Literature DB >> 18629084

Microarray probe expression measures, data normalization and statistical validation.

Silvia Saviozzi1, Raffaele A Calogero.   

Abstract

DNA microarray technology is a high-throughput method for gaining information on gene function. Microarray technology is based on deposition/synthesis, in an ordered manner, on a solid surface, of thousands of EST sequences/genes/oligonucleotides. Due to the high number of generated datapoints, computational tools are essential in microarray data analysis and mining to grasp knowledge from experimental results. In this review, we will focus on some of the methodologies actually available to define gene expression intensity measures, microarray data normalization, and statistical validation of differential expression.

Year:  2003        PMID： 18629084      PMCID： PMC2447370          DOI： 10.1002/cfg.312

Source DB:  PubMed          Journal:  Comp Funct Genomics        ISSN： 1531-6912


  9 in total

1.  Significance analysis of microarrays applied to the ionizing radiation response.

Authors:  V G Tusher; R Tibshirani; G Chu
Journal:  Proc Natl Acad Sci U S A       Date:  2001-04-17       Impact factor: 11.205

2.  Multivariate measurement of gene expression relationships.

Authors:  S Kim; E R Dougherty; Y Chen; K Sivakumar; P Meltzer; J M Trent; M Bittner
Journal:  Genomics       Date:  2000-07-15       Impact factor: 5.736

3.  A model for measurement error for gene expression arrays.

Authors:  D M Rocke; B Durbin
Journal:  J Comput Biol       Date:  2001       Impact factor: 1.479

4.  A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes.

Authors:  P Baldi; A D Long
Journal:  Bioinformatics       Date:  2001-06       Impact factor: 6.937

5.  A comparison of normalization methods for high density oligonucleotide array data based on variance and bias.

Authors:  B M Bolstad; R A Irizarry; M Astrand; T P Speed
Journal:  Bioinformatics       Date:  2003-01-22       Impact factor: 6.937

6.  Exploration, normalization, and summaries of high density oligonucleotide array probe level data.

Authors:  Rafael A Irizarry; Bridget Hobbs; Francois Collin; Yasmin D Beazer-Barclay; Kristen J Antonellis; Uwe Scherf; Terence P Speed
Journal:  Biostatistics       Date:  2003-04       Impact factor: 5.899

7.  Quantitative monitoring of gene expression patterns with a complementary DNA microarray.

Authors:  M Schena; D Shalon; R W Davis; P O Brown
Journal:  Science       Date:  1995-10-20       Impact factor: 47.728

8.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.

Authors:  T R Golub; D K Slonim; P Tamayo; C Huard; M Gaasenbeek; J P Mesirov; H Coller; M L Loh; J R Downing; M A Caligiuri; C D Bloomfield; E S Lander
Journal:  Science       Date:  1999-10-15       Impact factor: 47.728

9.  Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection.

Authors:  C Li; W H Wong
Journal:  Proc Natl Acad Sci U S A       Date:  2001-01-02       Impact factor: 11.205
  9 in total
  3 in total

1.  Identification of differentially expressed genes in HPV-positive and HPV-negative oropharyngeal squamous cell carcinomas.

Authors:  Ivan Martinez; Jun Wang; Kenosha F Hobson; Robert L Ferris; Saleem A Khan
Journal:  Eur J Cancer       Date:  2006-10-31       Impact factor: 9.162

2.  Internal standard-based analysis of microarray data. Part 1: analysis of differential gene expressions.

Authors:  Igor Dozmorov; Ivan Lefkovits
Journal:  Nucleic Acids Res       Date:  2009-08-31       Impact factor: 16.971

3.  Novel implementation of conditional co-regulation by graph theory to derive co-expressed genes from microarray data.

Authors:  Arun Rawat; Georg J Seifert; Youping Deng
Journal:  BMC Bioinformatics       Date:  2008-08-12       Impact factor: 3.169
  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.