Literature DB >> 20563291

Are a set of microarrays independent of each other?

Bradley Efron1.   

Abstract

Having observed an m x n matrix X whose rows are possibly correlated, we wish to test the hypothesis that the columns are independent of each other. Our motivation comes from microarray studies, where the rows of X record expression levels for m different genes, often highly correlated, while the columns represent n individual microarrays, presumably obtained independently. The presumption of independence underlies all the familiar permutation, cross-validation, and bootstrap methods for microarray analysis, so it is important to know when independence fails. We develop nonparametric and normal-theory testing methods. The row and column correlations of X interact with each other in a way that complicates test procedures, essentially by reducing the accuracy of the relevant estimators.

Entities:  

Year:  2009        PMID: 20563291      PMCID: PMC2887702          DOI: 10.1214/09-AOAS236

Source DB:  PubMed          Journal:  Ann Appl Stat        ISSN: 1932-6157            Impact factor:   2.083


  6 in total

1.  Significance analysis of microarrays applied to the ionizing radiation response.

Authors:  V G Tusher; R Tibshirani; G Chu
Journal:  Proc Natl Acad Sci U S A       Date:  2001-04-17       Impact factor: 11.205

2.  A comparison of normalization methods for high density oligonucleotide array data based on variance and bias.

Authors:  B M Bolstad; R A Irizarry; M Astrand; T P Speed
Journal:  Bioinformatics       Date:  2003-01-22       Impact factor: 6.937

3.  Correlation between gene expression levels and limitations of the empirical bayes methodology for finding differentially expressed genes.

Authors:  Xing Qiu; Lev Klebanov; Andrei Yakovlev
Journal:  Stat Appl Genet Mol Biol       Date:  2005-11-22

4.  Microarray expression profiling identifies genes with altered expression in HDL-deficient mice.

Authors:  M J Callow; S Dudoit; E L Gong; T P Speed; E M Rubin
Journal:  Genome Res       Date:  2000-12       Impact factor: 9.043

5.  Gene expression correlates of clinical prostate cancer behavior.

Authors:  Dinesh Singh; Phillip G Febbo; Kenneth Ross; Donald G Jackson; Judith Manola; Christine Ladd; Pablo Tamayo; Andrew A Renshaw; Anthony V D'Amico; Jerome P Richie; Eric S Lander; Massimo Loda; Philip W Kantoff; Todd R Golub; William R Sellers
Journal:  Cancer Cell       Date:  2002-03       Impact factor: 31.743

6.  The effects of normalization on the correlation structure of microarray data.

Authors:  Xing Qiu; Andrew I Brooks; Lev Klebanov; Ndrei Yakovlev
Journal:  BMC Bioinformatics       Date:  2005-05-16       Impact factor: 3.169

  6 in total
  8 in total

1.  Model Selection and Estimation in the Matrix Normal Graphical Model.

Authors:  Jianxin Yin; Hongzhe Li
Journal:  J Multivar Anal       Date:  2012-05-01       Impact factor: 1.473

2.  Correlated z-values and the accuracy of large-scale statistical estimates.

Authors:  Bradley Efron
Journal:  J Am Stat Assoc       Date:  2010-09-01       Impact factor: 5.033

3.  TRANSPOSABLE REGULARIZED COVARIANCE MODELS WITH AN APPLICATION TO MISSING DATA IMPUTATION.

Authors:  Genevera I Allen; Robert Tibshirani
Journal:  Ann Appl Stat       Date:  2010-06       Impact factor: 2.083

4.  Estimating a structured covariance matrix from multi-lab measurements in high-throughput biology.

Authors:  Alexander M Franks; Gábor Csárdi; D Allan Drummond; Edoardo M Airoldi
Journal:  J Am Stat Assoc       Date:  2015-03-01       Impact factor: 5.033

5.  Inference with Transposable Data: Modeling the Effects of Row and Column Correlations.

Authors:  Genevera I Allen; Robert Tibshirani
Journal:  J R Stat Soc Series B Stat Methodol       Date:  2012-03-16       Impact factor: 4.488

6.  On the choice and number of microarrays for transcriptional regulatory network inference.

Authors:  Elissa J Cosgrove; Timothy S Gardner; Eric D Kolaczyk
Journal:  BMC Bioinformatics       Date:  2010-09-09       Impact factor: 3.169

7.  Probabilistic analysis of gene expression measurements from heterogeneous tissues.

Authors:  Timo Erkkilä; Saara Lehmusvaara; Pekka Ruusuvuori; Tapio Visakorpi; Ilya Shmulevich; Harri Lähdesmäki
Journal:  Bioinformatics       Date:  2010-07-14       Impact factor: 6.937

8.  Pathway analysis of expression data: deciphering functional building blocks of complex diseases.

Authors:  Frank Emmert-Streib; Galina V Glazko
Journal:  PLoS Comput Biol       Date:  2011-05-26       Impact factor: 4.475

  8 in total

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