normICE: Iterative Correction of Hi-C data (ICE)

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/normalize_hiC.R

Description

Iterative correction leverages the unique pairwise genome-wide structure of Hi-C data to decompose the data into a set of biases and a map of relative contact probabilities between any two genomic loci, achieving equal visibility across all genomic regions.

Usage

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normICE(x, max_iter=50, eps=1e-4, sparse.filter=0.02)

Arguments

x

object that inherits from class HTCexp

max_iter

maximum number of iteration

eps

the relative increment in the results before declaring convergence

sparse.filter

Define which pourcentage of bins with high sparsity should be force to zero

Details

The normalization of Hi-C data is based on matrix balancing algorithm which consists of iteratively estimating the matrix bias using the l1 norm. The method implemented here is the Sinkhorn-Knopp algorithm as used in the Imakaev et al. paper. Note that the original method is applied on the genome-wide Hi-C map, but that the method could be applied on intra-chromosomal maps at high resolution.

Value

Returns a HTCexp object with a corrected contact map.

Author(s)

N. Servant, N. Varoqaux

References

Imakaev M, Fudenberg G, McCord RP, Naumova N, Goloborodko A, Lajoie BR, Dekker J, Mirny LA. Iterative correction of Hi-C data reveals hallmarks of chromosome organization.Nat Methods. 2012 Oct;9(10):999-1003.

See Also

normLGF

Examples

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## Not run: 
##Lieberman data
exDir <- system.file("extdata", package="HiTC")
l <- sapply(list.files(exDir, pattern=paste("HIC_gm06690_"), full.names=TRUE),
            import.my5C)
hiC <- HTClist(l)
hiC <- hiC[isIntraChrom(hiC)]

## Run ICE
hiC_iced <- HTClist(lapply(hiC, normICE))

## End(Not run)

HiTC documentation built on Nov. 8, 2020, 8:27 p.m.