Replication data for: Diverse Correlation Structures in Microarray Gene Expression Data (doi:10.7910/DVN/Z6UE8D)

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Replication data for: Diverse Correlation Structures in Microarray Gene Expression Data

doi:10.7910/DVN/Z6UE8D

Harvard Dataverse

2008-01-04

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Lev Klebanov; Andrei Yakovlev, 2008, "Replication data for: Diverse Correlation Structures in Microarray Gene Expression Data", https://doi.org/10.7910/DVN/Z6UE8D, Harvard Dataverse, V1

Replication data for: Diverse Correlation Structures in Microarray Gene Expression Data

doi:10.7910/DVN/Z6UE8D

Lev Klebanov (Department of Probability and Statistics, Charles University, Sokolovska)

Andrei Yakovlev (Department of Biostatistics and Computational Biology, University of Rochester)

Harvard Dataverse

Institute for Mathematical Statistics

2007-10-01

https://doi.org/10.7910/DVN/Z6UE8D

correlation structure, gene expression, microarrays

It is well-known that correlations in microarray data represent a serious nuisance deteriorating the performance of gene selection procedures. This paper is intended to demonstrate that the correlation structure of microarray data provides a rich source of useful information. We discuss distinct correlation substructures revealed in microarray gene expression data by an appropriate ordering of genes. These substructures include stochastic proportionality of expression signals in a large percentage of all gene pairs, negative correlations hidden in ordered gene triples, and a long sequence of weakly dependent random variables associated with ordered pairs of genes. The reported striking regularities are of general biological interest and they also have far-reaching implications for theory and practice of statistical methods of microarray data analysis. We illustrate the latter point with a method for testing differential expression of non-overlapping gene pairs. While designed for testing a different null hypothesis, this method provides an order of magnitude more accurate control of type 1 error rate compared to conventional methods of individual gene expre ssion profiling. In addition, this method is robust to the technical noise. Quantitative inference of the correlation structure has the potential to extend the analysis of microarray data far beyond currently practiced methods.

Subject: STANDARD DEPOSIT TERMS 1.0 Type: DATAPASS:TERMS:STANDARD:1.0 Notes: This study was deposited under the of the Data-PASS standard deposit terms. A copy of the usage agreement is included in the file section of this study.;

<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a>

Lev Klebanov, and Andrei Yakovlev. Forthcoming. "Diverse Correlation Structures in Microarray Gene Expression Data." Ann. Appl. Statist.

Lev Klebanov, and Andrei Yakovlev. Forthcoming. "Diverse Correlation Structures in Microarray Gene Expression Data." Ann. Appl. Statist.

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AOAS0703-010R2A0.pdf

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Original article for this study

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supplement1.pdf

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Effects of cross-hybridization and their relevance to correlation analysis of microarray data

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supplement2.pdf

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The figures presented are counterparts of Figure 2 in the main text; they refer to other sets of microarray data.

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supplement3.pdf

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Supporting material for Remark 3

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supplement4.pdf

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Distribution of the effect sizes specified for 350 elements of the sequence

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supplement5.pdf

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Outcomes of testing in the course of subsampling

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