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

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: conrad.burden@anu.edu.au

Examples

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

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(MPFE)
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/MPFE/MPFE-package.Rd_%03d_medium.png", width=480, height=480)
> ### Name: MPFE-package
> ### Title: MPFE
> ### Aliases: MPFE-package MPFE
> ### Keywords: amplicon, bisulphite sequencing, bisufite sequencing,
> ###   methylation
> 
> ### ** Examples
> 
>   data(patternsExample)
>   estimates <- estimatePatterns(patternsExample, epsilon=0.02, eta=0.01)
>   estimates
[[1]]
   pattern coverage observedDistribution estimatedDistribution spurious
1    00000      629          0.825459318          0.9616800105    FALSE
2    00001       26          0.034120735          0.0089304716    FALSE
3    00010       20          0.026246719          0.0003110634    FALSE
4    00011        2          0.002624672          0.0015407424    FALSE
5    00100       24          0.031496063          0.0061214122    FALSE
6    00101        3          0.003937008          0.0027064505    FALSE
7    00110        1          0.001312336          0.0002852839    FALSE
8    01000       23          0.030183727          0.0046657976    FALSE
9    01010        1          0.001312336          0.0003288084    FALSE
10   01100        1          0.001312336          0.0001547232    FALSE
11   10000       28          0.036745407          0.0105739396    FALSE
12   10001        1          0.001312336          0.0000000000     TRUE
13   11000        3          0.003937008          0.0027012968    FALSE

[[2]]
   pattern coverage observedDistribution estimatedDistribution spurious
1    00000     2257         0.8405959032          9.791739e-01    FALSE
2    00001       90         0.0335195531          6.386933e-03    FALSE
3    00010       75         0.0279329609          1.848086e-03    FALSE
4    00011        3         0.0011173184          1.736963e-05    FALSE
5    00100       82         0.0305400372          4.518741e-03    FALSE
6    00110       11         0.0040968343          3.113596e-03    FALSE
7    01000       80         0.0297951583          2.480130e-03    FALSE
8    01010        1         0.0003724395          0.000000e+00     TRUE
9    01100        5         0.0018621974          8.139170e-04    FALSE
10   10000       69         0.0256983240          0.000000e+00     TRUE
11   10001        2         0.0007448790          0.000000e+00     TRUE
12   10010        2         0.0007448790          0.000000e+00     TRUE
13   10100        7         0.0026070764          1.647322e-03    FALSE
14   11000        1         0.0003724395          0.000000e+00     TRUE

>   plotPatterns(estimates[[2]])
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>