calculateSignificantLevel: Calculate significant level for hypo and hyper conserved...

In methylInheritance: Permutation-Based Analysis associating Conserved Differentially Methylated Elements Across Multiple Generations to a Treatment Effect

Description

Calculate significant level for hypo and hyper conserved elements using permutation results as well as observed results

Usage

calculateSignificantLevel(formatForGraphDataFrame)

Arguments

formatForGraphDataFrame

a data.frame containing the observation results (using real data) and the permutation results (using shuffled data). Both hyper and hypo differentially conserved methylation results must be present. The data.frame must have 3 columns : "TYPE", "RESULT" and "SOURCE". The "TYPE" can be either "HYPER" or "HYPO". The "RESULT" is the number of conserved differentially elements. The "SOURCE" can be either "OBSERVATION" or "PERMUTATION".

Value

a list containing two elements:

• HYPER a double, the significant level for the hyper differentially methylated conserved elements

• HYPO a double, the significant level for the hypo differentially methylated conserved elements

Author(s)

Astrid Deschenes, Pascal Belleau

Examples

## Loading dataset containing all results
data(methylInheritanceResults)

## Extract information for the intersection between conserved differentially
## methylated sites (type = sites) between the intersection of 2
## generations (inter = i2): F2 and F3 (position = 2)
info <- extractInfo(allResults = methylInheritanceResults,
    type = "sites", inter="i2", 2)

## Create graph
methylInheritance:::calculateSignificantLevel(info)

methylInheritance documentation built on Nov. 8, 2020, 8:21 p.m.