get.scc: calculate the stratum-adjusted correlation coefficient

In hicrep: Measuring the reproducibility of Hi-C data

Description

calculate the stratum-adjusted correlation coefficient

Usage

get.scc(dat, resol, max)

Arguments

dat	A matrix of four columns. The first two are the mid-point coordinates of two interacting bin.
resol	An integer indicating the resolution of the Hi-C matrix.
max	An integer indicating the maximum distance of interaction that is considered.

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, Ross C Hardison, William Stafford Noble, Feng Yue, Qunhua Li. bioRxiv 101386; doi: https://doi.org/10.1101/101386.

Examples

data(HiCR1)
data(HiCR2)
processed <- prep(HiCR1, HiCR2, 1000000, 0, 5000000)

scc.out = get.scc(processed, 1000000, 5000000)
scc.out$scc
scc.out$std

Example output

          [,1]
[1,] 0.9720146
[1] 0.006901945

hicrep documentation built on April 28, 2020, 7:51 p.m.