Description Usage Arguments Value Examples

View source: R/calcLocEnrichment.R

Workhorse function that calculates overlaps between userSets, and then uses a fisher's exact test rank them by significance of the overlap.

1 2 3 4 5 6 7 8 9 | ```
runLOLA(
userSets,
userUniverse,
regionDB,
minOverlap = 1,
cores = 1,
redefineUserSets = FALSE,
direction = "enrichment"
)
``` |

`userSets` |
Regions of interest |

`userUniverse` |
Regions tested for inclusion in userSets |

`regionDB` |
Region DB to check for overlap, from loadRegionDB() |

`minOverlap` |
(Default:1) Minimum bases required to count an overlap |

`cores` |
Number of processors |

`redefineUserSets` |
run redefineUserSets() on your userSets? |

`direction` |
Defaults to "enrichment", but may also accept "depletion", which will swap the direction of the fisher test (use 'greater' or less' value passed to the 'alternative' option of fisher.test) |

Data.table with enrichment results. Rows correspond to individual pairwise fisher's tests comparing a single userSet with a single databaseSet. The columns in this data.table are: userSet and dbSet: index into their respective input region sets. pvalueLog: -log10(pvalue) from the fisher's exact result; oddsRatio: result from the fisher's exact test; support: number of regions in userSet overlapping databaseSet; rnkPV, rnkOR, rnkSup: rank in this table of p-value, oddsRatio, and Support respectively. The â€“value is the negative natural log of the p-value returned from a one-sided fisher's exact test. maxRnk, meanRnk: max and mean of the 3 previous ranks, providing a combined ranking system. b, c, d: 3 other values completing the 2x2 contingency table (with support). The remaining columns describe the dbSet for the row.

If you have the qvalue package installed from bioconductor, runLOLA will add a q-value transformation to provide FDR scores automatically.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
dbPath = system.file("extdata", "hg19", package="LOLA")
regionDB = loadRegionDB(dbLocation=dbPath)
data("sample_universe", package="LOLA")
data("sample_input", package="LOLA")
getRegionSet(regionDB, collections="ucsc_example", filenames="vistaEnhancers.bed")
getRegionSet(dbPath, collections="ucsc_example", filenames="vistaEnhancers.bed")
getRegionFile(dbPath, collections="ucsc_example", filenames="vistaEnhancers.bed")
res = runLOLA(userSets, userUniverse, regionDB, cores=1)
locResult = res[2,]
extractEnrichmentOverlaps(locResult, userSets, regionDB)
writeCombinedEnrichment(locResult, "temp_outfolder")
userSetsRedefined = redefineUserSets(userSets, userUniverse)
resRedefined = runLOLA(userSetsRedefined, userUniverse, regionDB, cores=1)
g = plotTopLOLAEnrichments(resRedefined)
``` |

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