# depth.adj: Sequencing depth adjustment In hicrep: Measuring the reproducibility of Hi-C data

## Description

Sequencing depth could be a confounding effect when measuring the reproducibility. This function will adjust sequencing depth of a given matrix to a specified total number of reads through random sampling.

## Usage

 `1` ```depth.adj(d, size, resol, out = 0) ```

## Arguments

 `d` a Hi-C matrix needed to be adjusted. `size` the size the total number one wants to adjust to. `resol` the resolution of the input matrix. `out` either 0 or 1. If it is 0, the function returns matrix format; if 1, it returns vection format.

## Value

a matrix or vec which has the adjusted total number of reads.

## 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 12 13``` ```data(HiCR1) #total number of reads sum(HiCR1[,-c(1:3)]) #Adjust it to 200000 reads, output Hi-C matrix HiC_R1_200k = depth.adj(HiCR1, 200000, 1000000, out = 0) #check total number of reads after adjustment sum(HiC_R1_200k[,-c(1:3)]) #output vector HiC_R1_200k = depth.adj(HiCR1, 200000, 1000000, out = 1) #check total number of reads after adjustment sum(HiC_R1_200k[,3]) ```

### Example output

```[1] 2065436
[1] 2e+05
[1] 2e+05
```

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