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Software
- Meta-analysis for Differential Expression Analysis (MetaDE):
- Optimally weighted (OW) statistic:
A genomic meta-analysis method for biomarker detection that categorize differential expression in partial studies.
Jia Li and George C. Tseng. (2009) Optimally weighted statistic for combining multiple genomic studies. (submitted)
- Multi-class correlation (MCC) measure and ANOVA-maxP:
A genomic meta-analysis method for biomarker detection of concordant pattern in multi-classes.
Shuya Lu, Jia Li, Chi Song, Kui Shen and George C Tseng. (2009) Biomarker Detection in the Integration of Multiple Multi-class Genomic Studies. Bioinformatics. (in revision)
- Meta-analysis for Pathway Analysis (MetaPath):
- Meta-analysis for Pathway Enrichment (MAPE):
- Inter-study prediction in microarray studies:
- Ratio-adjusted gene-wise normalization (rGN): Download (R package)
Chunrong Cheng, Kui Shen, Chi Song, Jianhua Luo and George C Tseng. (2009) Ratio Adjustment and Calibration Scheme for Gene-wise Normalization to Enhance Microarray Inter-study Prediction. Bioinformatics. 25:1655-1661.
- Module-based preidction approach (MBP):
- Gene clustering in microarray data: Our group has developed two complementary gene clustering methods for microarray data (or for clustering high-dimensional complex data in general). Both methods directly identify small and tight clusters in the data and allow a set of scattered genes without being clustered. Tight clustering utilizes resampling techniques to obtain consistent tight clusters in repeated subsampling evaluations. Penalized and weighted K-means extends the target function of K-means. It has faster computation than tight clustering and can allow incorporation of prior biological information.
- Tight clustering: Download (ANSI C source code and R package)
George C. Tseng and Wing H. Wong. (2005) Tight Clustering: A Resampling-based Approach for Identifying Stable and Tight Patterns in Data. Biometrics.61:10-16.
- PWKmeans: Download (ANSI C source code)
George C. Tseng. (2007). Penalized and weighted K-means for clustering with scattered objects and prior information in high-throughput biological data. Bioinformatics. 23:2247-2255.
- Quantile maps: This is a visualization tool to compactly and unbiasedly demonstrate multiple (hundreds) distributions.
George C. Tseng. (2009) Visualization of multiple distributions with quantiles and Fisher information with application to tandem mass spectrometry data. Computational Statistics and Data Analysis.
- R functions for cDNA array analysis: a set of R functions for filtering, normalization, Bayesian hierarchical modelling and MCMC procedures in cDNA microarray analysis.
The method is developed to assess gene expression level with replicates in cDNA microarray data. A Bayesian hierarchical model is established to model gene-specific replicate variations with prior information from calibration experiments. A version of empirical Bayes procedure is used. MCMC simulation is then used to generate the posterior distribution.
This program provides a browser interface to implement methods described in the paper. The interface is written in JavaScript but runs in R at the background. The plug-in between JavaScript and R may no longer be maintained. In that case, users can still use the functions directly in R.
George C. Tseng, Min-Kyu Oh, Lars Rohlin, James C. Liao, and Wing Hung Wong. (2001) Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects. Nucleic Acids Research. 29: 2549-2557.
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