normICE {HiTC} | R Documentation |
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.
normICE(x, max_iter=50, eps=1e-4, sparse.filter=0.02)
x |
object that inherits from class |
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.
## 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)