Literature DB >> 12925512

The efficiency of pooling mRNA in microarray experiments.

C M Kendziorski1, Y Zhang, H Lan, A D Attie.   

Abstract

In a microarray experiment, messenger RNA samples are oftentimes pooled across subjects out of necessity, or in an effort to reduce the effect of biological variation. A basic problem in such experiments is to estimate the nominal expression levels of a large number of genes. Pooling samples will affect expression estimation, but the exact effects are not yet known as the approach has not been systematically studied in this context. We consider how mRNA pooling affects expression estimates by assessing the finite-sample performance of different estimators for designs with and without pooling. Conditions under which it is advantageous to pool mRNA are defined; and general properties of estimates from both pooled and non-pooled designs are derived under these conditions. A formula is given for the total number of subjects and arrays required in a pooled experiment to obtain gene expression estimates and confidence intervals comparable to those obtained from the no-pooling case. The formula demonstrates that by pooling a perhaps increased number of subjects, one can decrease the number of arrays required in an experiment without a loss of precision. The assumptions that facilitate derivation of this formula are considered using data from a quantitative real-time PCR experiment. The calculations are not specific to one particular method of quantifying gene expression as they assume only that a single, normalized, estimate of expression is obtained for each gene. As such, the results should be generally applicable to a number of technologies provided sufficient pre-processing and normalization methods are available and applied.

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Year:  2003        PMID: 12925512     DOI: 10.1093/biostatistics/4.3.465

Source DB:  PubMed          Journal:  Biostatistics        ISSN: 1465-4644            Impact factor:   5.899


  69 in total

1.  Transcriptomic "portraits" of canine mammary cancer cell lines with various phenotypes.

Authors:  M Król; K M Pawłowski; J Skierski; P Turowski; A Majewska; J Polańska; M Ugorski; R E Morty; T Motyl
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Review 2.  Statistical issues in the design and analysis of gene expression microarray studies of animal models.

Authors:  Lisa M McShane; Joanna H Shih; Aleksandra M Michalowska
Journal:  J Mammary Gland Biol Neoplasia       Date:  2003-07       Impact factor: 2.673

Review 3.  Physiological insights gained from gene expression analysis in obesity and diabetes.

Authors:  Mark P Keller; Alan D Attie
Journal:  Annu Rev Nutr       Date:  2010-08-21       Impact factor: 11.848

4.  DNA methylation changes in plasticity genes accompany the formation and maintenance of memory.

Authors:  Rashi Halder; Magali Hennion; Ramon O Vidal; Orr Shomroni; Raza-Ur Rahman; Ashish Rajput; Tonatiuh Pena Centeno; Frauke van Bebber; Vincenzo Capece; Julio C Garcia Vizcaino; Anna-Lena Schuetz; Susanne Burkhardt; Eva Benito; Magdalena Navarro Sala; Sanaz Bahari Javan; Christian Haass; Bettina Schmid; Andre Fischer; Stefan Bonn
Journal:  Nat Neurosci       Date:  2015-12-14       Impact factor: 24.884

5.  On the utility of pooling biological samples in microarray experiments.

Authors:  C Kendziorski; R A Irizarry; K-S Chen; J D Haag; M N Gould
Journal:  Proc Natl Acad Sci U S A       Date:  2005-03-08       Impact factor: 11.205

6.  Assessment of a systematic expression profiling approach in ENU-induced mouse mutant lines.

Authors:  Matthias Seltmann; Marion Horsch; Alexei Drobyshev; Yali Chen; Martin Hrabé de Angelis; Johannes Beckers
Journal:  Mamm Genome       Date:  2005-01       Impact factor: 2.957

7.  Microarray methods in Drosophila neurobiology.

Authors:  Christopher J Mee
Journal:  Invert Neurosci       Date:  2005-10-24

Review 8.  A review of statistical methods for expression quantitative trait loci mapping.

Authors:  Christina Kendziorski; Ping Wang
Journal:  Mamm Genome       Date:  2006-06-12       Impact factor: 2.957

9.  A discussion of statistical methods for design and analysis of microarray experiments for plant scientists.

Authors:  Dan Nettleton
Journal:  Plant Cell       Date:  2006-09       Impact factor: 11.277

10.  A multiple-loop, double-cube microarray design applied to prostate cancer cell lines with variable sensitivity to histone deacetylase inhibitors.

Authors:  Madeleine S Q Kortenhorst; Marianna Zahurak; Shabana Shabbeer; Sushant Kachhap; Nathan Galloway; Giovanni Parmigiani; Henk M W Verheul; Michael A Carducci
Journal:  Clin Cancer Res       Date:  2008-11-01       Impact factor: 12.531

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