Package: LOLA 1.37.0

Nathan Sheffield

LOLA: Locus overlap analysis for enrichment of genomic ranges

Provides functions for testing overlap of sets of genomic regions with public and custom region set (genomic ranges) databases. This makes it possible to do automated enrichment analysis for genomic region sets, thus facilitating interpretation of functional genomics and epigenomics data.

Authors:Nathan Sheffield <http://www.databio.org> [aut, cre], Christoph Bock [ctb]

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NEWS

# Install 'LOLA' in R:
install.packages('LOLA', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/nsheff/lola/issues

Datasets:
  • userSets - An example set of regions, sampled from the example database.
  • userUniverse - A reduced GRanges object from the example regionDB database

On BioConductor:LOLA-1.35.0(bioc 3.20)LOLA-1.34.0(bioc 3.19)

genesetenrichmentgeneregulationgenomeannotationsystemsbiologyfunctionalgenomicschipseqmethylseqsequencing

9.33 score 71 stars 167 scripts 424 downloads 56 mentions 20 exports 29 dependencies

Last updated 23 days agofrom:9642694af5. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 30 2024
R-4.5-winOKOct 31 2024
R-4.5-linuxOKOct 30 2024
R-4.4-winOKOct 30 2024
R-4.4-macOKOct 31 2024
R-4.3-winOKOct 30 2024
R-4.3-macOKOct 31 2024

Exports:buildRestrictedUniversecheckUniverseAppropriatenessextractEnrichmentOverlapsgetRegionFilegetRegionSetlistRegionSetsloadRegionDBmergeRegionDBsplotTopLOLAEnrichmentsreadBedreadCollectionreadCollectionAnnotationreadCollectionFilesreadRegionGRLreadRegionSetAnnotationredefineUserSetsrunLOLAsetSharedDataDirsplitFileIntoCollectionwriteCombinedEnrichment

Dependencies:askpassBiocGenericsclicurldata.tableGenomeInfoDbGenomeInfoDbDataGenomicRangesgluehttrIRangesjsonlitelifecyclemagrittrmimeopensslplyrR6Rcppreshape2rlangS4VectorsstringistringrsysUCSC.utilsvctrsXVectorzlibbioc

Getting Started with LOLA

Nathan Sheffield

Rendered fromgettingStarted.Rmdusingknitr::rmarkdownon Oct 30 2024.

Last update: 2019-06-14
Started: 2015-04-09

Using LOLA Core

Nathan Sheffield

Rendered fromusingLOLACore.Rmdusingknitr::rmarkdownon Oct 30 2024.

Last update: 2018-02-06
Started: 2015-04-16

Choosing a LOLA Universe

Nathan Sheffield

Rendered fromchoosingUniverse.Rmdusingknitr::rmarkdownon Oct 30 2024.

Last update: 2018-02-06
Started: 2015-04-20

Readme and manuals

Help Manual

Help pageTopics
If you want to test for differential enrichment within your usersets, you can restrict the universe to only regions that are covered in at least one of your sets. This function helps you build just such a restricted universebuildRestrictedUniverse
Check universe appropriatenesscheckUniverseAppropriateness
cleanws takes multi-line, code formatted strings and just formats them as simple stringscleanws
Just a reverser. Reverses the order of arguments and passes them untouched to countOverlapsAny - so you can use it with lapply.countOverlapsAnyRev
Given a single row from an enrichment table calculation, finds the set of overlaps between the user set and the test set. You can then use these, for example, to get sequences for those regions.extractEnrichmentOverlaps
Grab the filename for a a single region set from a database specified by filename.getRegionFile
Grab a single region set from a database, specified by filename.getRegionSet
Function to run lapply or mclapply, depending on the option set in getOption("mc.cores"), which can be set with setLapplyAlias().lapplyAlias
Lists the region sets for given collection(s) in a region database on disk.listRegionSets
converts a list of GRanges into a GRangesList; strips all metadata.listToGRangesList
Helper function to annotate and load a regionDB, a folder with subfolder collections of regions.loadRegionDB
Genome locus overlap analysis.LOLA
Given two regionDBs, (lists returned from loadRegionDB()), This function will combine them into a single regionDB. This will enable you to combine, for example, LOLA Core databases with custom databases into a single analysis.mergeRegionDBs
Named list function.nlist
Given some results (you grab the top ones on your own), this plots a barplot visualizing their odds ratios.plotTopLOLAEnrichments
Imports bed files and creates GRanges objects, using the fread() function from data.table.readBed
Given a bunch of region set files, read in all those flat (bed) files and create a GRangesList object holding all the region sets. This function is used by readRegionGRL to process annotation objects.readCollection
Read collection annotationreadCollectionAnnotation
Given a database and a collection, this will create the region annotation data.table; either giving a generic table based on file names, or by reading in the annotation data.readCollectionFiles
This function takes a region annotation object and reads in the regions, returning a GRangesList object of the regions.readRegionGRL
Given a folder containing region collections in subfolders, this function will either read the annotation file if one exists, or create a generic annotation file.readRegionSetAnnotation
This function will take the user sets, overlap with the universe, and redefine the user sets as the set of regions in the user universe that overlap at least one region in user sets. this makes for a more appropriate statistical enrichment comparison, as the user sets are actually exactly the same regions found in the universe otherwise, you can get some weird artifacts from the many-to-many relationship between user set regions and universe regions.redefineUserSets
This will change the string in filename to have a new extensionreplaceFileExtension
Enrichment CalculationrunLOLA
Function to sample regions from a GRangesList object, in specified proportionsampleGRL
To make parallel processing a possibility but not required, I use an lapply alias which can point at either the base lapply (for no multicore), or it can point to mclapply, and set the options for the number of cores (what mclapply uses). With no argument given, returns intead the number of cpus currently selected.setLapplyAlias
setSharedDataDir Sets global variable specifying the default data directory.setSharedDataDir
Efficiently split a data.table by a column in the tablesplitDataTable
This function will take a single large bed file that is annotated with a column grouping different sets of similar regions, and split it into separate files for use with the LOLA collection format.splitFileIntoCollection
An example set of regions, sampled from the example database.userSets
A reduced GRanges object from the example regionDB databaseuserUniverse
Wrapper of write.table that provides defaults to write a simple .tsv file. Passes additional arguments to write.tablewrite.tsv
Function for writing output all at once: combinedResults is an table generated by "locationEnrichment()" or by rbinding category/location results. Writes all enrichments to a single file, and also spits out the same data divided into groups based on userSets, and Databases, just for convenience. disable this with an option.writeCombinedEnrichment
Given a data table and a factor variable to split on, efficiently divides the table and then writes the different splits to separate files, named with filePrepend and numbered according to split.writeDataTableSplitByColumn