glmDM-methods {exomePeak2} | R Documentation |
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
.
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"), ... )
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_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)