Multivariate search for differentially expressed gene combinationsYuanhui Xiao
Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Rochester, New York 14642, USA
BMC Bioinformatics 5:164. 2004
..Such tests are essentially univariate and disregard the multidimensional structure of microarray data. A more general two-sample hypothesis is formulated in terms of the joint distribution of any sub-vector of expression signals...
Testing differential expression in nonoverlapping gene pairs: a new perspective for the empirical Bayes methodLev Klebanov
Department of Probability and Statistics, Charles University, Sokolovska 83, Praha 8, CZ 18675, Czech Republic
J Bioinform Comput Biol 6:301-16. 2008
..The new paradigm arising from the existence of the delta-sequence in biological data offers considerable scope for future developments in this area of methodological research...
- Revisiting adverse effects of cross-hybridization in Affymetrix gene expression data: do they matter for correlation analysis?
Lev Klebanov
Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Rochester, Box 630, New York 14642, USA
Biol Direct 2:28. 2007
..The work by Okoniewski and Miller drove us to revisit the issue by means of experimentation with biological data and probabilistic modeling of cross-hybridization effects...
- A new type of stochastic dependence revealed in gene expression data
Lev Klebanov
Department of Probability and Statistics, Charles University
Stat Appl Genet Mol Biol 5:Article7. 2006
..The ability to identify genes that act as ;;modulators'' provides a potential strategy of prioritizing candidate genes...
- A nitty-gritty aspect of correlation and network inference from gene expression data
Lev B Klebanov
Department of Probability and Statistics, Charles University, Sokolovska 83, Praha 8, CZ 18675, Czech Republic
Biol Direct 3:35. 2008
....
- A multivariate extension of the gene set enrichment analysis
Lev Klebanov
Department of Probability and Statistics, Charles University, Sokolovska 83, Praha 8, CZ 18675, Czech Republic
J Bioinform Comput Biol 5:1139-53. 2007
..We also discuss some other aspects of the GSEA paradigm and suggest new avenues for future research...
- The effects of normalization on the correlation structure of microarray data
Xing Qiu
Department of Biostatistics and Computational Biology, University of Rochester, New York 14642, USA
BMC Bioinformatics 6:120. 2005
..A potential impact of between-gene correlations on the performance of such methods has yet to be explored...
- Detecting intergene correlation changes in microarray analysis: a new approach to gene selection
Rui Hu
Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Box 630, Rochester, New York 14642, USA
BMC Bioinformatics 10:20. 2009
..We intend to enrich the above procedure by proposing a nonparametric selection procedure that selects differentially correlated genes...
- Synergistic response to oncogenic mutations defines gene class critical to cancer phenotype
Helene R McMurray
Department of Biomedical Genetics, University of Rochester Medical Center, 601 Elmwood Avenue, Rochester, New York 14642, USA
Nature 453:1112-6. 2008
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Gene selection with the δ-sequence methodXing Qiu
Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, USA
Methods Mol Biol 972:57-71. 2013
..Furthermore, its outcomes are entirely free from the log-additive array-specific technical noise. The new paradigm offers considerable scope for future developments in this area of methodological research...
- Aggregation effect in microarray data analysis
Linlin Chen
School of Mathematical Sciences, Rochester Institute of Technology, Rochester, NY, USA
Methods Mol Biol 972:177-91. 2013
..A critical discussion of such pitfalls is long overdue. Here we discuss one feature of microarray data the investigators need to be aware of when embarking on a study of putative associations between elements of networks and pathways...
- Treating expression levels of different genes as a sample in microarray data analysis: is it worth a risk?
Lev Klebanov
Stat Appl Genet Mol Biol 5:Article9. 2006
..This dependence represents a very serious pitfall in microarray data analysis...
- Statistical methods and microarray data
Lev Klebanov
Nat Biotechnol 25:25-6; author reply 26-7. 2007
