normalizeChIPtoInput {edgeR} | R Documentation

## Normalize ChIP-Seq Read Counts to Input and Test for Enrichment

### Description

Normalize ChIP-Seq read counts to input control values, then test for significant enrichment relative to the control.

### Usage

    normalizeChIPtoInput(input, response, dispersion=0.01, niter=6, loss="p", plot=FALSE,
                         verbose=FALSE, ...)
    calcNormOffsetsforChIP(input, response, dispersion=0.01, niter=6, loss="p", plot=FALSE,
                           verbose=FALSE, ...)

### Arguments

`input` | numeric vector of non-negative input values, not necessarily integer.
`response` | vector of non-negative integer counts of some ChIP-Seq mark for each gene or other genomic feature.
`dispersion` | negative binomial dispersion, must be positive.
`niter` | number of iterations.
`loss` | loss function to be used when fitting the response counts to the input: `"p"` for cumulative probabilities or `"z"` for z-value.
`plot` | if `TRUE`, a plot of the fit is produced.
`verbose` | if `TRUE`, working estimates from each iteration are output.
`...` | other arguments are passed to the `plot` function.

### Details

`normalizeChIPtoInput` identifies significant enrichment for a ChIP-Seq mark relative to input values. The ChIP-Seq mark might be for example transcriptional factor binding or an epigenetic mark. The function works on the data from one sample. Replicate libraries are not explicitly accounted for; this function can either be run on each sample individually or on a pooled of replicates.

ChIP-Seq counts are assumed to be summarized by gene or similar genomic feature of interest.

This function makes the assumption that a non-negligible proportion of the genes, say 25% or more, are not truly marked by the ChIP-Seq feature of interest. Unmarked genes are further assumed to have counts at a background level proportional to the input. The function aligns the counts to the input so that the counts for the unmarked genes behave like a random sample. The function estimates the proportion of marked genes, and removes marked genes from the fitting process. For this purpose, marked genes are those with a Holm-adjusted mid-p-value less than 0.5.

When `plot=TRUE`, the genes shown in red are the marked genes (with Holm mid-p-value < 0.5) that have been removed as probably enriched during the fitting process. The normalization line has been fitted to the non-marked genes plotted in black.

The read counts are treated as negative binomial. The dispersion parameter is not estimated from the data; instead a reasonable value is assumed to be given.

`calcNormOffsetsforChIP` returns a numeric matrix of offsets, ready for linear modelling.

### Value

`normalizeChIPtoInput` returns a list with components

`p.value` | numeric vector of p-values for enrichment.
`scaling.factor` | factor by which input is scaled to align with response counts for unmarked genes.
`prop.enriched` | proportion of marked genes, as internally estimated

`calcNormOffsetsforChIP` returns a numeric matrix of offsets.

### Author(s)

Gordon Smyth

[Package edgeR version 3.36.0 Index]