CONTRIBUTING.md

Contributing to PCprophet

I only have a question!

Note: Please don't file an issue to ask a question

What should I know before I get started?

PCprophet modules

PCprophet is organized in a modular way. We have two kind of modules. First are core modules, which are characterized by the runner() method and that are called directly by the main.py. The rest are modules which have functions used by more than one module, or for which having a core module was unnecessary.

Core modules

• main.py - Top level module controlling the program flow and the setup of all parameters. Creates the .conf file
• map_to_database.py - Reads in the database (either PPI or complexes) and map the protein matrix into complexes. Performs rescaling and normalization and create the transf_matrix.txt file
• hypothesis.py - Performs hypothesis generation as described in the vignette and creates the transf_matrix_splitted.txt file
• merge.py - Merge hypothesis and database together before feature generation.
• generate_feature.py - Generate features and creates the peak_list.txt file
• predict.py - Load pickled sklearn module and returns class probability for every sample
• collapse.py - Experiment-wide and protein-centric merging of all complexes after FDR control
• differential.py - Performs differential analysis
• plots.py - Ensamble of plotting functions

Other modules

• aligner.py - Experiment-wide alignement using internal housekeeping complexes
• exceptions.py - Specific PCprophet exceptions
• go_fdr.py - GO-based FDR calculation as described in the vignette
• io_.py - I/O methods
• mcl.py - Markov based clustering for generating possible complexes from a ppi network
• parse_go.py - Implements GO based tree search and similarity methods calculation
• stats_py - Frequently used statistical metrics
• validate_input.py - Input tester

How Can I Contribute?

Reporting Bugs

This section guides you through submitting a bug report for PCprophet.

Note: If you find a Closed issue that seems like it is the same thing that you're experiencing, open a new issue and include a link to the original issue in the body of your new one.

Before Submitting A Bug Report

• Check the Vignette for a list of common questions and problems.

How Do I Submit A (Good) Bug Report?

Explain the problem and include details to help maintainers reproduce the problem:

• Use a clear and descriptive title for the issue to identify the problem.
• Provide example of sample_ids.txt and a sample of the input matrix used. Include links to files that can be useful for us to reproduce the error and to help us solving this issue faster
• Report the error/exception raised and which parameters were used
• Copy the .conf file and .spec file from PCprophet
• Copy the terminal output Every module in case of correct behaviour will print a message at the end of the runner() method. This ease up to point at faulty modules and can tell us if the previous modules failed or behaved as expected. Please copy that and attach it to the bug report

Provide more context by answering these questions:

• Can you reproduce the problem in by running different files?
• Did the problem start happening recently (e.g. after updating to a new version of Python or dependencies) or was this always a problem?
• If the problem started happening recently, Can you reproduce the problem in a older environment

Include details about your configuration and environment:

• Which version of Python are you using?

• What's the name and version of the OS you're using?

• Are all the Python packages needed meeting the minimum version required?

How to propose a new feature

Our final goal is to allow biologists to have a quick and easy way to analyze co-fractionation data. We tried to implement most of the features we would like to have but of course something can always be added. Just contact FossatiA explaining

• Which feature would need to be added

• Rational

• In which module it should be added

Styleguides

For contributing we have the following code style for Python

Python Styleguide

We mostly refers to PEP8 for syntax and in general coding styles. Below are some of the major styleguides

• Functions lowercase and _ are used when a space would be needed

def do_something_important():
    """
    does something important
    """
    pass

• Classes definition in CamelCase

class ImportantClass(object):
    """
    docstring for ImportantClass
    """
    def __init__(self, arg):
        super(ImportantClass, self).__init__()
        self.arg = arg

• Line length is 80 characters, apart when a long chained Pandas statement is used. We use spaces over tabs as separator due to the consistency across different ide

(df.groupby[['x','y']]
.apply(lambda x: (np.max(x['z'])-np.min(x['z'])))
.sort_values(ascending=False))

• Functions that do not returns explicitly a value should return True so they can be tested easily

def do_something_important():
    """
    does something important
    """
    return True


def test_do_something_important():
    """
    test do something important
    """
    if do_something_important():
        pass
    else:
        raise DoSomeThingImportException

• Functions used only once in a specific module or function should be encapsulated within the function they are called in. We used both lambdas or functions depending on the context

def do_something_important(df):
    """
    does something important
    """
    def does_something_important_but_only_here(things):
        """
        does something important but only here
        very long function
        """
        pass
    does_something_important_but_only_here()
    return True

Documentation Styleguide

• Use Markdown

Code formatting Guidelines

• Use Black