MethylAid : Visual and Interactive quality control of large Illumina - - PowerPoint PPT Presentation

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MethylAid : Visual and Interactive quality control of large Illumina - - PowerPoint PPT Presentation

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MethylAid : Visual and Interactive quality control of large Illumina 450k data sets BioC Europe 2015 Maarten van Iterson Leiden University Medical Center Department of Molecular Epidemiology January 9, 2015 Epigenome-wide association studies

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SLIDE 1

MethylAid: Visual and Interactive quality control

  • f large Illumina 450k data sets

BioC Europe 2015

Maarten van Iterson

Leiden University Medical Center Department of Molecular Epidemiology

January 9, 2015

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Epigenome-wide association studies (EWAS)

  • DNA methylation
  • cytosine of CpG sites can be convered to 5-methylcytosine
  • smoking, bmi and several autoimmune diseases
  • sample sizes are hundreds to several thousands
  • Illumina 450K HumanMethylation array
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SLIDE 3

Illumina 450K HumanMethylation array

  • genotyping of bisulfite-converted genomic DNA
  • 480K CpG sites
  • 99% of RefSeq genes, CpG island, shores and shelves
  • bisulfite conversion, amplification, hybridization, extending,

staining and scanning

  • several control probes to monitor different aspects of the

protocol and quality of the DNA

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MethylAid: Visual and Interactive quality control of large Illumina 450k data sets

  • wateRmelon, minfi, methylumi, lumi, COHCAP, ChAMP,

shinyMethyl, · · ·

  • detect bad quality samples/runs using predefined thresholds
  • fast and efficient: using BiocParallel and an option for reading

data in batches

  • interactive graphics: using shiny
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SLIDE 5

Using MethylAid

library(minfiData) baseDir <- system.file("extdata", package = "minfiData") targets <- read.450k.sheet(baseDir) library(MethylAid) sdata <- summarize(targets) visualize(sdata) ##this will launch the web application

summarizing in parallel using BiocParallel and the bathSize-option

library(BiocParallel) conffile <- system.file("scripts/config.R", package="MethylAid") BPPARAM <- BatchJobsParam(workers = 10, progressbar = FALSE, conffile = conffile) summarize(targets, batchSize = 50, BPPARAM = BPPARAM)

demo: http://shiny.bioexp.nl/MethylAid

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SLIDE 6

Further information

  • vignette shows how to use data from TCGA and from GEO
  • and gives more details on the parallel summarization
  • application note1
  • a larger demo (approx. 6000 samples) running at:

http://shiny.bioexp.nl/BIOS

1van Iterson, M., Tobi, E., Slieker, R., den Hollander, W., Luijk, R.,

Slagboom, P., and Heijmans, B. (2014). Methylaid: Visual and interactive quality control of large illumina 450k data sets. Bioinformatics