# hicrep-package: HiCRep pipeline calculates reproducibility of Hi-C... In hicrep: Measuring the reproducibility of Hi-C data

## Description

The pipelne is a two-step method. The first step is to smooth the Hi-C matrix, and the #' second step is to calculate the stratum-adjusted correlation coefficient (scc). The method also provides the estimation of asymptotic standard deviation of scc.

## Details

• Package: hicrep

• Type: Package

• Version: 0.99.6

• Date: 2017-2-5

The main functions are `prep`, `get.scc` and `htrain`. The function `prep` will take the two replicates of N*(3+N) matrix format as input, and return the vectorized, smoothed or unsmoothed (when smoothing neighborhood size parameter h = 0) Hi-C data, which will subsequently used to compute stratum-adjusted correlation coefficients (scc). The function `get.scc` computes scc and its asymptotic standard deviation, and the function `htrain` estimates optimal smoothing neighborhood size from the input matrices.

## Author(s)

Tao Yang Maintainer: Tao Yang <xadmyangt@gmail.com>

## 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

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```data(HiCR1) data(HiCR2) #Estimate the optimial smoothing neighborhood size parameter h_hat <- htrain(HiCR1, HiCR2, 1000000, 5000000, 0:2) h_hat <- 0 processed <- prep(HiCR1, HiCR2, 1000000, h_hat, 5000000) scc.out <- get.scc(processed, 1000000, 5000000) scc.out\$scc scc.out\$std ```

### Example output

```smoothing:0
smoothing:1
smoothing:2
Warning message:
In htrain(HiCR1, HiCR2, 1e+06, 5e+06, 0:2) :
Note: It's likely that your searching range is too narrow.
Try to expand the range and rerun it
[,1]
[1,] 0.9720146
[1] 0.006901945
```

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