Draw aggregation plot.

Draw aggregation plot from aggregation matrices.

Usage

PlotAPA(
  aggregatedMtx = NULL,
  trim = 0,
  colMin = NULL,
  colMid = NULL,
  colMax = NULL,
  colMinCond = NULL,
  colMaxCond = NULL,
  extra_info = FALSE,
  ...
)

Arguments

aggregatedMtx

: The aggregated matrix.

trim

: A number between 0 and 100 that gives the percentage of triming in matrices.

colMin

: The minimal value in color scale. If Null automaticaly find.

colMid

: The middle value in color scale. If Null automaticaly find.

colMax

: The mximal value in color scale. If Null automaticaly find.

colMinCond

: Avalaible for plotting differential aggregation. The minimal value in color scale in the classsical aggregation plot. If NULL automaticaly find.

colMaxCond

: Avalaible for plotting differantial aggregation. The maxiaml value in color scale in the classsical aggregation plot. If NULL automaticaly find.

extra_info

do you want to have a recall of your arguments values? (Default FALSE)

...

additional arguments to ggAPA()

Value

None

Details

PlotAPA

Examples

# Data
data(Beaf32_Peaks.gnr)
data(HiC_Ctrl.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 exemple

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

# Aggregate matrices in one matrix
aggreg.mtx <- Aggregation(interactions_PF.mtx_lst)

# Visualization
PlotAPA(
    aggregatedMtx = aggreg.mtx
)


PlotAPA(
    aggregatedMtx= aggreg.mtx,
    trim= 20,
    colMin= -2,
    colMid= 0,
    colMax= 2,
    colMinCond = 0,
    colMaxCond = 2
)