Literature DB >> 17099941

Some comments on instability of false discovery rate estimation.

Xing Qiu1, Andrei Yakovlev.   

Abstract

Some extended false discovery rate (FDR) controlling multiple testing procedures rely heavily on empirical estimates of the FDR constructed from gene expression data. Such estimates are also used as performance indicators when comparing different methods for microarray data analysis. The present communication shows that the variance of the proposed estimators may be intolerably high, the correlation structure of microarray data being the main cause of their instability.

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Year:  2006        PMID: 17099941     DOI: 10.1142/s0219720006002338

Source DB:  PubMed          Journal:  J Bioinform Comput Biol        ISSN: 0219-7200            Impact factor:   1.122


  14 in total

1.  Comments on the analysis of unbalanced microarray data.

Authors:  Kathleen F Kerr
Journal:  Bioinformatics       Date:  2009-06-15       Impact factor: 6.937

2.  A new gene selection procedure based on the covariance distance.

Authors:  Rui Hu; Xing Qiu; Galina Glazko
Journal:  Bioinformatics       Date:  2009-12-08       Impact factor: 6.937

3.  The effect of correlation in false discovery rate estimation.

Authors:  Armin Schwartzman; Xihong Lin
Journal:  Biometrika       Date:  2011-03       Impact factor: 2.445

4.  HYPOTHESIS SETTING AND ORDER STATISTIC FOR ROBUST GENOMIC META-ANALYSIS.

Authors:  Chi Song; George C Tseng
Journal:  Ann Appl Stat       Date:  2014       Impact factor: 2.083

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.  Balancing Type One and Two Errors in Multiple Testing for Differential Expression of Genes.

Authors:  Alexander Gordon; Linlin Chen; Galina Glazko; Andrei Yakovlev
Journal:  Comput Stat Data Anal       Date:  2009-03-15       Impact factor: 1.681

7.  Sources of variation in false discovery rate estimation include sample size, correlation, and inherent differences between groups.

Authors:  Jiexin Zhang; Kevin R Coombes
Journal:  BMC Bioinformatics       Date:  2012-08-24       Impact factor: 3.169

8.  Capturing heterogeneity in gene expression studies by surrogate variable analysis.

Authors:  Jeffrey T Leek; John D Storey
Journal:  PLoS Genet       Date:  2007-08-01       Impact factor: 5.917

9.  Detecting intergene correlation changes in microarray analysis: a new approach to gene selection.

Authors:  Rui Hu; Xing Qiu; Galina Glazko; Lev Klebanov; Andrei Yakovlev
Journal:  BMC Bioinformatics       Date:  2009-01-15       Impact factor: 3.169

10.  Is there an alternative to increasing the sample size in microarray studies?

Authors:  Lev Klebanov; Andrei Yakovlev
Journal:  Bioinformation       Date:  2007-04-10
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