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Rotation Testing in Gene Set Enrichment Analysis for Small Direct Comparison Experiments

Dørum, Guro; Snipen, Lars; Solheim, Margrete; Sæbø, Solve - In: Statistical Applications in Genetics and Molecular Biology 8 (2009) 1, pp. 34-34

Gene Set Enrichment Analysis (GSEA) is a method for analysing gene expression data with a focus on a priori defined gene sets. The permutation test generally used in GSEA for testing the significance of gene set enrichment involves permutation of a phenotype vector and is developed for data from...

Persistent link: https://www.econbiz.de/10005046584

Normalization Method for Transcriptional Studies of Heterogeneous Samples Ã¢ÂÂ Simultaneous Array Normalization and Identification of Equivalent Expression

Qin, Li-Xuan; Satagopan, Jaya - In: Statistical Applications in Genetics and Molecular Biology 8 (2009) 1, pp. 10-10

Normalization is an important step in the analysis of microarray data of transcription profiles as systematic non-biological variations often arise from the multiple steps involved in any transcription profiling experiment. Existing methods for data normalization often assume that there are few...

Persistent link: https://www.econbiz.de/10005046613

Early Diagnostic Marker Panel Determination for Microarray Based Clinical Studies

Jaeger, Jochen; Weichenhan, Dieter; Ivandic, Boris; … - In: Statistical Applications in Genetics and Molecular Biology 4 (2007) 1, pp. 9-9

determination (EMPD). In Phase-1, genome-wide microarrays are used only for a small number of individual patient samples. From this … the use of expensive whole genome microarrays, thus making EMPD a cost efficient alternative for current trials. The … expected performance loss of EMPD is compared to designs which use genome-wide microarrays for all patients. We also examine …

Persistent link: https://www.econbiz.de/10005046621

A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics

Juliane SchÃÂ¤fer; Strimmer, Korbinian - In: Statistical Applications in Genetics and Molecular Biology 4 (2007) 1, pp. 32-32

Inferring large-scale covariance matrices from sparse genomic data is an ubiquitous problem in bioinformatics. Clearly, the widely used standard covariance and correlation estimators are ill-suited for this purpose. As statistically efficient and computationally fast alternative we propose a...

Persistent link: https://www.econbiz.de/10005046623

Error-Rate and Decision-Theoretic Methods of Multiple Testing: Which Genes Have High Objective Probabilities of Differential Expression?

Bickel, David - In: Statistical Applications in Genetics and Molecular Biology 3 (2009) 1, pp. 8-8

. Decision theory can also inform the selection of false discovery rate weights. An application to gene expression microarrays is …

Persistent link: https://www.econbiz.de/10005046625

PLS Dimension Reduction for Classification with Microarray Data

Boulesteix, Anne-Laure - In: Statistical Applications in Genetics and Molecular Biology 3 (2009) 1, pp. 33-33

Partial Least Squares (PLS) dimension reduction is known to give good prediction accuracy in the context of classification with high-dimensional microarray data. In this paper, the classification procedure consisting of PLS dimension reduction and linear discriminant analysis on the new...

Persistent link: https://www.econbiz.de/10005046627

Covariate adjustment in the analysis of microarray data from clinical studies

Ghosh, Debashis; Chinnaiyan, Arul - Berkeley Electronic Press - 2004

There is tremendous scientific interest in the analysis of gene expression data in clinical settings, such as oncology. In this paper, we describe the importance of adjusting for confounders and other prognostic factors in order to select for differentially expressed genes for followup...

Persistent link: https://www.econbiz.de/10005458805

Semiparametric methods for identification of tumor progression genes from microarray data

Ghosh, Debashis; Chinnaiyan, Arul - Berkeley Electronic Press - 2004

The use of microarray data has become quite commonplace in medical and scientific experiments. We focus here on microarray data generated from cancer studies. It is potentially important for the discovery of biomarkers to identify genes whose expression levels correlate with tumor progression....

Persistent link: https://www.econbiz.de/10005458806

The false discovery rate: a variable selection perspective

Ghosh, Debashis; Chen, Wei; Raghuanthan, Trivellore - Berkeley Electronic Press - 2004

In many scientific and medical settings, large-scale experiments are generating large quantities of data that lead to inferential problems involving multiple hypotheses. This has led to recent tremendous interest in statistical methods regarding the false discovery rate (FDR). Several authors...

Persistent link: https://www.econbiz.de/10005458807

Error Distribution for Gene Expression Data

Purdom, Elizabeth; Holmes, Susan - In: Statistical Applications in Genetics and Molecular Biology 4 (2007) 1, pp. 16-16

We present a new instance of Laplace's second Law of Errors and show how it can be used in the analysis of data from microarray experiments. This error distribution is shown to fit microarray expression data much better than a normal distribution. The use of this distribution in a parametric...

Persistent link: https://www.econbiz.de/10005459171