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"description": "crr_labels\n=========================================================================================\n|travis| |sonar_quality| |sonar_maintainability| |codacy| |code_climate_maintainability| |pip| |downloads|\n\nPython package wrapping over FANTOM and Roadmap labels for cis-regulatory regions.\n\nHow do I install this package?\n----------------------------------------------\nAs usual, just download it using pip:\n\n.. code:: shell\n\n pip install crr_labels\n\nTests Coverage\n----------------------------------------------\nSince some software handling coverages sometimes get slightly different results, here's three of them:\n\n|coveralls| |sonar_coverage| |code_climate_coverage|\n\nUsage examples\n-----------------------------------------------\nCurrently, we support `FANTOM CAGE data `_ and `Roadmap `_ but in the future an additional\ncis-regulatory dataset based on open chromatin data will be added.\n\nFANTOM\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nTo retrieve the FANTOM promoters and enhancers you can proceed as follows:\n\n.. code:: python\n\n from crr_labels import fantom\n\n enhancers, promoters = fantom(\n cell_lines=[\"HelaS3\", \"GM12878\"], # list of cell lines to be considered.\n window_size=200, # window size to use for the various regions.\n genome = \"hg19\", # considered genome version. Currently supported only \"hg19\".\n center_enhancers = \"peak\", # how to center the enhancer window, either around \"peak\" or the \"center\" of the region.\n enhancers_threshold = 0, # activation threshold for the enhancers.\n promoters_threshold = 5, # activation threshold for the promoters.\n drop_always_inactive_rows = True, # whether to drop the rows where no activation is detected for every row.\n binarize = True, # whether to return the data binary-encoded, zero for inactive, one for active.\n nrows = None # the number of rows to read, useful when testing pipelines for creating smaller datasets.\n )\n\nThe library will download and parse the fantom project raw data and return two DataFrames for the required cell lines.\nConsider reading the method docstring for more in-depth information about the method.\n\nThe main steps are the following:\n\n- The raw files are retrieved from the fantom dataset from the link specified in the `fantom_data.json file `_\n- The window for the enhancers and promoters are expanded or compressed to the given window size. In particular:\n\n - The enhancers' window can either be centered on the region center with the \"center\" mode or around the \"peak\" with the \"peak\" mode.\n - 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.\n- When multiple experiments are present for a cell line, for instance for \"HelaS3\", an average of the activation peaks is executed.\n- 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\".\n\n\nRoadmap\n~~~~~~~~~~~~~~~~~~~~~~~~~~~\nTo retrieve the Roadmap promoters and enhancers you can proceed as follows:\n\n.. code:: python\n\n from crr_labels import roadmap\n\n enhancers, promoters = roadmap(\n cell_lines = [\"HelaS3\", \"GM12878\"], # List of cell lines to be considered.\n window_size = 200, # Window size to use for the various regions.\n genome = \"hg19\", # Considered genome version. Currently supported only \"hg19\".\n states = 18, # Number of the states of the model to consider. Currently supported only \"15\" and \"18\".\n enhancers_labels = (\"7_Enh\", \"9_EnhA1\", \"10_EnhA2\"), # Labels to encode as active enhancers.\n promoters_labels = (\"1_TssA\",), # Labels to enode as active promoters.\n nrows = None # the number of rows to read, useful when testing pipelines for creating smaller datasets.\n )\n\nConsider reading the method docstring for more in-depth information about the method.\n\nRendered datasets\n----------------------------------\nThe 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.\n\nFANTOM\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nThe following datasets contain data for the cell lines GM12878, HelaS3, HepG2, K562, A549, H1, H9, JURKAT, MCF7, HEK293, Caco2, HL60 and PC3.\n\nTODO: Render the datasets\n\nRoadmap\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nThe following datasets contain data for the cell lines GM12878, HelaS3, HepG2, K562, A549, H1, H9, DND41, HUES48, HUES6, HUES64 and IMR90.\n\nTODO: The updated processed labels will be added as soon as we decide on the new states to be used.\n\n.. |travis| image:: https://travis-ci.org/LucaCappelletti94/crr_labels.png\n :target: https://travis-ci.org/LucaCappelletti94/crr_labels\n :alt: Travis CI build\n\n.. |sonar_quality| image:: https://sonarcloud.io/api/project_badges/measure?project=LucaCappelletti94_crr_labels&metric=alert_status\n :target: https://sonarcloud.io/dashboard/index/LucaCappelletti94_crr_labels\n :alt: SonarCloud Quality\n\n.. |sonar_maintainability| image:: https://sonarcloud.io/api/project_badges/measure?project=LucaCappelletti94_crr_labels&metric=sqale_rating\n :target: https://sonarcloud.io/dashboard/index/LucaCappelletti94_crr_labels\n :alt: SonarCloud Maintainability\n\n.. |sonar_coverage| image:: https://sonarcloud.io/api/project_badges/measure?project=LucaCappelletti94_crr_labels&metric=coverage\n :target: https://sonarcloud.io/dashboard/index/LucaCappelletti94_crr_labels\n :alt: SonarCloud Coverage\n\n.. |coveralls| image:: https://coveralls.io/repos/github/LucaCappelletti94/crr_labels/badge.svg?branch=master\n :target: https://coveralls.io/github/LucaCappelletti94/crr_labels?branch=master\n :alt: Coveralls Coverage\n\n.. |pip| image:: https://badge.fury.io/py/crr-labels.svg\n :target: https://badge.fury.io/py/crr-labels\n :alt: Pypi project\n\n.. |downloads| image:: https://pepy.tech/badge/crr-labels\n :target: https://pepy.tech/badge/crr-labels\n :alt: Pypi total project downloads \n\n.. |codacy| image:: https://api.codacy.com/project/badge/Grade/c0a7e110045a4d25933c65fe2014a33c\n :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\n :alt: Codacy Maintainability\n\n.. |code_climate_maintainability| image:: https://api.codeclimate.com/v1/badges/7c18ec5176f2ebebef96/maintainability\n :target: https://codeclimate.com/github/LucaCappelletti94/crr_labels/maintainability\n :alt: Maintainability\n\n.. |code_climate_coverage| image:: https://api.codeclimate.com/v1/badges/7c18ec5176f2ebebef96/test_coverage\n :target: https://codeclimate.com/github/LucaCappelletti94/crr_labels/test_coverage\n :alt: Code Climate Coverate",
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