glmM-methods: Method glmM

In exomePeak2: Bias Awared Peak Calling and Quantification for MeRIP-Seq

Description

glmM performs inference and estimation on IP/input log2FC.

GLMs with the design of an indicator of IP samples are fitted for each peaks/sites:

log2(Q) = intercept + I(IP)

The log2FC and the associated statistics are based on the coefficient estimate of the dummy variable term: I(IP).

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

Usage

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

## S4 method for signature 'SummarizedExomePeak'
glmM(
  sep,
  glm_type = c("DESeq2", "NB", "Poisson"),
  LFC_shrinkage = c("apeglm", "Gaussian", "ashr"),
  ...
)

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", "Gaussian", "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

glmDM

Examples

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

sep <- readRDS(f1)

### Select only the control group to avoid warning.
sep <- sep[,!colData(sep)$design_Treatment]

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

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

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