run_methylation_array

Running on methylation array data

This function allows to get estimates of purity and ploidy values as well as integer copy-number fitting from methylation array data, derived with the illumina platforms 450K, Epicv1 or Epicv2.

This module is different from the other uses of ASCAT.sc, as it works on methylation data nor genomic data, and on array data, not sequencing. However, the methodology is similar, as it uses a logR track that is a proxy for DNA content in a bulk tissue. So, the equations, the fitting strategy and core functions to derive and print the profiles are the same.

Illumina methylation arrays

These can be either 450K, Epicv1 or Epicv2. Pre-computed bad loci and panel of diploid normal samples are in the dependency package ASCAT.scDataMeth and should be downloaded and installed from this google drive.

Minimal run with defaults

Example run:

library(ASCAT.sc)

res <- run_methylation_array(idat_dir=idat_dir,#path to the directory containing *all* the idat raw files
                             id_normals=id_normals,#optional: IDs of the normal diploid samples in your dataset
                             outdir="/path/to/output/diretory/",#path to the outputs (should exist)
	                         platform="450K",#platform, one of: "450K", "epicv1" or "epicv2"
	                         projectname="name_of_this_project",#nametagof your project or of your run
                             MC.CORES=4)#number of cores to use

Other parameters

Example run:

library(ASCAT.sc)

res <- run_methylation_array(idat_dir=idat_dir,#path to the directory containing *all* the idat raw files
                             id_normals=id_normals,#optional: IDs of the normal diploid samples in your dataset
                             purs = seq(0.05, 1, 0.01),#purities to consider
                             ploidies = seq(1.7,5, 0.01),#ploidies to consider
                             allchr=paste0("chr",c(1:22,"X","Y")),#chromosomes to fit
                             maxtumourpsi=5,#maximum tumor ploidy to consider
                             segmentation_alpha=0.01,#segmentation alpha
                             outdir="/path/to/output/diretory/",#path to the outputs (should exist)
                             projectname="name_of_this_project",#nametagof your project or of your run
                             predict_refit=TRUE,#should automatic refitting be performed
                             print_results=TRUE,#print results to disk
                             MC.CORES=4)#number of cores to use

Note on Epicv2

Currently the panel of diploid normal samples is limited, so we recommend using your own diploid normal samples as well. We are looking for datasets containing diploid samples to improve this, please do not hesitate to contact us if you have some.

If you experience that the logR tracks are noisy or oversegmented, we recommend decreasing the segmentation_alpha values.