BackgroundThe 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.FindingsHere 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.ConclusionsThe 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
[1]
Ian B. Jeffery,et al.
Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data
,
2006,
BMC Bioinformatics.
[2]
RAINER BREITLING,et al.
Rank-based Methods as a Non-parametric Alternative of the T-statistic for the Analysis of Biological Microarray Data
,
2005,
J. Bioinform. Comput. Biol..
[3]
Rainer Breitling,et al.
RankProd: a bioconductor package for detecting differentially expressed genes in meta-analysis
,
2006,
Bioinform..
[4]
J. Koziol.
Comments on the rank product method for analyzing replicated experiments
,
2010,
FEBS letters.
[5]
Rainer Breitling,et al.
A comparison of meta-analysis methods for detecting differentially expressed genes in microarray experiments
,
2008,
Bioinform..
[6]
Rainer Breitling,et al.
Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments
,
2004,
FEBS letters.