pca.hic: Perform Principle Component Analysis on Hi-C contact map
In bioinfo-pf-curie/HiTC: High Throughput Chromosome Conformation Capture analysis

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

Perform Principle Component Analysis on Hi-C contact map

1 2	pca.hic(x, normPerExpected=TRUE, npc=2, asGRangesList=TRUE, gene.gr=NULL, ...)

`x`	object that inherits from class `HTCexp`
`normPerExpected`	normalized by expected interaction using the loess calculation of distance dependency. see normPerExpected
`npc`	numeric; number of first principal component to return
`asGRangesList`	if TRUE a GRangesList object is returned where the scores represent the eigenvector
`gene.gr`	object of class `GenomicRanges` describing the genes position. If used, the A/B compartments classes are defined based on gene density
`...`	additional parameters passed to `normPerExpected` function

This method was apply by Lieberman-Aiden et al. 2009 to correlate the annotation profiles (genes, ChIP-seq, etc.) with the topological domains observed in Hi-C (see Fig3G of Lieberman-Aiden et al. 2009)

A list with the eigen vector(s) of the npc first principal component(s), and their importance

N. Servant, B. Lajoie, R. McCord

normPerExpected

## Get Lieberman-Aiden Hi-C data
exDir <- system.file("extdata", package="HiTC")
l <- sapply(list.files(exDir, pattern=paste("HIC_gm06690_"), full.names=TRUE),
import.my5C)
hiC <- HTClist(l)

## Performed PCA
pr<-pca.hic(hiC$chr14chr14, npc=1, asGRangesList=TRUE)