Biological data is often compared to reference databases and searching for interesting patterns of enrichment and depletion. For example, gene set analysis has been pivotal for making connections between diverse types of genomic data. However, it suffers from one major limitation: it requires gene-centric data. This is becoming increasingly limiting as our understanding of gene regulation advances. It has become evident that gene expression and chromatin organization are controlled by hundreds of thousands of enhancers and other functional elements, which are often difficult to map to gene symbols. The increasing emphasis on genomic region sets has been propelled by next generation sequencing technology that produces data most naturally analyzed in the context of genomic regions – as peaks and segmentations. The research community has now established large catalogs of regulatory elements and other genomic features across many cell types. LOLA makes use of these catalogs to perform enrichment analysis of genomic ranges.
In this vignette, you’ll use small example datasets that come with the LOLA package to get a first look at the most common functions in a LOLA workflow.
You need 3 things to run a LOLA analysis:
Let’s load an example regionDB with loadRegionDB(). Here’s a small example that comes with LOLA. The database location should point to a folder that contains collection subfolders:
library("LOLA")
dbPath = system.file("extdata", "hg19", package="LOLA")
regionDB = loadRegionDB(dbPath)The regionDB is an R (list) object that has a few elements:
names(regionDB)## [1] "dbLocation" "regionAnno" "collectionAnno" "regionGRL"loadRegionDB().data.table annotating the collections, with rows corresponding to the rows in your collection annotation files in the database.data.table annotating each region set, with rows corresponding to bed files in the database (there is also a collection column recording which collection each region set belongs to).GRangesList object holding the actual regions, with one list element per region set, ordered as in regionAnno.Now with the database loaded, let’s load up some sample data (the regions of interest, and the tested universe):
data("sample_input", package="LOLA") # load userSets
data("sample_universe", package="LOLA") # load userUniverseNow we have a GRanges object called userSets and a GRanges object called userUniverse. This is all we need to run the enrichment calculation.
runLOLA() will test the overlap between your userSets, and each region set in the regionDB.
locResults = runLOLA(userSets, userUniverse, regionDB, cores=1)runLOLA tests for pairwise overlap between each user set and each region set in regionDB. It then uses a Fisher’s exact test to assess significance of the overlap. The results are a data.table with several columns:
colnames(locResults)
head(locResults)## [1] "userSet" "dbSet" "collection" "pValueLog" "oddsRatio"
## [6] "support" "rnkPV" "rnkOR" "rnkSup" "maxRnk"
## [11] "meanRnk" "b" "c" "d" "description"
## [16] "cellType" "tissue" "antibody" "treatment" "dataSource"
## [21] "filename" "qValue" "size"
## userSet dbSet collection pValueLog oddsRatio support rnkPV rnkOR rnkSup
## 1: setB 2 ucsc_example 264.4863407 7.7578283 850 1 1 2
## 2: setA 2 ucsc_example 254.6188080 8.6487312 632 1 1 2
## 3: setB 1 ucsc_example 34.6073689 3.3494078 5747 2 2 1
## 4: setA 4 ucsc_example 1.7169689 1.2377725 124 2 2 3
## 5: setA 5 ucsc_example 1.7169689 1.2377725 124 2 2 3
## 6: setA 3 ucsc_example 0.1877354 0.9135696 8 4 4 5
## maxRnk meanRnk b c d description cellType
## 1: 2 1.33 452 4981 20546 ucsc_example <NA>
## 2: 2 1.33 670 2510 23017 ucsc_example <NA>
## 3: 2 1.67 20018 84 980 CpG islands from UCSC annotation <NA>
## 4: 3 2.33 761 3018 22926 ucsc_example <NA>
## 5: 3 2.33 761 3018 22926 ucsc_example <NA>
## 6: 5 4.33 66 3134 23621 ucsc_example <NA>
## tissue antibody treatment dataSource filename
## 1: <NA> <NA> <NA> <NA> laminB1Lads.bed
## 2: <NA> <NA> <NA> <NA> laminB1Lads.bed
## 3: <NA> <NA> <NA> <NA> cpgIslandExt.bed
## 4: <NA> <NA> <NA> <NA> vistaEnhancers.bed
## 5: <NA> <NA> <NA> <NA> vistaEnhancers_colNames.bed
## 6: <NA> <NA> <NA> <NA> numtSAssembled.bed
## qValue size
## 1: 3.263317e-264 1302
## 2: 1.202713e-254 1302
## 3: 8.232086e-35 28691
## 4: 3.837612e-02 1339
## 5: 3.837612e-02 1340
## 6: 1.000000e+00 78If you’re not familiar with how data.table works in R, it’s worth reading some of the documentation of this powerful package.
Columns userSet and dbSet are indexes into the respective GRangeList objects, identifying each pairwise comparison. There are a series of columns describing the results of the statistical test, such as pValueLog, logOdds, and the actual values from the contingency table (support is the overlap, and b, c, and d complete the 2x2 table). Rank columns simply rank the tests by pValueLog, logOdds, or support; following these are a series of columns annotating the database regions, depending on how you populated the index table in the regionDB folder.
You can explore these results in R by, for example, ranking with different orders:
locResults[order(support, decreasing=TRUE),]## userSet dbSet collection pValueLog oddsRatio support rnkPV rnkOR
## 1: setB 1 ucsc_example 3.460737e+01 3.3494078 5747 2 2
## 2: setA 1 ucsc_example 2.818334e-02 0.8704355 3002 5 5
## 3: setB 2 ucsc_example 2.644863e+02 7.7578283 850 1 1
## 4: setA 2 ucsc_example 2.546188e+02 8.6487312 632 1 1
## 5: setA 4 ucsc_example 1.716969e+00 1.2377725 124 2 2
## 6: setA 5 ucsc_example 1.716969e+00 1.2377725 124 2 2
## 7: setB 4 ucsc_example 0.000000e+00 0.3489379 80 4 3
## 8: setB 5 ucsc_example 0.000000e+00 0.3489379 80 4 3
## 9: setA 3 ucsc_example 1.877354e-01 0.9135696 8 4 4
## 10: setB 3 ucsc_example 9.184826e-06 0.2052377 4 3 5
## rnkSup maxRnk meanRnk b c d description
## 1: 1 2 1.67 20018 84 980 CpG islands from UCSC annotation
## 2: 1 5 3.67 22763 140 924 CpG islands from UCSC annotation
## 3: 2 2 1.33 452 4981 20546 ucsc_example
## 4: 2 2 1.33 670 2510 23017 ucsc_example
## 5: 3 3 2.33 761 3018 22926 ucsc_example
## 6: 3 3 2.33 761 3018 22926 ucsc_example
## 7: 3 4 3.33 805 5751 20193 ucsc_example
## 8: 3 4 3.33 805 5751 20193 ucsc_example
## 9: 5 5 4.33 66 3134 23621 ucsc_example
## 10: 5 5 4.33 70 5827 20928 ucsc_example
## cellType tissue antibody treatment dataSource filename
## 1: <NA> <NA> <NA> <NA> <NA> cpgIslandExt.bed
## 2: <NA> <NA> <NA> <NA> <NA> cpgIslandExt.bed
## 3: <NA> <NA> <NA> <NA> <NA> laminB1Lads.bed
## 4: <NA> <NA> <NA> <NA> <NA> laminB1Lads.bed
## 5: <NA> <NA> <NA> <NA> <NA> vistaEnhancers.bed
## 6: <NA> <NA> <NA> <NA> <NA> vistaEnhancers_colNames.bed
## 7: <NA> <NA> <NA> <NA> <NA> vistaEnhancers.bed
## 8: <NA> <NA> <NA> <NA> <NA> vistaEnhancers_colNames.bed
## 9: <NA> <NA> <NA> <NA> <NA> numtSAssembled.bed
## 10: <NA> <NA> <NA> <NA> <NA> numtSAssembled.bed
## qValue size
## 1: 8.232086e-35 28691
## 2: 1.000000e+00 28691
## 3: 3.263317e-264 1302
## 4: 1.202713e-254 1302
## 5: 3.837612e-02 1339
## 6: 3.837612e-02 1340
## 7: 1.000000e+00 1339
## 8: 1.000000e+00 1340
## 9: 1.000000e+00 78
## 10: 1.000000e+00 78You can order by one of the rank columns:
locResults[order(maxRnk, decreasing=TRUE),]## userSet dbSet collection pValueLog oddsRatio support rnkPV rnkOR
## 1: setA 3 ucsc_example 1.877354e-01 0.9135696 8 4 4
## 2: setA 1 ucsc_example 2.818334e-02 0.8704355 3002 5 5
## 3: setB 3 ucsc_example 9.184826e-06 0.2052377 4 3 5
## 4: setB 4 ucsc_example 0.000000e+00 0.3489379 80 4 3
## 5: setB 5 ucsc_example 0.000000e+00 0.3489379 80 4 3
## 6: setA 4 ucsc_example 1.716969e+00 1.2377725 124 2 2
## 7: setA 5 ucsc_example 1.716969e+00 1.2377725 124 2 2
## 8: setB 2 ucsc_example 2.644863e+02 7.7578283 850 1 1
## 9: setA 2 ucsc_example 2.546188e+02 8.6487312 632 1 1
## 10: setB 1 ucsc_example 3.460737e+01 3.3494078 5747 2 2
## rnkSup maxRnk meanRnk b c d description
## 1: 5 5 4.33 66 3134 23621 ucsc_example
## 2: 1 5 3.67 22763 140 924 CpG islands from UCSC annotation
## 3: 5 5 4.33 70 5827 20928 ucsc_example
## 4: 3 4 3.33 805 5751 20193 ucsc_example
## 5: 3 4 3.33 805 5751 20193 ucsc_example
## 6: 3 3 2.33 761 3018 22926 ucsc_example
## 7: 3 3 2.33 761 3018 22926 ucsc_example
## 8: 2 2 1.33 452 4981 20546 ucsc_example
## 9: 2 2 1.33 670 2510 23017 ucsc_example
## 10: 1 2 1.67 20018 84 980 CpG islands from UCSC annotation
## cellType tissue antibody treatment dataSource filename
## 1: <NA> <NA> <NA> <NA> <NA> numtSAssembled.bed
## 2: <NA> <NA> <NA> <NA> <NA> cpgIslandExt.bed
## 3: <NA> <NA> <NA> <NA> <NA> numtSAssembled.bed
## 4: <NA> <NA> <NA> <NA> <NA> vistaEnhancers.bed
## 5: <NA> <NA> <NA> <NA> <NA> vistaEnhancers_colNames.bed
## 6: <NA> <NA> <NA> <NA> <NA> vistaEnhancers.bed
## 7: <NA> <NA> <NA> <NA> <NA> vistaEnhancers_colNames.bed
## 8: <NA> <NA> <NA> <NA> <NA> laminB1Lads.bed
## 9: <NA> <NA> <NA> <NA> <NA> laminB1Lads.bed
## 10: <NA> <NA> <NA> <NA> <NA> cpgIslandExt.bed
## qValue size
## 1: 1.000000e+00 78
## 2: 1.000000e+00 28691
## 3: 1.000000e+00 78
## 4: 1.000000e+00 1339
## 5: 1.000000e+00 1340
## 6: 3.837612e-02 1339
## 7: 3.837612e-02 1340
## 8: 3.263317e-264 1302
## 9: 1.202713e-254 1302
## 10: 8.232086e-35 28691And finally, record the results to file like this:
writeCombinedEnrichment(locResults, outFolder= "lolaResults")By default, this function will write the entire table to a tsv file. I recommend using the includeSplits parameter, which tells the function to also print out additional tables that are subsetted by userSet, so that each region set you test has its own result table. It just makes it a little easier to explore the results.
writeCombinedEnrichment(locResults, outFolder= "lolaResults", includeSplits=TRUE)Say you’d like to know which regions are responsible for the enrichment we see; or, in other words, you’d like to extract the regions that are actually overlapping a particular database. For this, you can use the function extractEnrichmentOverlaps():
oneResult = locResults[2,]
extractEnrichmentOverlaps(oneResult, userSets, regionDB)## GRanges object with 632 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr1 18229570-19207602 *
## [2] chr1 35350878-35351854 *
## [3] chr1 38065507-38258622 *
## [4] chr1 38499473-39306315 *
## [5] chr1 42611485-42611691 *
## ... ... ... ...
## [628] chrX 125299245-125300436 *
## [629] chrX 136032577-138821238 *
## [630] chrX 139018365-148549454 *
## [631] chrX 154066672-154251301 *
## [632] chrY 2880166-7112793 *
## -------
## seqinfo: 69 sequences from an unspecified genome; no seqlengthsIf you have a large database, you may be interested in using the LOLA database format for other projects, or for additional follow-up analysis. In this case, you may be interested in just a single region set within a database, or perhaps just a few of them. LOLA provides a function to extract certain region sets from either a loaded or an unloaded database.
Say you just want an object with regions from the “vistaEnhancers” region set. You can grab it from a loaded database like this:
getRegionSet(regionDB, collections="ucsc_example", filenames="vistaEnhancers.bed")## GRangesList object of length 1:
## [[1]]
## GRanges object with 1339 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## 1 chr1 3190582-3191428 *
## 2 chr1 8130440-8131887 *
## 3 chr1 10593124-10594209 *
## 4 chr1 10732071-10733118 *
## 5 chr1 10757665-10758631 *
## ... ... ... ...
## 1335 chrX 139380917-139382199 *
## 1336 chrX 139593503-139594774 *
## 1337 chrX 139674500-139675403 *
## 1338 chrX 147829017-147830159 *
## 1339 chrX 150407693-150409052 *
## -------
## seqinfo: 69 sequences from an unspecified genome; no seqlengthsOr, if you haven’t already loaded the database, you can just give the path to the database and LOLA will only load the specific region set(s) you are interested in. This can take more than one filename or collection:
getRegionSet(dbPath, collections="ucsc_example", filenames="vistaEnhancers.bed")## GRangesList object of length 1:
## [[1]]
## GRanges object with 1339 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## 1 chr1 3190582-3191428 *
## 2 chr1 8130440-8131887 *
## 3 chr1 10593124-10594209 *
## 4 chr1 10732071-10733118 *
## 5 chr1 10757665-10758631 *
## ... ... ... ...
## 1335 chrX 139380917-139382199 *
## 1336 chrX 139593503-139594774 *
## 1337 chrX 139674500-139675403 *
## 1338 chrX 147829017-147830159 *
## 1339 chrX 150407693-150409052 *
## -------
## seqinfo: 23 sequences from an unspecified genome; no seqlengthsNow that you have a basic idea of what the functions are, you can follow some other vignettes, such as Using LOLA Core, to see how this works on a realistic dataset.