Search Results - subject:"Microarray"

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

Detecting differentially expressed genes in microarray experiments is a topic that has been well studied in the … 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

Inference on the Limiting False Discovery Rate and the P-value Threshold Parameter Assuming Weak Dependence between Gene Expression Levels within Subject

Heller, Glenn; Qin, Jing - In: Statistical Applications in Genetics and Molecular Biology 6 (2007) 1, pp. 14-14

An objective of microarray data analysis is to identify gene expressions that are associated with a disease related …

Persistent link: https://www.econbiz.de/10005585087

Calculating the Statistical Significance of Changes in Pathway Activity From Gene Expression Data

JÃÂ¶rg RahnenfÃÂ¼hrer; Domingues, Francisco; … - In: Statistical Applications in Genetics and Molecular Biology 3 (2007) 1, pp. 16-16

findings from microarray experiments.Recently introduced methods test if members of predefined gene sets are enriched in a list … of top-ranked genes in a microarray study. We improve this approach by defining scores that depend on all members of the …

Persistent link: https://www.econbiz.de/10005585090

Fully Bayesian Mixture Model for Differential Gene Expression: Simulations and Model Checks

Lewin, Alex; Bochkina, Natalia; Richardson, Sylvia - In: Statistical Applications in Genetics and Molecular Biology 6 (2007) 1, pp. 36-36

We present a Bayesian hierarchical model for detecting differentially expressed genes using a mixture prior on the parameters representing differential effects. We formulate an easily interpretable 3-component mixture to classify genes as over-expressed, under-expressed and non-differentially...

Persistent link: https://www.econbiz.de/10005585098

On the Operational Characteristics of the Benjamini and Hochberg False Discovery Rate Procedure

Green, Gerwyn; Diggle, Peter - In: Statistical Applications in Genetics and Molecular Biology 6 (2007) 1, pp. 27-27

Multiple testing procedures are commonly used in gene expression studies for the detection of differential expression, where typically thousands of genes are measured over at least two experimental conditions. Given the need for powerful testing procedures, and the attendant danger of false...

Persistent link: https://www.econbiz.de/10005585100

Treating Expression Levels of Different Genes as a Sample in Microarray Data Analysis: Is it Worth a Risk?

Klebanov, Lev; Yakovlev, Andrei - In: Statistical Applications in Genetics and Molecular Biology 5 (2007) 1, pp. 9-9

One of the prevailing ideas in the literature on microarray data analysis is to pool the expression measures across … dependence represents a very serious pitfall in microarray data analysis. …

Persistent link: https://www.econbiz.de/10005585102

Statistical Significance Threshold Criteria For Analysis of Microarray Gene Expression Data

Cheng, Cheng; Pounds, Stanley; Boyett, James; Pei, Deqing; … - In: Statistical Applications in Genetics and Molecular Biology 3 (2007) 1, pp. 36-36

The methodological advancement in microarray data analysis on the basis of false discovery rate (FDR) control, such as …

Persistent link: https://www.econbiz.de/10005752555

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

Correlation Between Gene Expression Levels and Limitations of the Empirical Bayes Methodology for Finding Differentially Expressed Genes

Qiu, Xing; Klebanov, Lev; Yakovlev, Andrei - In: Statistical Applications in Genetics and Molecular Biology 4 (2007) 1, pp. 34-34

Stochastic dependence between gene expression levels in microarray data is of critical importance for the methods of …

Persistent link: https://www.econbiz.de/10005752557

Evaluation of Multiple Models to Distinguish Closely Related Forms of Disease Using DNA Microarray Data: an Application to Multiple Myeloma

Hardin, Johanna; Waddell, Michael; Page, C. David; … - In: Statistical Applications in Genetics and Molecular Biology 3 (2007) 1, pp. 10-10

themselves in altered gene expression patterns in the clonally related cancer cells. Microarray technology allows for qualitative … expression of 12,000 genes derived from a high density oligonucleotide microarray analysis of highly purified plasma cells from …

Persistent link: https://www.econbiz.de/10005752563