callPeaks {NADfinder} | R Documentation

## Call peaks using transformed, background corrected, and smoothed ratios with biological replicates

### Description

Use limma to calculate p-values for NADs

### Usage

    callPeaks(se, backgroundCorrectedAssay = "bcRatio",
      normalization.method = "quantile", N = 100,
      cutoffAdjPvalue = 1e-04, countFilter = 1000,
      combineP.method = "minimump", smooth.method = "loess",
      lfc = log2(1.5), ...)

### Arguments

`se` | An object of RangedSummarizedExperiment with assays of raw counts, tranformed ratios, background corrected ratios, smoothed ratios and z-scores. It should be an element of output of smoothRatiosByChromosome
`backgroundCorrectedAssay` | character(1). Assays names for background corrected log2-transformed ratios, CPMRatios or OddRatios.
`normalization.method` | character(1) specifying the normalization method to be used. Choices are "none", "scale", "quantile" or "cyclicloess". See normalizeBetweenArrays for details.
`N` | numeric(1) or integer(1). The number of neighboring windows used for loess smoothing or the inverse of the critical frequencies of the low pass filter for butterworth filter. 1/N is a cutoff at 1/N-th of the Nyquist frequency. Default 100.
`cutoffAdjPvalue` | numeric(1). Cutoff adjust p-value.
`countFilter` | numeric(1). Cutoff value for mean of raw reads count in each window.
`combineP.method` | A method used to combine P-values. Default minimump
`smooth.method` | A method used to smooth the ratios. Choices are "loess", "none" and "butterworthfilter".
`lfc` | the minimum log2-fold-change that is considered scientifically meaningful
`...` | Parameter not used.

### Details

By default, use the mean smoothed ratio for each peak region to calculate p-values

### Value

An object of GRanges of peak list with metadata "AveSig", "P.Value", and "adj.P.Val", where "AveSig" means average signal such as average log2OddsRatio, log2CPMRatio or log2Ratio.

### Author(s)

Jianhong Ou, Haibo Liu and Julie Zhu

### Examples

    data(triplicate.count)
    se <- triplicate.count
    se <- log2se(se, transformation = "log2CPMRatio",
                 nucleolusCols = c("N18.subsampled.srt-2.bam",
                 "N18.subsampled.srt-3.bam",
                 "N18.subsampled.srt.bam"),
                 genomeCols = c("G18.subsampled.srt-2.bam",
                 "G18.subsampled.srt-3.bam",
                 "G18.subsampled.srt.bam"))
    se<- smoothRatiosByChromosome(se, chr="chr18")
    #add some variability to the data since the triplicate.count data was created using one sample only
    assays(se[[1]])$bcRatio[,2] <- assays(se[[1]])$bcRatio[,2] + 0.3
    assays(se[[1]])$bcRatio[,3] <- assays(se[[1]])$bcRatio[,3] - 0.3
    peaks <- callPeaks(se[[1]],
                    cutoffAdjPvalue=0.001, countFilter=10)

[Package NADfinder version 1.8.1 Index]