============================================================
scTail for alternative PAS analysis in single cells
============================================================
|pypi|
.. |pypi| image:: https://badge.fury.io/py/scTail.svg
:target: https://pypi.org/project/scTail/
.. image:: https://zenodo.org/badge/497821671.svg
:target: https://zenodo.org/badge/latestdoi/497821671
Note
============
Hi there, my github account did not notify me when there are issue.
So if you are in a hurry, you can email me. ruiyan@connect.hku.hk.
I check email every day.
Installation
============
scTail was developed by using python 3.9. You can build a environment by
using the following code at first.
.. code-block:: bash
conda create -n run_scTail python=3.9
You can install from this GitHub repository for latest (often development)
version by following command line
.. code-block:: bash
pip install -U git+https://github.com/StatBiomed/scTail
In either case, add ``--user`` if you don't have the write permission for your
Python environment.
Quick start
===========
Download test file
===================
You can download test file from figshare_.
.. _figshare: https://figshare.com/articles/dataset/scTail_supplementary_data/25902508
Here, you can download test data and also gene and PAS expression profiles for three dataset: human intestinal, mouse forelimb and ESCC.
Run scTail
=============
Here are three steps in scTail : **scTail-callPeak**, **scTail-peakMerge** and **scTail-count**.
We set these three steps to speed up when running some large file (file size > 30G).
Please check your reads1 (the one that contains cellbarcode and UMI) at first before you run scTail to make sure the length of it more than 100bp. In the most situations, it is perfect that length of reads 1 is 150bp or 151bp.
scTail only support two species: mouse and human. Because classifier embedded in it only trains with sequence of mouse and human.
When you get fastq file, you should follow this instruction_ to run scTail step by step.
.. _instruction: https://sctail.readthedocs.io/en/latest/run_scTail.html
Differential APA usage detecting
=================================
To identify differential alternative PAS usage, BRIE2 (Huang & Sanguinetti, 2021) is recommend to be used.
Here, we provide an example exploiting BRIE2 to detect differential PAS usage.
You can check it in our manual_.
.. _manual: https://sctail.readthedocs.io/en/latest/runBRIE.html
Detailed Manual
================
The full manual is here_, including:
`Preprocess`_
`Run scTail`_
`Detect alternative PAS`_
.. _here: https://sctail.readthedocs.io/en/latest/index.html
.. _Preprocess: https://sctail.readthedocs.io/en/latest/preprocess.html
.. _Run scTail: https://sctail.readthedocs.io/en/latest/run_scTail.html
.. _Detect alternative PAS: https://sctail.readthedocs.io/en/latest/runBRIE.html
Reference
===========
coming soon
Raw data
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"description": "============================================================\nscTail for alternative PAS analysis in single cells\n============================================================\n|pypi| \n\n.. |pypi| image:: https://badge.fury.io/py/scTail.svg\n :target: https://pypi.org/project/scTail/\n\n.. image:: https://zenodo.org/badge/497821671.svg\n :target: https://zenodo.org/badge/latestdoi/497821671\n\n\nNote\n============\nHi there, my github account did not notify me when there are issue. \nSo if you are in a hurry, you can email me. ruiyan@connect.hku.hk.\nI check email every day. \n\n\n\nInstallation\n============\nscTail was developed by using python 3.9. You can build a environment by \nusing the following code at first. \n\n.. code-block:: bash\n\n conda create -n run_scTail python=3.9\n\nYou can install from this GitHub repository for latest (often development) \nversion by following command line\n\n.. code-block:: bash\n\n pip install -U git+https://github.com/StatBiomed/scTail\n\nIn either case, add ``--user`` if you don't have the write permission for your \nPython environment.\n\n\nQuick start\n===========\n\nDownload test file\n===================\n\nYou can download test file from figshare_.\n\n.. _figshare: https://figshare.com/articles/dataset/scTail_supplementary_data/25902508\n\nHere, you can download test data and also gene and PAS expression profiles for three dataset: human intestinal, mouse forelimb and ESCC.\n \nRun scTail\n=============\n\nHere are three steps in scTail : **scTail-callPeak**, **scTail-peakMerge** and **scTail-count**.\n\nWe set these three steps to speed up when running some large file (file size > 30G).\n\nPlease check your reads1 (the one that contains cellbarcode and UMI) at first before you run scTail to make sure the length of it more than 100bp. In the most situations, it is perfect that length of reads 1 is 150bp or 151bp.\n\nscTail only support two species: mouse and human. Because classifier embedded in it only trains with sequence of mouse and human.\n\nWhen you get fastq file, you should follow this instruction_ to run scTail step by step. \n\n.. _instruction: https://sctail.readthedocs.io/en/latest/run_scTail.html\n\n\n\nDifferential APA usage detecting\n=================================\n\nTo identify differential alternative PAS usage, BRIE2 (Huang & Sanguinetti,\u20092021) is recommend to be used. \n\nHere, we provide an example exploiting BRIE2 to detect differential PAS usage. \n\nYou can check it in our manual_.\n\n.. _manual: https://sctail.readthedocs.io/en/latest/runBRIE.html \n\n\nDetailed Manual\n================\n\nThe full manual is here_, including:\n\n`Preprocess`_\n\n`Run scTail`_\n\n`Detect alternative PAS`_\n\n.. _here: https://sctail.readthedocs.io/en/latest/index.html\n\n.. _Preprocess: https://sctail.readthedocs.io/en/latest/preprocess.html\n\n.. _Run scTail: https://sctail.readthedocs.io/en/latest/run_scTail.html\n\n.. _Detect alternative PAS: https://sctail.readthedocs.io/en/latest/runBRIE.html\n\n\n\nReference\n===========\n\ncoming soon\n\n\n\n\n\n\n\n\n\n\n\n\n\n",
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