MethLAB software for analysis of DNA methylation microarray data

MethLAB is a GUI package for analysis of DNA methylation microarray data

Version 2.0 may be downloaded below.

Download Windows version (.zip file)

Download Mac version (.tar.gz file)

For instructions on installing and running MethLAB, please see the tutorial:

Download tutorial

New users may wish to initially test MethLAB on the example input datasets:

 Download example files

Please report errors or suggestions to Varun Kilaru at vkilaru@emory.edu.

More experienced R users may also be interested in our non-GUI package CpGassoc, which has similar capabilities and can also perform permutation tests and basic QC.

If you have any issues using MethLAB, please contact us either via email (email: vkilaru@emory.edu) or via Skype(id: methlab_software).  To receive emails about future version releases, please email vkilaru@emory.edu to be added to our mailing list.

For information on installing binary R packages, see http://cran.r-project.org/doc/manuals/R-admin.html#Installing-packages.  To download R, visit http://cran.r-project.org/.  For large datasets (e.g. Illumina 450K data), we recommend running 64-bit R to avoid running into R's memory limits.

Thank you for your interest in MethLAB!  To reference MethLAB in a publication, please cite:

Varun Kilaru, Richard T. Barfield, James W. Schroeder, Alicia K. Smith, Karen N. Conneely.  MethLAB: A GUI package for the analysis of array-based DNA methylation data (2012).  Epigenetics, 7(3):225-9.

  • Version 0.1: Initial release.
  • Version 0.2: Improved Manhattan plots and subsetting of CpG sites.
  • Version 0.3: Added option for global methylation analysis.
  • Version 1.0: Improved handling of large datasets, improved Manhattan plots
  • Version 1.1: Improved box plot labels, added workarounds for phenotypes with extremely low variance and methylation datasets containing values <0 or >1"
  • Version 1.5: Improved Manhattan plot colors, added option to subset by gene, made fixes specific to 450k datasets.
  • Version 2.0: Compatible with R-3.x.x, as well as older versions. Also has several performance improvements and bug fixes related to file loading.