ChIPseq: ChIP-seq dataset

In FPZhang2015/segCCR: Segmented correspondence curve regression

ChIP-seq dataset

Description

ChIP-seq data from Chen et al. (2012) to characterize the effect of sequencing depth on the reproducibility of binding site identification. At the sequencing depth of 0.45, 0.9, 2.7, 5.4 and 16.2 million reads, 1335, 2198, 3813, 4631, and 5499 binding sites are identified on both replicates, respectively. It has three columns, y1, y2 and x.

The variables are as follows:

Usage

data(ChIPseq)

Format

A data frame with 17476 rows and 3 variables

y1

the scores of replicate 1

y2

the scores of replicate 2

x

the factor variabel of 5 depths. 0 for baseline at depth 0.45M, and 1,2,3,4 are for the for the depths of 0.9M, 2.7M, 5.4M and 16.2M, respectively.

Source

Data is from Chen et al. (2012).

References

• Chen, Y., Negre, N., Li, Q., Mieczkowska, J. O., Slattery, M., Liu, T., Zhang, Y., Kim, T.-K., He, H. H., Zieba, J., et al. (2012). Systematic evaluation of factors influencing ChIP-seq fidelity. Nature Methods, 9:609鈥?14.

Examples

## Not run: 
data(ChIPseq)
## estimate
m = 100
tm <- seq(0.01, 0.999, length.out = m)
nx = nlevels(factor(ChIPseq$x))
par.ini = c(0.5, 2, 1, rep(0.1, 2*(nx-1)))  # initial value
nx = nlevels(factor(ChIPseq$x))
par.ini = c(0.5, 2, 1, rep(0.1, 2*(nx-1)))  # initial value
fit = segCCR(data = ChIPseq,
           par.ini = par.ini,
           tm=tm,
           NB = 5)

## End(Not run)

FPZhang2015/segCCR documentation built on June 25, 2022, 5:20 a.m.