Entropy comparison between healthy and tumor samples can identify significant CpG sites which are contributing most in the tumor development either by hypomethylation or hypermethylation. Also such way can help in understanding the randomness in methylation status.
Sliding window of 4 was used to calculate the entropy in the sample, which can analyze 16 different pattern for entropy calculation.
Usage
methEntropy(x)
Arguments
x
Matrix from methAlign. Also matrix where columns represents Cytosine of CpG sites and rows represents sequences
Value
Matrix containing entropy for every sequence and group of 4 cpg sites.
Note
This function needs time to process depending on the number of rows in matrix
Author(s)
Muhammad Ahmer Jamil, Prof. Holger Frohlich, Priv.-Doz. Dr. Osman El-Maarri
Xie, H., Wang, M., de Andrade, A., Bonaldo, M.d.F., Galat, V., Arndt, K., Rajaram, V.,
Goldman, S., Tomita, T. and Soares, M.B. (2011) Genome-wide quantitative assessment of
variation in DNA methylation patterns. Nucleic Acids Research, 39, 4099-4108.
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(MethTargetedNGS)
Loading required package: stringr
Loading required package: seqinr
Loading required package: gplots
Attaching package: 'gplots'
The following object is masked from 'package:stats':
lowess
Loading required package: Biostrings
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
rbind, rownames, sapply, setdiff, sort, table, tapply, union,
unique, unsplit
Loading required package: S4Vectors
Loading required package: stats4
Attaching package: 'S4Vectors'
The following object is masked from 'package:gplots':
space
The following objects are masked from 'package:base':
colMeans, colSums, expand.grid, rowMeans, rowSums
Loading required package: IRanges
Loading required package: XVector
Attaching package: 'Biostrings'
The following object is masked from 'package:seqinr':
translate
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/MethTargetedNGS/methEntropy.Rd_%03d_medium.png", width=480, height=480)
> ### Name: methEntropy
> ### Title: Calculate Methylation Entropy
> ### Aliases: methEntropy
> ### Keywords: Entropy Methylation Entropy
>
> ### ** Examples
>
> healthy = system.file("extdata", "Healthy.fasta", package = "MethTargetedNGS")
> reference = system.file("extdata", "Reference.fasta", package = "MethTargetedNGS")
> methP <- methAlign(healthy,reference)
Time difference of 2.01 secs
> entMeth <- methEntropy(methP)
Time difference of 0.02 secs
> plot(entMeth,type="l")
>
>
>
>
>
> dev.off()
null device
1
>