Inference_merip: Analysis MeRIP datasets with DESeq2.

Description Usage Arguments Value

View source: R/Inference_merip.R

Description

Inference_merip is a function to infer methylation and differential methylation given merip datasets.

Usage

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Inference_merip(SE_M, MODE = "Meth", DM_METHOD = "DESeq2", PCA = FALSE,
  HDER = "Unknown", ROW_FILTER = 0, CQN = FALSE, GC_INDX = NULL)

Arguments

SE_M

A SummarizedExperiment object with 1 necessary column in colData named "IP_input", its content should be a character vector consists of 2 values: c("IP", "input").

MODE

Could be either "Meth" or "DM", the later will conduct differential methylation analysis with the design:

DM_METHOD

A character string indicating the statistical method used in differential methylation analysis, could be one in c("DESeq2","QNB").

PCA

Wheather to save the PCA plot after rlog transformation, default is FALSE, the plot will not be generated when DM_METHOD = "QNB" while the MODE = "DM".

HDER

What should be the header of the PCA plot, applied when PCA = TRUE.

CQN

Wheather to normalize GC content dependency of methylation / differential methylation log2FC, default is FALSE.

GC_INDX

The GC content values for each features (rows) of the count matrix, it is required when CQN = TRUE.

log2(Q) = intercept + I(Treated) + I(IP) + I(IP):I(Treated).

The result is just the differences in conditioning effect, or the statistics for estimate before the term I(IP):I(Treated).

We don't get this by contrast, because the information is already contained in coefficient estimate under the design above, and we don't need to linear combine (or a linear combination by t(c(0,0,0,1))) the estimates to get it.

An additional column c("Perturbation") is necessary for this option, the Perturbation column has to include character "C" for control condition.

Value

A DESeq2 result object for DESeq2 analysis; for other analysis, it will generate a data.frame object.


ZhenWei10/meripQC documentation built on April 18, 2018, 9:37 p.m.