Literature DB >> 15208188

Numerical deconvolution of cDNA microarray signal: simulation study.

Simon Rosenfeld1, Thomas Wang, Young Kim, John Milner.   

Abstract

A computational model for simulation of the cDNA microarray experiments has been created. The simulation allows one to foresee the statistical properties of replicated experiments without actually performing them. We introduce a new concept, the so-called bio-weight, which allows for reconciliation between conflicting meanings of biological and statistical significance in microarray experiments. It is shown that, for a small sample size, the bio-weight is a more powerful criterion of the presence of a signal in microarray data as compared to the standard approach based on t test. Joint simulation of microarray and quantitative PCR data shows that the genes recovered by using the bio-weight have better chances to be confirmed by PCR than those obtained by the t test technique. We also employ extreme value considerations to derive plausible cutoff levels for hypothesis testing.

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Year:  2004        PMID: 15208188     DOI: 10.1196/annals.1310.012

Source DB:  PubMed          Journal:  Ann N Y Acad Sci        ISSN: 0077-8923            Impact factor:   5.691


  4 in total

1.  Polyribosome and ribonucleoprotein complex redistribution of mRNA induced by GnRH involves both EIF2AK3 and MAPK signaling.

Authors:  Minh-Ha T Do; Taeshin Kim; Feng He; Hiral Dave; Rachel E Intriago; Uriah A Astorga; Sonia Jain; Mark A Lawson
Journal:  Mol Cell Endocrinol       Date:  2013-10-23       Impact factor: 4.102

2.  Global developmental gene expression and pathway analysis of normal brain development and mouse models of human neuronal migration defects.

Authors:  Tiziano Pramparo; Ondrej Libiger; Sonia Jain; Hong Li; Yong Ha Youn; Shinji Hirotsune; Nicholas J Schork; Anthony Wynshaw-Boris
Journal:  PLoS Genet       Date:  2011-03-10       Impact factor: 5.917

3.  Adolescent mouse takes on an active transcriptomic expression during postnatal cerebral development.

Authors:  Wei Xu; Chengqi Xin; Qiang Lin; Feng Ding; Wei Gong; Yuanyuan Zhou; Jun Yu; Peng Cui; Songnian Hu
Journal:  Genomics Proteomics Bioinformatics       Date:  2014-06-18       Impact factor: 7.691

4.  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

  4 in total

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