ORIOGEN 4.01 - Order Restricted Inference for Ordered Gene Expression and multidimensional pairwise comparisons
ORIOGEN 4.01 Software Usage Agreement and Download
Shyamal D. Peddada
National Institute of Environmental Health Sciences
(Programmed by Mr. Shawn Harris, SRA, Inc. Durham, NC)
This is a JAVA based software package that can be used for the following purposes:
- Comparison of high dimensional data (e.g. gene expression) among two or more ordered experimental conditions, such as in dose-response studies or time-course experiments. This software can be used when the data are independent among experimental conditions or they are dependent as in repeated measurement designs. The underlying methodology is described in Peddada et al. (2003, 2005, 2010). Since the methodology is based on bootstrapping the residuals, this software may not be suitable if the sample size per group is very small (e.g. 3), especially when the data between groups are correlated. For computational efficiency, this methodology uses adaptive bootstrap as described in Guo and Peddada (2008). 2. This methodology attempts to control the false discovery rate (FDR) at the desired nominal level while being computationally efficient.
- Pairwise comparisons of high dimensional data (e.g. gene expression) among two or more experimental conditions. Pairs to be compared are chosen a priori by the user. This software can be used when the data are independent among experimental conditions. The methodology not only controls for the overall false discovery rate for making all desired pairwise comparisons, but also controls for the error committed in the direction of inequality between groups for each differentially expressed variable (e.g. gene). 4. Thus the methodology controls the mixed directional FDR (mdFDR) at the desired nominal level provided by the user. This software is based on the methodology described in Guo, Sarkar and Peddada (2010).
Guo, W., and Peddada, SD. (2008). Adaptive Choice of the Number of Bootstrap Samples in Large Scale Multiple Testing. Statistical Applications in Genetics and Molecular Biology, 7 (1), Art. 13.
Guo W, Sarkar SK, Peddada, SD (2010). Controlling False Discoveries in Multidimensional Directional Decisions, with Applications to Gene Expression Data on Ordered Categories. Biometrics, 66, 485 - 492.
Peddada, S., Lobenhofer, E., Li, L., Afshari, C., Weinberg, C., and Umbach. D. M. (2003). Gene selection and clustering for time-course and dose-response microarray experiments using order restricted inference. Bioinformatics, 7, 834-841.
Peddada SD, Harris S, Zajd J and Harvey E. (2005). ORIOGEN: Order Restricted Inference for Ordered Gene Expression data. Bioinformatics, 21, 3933-3934.
Peddada SD, Harris SF, Davidov O (2010). Analysis of Correlated Gene Expression Data on Ordered Categories. J. Indian Society of Agricultural Statistics Indian Society of Agricultural Statistics. 64(1), 45-60.
Shyamal Peddada, Ph.D.
Former Chief, Biostatistics & Computational Biology Branch
7128 Parran Hall
130 DeSoto Street
Pittsburgh, PA 15261