normICE: Iterative Correction of Hi-C data (ICE)
In bioinfo-pf-curie/HiTC: High Throughput Chromosome Conformation Capture analysis

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

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.

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

`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

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.

Returns a HTCexp object with a corrected contact map.

N. Servant, N. Varoqaux

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.

normLGF

## 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)