Software (For running on Linux; in a zipped, tar file; compiled with R 2.15)
PURPOSE:
R package that considers an ensemble of finite mixture models combined
with a local false discovery rate (fdr) for analyzing ChIP-seq data comparing
two samples. This package can also be used to identify differential in other
high throughput data such as microarray and DNA methylation.
The package may be downloaded from the links below or from http://cran.r-project.org/web/packages/DIME/.
Software (For running on Linux; in a zipped, tar file; compiled with R 2.15)
Software (For running on Mac; in a gzip-compressed archive)
Software (For running on Windows; in a zipped)
Instructions
for installing DIME.
Manual
for running DIME.
Sample data Pol II Chip-seq data (R object)
This package is primarily authored by Cenny Taslim, with contributions from
Dustin Potter, Abbas Khalili, and Shili Lin. Integrative Cancer Biology
Program (ICBP) trainees Yue (Phoenix) Xu and Leslie Watkins also contributed
to the testing of the software.
References:
Khalili, A., Huang, T., and Lin, S. (2009). A robust unified approach to analyzing methylation and gene expression data. Computational Statistics and Data Analysis, 53(5), 1701-1710.
Taslim, C., Wu, J., Yan, P., Singer, G., Parvin, J., Huang, T., Lin, S., and Huang, K. (2009). Comparative study on chip-seq data: normalization and binding pattern characterization. Bioinformatics, 25(18), 2334-2340.
Dean, N. and Raftery, A. E. (2005). Normal uniform mixture differential gene expression detection for cDNA microarrays. BMC Bioinformatics, 6, 173.
please contact Cenny Taslim at Cenny.Taslim@osumc.edu or Shili Lin at
shili@stat.osu.edu for questions.