*******************************************
LISA: Landscape In-Silico deletion Analysis
*******************************************
.. image:: https://raw.githubusercontent.com/liulab-dfci/lisa2/master/docs/example_clustermap.png
:width: 200px
.. contents:: Table of Contents
About
-----
LISA is a statistical test for the influence of Transcription Factors on a set of genes. We leverage integrative modeling of public chromatin accessiblity and factor binding to make predictions that go beyond simple co-expression analysis.
The minimum you need to run LISA is a list of genes-of-interest, but you can also supply your own epigenetic background. For more information, see `Qin et al., 2020 <https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1934-6>`_.
This implementation extends the original, running faster, reducing dependencies, and adding useful CLI functions for pipeline integration.
The python package is easy to install and has a rich set of features and options.
For a quick introduction to the method, check out the `web interface <http://lisa.cistrome.org/>`_.
The Model
---------
The key components of the LISA test are the:
1. profile, a distribution of accessibility over regions in the genome, supplied by user or predicted from public data
2. hits, the regions where a TF is predicted to bind (through ChIP-seq or motif)
3. region-gene map, maps the influence of a region to nearby genes.
First, LISA constructs a null model of gene influence, which assumes each accessible region is occupied by its associated factors, and that all factor-bound regions exert influence on nearby genes.
LISA then tests for the influence of a factor on a gene by calculating what proportion of that gene's influence could be attributed to that factor binding nearby regions.
When you provide genes-of-interest, LISA finds factors that preferentially affects these genes over a sampling of background genes.
.. image:: https://raw.githubusercontent.com/liulab-dfci/lisa2/master/docs/model_diagram.png
:width: 300
Refer to the `User Guide <https://github.com/liulab-dfci/lisa2/blob/master/docs/user_guide.md>`_ to see it in action.
Refer to the `Data Analysis Guide <https://github.com/liulab-dfci/lisa2/blob/master/docs/DataAnalysisGuide.md>`_ to see the questions LISA can help you answer.
Requirements
------------
* Mac or Linux OS
* Python 3.6+
* 15 GB of available storage space
Installation
------------
**LISA will install data into the virutal environment's "site_packages" directory, so ensure the env's location can store ~10GB.**
PyPI
~~~~
It is recommended to install lisa to a virtual environment:
.. code-block:: bash
$ python3 -m venv .venvs/lisa_env
$ source .venvs/lisa_env/bin/activate
Install LISA to this virtual env using this command:
.. code-block:: bash
(lisa_env) $ pip install lisa2
Conda
~~~~~
First, create a virtual environment:
.. code-block:: bash
(base) $ conda create --name lisa_env
(base) $ conda activate lisa_env
Then install from Conda:
.. code-block:: bash
(lisa_env) $ conda install -c liulab-dfci lisa2
Dataset Installation Issues
~~~~~~~~~~~~~~~~~~~~~~~~~~~
If you successfully install lisa but the program fails while downloading data, follow these `manual dataset installation instructions <https://github.com/liulab-dfci/lisa2/blob/master/docs/troubleshooting.md>`_.
Usage
-----
Command Line Interface
~~~~~~~~~~~~~~~~~~~~~~
LISA's cli offers convenient methods for the most common use cases. See the `API <https://github.com/liulab-dfci/lisa2/blob/master/docs/cli.rst>`_, or try:
.. code-block::
(lisa_env) $ lisa {command} --help
for parameter descriptions. See the `User Guide <https://github.com/liulab-dfci/lisa2/blob/master/docs/user_guide.md>`_ for best practices.
Python Interface
~~~~~~~~~~~~~~~~
The python module allows more control over the LISA test and more convenient data analysis. See the `Python API <https://github.com/liulab-dfci/lisa2/blob/master/docs/python_api.rst>`_
and the `User Guide <https://github.com/liulab-dfci/lisa2/blob/master/docs/user_guide.md>`_.
Changelog
---------
**[2.3.0] - 2022-03-15**
Removed
~~~~~~~
Removed coverage test from base LISA install because pyBigWig was causing problems with installation.
Now, to install the coverage test, do
.. code-block:: bash
$ pip install lisa2[coverage]
Changed
~~~~~~~
* Loosening H5py requirements for easier install.
**[2.2.4] - 2021-03-01**
* Added "lisa deseq" interface for parsing DESeq2 output files for fast LISA tests of DE genes
**[2.2.0] - 2021-01-10**
Added
~~~~~
* Added "FromRegions" test, and moved all older functionalities to "FromGenes". New feature allows user to run LISA test with their own regions-of-interest
* Added "query_reg_score" and "background_reg_score" matrices to output metadata of "FromRegions" test, which allows user to see which genes are likely regulated by each factor.
* New backend interface for faster file transfers
* Added ability to append more data to backend for future updates, including ATAC-seq epigenetic backgrounds
* Added more documentation and user guide
* Appended new ATAC data and reprocessed motifs using JASPAR database
Removed
~~~~~~~
* Removed "cores" option from multi and oneshot tests, and removed mutliprocessing from package.
* Removed "one-vs-rest" test because proved to provide unstable results
**[2.1.0] - 2020-12-01**
* Bugfixes in output of "lisa multi" test
* Refactored classes for future extension to user-supplied fragment files and peaks
* Added integration testing
* Added factor accessibility introspection to results printout
* Made RP maps substitutable for future tests
* Made assays modular so users can specify which statistical tests they are interested in
**[2.0.6] - 2020-11-22**
* Support for Lisa version 1 API for integration with LISA website
* Bugfixes in motif mode results
* Slight speedups in parallelization of insilico-delition computing
Support
-------
If you have questions, requests, or issues, please email alynch@ds.dfci.harvard.edu.
Raw data
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"description": "*******************************************\nLISA: Landscape In-Silico deletion Analysis\n*******************************************\n\n.. image:: https://raw.githubusercontent.com/liulab-dfci/lisa2/master/docs/example_clustermap.png\n :width: 200px\n\n.. contents:: Table of Contents\n\nAbout\n-----\n\nLISA is a statistical test for the influence of Transcription Factors on a set of genes. We leverage integrative modeling of public chromatin accessiblity and factor binding to make predictions that go beyond simple co-expression analysis. \nThe minimum you need to run LISA is a list of genes-of-interest, but you can also supply your own epigenetic background. For more information, see `Qin et al., 2020 <https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1934-6>`_. \nThis implementation extends the original, running faster, reducing dependencies, and adding useful CLI functions for pipeline integration.\n\nThe python package is easy to install and has a rich set of features and options. \nFor a quick introduction to the method, check out the `web interface <http://lisa.cistrome.org/>`_.\n\nThe Model\n---------\n\nThe key components of the LISA test are the:\n 1. profile, a distribution of accessibility over regions in the genome, supplied by user or predicted from public data\n 2. hits, the regions where a TF is predicted to bind (through ChIP-seq or motif)\n 3. region-gene map, maps the influence of a region to nearby genes.\n\nFirst, LISA constructs a null model of gene influence, which assumes each accessible region is occupied by its associated factors, and that all factor-bound regions exert influence on nearby genes. \nLISA then tests for the influence of a factor on a gene by calculating what proportion of that gene's influence could be attributed to that factor binding nearby regions.\nWhen you provide genes-of-interest, LISA finds factors that preferentially affects these genes over a sampling of background genes.\n\n.. image:: https://raw.githubusercontent.com/liulab-dfci/lisa2/master/docs/model_diagram.png\n :width: 300\n\nRefer to the `User Guide <https://github.com/liulab-dfci/lisa2/blob/master/docs/user_guide.md>`_ to see it in action. \nRefer to the `Data Analysis Guide <https://github.com/liulab-dfci/lisa2/blob/master/docs/DataAnalysisGuide.md>`_ to see the questions LISA can help you answer.\n\nRequirements\n------------\n\n* Mac or Linux OS\n* Python 3.6+\n* 15 GB of available storage space\n\nInstallation\n------------\n\n**LISA will install data into the virutal environment's \"site_packages\" directory, so ensure the env's location can store ~10GB.**\n\nPyPI\n~~~~\n\nIt is recommended to install lisa to a virtual environment:\n\n.. code-block:: bash\n\n $ python3 -m venv .venvs/lisa_env\n $ source .venvs/lisa_env/bin/activate\n \nInstall LISA to this virtual env using this command:\n\n.. code-block:: bash\n\n (lisa_env) $ pip install lisa2\n\nConda\n~~~~~\n\nFirst, create a virtual environment:\n\n.. code-block:: bash\n\n (base) $ conda create --name lisa_env\n (base) $ conda activate lisa_env\n\nThen install from Conda:\n\n.. code-block:: bash\n\n (lisa_env) $ conda install -c liulab-dfci lisa2\n\nDataset Installation Issues\n~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nIf you successfully install lisa but the program fails while downloading data, follow these `manual dataset installation instructions <https://github.com/liulab-dfci/lisa2/blob/master/docs/troubleshooting.md>`_.\n\nUsage\n-----\n\nCommand Line Interface\n~~~~~~~~~~~~~~~~~~~~~~\n\nLISA's cli offers convenient methods for the most common use cases. See the `API <https://github.com/liulab-dfci/lisa2/blob/master/docs/cli.rst>`_, or try:\n\n.. code-block::\n\n (lisa_env) $ lisa {command} --help\n\nfor parameter descriptions. See the `User Guide <https://github.com/liulab-dfci/lisa2/blob/master/docs/user_guide.md>`_ for best practices.\n\nPython Interface\n~~~~~~~~~~~~~~~~\n\nThe python module allows more control over the LISA test and more convenient data analysis. 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