Literature DB >> 11389458

Computational analysis of microarray data.

J Quackenbush1.   

Abstract

Microarray experiments are providing unprecedented quantities of genome-wide data on gene-expression patterns. Although this technique has been enthusiastically developed and applied in many biological contexts, the management and analysis of the millions of data points that result from these experiments has received less attention. Sophisticated computational tools are available, but the methods that are used to analyse the data can have a profound influence on the interpretation of the results. A basic understanding of these computational tools is therefore required for optimal experimental design and meaningful data analysis.

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Year:  2001        PMID: 11389458     DOI: 10.1038/35076576

Source DB:  PubMed          Journal:  Nat Rev Genet        ISSN: 1471-0056            Impact factor:   53.242


  305 in total

1.  An assessment of Motorola CodeLink microarray performance for gene expression profiling applications.

Authors:  Ramesh Ramakrishnan; David Dorris; Anna Lublinsky; Allen Nguyen; Marc Domanus; Anna Prokhorova; Linn Gieser; Edward Touma; Randall Lockner; Murthy Tata; Xiaomei Zhu; Marcus Patterson; Richard Shippy; Timothy J Sendera; Abhijit Mazumder
Journal:  Nucleic Acids Res       Date:  2002-04-01       Impact factor: 16.971

2.  Analysis of DNA microarrays using algorithms that employ rule-based expert knowledge.

Authors:  Kuang-Hung Pan; Chih-Jian Lih; Stanley N Cohen
Journal:  Proc Natl Acad Sci U S A       Date:  2002-02-19       Impact factor: 11.205

3.  Monitoring global messenger RNA changes in externally controlled microarray experiments.

Authors:  Jeroen van de Peppel; Patrick Kemmeren; Harm van Bakel; Marijana Radonjic; Dik van Leenen; Frank C P Holstege
Journal:  EMBO Rep       Date:  2003-04       Impact factor: 8.807

4.  Identification and removal of contaminating fluorescence from commercial and in-house printed DNA microarrays.

Authors:  M Juanita Martinez; Anthony D Aragon; Angelina L Rodriguez; Jose M Weber; Jerilyn A Timlin; Michael B Sinclair; David M Haaland; Margaret Werner-Washburne
Journal:  Nucleic Acids Res       Date:  2003-02-15       Impact factor: 16.971

5.  A family-based test for correlation between gene expression and trait values.

Authors:  Peter Kraft; Eric Schadt; Jason Aten; Steve Horvath
Journal:  Am J Hum Genet       Date:  2003-04-08       Impact factor: 11.025

6.  Spearman correlation identifies statistically significant gene expression clusters in spinal cord development and injury.

Authors:  Max Kotlyar; Stefanie Fuhrman; Alan Ableson; Roland Somogyi
Journal:  Neurochem Res       Date:  2002-10       Impact factor: 3.996

7.  Expression profiling with oligonucleotide arrays: technologies and applications for neurobiology.

Authors:  Timothy J Sendera; David Dorris; Ramesh Ramakrishnan; Allen Nguyen; Dionisios Trakas; Abhijit Mazumder
Journal:  Neurochem Res       Date:  2002-10       Impact factor: 3.996

8.  False Discovery Rate Control With Groups.

Authors:  James X Hu; Hongyu Zhao; Harrison H Zhou
Journal:  J Am Stat Assoc       Date:  2010-09-01       Impact factor: 5.033

9.  HIV envelope induces a cascade of cell signals in non-proliferating target cells that favor virus replication.

Authors:  Claudia Cicala; James Arthos; Sara M Selig; Glynn Dennis; Douglas A Hosack; Donald Van Ryk; Marion L Spangler; Tavis D Steenbeke; Prateeti Khazanie; Neil Gupta; Jun Yang; Marybeth Daucher; Richard A Lempicki; Anthony S Fauci
Journal:  Proc Natl Acad Sci U S A       Date:  2002-06-27       Impact factor: 11.205

10.  DAVID: Database for Annotation, Visualization, and Integrated Discovery.

Authors:  Glynn Dennis; Brad T Sherman; Douglas A Hosack; Jun Yang; Wei Gao; H Clifford Lane; Richard A Lempicki
Journal:  Genome Biol       Date:  2003-04-03       Impact factor: 13.583

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