Literature DB >> 18629036

ArraySolver: an algorithm for colour-coded graphical display and Wilcoxon signed-rank statistics for comparing microarray gene expression data.

Haseeb Ahmad Khan1.   

Abstract

The massive surge in the production of microarray data poses a great challenge for proper analysis and interpretation. In recent years numerous computational tools have been developed to extract meaningful interpretation of microarray gene expression data. However, a convenient tool for two-groups comparison of microarray data is still lacking and users have to rely on commercial statistical packages that might be costly and require special skills, in addition to extra time and effort for transferring data from one platform to other. Various statistical methods, including the t-test, analysis of variance, Pearson test and Mann-Whitney U test, have been reported for comparing microarray data, whereas the utilization of the Wilcoxon signed-rank test, which is an appropriate test for two-groups comparison of gene expression data, has largely been neglected in microarray studies. The aim of this investigation was to build an integrated tool, ArraySolver, for colour-coded graphical display and comparison of gene expression data using the Wilcoxon signed-rank test. The results of software validation showed similar outputs with ArraySolver and SPSS for large datasets. Whereas the former program appeared to be more accurate for 25 or fewer pairs (n < or = 25), suggesting its potential application in analysing molecular signatures that usually contain small numbers of genes. The main advantages of ArraySolver are easy data selection, convenient report format, accurate statistics and the familiar Excel platform.

Year:  2004        PMID: 18629036      PMCID: PMC2447319          DOI: 10.1002/cfg.369

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


  41 in total

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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.  Making the most of microarray data.

Authors:  T Gaasterland; S Bekiranov
Journal:  Nat Genet       Date:  2000-03       Impact factor: 38.330

3.  Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation.

Authors:  P Tamayo; D Slonim; J Mesirov; Q Zhu; S Kitareewan; E Dmitrovsky; E S Lander; T R Golub
Journal:  Proc Natl Acad Sci U S A       Date:  1999-03-16       Impact factor: 11.205

4.  Genome-wide expression profiling of mid-gestation placenta and embryo using a 15,000 mouse developmental cDNA microarray.

Authors:  T S Tanaka; S A Jaradat; M K Lim; G J Kargul; X Wang; M J Grahovac; S Pantano; Y Sano; Y Piao; R Nagaraja; H Doi; W H Wood; K G Becker; M S Ko
Journal:  Proc Natl Acad Sci U S A       Date:  2000-08-01       Impact factor: 11.205

5.  Anti-dsDNA antibody up-regulates interleukin 6, but not cyclo-oxygenase, gene expression in glomerular mesangial cells: a marker of immune-mediated renal damage?

Authors:  C L Yu; K H Sun; C Y Tsai; S C Hsieh; H S Yu
Journal:  Inflamm Res       Date:  2001-01       Impact factor: 4.575

6.  Transcriptional gene expression profiles of colorectal adenoma, adenocarcinoma, and normal tissue examined by oligonucleotide arrays.

Authors:  D A Notterman; U Alon; A J Sierk; A J Levine
Journal:  Cancer Res       Date:  2001-04-01       Impact factor: 12.701

7.  Monitoring gene expression profile changes in ovarian carcinomas using cDNA microarray.

Authors:  K Wang; L Gan; E Jeffery; M Gayle; A M Gown; M Skelly; P S Nelson; W V Ng; M Schummer; L Hood; J Mulligan
Journal:  Gene       Date:  1999-03-18       Impact factor: 3.688

8.  Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling.

Authors:  A A Alizadeh; M B Eisen; R E Davis; C Ma; I S Lossos; A Rosenwald; J C Boldrick; H Sabet; T Tran; X Yu; J I Powell; L Yang; G E Marti; T Moore; J Hudson; L Lu; D B Lewis; R Tibshirani; G Sherlock; W C Chan; T C Greiner; D D Weisenburger; J O Armitage; R Warnke; R Levy; W Wilson; M R Grever; J C Byrd; D Botstein; P O Brown; L M Staudt
Journal:  Nature       Date:  2000-02-03       Impact factor: 49.962

9.  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

10.  Identification of potential diagnostic markers of prostate cancer and prostatic intraepithelial neoplasia using cDNA microarray.

Authors:  J H Bull; G Ellison; A Patel; G Muir; M Walker; M Underwood; F Khan; L Paskins
Journal:  Br J Cancer       Date:  2001-06-01       Impact factor: 7.640

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  3 in total

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2.  Importance of customized (task oriented) software tools for biomedical applications.

Authors:  Haseeb A Khan
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3.  Current trends for customized biomedical software tools.

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