GLM_inference {exomePeak2} | R Documentation |
Statistical Inference with DESeq2 on IP over input fold change.
Description
GLM_inference
conduct inference on log2 fold changes of IP over input using the GLM defined in DESeq2.
Usage
GLM_inference(
SE_bins,
glm_type = c("Poisson", "NB", "DESeq2"),
p_cutoff = 1e-05,
p_adj_cutoff = NULL,
count_cutoff = 0,
log2FC_mod = 1,
min_mod_number = NA,
correct_GC_bg = TRUE,
qtnorm = TRUE
)
Arguments
SE_bins |
a SummarizedExperiment of read count. It should contains a colData with column named design_IP,
which is a character vector with values of "IP" and "input". The column helps to index the design of MeRIP-Seq experiment.
|
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.
|
p_cutoff |
a numeric for the p value cutoff used in DESeq inference.
|
p_adj_cutoff |
a numeric for the adjusted p value cutoff used in DESeq2 inference; if provided, values in p_cutoff will be ignored.
|
count_cutoff |
an integer indicating the cutoff of the mean of reads count in a row, inference is only performed on the windows with read count bigger than the cutoff. Default value is 10.
|
log2FC_mod |
a non negative numeric for the log2 fold change cutoff used in DESeq inferene for modification containing peaks (IP > input).
|
min_mod_number |
a non negative numeric for the minimum number of the reported modification containing bins.
If the bins are filtered less than this number by the p values or effect sizes,
more sites will be reported by the order of the p value until it reaches this number; Default to be calculated by floor( sum(rowSums( assay(SE_bins) ) > 0)*0.001 ).
|
correct_GC_bg |
a logical of whether to estimate the GC content linear effect on background regions; default = TRUE .
If correct_GC_bg = TRUE , it may 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.
|
Value
a list of the index for the significant modified peaks (IP > input) and control peaks (peaks other than modification containing peaks).
[Package
exomePeak2 version 1.4.2
Index]