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https://github.com/lucacappelletti94/crr_labels

Python package wrapping over FANTOM and Roadmap labels for cis regulatory regions.
https://github.com/lucacappelletti94/crr_labels

Last synced: 9 days ago
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Python package wrapping over FANTOM and Roadmap labels for cis regulatory regions.

Host: GitHub
URL: https://github.com/lucacappelletti94/crr_labels
Owner: LucaCappelletti94
Created: 2019-10-25T06:26:18.000Z (about 5 years ago)
Default Branch: master
Last Pushed: 2021-06-02T12:13:16.000Z (over 3 years ago)
Last Synced: 2024-10-13T13:45:31.623Z (23 days ago)
Language: Jupyter Notebook
Homepage:
Size: 169 MB
Stars: 0
Watchers: 2
Forks: 1
Open Issues: 0
Metadata Files:
- Readme: README.rst

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README

        crr_labels

=========================================================================================

|travis| |sonar_quality| |sonar_maintainability| |codacy| |code_climate_maintainability| |pip| |downloads|

Python package wrapping over FANTOM and Roadmap labels for cis-regulatory regions.

How do I install this package?

----------------------------------------------

As usual, just download it using pip:

.. code:: shell

    pip install crr_labels

Tests Coverage

----------------------------------------------

Since some software handling coverages sometimes get slightly different results, here's three of them:

|coveralls| |sonar_coverage| |code_climate_coverage|

Usage examples

-----------------------------------------------

Currently, we support `FANTOM CAGE data `_ and `Roadmap `_ but in the future an additional

cis-regulatory dataset based on open chromatin data will be added.

FANTOM

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

To retrieve the FANTOM promoters and enhancers you can proceed as follows:

.. code:: python

    from crr_labels import fantom

    enhancers, promoters = fantom(

        cell_lines=["HelaS3", "GM12878"], # list of cell lines to be considered.

        window_size=200, # window size to use for the various regions.

        genome = "hg19", # considered genome version. Currently supported only "hg19".

        center_enhancers = "peak", # how to center the enhancer window, either around "peak" or the "center" of the region.

        enhancers_threshold = 0, # activation threshold for the enhancers.

        promoters_threshold = 5, # activation threshold for the promoters.

        drop_always_inactive_rows = True, # whether to drop the rows where no activation is detected for every row.

        binarize = True, # whether to return the data binary-encoded, zero for inactive, one for active.

        nrows = None # the number of rows to read, useful when testing pipelines for creating smaller datasets.

    )

The library will download and parse the fantom project raw data and return two DataFrames for the required cell lines.

Consider reading the method docstring for more in-depth information about the method.

The main steps are the following:

- The raw files are retrieved from the fantom dataset from the link specified in the `fantom_data.json file `_

- The window for the enhancers and promoters are expanded or compressed to the given window size. In particular:

  - The enhancers' window can either be centered on the region center with the "center" mode or around the "peak" with the "peak" mode.

  - The promoters' window is upstream in the positive strand from the end of the promoter and downstream on the negative strand from the start of the promoter.

- When multiple experiments are present for a cell line, for instance for "HelaS3", an average of the activation peaks is executed.

- Optionally (and by default) the rows that are always inactive for the chosen cell lines are dropped. You can specify this behaviour using the parameter "drop_always_inactive_rows".

Roadmap

~~~~~~~~~~~~~~~~~~~~~~~~~~~

To retrieve the Roadmap promoters and enhancers you can proceed as follows:

.. code:: python

    from crr_labels import roadmap

    enhancers, promoters = roadmap(

        cell_lines = ["HelaS3", "GM12878"], # List of cell lines to be considered.

        window_size = 200, # Window size to use for the various regions.

        genome = "hg19", # Considered genome version. Currently supported only "hg19".

        states = 18, # Number of the states of the model to consider. Currently supported only "15" and "18".

        enhancers_labels = ("7_Enh", "9_EnhA1", "10_EnhA2"), # Labels to encode as active enhancers.

        promoters_labels = ("1_TssA",), # Labels to enode as active promoters.

        nrows = None # the number of rows to read, useful when testing pipelines for creating smaller datasets.

    )

Consider reading the method docstring for more in-depth information about the method.

Rendered datasets

----------------------------------

The following two datasets have labels for 7 common cell lines (GM12878, HelaS3, HepG2, K562, A549, H1, H9) and for various other that were not available in the other dataset.

FANTOM

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The following datasets contain data for the cell lines GM12878, HelaS3, HepG2, K562, A549, H1, H9, JURKAT, MCF7, HEK293, Caco2, HL60 and PC3.

TODO: Render the datasets

Roadmap

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The following datasets contain data for the cell lines GM12878, HelaS3, HepG2, K562, A549, H1, H9, DND41, HUES48, HUES6, HUES64 and IMR90.

TODO: The updated processed labels will be added as soon as we decide on the new states to be used.

.. |travis| image:: https://travis-ci.org/LucaCappelletti94/crr_labels.png

   :target: https://travis-ci.org/LucaCappelletti94/crr_labels

   :alt: Travis CI build

.. |sonar_quality| image:: https://sonarcloud.io/api/project_badges/measure?project=LucaCappelletti94_crr_labels&metric=alert_status

    :target: https://sonarcloud.io/dashboard/index/LucaCappelletti94_crr_labels

    :alt: SonarCloud Quality

.. |sonar_maintainability| image:: https://sonarcloud.io/api/project_badges/measure?project=LucaCappelletti94_crr_labels&metric=sqale_rating

    :target: https://sonarcloud.io/dashboard/index/LucaCappelletti94_crr_labels

    :alt: SonarCloud Maintainability

.. |sonar_coverage| image:: https://sonarcloud.io/api/project_badges/measure?project=LucaCappelletti94_crr_labels&metric=coverage

    :target: https://sonarcloud.io/dashboard/index/LucaCappelletti94_crr_labels

    :alt: SonarCloud Coverage

.. |coveralls| image:: https://coveralls.io/repos/github/LucaCappelletti94/crr_labels/badge.svg?branch=master

    :target: https://coveralls.io/github/LucaCappelletti94/crr_labels?branch=master

    :alt: Coveralls Coverage

.. |pip| image:: https://badge.fury.io/py/crr-labels.svg

    :target: https://badge.fury.io/py/crr-labels

    :alt: Pypi project

.. |downloads| image:: https://pepy.tech/badge/crr-labels

    :target: https://pepy.tech/badge/crr-labels

    :alt: Pypi total project downloads 

.. |codacy| image:: https://api.codacy.com/project/badge/Grade/c0a7e110045a4d25933c65fe2014a33c

    :target: https://www.codacy.com/manual/LucaCappelletti94/crr_labels?utm_source=github.com&utm_medium=referral&utm_content=LucaCappelletti94/crr_labels&utm_campaign=Badge_Grade

    :alt: Codacy Maintainability

.. |code_climate_maintainability| image:: https://api.codeclimate.com/v1/badges/7c18ec5176f2ebebef96/maintainability

    :target: https://codeclimate.com/github/LucaCappelletti94/crr_labels/maintainability

    :alt: Maintainability

.. |code_climate_coverage| image:: https://api.codeclimate.com/v1/badges/7c18ec5176f2ebebef96/test_coverage

    :target: https://codeclimate.com/github/LucaCappelletti94/crr_labels/test_coverage

    :alt: Code Climate Coverate