consDESeq2_M: Calculate the index for across sample modification...
In exomePeak2: Bias Awared Peak Calling and Quantification for MeRIP-Seq

Description Usage Arguments Details Value

Calculate the index for across sample modification consistency in a DESeqDataSet object

consDESeq2_M(
  dds,
  consistent_log2FC_cutoff = 1,
  consistent_fdr_cutoff = 0.05,
  p0 = 0.8,
  alpha = 0.05
)

`dds`	a DESeqDataSet object.
`consistent_log2FC_cutoff`	a `numeric` for the modification log2 fold changes cutoff in the peak consisency calculation; default = 1.
`consistent_fdr_cutoff`	a `numeric` for the BH adjusted C-test p values cutoff in the peak consistency calculation; default = 0.05. Check `ctest`.
`p0`	a `numeric` for the binomial proportion parameter used in the consistent peak filter; default `= 0.8`. For a peak to be consistently methylated, the minimum number of significant enriched replicate pairs is defined as the 1 - alpha quantile of a binomial distribution with p = p0 and N = number of possible pairs between replicates. The consistency defined in this way is equivalent to the rejection of an exact binomial test with null hypothesis of p < p0 and N = replicates number of IP * replicates number of input.
`alpha`	a `numeric` for the binomial quantile used in the consitent peak filter; default `= 0.05`.

The minimum consistent number cutcoff is defined by 1-alpha quantile of a binomial distribution with probability of success = p, and number of trials = number of possible pairs between replicates. This is equivalent to the rejection of an exact binomial test with null hypothesis of p < 0.8 and N = number of possible pairs.

a logical index for the consistently modified rows in the DESeqDataSet.

exomePeak2 documentation built on Nov. 8, 2020, 5:27 p.m.