Draw aggregation plots for interactions with different distances.

Separates matrices based on interaction distance, performs aggregation and plots Aggregated signal for each chunk of interaction distances.

Usage

plotMultiAPA(submatrices = NULL, ctrlSubmatrices = NULL, ..., plot.opts = NULL)

Arguments

submatrices

: The matrices list to separate using interaction distances and aggregate. Chunks of distances are created with: c(0,50000*2 ^ seq(0,5,by=1)). Other matrices with distances over 1.6 Mb are aggregated in the same final chunk.

ctrlSubmatrices

: The matrices list to use as control condition for differential aggregation.

...

: Additional arguments to pass to [Aggregation()]. For differential aggregation plot, submatrices will take the matrices of the treated condition. eg:

plotMultiAPA(
submatrices = interactions_HS.mtx_lst,
ctrlSubmatrices = interactions_Ctrl.mtx_lst)```

[Aggregation()]: R:Aggregation()

plot.opts

list of arguments to pass to ggAPA().

Value

A plot with separate APAs per distance and a list of aggregated matrices as invisible output.

Details

plotMultiAPA

Examples

#' # Data
data(Beaf32_Peaks.gnr)
data(HiC_Ctrl.cmx_lst)
data(HiC_HS.cmx_lst)

# Index Beaf32
Beaf32_Index.gnr <- IndexFeatures(
    gRangeList = list(Beaf = Beaf32_Peaks.gnr),
    chromSizes = data.frame(seqnames = c("2L", "2R"),
        seqlengths = c(23513712, 25286936)),
    binSize = 100000
)

# Beaf32 <-> Beaf32 Pairing
Beaf_Beaf.gni <- SearchPairs(indexAnchor = Beaf32_Index.gnr)
Beaf_Beaf.gni <- Beaf_Beaf.gni[seq_len(2000)] # subset 2000 first for eg

# Matrices extractions center on Beaf32 <-> Beaf32 point interaction
interactions_Ctrl.mtx_lst <- ExtractSubmatrix(
    genomicFeature = Beaf_Beaf.gni,
    hicLst = HiC_Ctrl.cmx_lst,
    referencePoint = "pf"
)
interactions_HS.mtx_lst <- ExtractSubmatrix(
    genomicFeature = Beaf_Beaf.gni,
    hicLst = HiC_HS.cmx_lst,
    referencePoint = "pf"
)
interactions_Ctrl.mtx_lst <- PrepareMtxList(
    matrices = interactions_Ctrl.mtx_lst
)

# Aggregate matrices in one matrix
plotMultiAPA(submatrices = interactions_Ctrl.mtx_lst)



interactions_HS.mtx_lst <- PrepareMtxList(
    matrices = interactions_HS.mtx_lst
)

# Differential Aggregation
plotMultiAPA(
    submatrices = interactions_HS.mtx_lst,
    ctrlSubmatrices = interactions_Ctrl.mtx_lst,
    diffFun = "ratio",
    plot.opts = list(colors = list("blue","white","red"))
)