Description
Usage
Arguments
Value
View source: R/GLM_inference.R
GLM_inference
conduct inference on log2 fold changes of IP over input using the GLM defined in DESeq2.
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16 | (
SE_bins,
glm_type = ("Poisson", "NB", "DESeq2"),
p_cutoff = 1e-05,
p_adj_cutoff = ,
count_cutoff = 5,
log2FC_mod = 1,
min_mod_number = ,
correct_GC_bg = ,
qtnorm = ,
consistent_peak = ,
consistent_log2FC_cutoff = 1,
consistent_fdr_cutoff = 0.05,
alpha = 0.05,
p0 = 0.8
)
|
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 = FALSE .
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.
|
consistent_peak |
a logical of whether the positive peaks returned should be consistent among replicates; default = TRUE .
|
consistent_log2FC_cutoff |
a numeric for the modification log2 fold changes cutoff in the peak consisency calculation; default = 1.
|
consistent_fdr_cutoff |
a numeric for the BH adjusted C-test p values cutoff in the peak consistency calculation; default = 0.05. Check ctest .
|
alpha |
a numeric for the binomial quantile used in the consitent peak filter; default = 0.05 .
|
p0 |
a numeric for the binomial proportion parameter used in the consistent peak filter; default = 0.8 .
For a peak to be consistently methylated, the minimum number of significant enriched replicate pairs is defined as the 1 - alpha quantile of a binomial distribution with p = p0 and N = number of possible pairs between replicates.
The consistency defined in this way is equivalent to the rejection of an exact binomial test with null hypothesis of p < p0 and N = replicates number of IP * replicates number of input.
|
a list of the index for the significant modified peaks (IP > input) and control peaks (peaks other than modification containing peaks).
exomePeak2 documentation built on Nov. 8, 2020, 5:27 p.m.