R/aggregate.R, R/arithmetics.R, R/boost.R, and 3 more
arithmetics.RdDifferent arithmetic operations can be performed on Hi-C contact matrices:
normalize a contact matrix using iterative correction;
detrend a contact matrix, i.e. remove the distance-dependent
contact trend;
autocorrelate a contact matrix: this is is typically done to highlight
large-scale compartments;
divide one contact matrix by another;
merge multiple contact matrices;
despeckle (i.e. smooth out) a contact matrix out;
aggregate (average) a contact matrices over a set of genomic loci of
interest;
boost Hi-C signal by enhancing long-range interactions while preserving short-
range interactions (this is adapted from Boost-HiC);
subsample interactions using a proportion or a fixed number of final
interactions.
coarsen a contact matrix to a larger (coarser) resolution
# S4 method for class 'HiCExperiment'
aggregate(
x,
targets,
flankingBins = 51,
maxDistance = NULL,
BPPARAM = BiocParallel::bpparam()
)
detrend(x, use.scores = "balanced")
autocorrelate(x, use.scores = "balanced", detrend = TRUE, ignore_ndiags = 3)
divide(x, by, use.scores = "balanced", pseudocount = 0)
# S4 method for class 'HiCExperiment,HiCExperiment'
merge(x, y, ..., use.scores = "balanced", FUN = mean)
despeckle(x, use.scores = "balanced", focal.size = 1)
boost(x, use.scores = "balanced", alpha = 1, full.replace = FALSE)
coarsen(x, bin.size)
# S4 method for class 'HiCExperiment'
normalize(
object,
use.scores = "count",
niters = 200,
min.nnz = 10,
mad.max = 3
)
subsample(x, prop)a HiCExperiment object
Set of chromosome coordinates for which interaction counts are extracted from the Hi-C contact file, provided as a GRanges object (for diagnoal-centered loci) or as a GInteractions object (for off-diagonal coordinates).
Number of bins on each flank of the bins containing input targets.
Maximum distance to use when compiling distance decay
BiocParallel parameters
Which scores to use to perform operations
Detrend matrix before performing autocorrelation
ignore N diagonals when calculating correlations
a HiCExperiment object
Add a pseudocount when dividing matrices (Default: 0)
HiCExperiment objects. For aggregate, targets (a set of
GRanges or GInteractions).
merging function
Size of the smoothing rectangle
Power law scaling factor. As indicated in Boost-HiC documentation, the alpha parameter influences the weighting of contacts: if alpha < 1 long-range interactions are prioritized; if alpha >> 1 short-range interactions have more weight when computing the distance matrix.
Whether to replace the entire set of contacts, rather than only filling the missing interactions (count=0) (Default: FALSE)
Bin size to coarsen a HiCExperiment at
Number of iterations for ICE matrix balancing
Filter bins with less than min.nnz non-zero elements when
performing ICE matrix balancing
Filter out bins whose log coverage is less than mad.max
median absolute deviations below the median bin log coverage.
Float between 0 and 1, or integer corresponding to the # of
a HiCExperiment object with extra scores
#### -----
#### Normalize a contact matrix
#### -----
library(HiContacts)
contacts_yeast <- contacts_yeast()
#> see ?HiContactsData and browseVignettes('HiContactsData') for documentation
#> loading from cache
normalize(contacts_yeast)
#>
#> `HiCExperiment` object with 8,757,906 contacts over 763 regions
#> -------
#> fileName: "/github/home/.cache/R/ExperimentHub/1bea36c87b1b_7752"
#> focus: "whole genome"
#> resolutions(5): 1000 2000 4000 8000 16000
#> active resolution: 16000
#> interactions: 267709
#> scores(3): count balanced ICE
#> topologicalFeatures: compartments(0) borders(0) loops(0) viewpoints(0) centromeres(16)
#> pairsFile: N/A
#> metadata(0):
#### -----
#### Detrending a contact matrix
#### -----
detrend(contacts_yeast)
#> `HiCExperiment` object with 8,757,906 contacts over 763 regions
#> -------
#> fileName: "/github/home/.cache/R/ExperimentHub/1bea36c87b1b_7752"
#> focus: "whole genome"
#> resolutions(5): 1000 2000 4000 8000 16000
#> active resolution: 16000
#> interactions: 267709
#> scores(4): count balanced expected detrended
#> topologicalFeatures: compartments(0) borders(0) loops(0) viewpoints(0) centromeres(16)
#> pairsFile: N/A
#> metadata(0):
#### -----
#### Auto-correlate a contact matrix
#### -----
autocorrelate(contacts_yeast)
#>
#> `HiCExperiment` object with 8,757,906 contacts over 763 regions
#> -------
#> fileName: "/github/home/.cache/R/ExperimentHub/1bea36c87b1b_7752"
#> focus: "whole genome"
#> resolutions(5): 1000 2000 4000 8000 16000
#> active resolution: 16000
#> interactions: 267709
#> scores(5): count balanced expected detrended autocorrelated
#> topologicalFeatures: compartments(0) borders(0) loops(0) viewpoints(0) centromeres(16)
#> pairsFile: N/A
#> metadata(0):
#### -----
#### Divide 2 contact matrices
#### -----
contacts_yeast <- refocus(contacts_yeast, 'II')
contacts_yeast_eco1 <- contacts_yeast_eco1() |> refocus('II')
#> see ?HiContactsData and browseVignettes('HiContactsData') for documentation
#> loading from cache
divide(contacts_yeast_eco1, by = contacts_yeast)
#> `HiCExperiment` object with 996,154 contacts over 51 regions
#> -------
#> fileName: N/A
#> focus: "II"
#> resolutions(1): 16000
#> active resolution: 16000
#> interactions: 1326
#> scores(6): count.x balanced.x count.by balanced.by balanced.fc balanced.l2fc
#> topologicalFeatures: ()
#> pairsFile: N/A
#> metadata(2): hce_list operation
#### -----
#### Merge 2 contact matrices
#### -----
merge(contacts_yeast_eco1, contacts_yeast)
#> `HiCExperiment` object with 733,836.5 contacts over 51 regions
#> -------
#> fileName: "/github/home/.cache/R/ExperimentHub/1bea73b99dd3_7754"
#> focus: "II"
#> resolutions(5): 1000 2000 4000 8000 16000
#> active resolution: 16000
#> interactions: 1326
#> scores(2): count balanced
#> topologicalFeatures: ()
#> pairsFile: N/A
#> metadata(2): hce_list operation
#### -----
#### Despeckle (smoothen) a contact map
#### -----
despeckle(contacts_yeast)
#> `HiCExperiment` object with 471,364 contacts over 51 regions
#> -------
#> fileName: "/github/home/.cache/R/ExperimentHub/1bea36c87b1b_7752"
#> focus: "II"
#> resolutions(5): 1000 2000 4000 8000 16000
#> active resolution: 16000
#> interactions: 1326
#> scores(3): count balanced balanced.despeckled
#> topologicalFeatures: compartments(0) borders(0) loops(0) viewpoints(0) centromeres(16)
#> pairsFile: N/A
#> metadata(0):
#### -----
#### Aggregate a contact matrix over centromeres, at different scales
#### -----
contacts <- contacts_yeast() |> zoom(resolution = 1000)
#> see ?HiContactsData and browseVignettes('HiContactsData') for documentation
#> loading from cache
centros <- topologicalFeatures(contacts, 'centromeres')
aggregate(contacts, targets = centros, flankingBins = 51)
#> Going through preflight checklist...
#> Parsing the entire contact matrice as a sparse matrix...
#> Modeling distance decay...
#> Filtering for contacts within provided targets...
#> `AggrHiCExperiment` object over 16 targets
#> -------
#> fileName: "/github/home/.cache/R/ExperimentHub/1bea36c87b1b_7752"
#> focus: 16 targets
#> resolutions(5): 1000 2000 4000 8000 16000
#> active resolution: 1000
#> interactions: 10609
#> scores(4): count balanced expected detrended
#> slices(4): count balanced expected detrended
#> topologicalFeatures: targets(16) compartments(0) borders(0) loops(0) viewpoints(0) centromeres(16)
#> pairsFile: N/A
#> metadata(0):
#### -----
#### Enhance long-range interaction signal
#### -----
contacts <- contacts_yeast() |> zoom(resolution = 1000) |> refocus('II')
#> see ?HiContactsData and browseVignettes('HiContactsData') for documentation
#> loading from cache
boost(contacts, alpha = 1)
#> `HiCExperiment` object with 469,569 contacts over 814 regions
#> -------
#> fileName: "/github/home/.cache/R/ExperimentHub/1bea36c87b1b_7752"
#> focus: "II"
#> resolutions(5): 1000 2000 4000 8000 16000
#> active resolution: 1000
#> interactions: 330763
#> scores(3): count balanced boosted
#> topologicalFeatures: compartments(0) borders(0) loops(0) viewpoints(0) centromeres(16)
#> pairsFile: N/A
#> metadata(1): alpha
#### -----
#### Subsample & "coarsen" contact matrix
#### -----
subcontacts <- subsample(contacts, prop = 100000)
coarsened_subcontacts <- coarsen(subcontacts, bin.size = 4000)