Package 'MPFE'

MPFE

Description

Estimate distribution of methylation patterns from a table of counts from a bisulphite sequencing experiment given a non-conversion rate and sequencing error rate.

Details

Package:	MPFE
Type:	Package
License:	GPL(>=3)

The main component of this package is the function estimatePatterns, which reads a table of read counts of bisulphite sequencing data for a given amplicon and generates a table and plot of the estimated distribution over methylation patterns.

Author(s)

Peijie Lin, Sylvain Foret, Conrad Burden

Maintainer: [email protected]

Examples

data(patternsExample)
  estimates <- estimatePatterns(patternsExample, epsilon=0.02, eta=0.01)
  estimates
  plotPatterns(estimates[[2]])

---

Estimate distribution of methylation patterns

Description

Estimate distribution of methylation patterns from a table of counts from a bisulphite sequencing experiment given a non-conversion rate and a sequencing error rate.

Usage

estimatePatterns(patternCounts,
                   epsilon=0,
                   eta=0,
                   column=NULL,
                   fast=TRUE,
                   steps=20000,
                   reltol=1e-12)

Arguments

patternCounts	data frame with methylation patterns in first column and pattern counts in subsequent columns.
epsilon	non-converson rate, a value between 0 and 1.
eta	error rate, either a vector of numbers between 0 and 1 of length equal to the number of CpG sites or a single value between 0 and 1 for a single error rate across all sites.
column	a vector that specifies the indices of the columns of ‘patternCounts’ to process. Its entries are integer values from 1 to the number of pattern counts columns in ‘patternCounts’. If NULL, defaults to all columns.
fast	logical, if TRUE, fast version implemented (default).
steps	number of steps for the optimiser, passed to constrOptim. If NULL, defaults to 20000 steps.
reltol	relative tolerance for the optimiser, passed to constrOptim. If NULL, defaults to 1e-12.

Value

The function returns a list of data frames.

The data frames contain the following columns:

pattern	the list of input patterns (factor)
coverage	the number of reads for each pattern (integer)
observedDistribution	the observed frequencies of each pattern (numeric)
estimatedDistribution	the estimated frequencies (numeric)
spurious	indicates whether the patterns are real or spurious (logical)

Author(s)

Peijie Lin, Sylvain Foret, Conrad Burden

Examples

data(patternsExample)
  estimatePatterns(patternsExample, 
                   epsilon=0.02, 
                   eta=0.01)
  estimatePatterns(patternsExample, 
                   epsilon=0.01, 
                   eta=c(0.015, 0.01, 0.01, 0.01, 0.015),
                   column=2)

---

Plot a representation of the patterns and their frequencies

Description

Plot the observed distribution and the estimated distribution of the methylation patterns

Usage

patternMap(patterns,
             minFreq=0,
             maxFreq=1,
             noSpurious=TRUE,
             estimatedDistribution=TRUE,
             topDown=TRUE,
             allTicks=FALSE,
             methCol='black',
             unMethCol='white',
             ...)

Arguments

patterns	A data frame obtained from the output of the function estimatePatterns.
minFreq	Only plot patterns with at least minFreq frequency.
maxFreq	Only plot patterns with more maxFreq frequency or more.
noSpurious	Don't plot spurious patterns (only relevant if estimatedDistribution is FALSE).
estimatedDistribution	Use the frequencies from the estimated distribution. If FALSE, use the observed distribution.
topDown	Put the most abundant patterns at the top. If FALSE the most abundant patterns are at the bottom.
allTicks	Draw a tick under every position.
methCol	The colour for the methylated positions. Can be a single colour, a vector of colours (recycled), or a function (for instance from colorRampPalette).
unMethCol	As methCol but for un-methylated positions.
...	Other arguments passed to plot

Details

This function draws a map of the different pattern and their frequencies based on the values returned by estimatePatterns.

Author(s)

Peijie Lin, Sylvain Foret, Conrad Burden

Examples

data(patternsExample)
  estimates <- estimatePatterns(patternsExample, 
                                epsilon=0.02, 
                                eta=0.01)
  patternMap(estimates[[1]])

---

patternsExample

Description

A data frame which contains a column of methylation patterns and two columns of counts. This data was obtained as described in Lyko, F., Forest, S., Kucharski, R., Wolf, S., Falckenhayn, C., and Maleszka, R. (2010). The honey bee epigenomes: differential methylation of brain DNA in queens and workers. PLoS Biol, 8(11), e1000506.

Usage

data(patternsExample)

Format

This data frame contains the following columns:

mPattern

methylation patterns

k1

first column of counts

k2

second column of counts

---

Plot methylation pattern distribution

Description

Plot the observed distribution and the estimated distribution of the methylation patterns

Usage

plotPatterns(compareData, yLimit1=NULL, yLimit2=NULL)

Arguments

compareData	data frame, obtained from the output of the function estimatePatterns
yLimit1	upper limit of y-axis on left hand scale of the first graph. If NULL, defaults to show all patterns
yLimit2	upper limit of y-axis on left hand scale of the second graph. If NULL, defaults to show most patterns

Details

The two graphs in the output plot are the same but have different ranges. The parameters yLimit1 and yLimit2 control the range of the y_axis on the plots produced.

Value

A plot that compares the observed read distribution with the estimated distribution.

Author(s)

Peijie Lin, Sylvain Foret, Conrad Burden

Examples

data(patternsExample)
  estimates <- estimatePatterns(patternsExample, 
                                epsilon=0.02, 
                                eta=0.01)
  plotPatterns(estimates[[1]])

---