glmDM-methods {exomePeak2} | R Documentation

## Method glmDM

### Description

`glmDM` perform inference and estimation on RNA differential modification log2FC.

GLMs with interactive design between dummy variables of IP/input and Treatment/control are fitted for each peaks/sites:

log2(Q) = intercept + I(Treatment) + I(IP) + I(IP)*I(Treatment)

The log2FC and the associated statistics are based on the coefficient estimate of the interactive term: I(IP)*I(Treated).

Under default setting, the returned log2FC are the RR estimates with Couchey priors defined in `apeglm`.

### Usage

    glmDM(
      sep,
      glm_type = c("DESeq2", "NB", "Poisson"),
      LFC_shrinkage = c("apeglm", "ashr"),
      ...
    )

    ## S4 method for signature 'SummarizedExomePeak'
    glmDM(
      sep,
      glm_type = c("DESeq2", "NB", "Poisson"),
      LFC_shrinkage = c("apeglm", "ashr", "none"),
      ...
    )

### Arguments

`sep` | a `SummarizedExomePeak` object.
`glm_type` | a `character` speciefies the type of Generalized Linear Model (GLM) fitted for the purpose of statistical inference during peak calling, which can be one of the `c("DESeq2", "NB", "Poisson")`. `DESeq2` Fit the GLM defined in the function `DESeq`, which is the NB GLM with regulated estimation of the overdispersion parameters. `NB` Fit the Negative Binomial (NB) GLM. `Poisson` Fit the Poisson GLM. By default, the DESeq2 GLMs are fitted on the data set with > 1 biological replicates for both the IP and input samples, the Poisson GLM will be fitted otherwise.
`LFC_shrinkage` | a `character` for the method of emperical bayes shrinkage on log2FC, could be one of `c("apeglm", "ashr", "none")`; Default `= "apeglm"`. see `lfcShrink` for details; if "none" is selected, only the MLE will be returned.
`...` | Optional arguments passed to `DESeq`

### Value

a `SummarizedExomPeak` object.

### See Also

`glmM`

### Examples

    ### Load the example SummarizedExomPeak object
    f1 = system.file("extdata", "sep_ex_dm.rds", package="exomePeak2")

    sep <- readRDS(f1)

    ### Normalize the GC contents biases
    sep <- normalizeGC(sep)

    ### Calculate GLM Statistics on the Modification Peaks
    sep <- glmDM(sep)

[Package exomePeak2 version 1.2.0 Index]