Search Results - subject:"microarray data"

Empirical Bayes analysis of unreplicated microarray data

Cho, HyungJun; Kang, Jaewoo; Lee, Jae - In: Computational Statistics 24 (2009) 3, pp. 393-408

Persistent link: https://www.econbiz.de/10005029238

Hierarchical Bayesian meta-analysis models for cross-platform microarray studies

Conlon, E. M.; Postier, B. L.; Methe, B. A.; Nevin, K. P.; … - In: Journal of Applied Statistics 36 (2009) 10, pp. 1067-1085

The development of new technologies to measure gene expression has been calling for statistical methods to integrate findings across multiple-platform studies. A common goal of microarray analysis is to identify genes with differential expression between two conditions, such as treatment versus...

Persistent link: https://www.econbiz.de/10008466709

Biclustering of Gene Expression Data by an Extension of Mixtures of Factor Analyzers

Martella, Francesca; Alfò, Marco; Vichi, Maurizio - In: International Journal of Biostatistics 4 (2008) 1, pp. 1078-1078

A challenge in microarray data analysis concerns discovering local structures composed by sets of genes that show …

Persistent link: https://www.econbiz.de/10005246595

Classifying Gene Expression Profiles from Pairwise mRNA Comparisons

Geman, Donald; d'Avignon, Christian; Naiman, Daniel; … - In: Statistical Applications in Genetics and Molecular Biology 3 (2007) 1, pp. 19-19

-scoring pair(s) (TSP) classifier, is motivated by current technical and practical limitations in using gene expression microarray … data for class prediction, for example to detect disease, identify tumors or predict treatment response. Accurate …

Persistent link: https://www.econbiz.de/10005459174

Approximate Sample Size Calculations with Microarray Data: An Illustration

Ferreira, José; Zwinderman, Aeilko - In: Statistical Applications in Genetics and Molecular Biology 5 (2007) 1, pp. 25-25

We outline a method of sample size calculation in microarray experiments on the basis of pilot data and illustrate its practical application with both simulated and real data. The method was shown to be consistent (as the number of `probed genes' tends to infinity) under general conditions in an...

Persistent link: https://www.econbiz.de/10005046608

Reader's Reaction to "Dimension Reduction for Classification with Gene Expression Microarray Data" by Dai et al (2006)

Boulesteix, Anne-Laure - In: Statistical Applications in Genetics and Molecular Biology 5 (2007) 1, pp. 16-16

This note is a comment on the article "Dimension Reduction for Classification with Gene Expression Microarray Data …

Persistent link: https://www.econbiz.de/10005585066

Hierarchical Inverse Gaussian Models and Multiple Testing: Application to Gene Expression Data

Labbe, Aurelie; Thompson, Mary - In: Statistical Applications in Genetics and Molecular Biology 4 (2007) 1, pp. 23-23

intensities. However, the shape of microarray data may vary substantially from one experiment to another, and model assumptions … may be seriously violated in many cases. The literature on microarray data is mainly based on two distributions: the log …

Persistent link: https://www.econbiz.de/10005585086

Normalization of Dye Bias in Microarray Data Using the Mixture of Splines Model

Joo, Yongsung; Casella, George; Booth, James; Lee, Keunbaik - In: Statistical Applications in Genetics and Molecular Biology 6 (2007) 1, pp. 2-2

This paper discusses characteristics of dye biases in microarray data that the conventional normalization methods do …

Persistent link: https://www.econbiz.de/10005752556

Approximate Power and Sample Size Calculations with the Benjamini-Hochberg Method

Ferreira, José; Zwinderman, Aeilko - In: International Journal of Biostatistics 2 (2006) 1, pp. 1018-1018

We provide a method for calculating the sample size required to attain a given average power (the ratio of rejected hypotheses to the number of false hypotheses) and a given false discovery rate (the number of incorrect rejections divided by the number of rejections) in adaptive versions of the...

Persistent link: https://www.econbiz.de/10005246577

A recursive network approach can identify constitutive regulatory circuits in gene expression data

Blasi, Monica Francesca; Casorelli, Ida; Colosimo, Alfredo - In: Physica A: Statistical Mechanics and its Applications 348 (2005) C, pp. 349-370

The activity of the cell is often coordinated by the organisation of proteins into regulatory circuits that share a common function. Genome-wide expression profiles might contain important information on these circuits. Current approaches for the analysis of gene expression data include...

Persistent link: https://www.econbiz.de/10010588977