Package: lisa2
Table of ContentsAboutThe ModelRequirementsInstallationPyPICondaDataset Installation IssuesUsageCommand Line InterfacePython InterfaceChangelogRemovedChangedAddedRemovedSupportAboutLISA 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, seeQin et al., 2020.
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 theweb interface.The ModelThe key components of the LISA test are the:profile, a distribution of accessibility over regions in the genome, supplied by user or predicted from public datahits, the regions where a TF is predicted to bind (through ChIP-seq or motif)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.Refer to theUser Guideto see it in action.
Refer to theData Analysis Guideto see the questions LISA can help you answer.RequirementsMac or Linux OSPython 3.6+15 GB of available storage spaceInstallationLISA will install data into the virutal environment’s “site_packages” directory, so ensure the env’s location can store ~10GB.PyPIIt is recommended to install lisa to a virtual environment:$python3-mvenv.venvs/lisa_env$source.venvs/lisa_env/bin/activateInstall LISA to this virtual env using this command:(lisa_env)$pipinstalllisa2CondaFirst, create a virtual environment:(base)$condacreate--namelisa_env(base)$condaactivatelisa_envThen install from Conda:(lisa_env)$condainstall-cliulab-dfcilisa2Dataset Installation IssuesIf you successfully install lisa but the program fails while downloading data, follow thesemanual dataset installation instructions.UsageCommand Line InterfaceLISA’s cli offers convenient methods for the most common use cases. See theAPI, or try:(lisa_env) $ lisa {command} --helpfor parameter descriptions. See theUser Guidefor best practices.Python InterfaceThe python module allows more control over the LISA test and more convenient data analysis. See thePython APIand theUser Guide.Changelog[2.3.0] - 2022-03-15RemovedRemoved coverage test from base LISA install because pyBigWig was causing problems with installation.
Now, to install the coverage test, do$pipinstalllisa2[coverage]ChangedLoosening H5py requirements for easier install.[2.2.4] - 2021-03-01Added “lisa deseq” interface for parsing DESeq2 output files for fast LISA tests of DE genes[2.2.0] - 2021-01-10AddedAdded “FromRegions” test, and moved all older functionalities to “FromGenes”. New feature allows user to run LISA test with their own regions-of-interestAdded “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 transfersAdded ability to append more data to backend for future updates, including ATAC-seq epigenetic backgroundsAdded more documentation and user guideAppended new ATAC data and reprocessed motifs using JASPAR databaseRemovedRemoved “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] - 2020-12-01Bugfixes in output of “lisa multi” testRefactored classes for future extension to user-supplied fragment files and peaksAdded integration testingAdded factor accessibility introspection to results printoutMade RP maps substitutable for future testsMade assays modular so users can specify which statistical tests they are interested in[2.0.6] - 2020-11-22Support for Lisa version 1 API for integration with LISA websiteBugfixes in motif mode resultsSlight speedups in parallelization of insilico-delition computingSupportIf you have questions, requests, or issues, please emailalynch@ds.dfci.harvard.edu.