Description Usage Arguments Value References Examples

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.

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

`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. |

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

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.

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])
``` |

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

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