Coercing functions available for HiCExperiment objects.
Usage
# S4 method for class 'HiCExperiment'
as.matrix(x, use.scores = "balanced", sparse = FALSE)
# S4 method for class 'HiCExperiment'
as.data.frame(x)
gi2cm(gi, use.scores = "score")
cm2matrix(cm, replace_NA = NA, sparse = FALSE)
df2gi(
df,
seqnames1 = "seqnames1",
start1 = "start1",
end1 = "end1",
seqnames2 = "seqnames2",
start2 = "start2",
end2 = "end2"
)Arguments
- x
HiCExperiment object
- use.scores
Which scores to use to inflate GInteractions
- sparse
Whether to return the contact matrix as a sparse matrix
- gi
GInteractions object
- cm
A
ContactMatrixobject- replace_NA
Replace NA values
- df
A
data.frameobject- seqnames1, start1, end1, seqnames2, start2, end2
Names (as strings) of columns containing corresponding information in a data.frame parsed into GInteractions (default: FALSE)
Examples
mcoolPath <- HiContactsData::HiContactsData('yeast_wt', 'mcool')
#> see ?HiContactsData and browseVignettes('HiContactsData') for documentation
#> loading from cache
contacts <- import(mcoolPath, focus = 'XVI', resolution = 16000, format = 'cool')
gis <- interactions(contacts)
cm <- gi2cm(gis, 'balanced')
cm
#> class: ContactMatrix
#> dim: 60 60
#> type: dgCMatrix
#> rownames: NULL
#> colnames: NULL
#> metadata(0):
#> regions: 60
cm2matrix(cm)[1:10, 1:10]
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> [1,] NaN NaN NaN NaN NaN NaN NaN
#> [2,] NaN 1.39502102 1.89623124 0.39560223 0.14820676 0.07426750 0.05980846
#> [3,] NaN 1.89623124 2.35751077 1.58129178 0.33905391 0.14339992 0.10552853
#> [4,] NaN 0.39560223 1.58129178 1.20960316 0.54168680 0.18242094 0.10413483
#> [5,] NaN 0.14820676 0.33905391 0.54168680 0.52832563 0.49867956 0.16116603
#> [6,] NaN 0.07426750 0.14339992 0.18242094 0.49867956 0.67745097 0.41256806
#> [7,] NaN 0.05980846 0.10552853 0.10413483 0.16116603 0.41256806 0.87399239
#> [8,] NaN 0.03340782 0.05614098 0.06121680 0.10343933 0.26074267 0.48711735
#> [9,] NaN 0.02780230 0.03659704 0.03372027 0.05460712 0.11214948 0.18101311
#> [10,] NaN 0.02204195 0.03877495 0.02539418 0.02878660 0.04762856 0.08132232
#> [,8] [,9] [,10]
#> [1,] NaN NaN NaN
#> [2,] 0.03340782 0.02780230 0.02204195
#> [3,] 0.05614098 0.03659704 0.03877495
#> [4,] 0.06121680 0.03372027 0.02539418
#> [5,] 0.10343933 0.05460712 0.02878660
#> [6,] 0.26074267 0.11214948 0.04762856
#> [7,] 0.48711735 0.18101311 0.08132232
#> [8,] 0.59450224 0.52204112 0.16856414
#> [9,] 0.52204112 0.61170845 0.48810265
#> [10,] 0.16856414 0.48810265 0.64767893
df2gi(data.frame(
chr1 = 'I', start1 = 10, end1 = 100,
chr2 = 'I', start2 = 40, end2 = 1000,
score = 12,
weight = 0.234,
filtered = TRUE
), seqnames1 = 'chr1', seqnames2 = 'chr2')
#> GInteractions object with 1 interaction and 3 metadata columns:
#> seqnames1 ranges1 seqnames2 ranges2 | score weight
#> <Rle> <IRanges> <Rle> <IRanges> | <numeric> <numeric>
#> [1] I 10-100 --- I 40-1000 | 12 0.234
#> filtered
#> <logical>
#> [1] TRUE
#> -------
#> regions: 2 ranges and 0 metadata columns
#> seqinfo: 1 sequence from an unspecified genome; no seqlengths