Literature DB >> 16772260

Normalization and quantification of differential expression in gene expression microarrays.

Christine Steinhoff1, Martin Vingron.   

Abstract

Array-based gene expression studies frequently serve to identify genes that are expressed differently under two or more conditions. The actual analysis of the data, however, may be hampered by a number of technical and statistical problems. Possible remedies on the level of computational analysis lie in appropriate preprocessing steps, proper normalization of the data and application of statistical testing procedures in the derivation of differentially expressed genes. This review summarizes methods that are available for these purposes and provides a brief overview of the available software tools.

Mesh:

Year:  2006        PMID: 16772260     DOI: 10.1093/bib/bbl002

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  17 in total

1.  Gene expression profiling in the rhesus macaque: methodology, annotation and data interpretation.

Authors:  Nigel C Noriega; Steven G Kohama; Henryk F Urbanski
Journal:  Methods       Date:  2009-05-23       Impact factor: 3.608

Review 2.  Gene set enrichment analysis: performance evaluation and usage guidelines.

Authors:  Jui-Hung Hung; Tun-Hsiang Yang; Zhenjun Hu; Zhiping Weng; Charles DeLisi
Journal:  Brief Bioinform       Date:  2011-09-07       Impact factor: 11.622

3.  Reference alignment of SNP microarray signals for copy number analysis of tumors.

Authors:  Stan Pounds; Cheng Cheng; Charles Mullighan; Susana C Raimondi; Sheila Shurtleff; James R Downing
Journal:  Bioinformatics       Date:  2008-12-03       Impact factor: 6.937

4.  A quick guide to large-scale genomic data mining.

Authors:  Curtis Huttenhower; Oliver Hofmann
Journal:  PLoS Comput Biol       Date:  2010-05-27       Impact factor: 4.475

5.  Diagnostic biomarkers for renal cell carcinoma: selection using novel bioinformatics systems for microarray data analysis.

Authors:  Adeboye O Osunkoya; Qiqin Yin-Goen; John H Phan; Richard A Moffitt; Todd H Stokes; May D Wang; Andrew N Young
Journal:  Hum Pathol       Date:  2009-08-19       Impact factor: 3.466

6.  Pathway analysis of expression data: deciphering functional building blocks of complex diseases.

Authors:  Frank Emmert-Streib; Galina V Glazko
Journal:  PLoS Comput Biol       Date:  2011-05-26       Impact factor: 4.475

7.  RegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks.

Authors:  Marco Grimaldi; Roberto Visintainer; Giuseppe Jurman
Journal:  PLoS One       Date:  2011-12-28       Impact factor: 3.240

8.  Influence of statistical estimators of mutual information and data heterogeneity on the inference of gene regulatory networks.

Authors:  Ricardo de Matos Simoes; Frank Emmert-Streib
Journal:  PLoS One       Date:  2011-12-29       Impact factor: 3.240

9.  Identification of novel endogenous antisense transcripts by DNA microarray analysis targeting complementary strand of annotated genes.

Authors:  Koji Numata; Yuko Osada; Yuki Okada; Rintaro Saito; Noriko Hiraiwa; Hajime Nakaoka; Naoyuki Yamamoto; Kazufumi Watanabe; Kazue Okubo; Chihiro Kohama; Akio Kanai; Kuniya Abe; Hidenori Kiyosawa
Journal:  BMC Genomics       Date:  2009-08-22       Impact factor: 3.969

10.  Influence of the experimental design of gene expression studies on the inference of gene regulatory networks: environmental factors.

Authors:  Frank Emmert-Streib
Journal:  PeerJ       Date:  2013-02-12       Impact factor: 2.984

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