===================== INSTRUCTIONS ==============================

This document contains instructions for installing and using the DIME
package for R.


1. Install

For Linux users, download the compiled R package file:
DIME_1.0.tar.gz
For Windows users, download the compiled R package fike:
DIME_1.0.zip

Then use the following function in R to install the package

>install.packages(pkgs=filename,repos=NULL)
where "filename" should be replaced with the corresponding file name
(DIME_1.0.tar.gz) with the correct path.

Alternatively, at the Linux command line, you may type 
"R CMD INSTALL PACKAGE_DIRECTORY/DIME_1.0.tar.gz" 
where PACKAGE_DIRECTORY is folder containing DIME_1.0.tar.gz (with the correct
page).


2. Usage

Use
>library(DIME)
to load the packages into R.

Use
>?DIME
to read the details about the functions and examples.

3. Reference

Taslim, C., Huang, T., and Lin, S. (2011). 
DIME: R-package for Identifying Differential ChIP-seq
Based on an Ensemble of Mixture Models.
Bioinformatics,  27, 1569-1570.


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.


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If you have any questions, please email Cenny Taslim at
Cenny.Taslim@osumc.edu or Shili Lin at shili@stat.osu.edu