
        - `sidak`, sidak correction
        - `holm`, hold correction
        - `fdr`, false discovery rate (fdr) implementation using resampling
        
        Additional methods are available if `statsmodels` is installed:
        
        - `sm_bonferroni`, bonferroni one-step correction
        - `sm_sidak`, sidak one-step correction
        - `sm_holm-sidak`, holm-sidak step-down method using Sidak adjustments
        - `sm_holm`, holm step-down method using Bonferroni adjustments
        - `simes-hochberg`, simes-hochberg step-up method (independent)
        - `hommel`, hommel closed method based on Simes tests (non-negative)
        - `fdr_bh`, fdr correction with Benjamini/Hochberg (non-negative)
        - `fdr_by`, fdr correction with Benjamini/Yekutieli (negative)
        - `fdr_tsbh`, two stage fdr correction (non-negative)
        - `fdr_tsbky`, two stage fdr correction (non-negative)
        - `fdr_gbs`, fdr adaptive Gavrilov-Benjamini-Sarkar
        
        In total 15 tests are available, which can be selected using option
        `--method`. Please note that the default FDR (`fdr`) uses a resampling
        strategy which may lead to slightly different q-values between runs.
        
        ## iPython [Notebooks](https://github.com/tanghaibao/goatools/tree/master/notebooks)
        
        ### Run a Gene Ontology Enrichment Analysis (GOEA)
        
        <https://github.com/tanghaibao/goatools/blob/master/notebooks/goea_nbt3102.ipynb>
        
        ### Show many study genes are associated with RNA, translation, mitochondria, and ribosomal
        
        <https://github.com/tanghaibao/goatools/blob/master/notebooks/goea_nbt3102_group_results.ipynb>
        
        ### Report level and depth counts of a set of GO terms
        
        <https://github.com/tanghaibao/goatools/blob/master/notebooks/report_depth_level.ipynb>
        
        ### Find all human protein-coding genes associated with cell cycle
        
        <https://github.com/tanghaibao/goatools/blob/master/notebooks/cell_cycle.ipynb>
        
        ### Calculate annotation coverage of GO terms on various species
        
        <https://github.com/tanghaibao/goatools/blob/master/notebooks/annotation_coverage.ipynb>
        
        ### Determine the semantic similarities between GO terms
        
        <https://github.com/tanghaibao/goatools/blob/master/notebooks/semantic_similarity.ipynb>
        
        ### Obsolete GO terms are loaded upon request
        
        <https://github.com/tanghaibao/goatools/blob/master/notebooks/godag_obsolete_terms.ipynb>
        
        ## Want to Help?
        
        Prior to submitting your pull request, please add a test which verifies your code, and run:
        
        ```console
        make test
        ```
        
        Items that we know we need include:
        
        - Add code coverage runs
        - Edit tests in the `makefile` under the comment, `# TBD`, suchthey run using `nosetests`
        - Help setting up [documentation](http://goatools.readthedocs.io/en/latest/). We
          are using Sphinx and Python docstrings to create documentation.
          For documentation practice, use make targets:
        
          ```bash
          make mkdocs_practice
          ```
        
          To remove practice documentation:
        
          ```bash
          make rmdocs_practice
          ```
        
          Once you are happy with the documentation do:
        
          ```bash
          make gh-pages
          ```
        
        Copyright (C) 2010-2018, Haibao Tang et al. All rights reserved.
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Description-Content-Type: text/markdown
