Calculates expected partiton overlap based on contribution of each feature (partition) to genome size. Expected and observed overlaps are then compared.

calcExpectedPartitions(
  query,
  partitionList,
  genomeSize = NULL,
  remainder = "intergenic",
  bpProportion = FALSE
)

Arguments

query: GRanges or GRangesList with regions to classify.
partitionList: An ORDERED (if bpProportion=FALSE) and NAMED list of genomic partitions GRanges. This list must be in priority order; the input will be assigned to the first partition it overlaps. However, if bpProportion=TRUE, the list does not need ordering.
genomeSize: The number of bases in the query genome. In other words, the sum of all chromosome sizes.
remainder: Which partition do you want to account for 'everything else'?
bpProportion: logical indicating if overlaps should be calculated based on number of base pairs overlapping with each partition. bpProportion=FALSE does overlaps in priority order, bpProportion=TRUE counts number of overlapping base pairs between query and each partition.

Value

A data.frame assigning each element of a GRanges object to a partition from a previously provided partitionList.The data.frame also contains Chi-square p-values calculated for observed/expected overlaps on each individual partition.

Examples

partitionList = genomePartitionList(geneModels_hg19$genesGR,
                                    geneModels_hg19$exonsGR,
                                    geneModels_hg19$threeUTRGR,
                                    geneModels_hg19$fiveUTRGR)
chromSizes = getChromSizes('hg19')
genomeSize = sum(chromSizes)
calcExpectedPartitions(vistaEnhancers, partitionList, genomeSize)
#> Warning: Chi-squared approximation may be incorrect
#>       partition observed   expected     log10OE Chi.square.pval
#> 1: promoterCore        5   0.801438  0.79510006         0.18400
#> 2: promoterProx       12  15.218192 -0.10318181         0.66900
#> 3:     threeUTR       12  16.693035 -0.14335406         0.48800
#> 4:      fiveUTR       11   4.461866  0.39187614         0.15800
#> 5:         exon       30  18.931636  0.19993312         0.14600
#> 6:       intron      441 510.750772 -0.06377044         0.00551
#> 7:   intergenic      828 772.143061  0.03033256         0.03060
#>                                                          method
#> 1: Pearson's Chi-squared test with Yates' continuity correction
#> 2: Pearson's Chi-squared test with Yates' continuity correction
#> 3: Pearson's Chi-squared test with Yates' continuity correction
#> 4: Pearson's Chi-squared test with Yates' continuity correction
#> 5: Pearson's Chi-squared test with Yates' continuity correction
#> 6: Pearson's Chi-squared test with Yates' continuity correction
#> 7: Pearson's Chi-squared test with Yates' continuity correction