methSeg: Segment methylation or differential methylation profile

Description

The function uses a segmentation algorithm (fastseg) to segment the methylation profiles. Following that, it uses gaussian mixture modelling to cluster the segments into k components. This process uses mean methylation value of each segment in the modeling phase. Each component ideally indicates quantitative classification of segments, such as high or low methylated regions.

Usage

methSeg(obj, diagnostic.plot = TRUE, ...)

Arguments

obj

GRanges, methylDiff, methylDiffDB, methylRaw or methylRawDB . If the object is a GRanges it should have one meta column with methylation scores

diagnostic.plot

if TRUE a diagnostic plot is plotted. The plot shows methylation and length statistics per segment group. In addition, it shows diagnostics from mixture modeling: the density function estimated and BIC criterion used to decide the optimum number of components in mixture modeling.

...

arguments to fastseg function in fastseg package, or to densityMclust in Mclust package, could be used to fine tune the segmentation algorithm. E.g. Increasing "alpha" will give more segments. Increasing "cyberWeight" will give also more segments."maxInt" controls the segment extension around a breakpoint. "minSeg" controls the minimum segment length. "G" argument denotes number of components used in BIC selection in mixture modeling. For more details see fastseg and Mclust documentation.

Value

A GRanges object with segment classification and information. 'seg.mean' column shows the mean methylation per segment. 'seg.group' column shows the segment groups obtained by mixture modeling

Details

To be sure that the algorithm will work on your data, the object should have at least 5000 records

Examples

Run this code

# NOT RUN {
# }
# NOT RUN {
 download.file("https://dl.dropboxusercontent.com/u/1373164/H1.chr21.chr22.rds",
               destfile="H1.chr21.chr22.rds",method="curl")

 mbw=readRDS("H1.chr21.chr22.rds")

 # it finds the optimal number of componets as 6
 res=methSeg(mbw,diagnostic.plot=TRUE,maxInt=100,minSeg=10)

 # however the BIC stabilizes after 4, we can also try 4 componets
 res=methSeg(mbw,diagnostic.plot=TRUE,maxInt=100,minSeg=10,G=1:4)

 # get segments to BED file
 methSeg2bed(res,filename="H1.chr21.chr22.trial.seg.bed")
# }
# NOT RUN {
unlink(list.files(pattern="H1.chr21.chr22",full.names=TRUE))

# }