RankProdIt: A web-interactive Rank Products analysis tool

被引:26
作者
Laing E. [1 ]
Smith C.P. [1 ]
机构
[1] Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey
关键词
Microarray Experiment; Input File; Rank Product; Slave Mode; Rank Product Analysis;
D O I
10.1186/1756-0500-3-221
中图分类号
学科分类号
摘要
Background. The first objective of a DNA microarray experiment is typically to generate a list of genes or probes that are found to be differentially expressed or represented (in the case of comparative genomic hybridizations and/or copy number variation) between two conditions or strains. Rank Products analysis comprises a robust algorithm for deriving such lists from microarray experiments that comprise small numbers of replicates, for example, less than the number required for the commonly used t-test. Currently, users wishing to apply Rank Products analysis to their own microarray data sets have been restricted to the use of command line-based software which can limit its usage within the biological community. Findings. Here we have developed a web interface to existing Rank Products analysis tools allowing users to quickly process their data in an intuitive and step-wise manner to obtain the respective Rank Product or Rank Sum, probability of false prediction and p-values in a downloadable file. Conclusions. The online interactive Rank Products analysis tool RankProdIt, for analysis of any data set containing measurements for multiple replicated conditions, is available at: http://strep-microarray.sbs. surrey.ac.uk/RankProducts. © 2010 Laing et al; licensee BioMed Central Ltd.
引用
收藏
相关论文
共 9 条
[1]  
Jeffery I.B., Desmond G.H., Culhane A.C., Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data, BMC Bioinformatics, 7, (2006)
[2]  
Hong F., Breitling R., A comparison of meta-analysis methods for detecting differentially expressed genes in microarray experiments, Bioinformatics, 24, pp. 374-382, (2008)
[3]  
Breitling R., Herzyk P., Rank-based methods as a non-parametric alternative of the T-statistic for the analysis of biological microarray data, J Bioinform Comput Biol, 3, pp. 1171-1189, (2005)
[4]  
Breitling R., Armengaud P., Amtmann A., Herzyk P., Rank products: A simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments, FEBS Letters, 573, pp. 83-92, (2004)
[5]  
R: A Language and Environment for Statistical Computing, (2005)
[6]  
Hong F., Breitling R., McEntee C.W., Wittner B.S., Nemhauser J.L., Chory J., RankProd: A Bioconductor package for detecting differentially expressed genes in meta-analysis, Bioinformatics, 22, pp. 2825-2827, (2006)
[7]  
GlaMA
[8]  
HaXe
[9]  
Koziol J.A., Comment son the rank product method for analyzing replicated experiments, FEBS Lett, 584, pp. 941-944, (2010)