call_peaks_with_GLM {exomePeak2} | R Documentation

## Statistical Inference with DESeq package based on the provided reads count for exomic bins.

### Description

`call_peaks_with_GLM` conduct inference on every exome bins using negative binomial model, the significant bins will be the merged into peaks.

### Usage

    call_peaks_with_GLM(
      SE_bins,
      glm_type = c("Poisson", "NB", "DESeq2"),
      correct_GC_bg = TRUE,
      qtnorm = TRUE,
      txdb,
      count_cutoff = 5,
      p_cutoff = NULL,
      p_adj_cutoff = 0.05,
      log2FC_cutoff = 0,
      consistent_peak = TRUE,
      consistent_log2FC_cutoff = 0,
      consistent_fdr_cutoff = 0.05,
      alpha = 0.05,
      p0 = 0.8
    )

### Arguments

`SE_bins` | a `SummarizedExperiment` object. The meta-data collumn should contain the design information of IP/input + treated/control.
`glm_type` | a character, which can be one of the "Poisson", "NB", and "DESeq2". This argument specify the type of generalized linear model used in peak calling; Default to be "Poisson". The DESeq2 method is only recommended for high power experiments with more than 3 biological replicates for both IP and input.
`correct_GC_bg` | a `logical` value of whether to estimate the GC content linear effect on background regions; default `= TRUE`. If `correct_GC_bg = TRUE`, it could result in a more accurate estimation of the technical effect of GC content for the RNA modifications that are highly biologically related to GC content.
`qtnorm` | a `logical` of whether to perform subset quantile normalization after the GC content linear effect correction; default `= TRUE`. Subset quantile normalization will be applied within the IP and input samples seperately to account for the inherent differences between the marginal distributions of IP and input samples.
`txdb` | the txdb object that is necessary for the calculation of the merge of the peaks.
`count_cutoff` | an integer value indicating the cutoff of the mean of reads count in a row, inference is only performed on the windows with the row average read count bigger than the cutoff. Default value is 5.
`p_cutoff` | a numeric value of the p value cutoff used in DESeq inference.
`p_adj_cutoff` | a numeric value of the adjusted p value cutoff used in DESeq2 inference; if provided, the value of `p_cutoff` will be ignored; Default = 0.05.
`log2FC_cutoff` | a non negative numeric value of the log2 fold change (log2 IP/input) cutoff used in the inferene of peaks.
`consistent_peak` | a `logical` of whether the positive peaks returned should be consistent among replicates; default `= TRUE`.
`consistent_log2FC_cutoff` | a `numeric` for the modification log2 fold changes cutoff in the peak consisency calculation; default = 0.
`consistent_fdr_cutoff` | a `numeric` for the BH adjusted C-test p values cutoff in the peak consistency calculation; default = 0.05. Check `ctest`.
`alpha` | a `numeric` for the binomial quantile used in the consitent peak filter; default`= 0.05`.
`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.

### Details

`call_peaks_with_GLM` will performe exome level peak calling using DESeq2 model,

The significant bins will be merged into modification peaks.

The insignificant bins (pass the row means filtering) will also be merged into control peaks.

### Value

This function will return a list of `GRangesList` object storing peaks for both modification and control.

[Package exomePeak2 version 1.2.0 Index]