pca.hic {HiTC} | R Documentation |
Perform Principle Component Analysis on Hi-C contact map
pca.hic(x, normPerExpected=TRUE, npc=2, asGRangesList=TRUE, gene.gr=NULL, ...)
x |
object that inherits from class |
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 |
... |
additional parameters passed to |
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
## 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)