Literature DB >> 16890548

Analysis of microarray experiments of gene expression profiling.

Adi L Tarca1, Roberto Romero, Sorin Draghici.   

Abstract

The study of gene expression profiling of cells and tissue has become a major tool for discovery in medicine. Microarray experiments allow description of genome-wide expression changes in health and disease. The results of such experiments are expected to change the methods employed in the diagnosis and prognosis of disease in obstetrics and gynecology. Moreover, an unbiased and systematic study of gene expression profiling should allow the establishment of a new taxonomy of disease for obstetric and gynecologic syndromes. Thus, a new era is emerging in which reproductive processes and disorders could be characterized using molecular tools and fingerprinting. The design, analysis, and interpretation of microarray experiments require specialized knowledge that is not part of the standard curriculum of our discipline. This article describes the types of studies that can be conducted with microarray experiments (class comparison, class prediction, class discovery). We discuss key issues pertaining to experimental design, data preprocessing, and gene selection methods. Common types of data representation are illustrated. Potential pitfalls in the interpretation of microarray experiments, as well as the strengths and limitations of this technology, are highlighted. This article is intended to assist clinicians in appraising the quality of the scientific evidence now reported in the obstetric and gynecologic literature.

Mesh:

Year:  2006        PMID: 16890548      PMCID: PMC2435252          DOI: 10.1016/j.ajog.2006.07.001

Source DB:  PubMed          Journal:  Am J Obstet Gynecol        ISSN: 0002-9378            Impact factor:   8.661


  112 in total

Review 1.  Exploring expression data: identification and analysis of coexpressed genes.

Authors:  L J Heyer; S Kruglyak; S Yooseph
Journal:  Genome Res       Date:  1999-11       Impact factor: 9.043

2.  Normalization strategies for cDNA microarrays.

Authors:  J Schuchhardt; D Beule; A Malik; E Wolski; H Eickhoff; H Lehrach; H Herzel
Journal:  Nucleic Acids Res       Date:  2000-05-15       Impact factor: 16.971

3.  Application of a functional genomics approach to identify differentially expressed genes in human myometrium during pregnancy and labour.

Authors:  K Aguan; J A Carvajal; L P Thompson; C P Weiner
Journal:  Mol Hum Reprod       Date:  2000-12       Impact factor: 4.025

Review 4.  Statistical design and the analysis of gene expression microarray data.

Authors:  M K Kerr; G A Churchill
Journal:  Genet Res       Date:  2001-04       Impact factor: 1.588

5.  Analysis of repeatability in spotted cDNA microarrays.

Authors:  Tor-Kristian Jenssen; Mette Langaas; Winston P Kuo; Birgitte Smith-Sørensen; Ola Myklebost; Eivind Hovig
Journal:  Nucleic Acids Res       Date:  2002-07-15       Impact factor: 16.971

6.  Sequence-matched probes produce increased cross-platform consistency and more reproducible biological results in microarray-based gene expression measurements.

Authors:  Brigham H Mecham; Gregory T Klus; Jeffrey Strovel; Meena Augustus; David Byrne; Peter Bozso; Daniel Z Wetmore; Thomas J Mariani; Isaac S Kohane; Zoltan Szallasi
Journal:  Nucleic Acids Res       Date:  2004-05-25       Impact factor: 16.971

7.  A robust neural networks approach for spatial and intensity-dependent normalization of cDNA microarray data.

Authors:  A L Tarca; J E K Cooke; J Mackay
Journal:  Bioinformatics       Date:  2005-03-29       Impact factor: 6.937

8.  Gene expression profiling of mid to late secretory phase endometrial biopsies from women with menstrual complaint.

Authors:  Hilary O D Critchley; Kevin A Robertson; Thorsten Forster; Teresa A Henderson; Alistair R W Williams; Peter Ghazal
Journal:  Am J Obstet Gynecol       Date:  2006-08       Impact factor: 8.661

9.  Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.

Authors:  Aravind Subramanian; Pablo Tamayo; Vamsi K Mootha; Sayan Mukherjee; Benjamin L Ebert; Michael A Gillette; Amanda Paulovich; Scott L Pomeroy; Todd R Golub; Eric S Lander; Jill P Mesirov
Journal:  Proc Natl Acad Sci U S A       Date:  2005-09-30       Impact factor: 11.205

10.  Detection of differentially expressed genes in lymphomas using cDNA arrays: identification of clusterin as a new diagnostic marker for anaplastic large-cell lymphomas.

Authors:  A Wellmann; C Thieblemont; S Pittaluga; A Sakai; E S Jaffe; P Siebert; M Raffeld
Journal:  Blood       Date:  2000-07-15       Impact factor: 22.113

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  68 in total

1.  Characterization of the transcriptome of chorioamniotic membranes at the site of rupture in spontaneous labor at term.

Authors:  Chia-Ling Nhan-Chang; Roberto Romero; Adi L Tarca; Pooja Mittal; Juan Pedro Kusanovic; Offer Erez; Shali Mazaki-Tovi; Tinnakorn Chaiworapongsa; John Hotra; Nandor Gabor Than; Jung-Sun Kim; Sonia S Hassan; Chong Jai Kim
Journal:  Am J Obstet Gynecol       Date:  2010-05       Impact factor: 8.661

2.  Peripheral CD300a+CD8+ T lymphocytes with a distinct cytotoxic molecular signature increase in pregnant women with chronic chorioamnionitis.

Authors:  Yi Xu; Federica Tarquini; Roberto Romero; Chong Jai Kim; Adi L Tarca; Gaurav Bhatti; JoonHo Lee; I Birgitta Sundell; Pooja Mittal; Juan Pedro Kusanovic; Sonia S Hassan; Jung-Sun Kim
Journal:  Am J Reprod Immunol       Date:  2011-11-13       Impact factor: 3.886

3.  The maternal plasma proteome changes as a function of gestational age in normal pregnancy: a longitudinal study.

Authors:  Roberto Romero; Offer Erez; Eli Maymon; Piya Chaemsaithong; Zhonghui Xu; Percy Pacora; Tinnakorn Chaiworapongsa; Bogdan Done; Sonia S Hassan; Adi L Tarca
Journal:  Am J Obstet Gynecol       Date:  2017-03-03       Impact factor: 8.661

4.  Effects of developmental lead exposure on the hippocampal transcriptome: influences of sex, developmental period, and lead exposure level.

Authors:  Jay S Schneider; David W Anderson; Keyur Talsania; William Mettil; Rajanikanth Vadigepalli
Journal:  Toxicol Sci       Date:  2012-05-28       Impact factor: 4.849

Review 5.  The use of high-dimensional biology (genomics, transcriptomics, proteomics, and metabolomics) to understand the preterm parturition syndrome.

Authors:  R Romero; J Espinoza; F Gotsch; J P Kusanovic; L A Friel; O Erez; S Mazaki-Tovi; N G Than; S Hassan; G Tromp
Journal:  BJOG       Date:  2006-12       Impact factor: 6.531

6.  A molecular signature of an arrest of descent in human parturition.

Authors:  Pooja Mittal; Roberto Romero; Adi L Tarca; Sorin Draghici; Chia-Ling Nhan-Chang; Tinnakorn Chaiworapongsa; John Hotra; Ricardo Gomez; Juan Pedro Kusanovic; Deug-Chan Lee; Chong Jai Kim; Sonia S Hassan
Journal:  Am J Obstet Gynecol       Date:  2011-02       Impact factor: 8.661

7.  Characterization of the myometrial transcriptome and biological pathways of spontaneous human labor at term.

Authors:  Pooja Mittal; Roberto Romero; Adi L Tarca; Juan Gonzalez; Sorin Draghici; Yi Xu; Zhong Dong; Chia-Ling Nhan-Chang; Tinnakorn Chaiworapongsa; Stephen Lye; Juan Pedro Kusanovic; Leonard Lipovich; Shali Mazaki-Tovi; Sonia S Hassan; Sam Mesiano; Chong Jai Kim
Journal:  J Perinat Med       Date:  2010-07-14       Impact factor: 1.901

8.  Integrated Systems Biology Approach Identifies Novel Maternal and Placental Pathways of Preeclampsia.

Authors:  Nandor Gabor Than; Roberto Romero; Adi Laurentiu Tarca; Katalin Adrienna Kekesi; Yi Xu; Zhonghui Xu; Kata Juhasz; Gaurav Bhatti; Ron Joshua Leavitt; Zsolt Gelencser; Janos Palhalmi; Tzu Hung Chung; Balazs Andras Gyorffy; Laszlo Orosz; Amanda Demeter; Anett Szecsi; Eva Hunyadi-Gulyas; Zsuzsanna Darula; Attila Simor; Katalin Eder; Szilvia Szabo; Vanessa Topping; Haidy El-Azzamy; Christopher LaJeunesse; Andrea Balogh; Gabor Szalai; Susan Land; Olga Torok; Zhong Dong; Ilona Kovalszky; Andras Falus; Hamutal Meiri; Sorin Draghici; Sonia S Hassan; Tinnakorn Chaiworapongsa; Manuel Krispin; Martin Knöfler; Offer Erez; Graham J Burton; Chong Jai Kim; Gabor Juhasz; Zoltan Papp
Journal:  Front Immunol       Date:  2018-08-08       Impact factor: 7.561

9.  Human blood gene signature as a marker for smoking exposure: computational approaches of the top ranked teams in the sbv IMPROVER Systems Toxicology challenge.

Authors:  Adi L Tarca; Xiaofeng Gong; Roberto Romero; Wenxin Yang; Zhongqu Duan; Hao Yang; Chengfang Zhang; Peixuan Wang
Journal:  Comput Toxicol       Date:  2017-07-18

10.  Gene ARMADA: an integrated multi-analysis platform for microarray data implemented in MATLAB.

Authors:  Aristotelis Chatziioannou; Panagiotis Moulos; Fragiskos N Kolisis
Journal:  BMC Bioinformatics       Date:  2009-10-27       Impact factor: 3.169

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