glmM-methods {exomePeak2} | R Documentation |
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
.
glmM( sep, glm_type = c("DESeq2", "NB", "Poisson"), LFC_shrinkage = c("apeglm", "Gaussian", "ashr", "none"), ... ) ## S4 method for signature 'SummarizedExomePeak' glmM( sep, glm_type = c("DESeq2", "NB", "Poisson"), LFC_shrinkage = c("apeglm", "Gaussian", "ashr", "none"), ... )
sep |
a |
glm_type |
a
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 see |
... |
Optional arguments passed to |
a SummarizedExomPeak
object.
### 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)