get.scc: calculate the stratum-adjusted correlation coefficient

In qunhualilab/hicrep: Measuring the reproducibility of Hi-C data

Description

calculate the stratum-adjusted correlation coefficient

Usage

get.scc(mat1, mat2, resol, h, lbr = 0, ubr = 5e+06)

Arguments

mat1	Replicate 1 : a n*n intrachromosome Hi-C contact map.
mat2	Replicate 2 : a n*n intrachromosome Hi-C contact map.
resol	An integer indicating the resolution of the Hi-C matrix.
h	An integer indicating the size of the smoothing neighborhood.
lbr	An integer indicating the minumum distance of interaction that is considered. Default is 0.
ubr	An integer indicating the maximum distance of interaction that is considered. Defalt is 5000000.

Details

The function stratifies the Hi-C reads count according to their interacting distance, calculates the Pearson correlation coefficient for each stratum, then aggregrates them using a weighted average.

Value

A list of results including stratum-specific correlation coefficients, weights, stratum-adjusted correlation coefficient (scc), and the asymptotic standard deviation of scc.

• corr A vector that contains the stratum specific Pearson correlation coefficients.

• wei A vector that contains the weights for each stratum.

• scc Stratum-adjusted correlation coefficients.

• std The asymptotic standard deviation of scc.

References

HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient. Tao Yang, Feipeng Zhang, Galip Gurkan Yardimci, Fan Song, Ross C Hardison, William Stafford Noble, Feng Yue, Qunhua Li. Genome Research 2017. doi: 10.1101/gr.220640.117

Examples

data(HiCR1)
data(HiCR2)
scc.out = get.scc(HiCR1, HiCR2, 100000, 0, 0, 5000000)
scc.out$scc
scc.out$std

qunhualilab/hicrep documentation built on Feb. 27, 2020, 9:17 p.m.