Use of within-array replicate spots for assessing differential expression in microarray experiments

Bioinformatics. 2005 May 1;21(9):2067-75. doi: 10.1093/bioinformatics/bti270. Epub 2005 Jan 18.


Motivation: Spotted arrays are often printed with probes in duplicate or triplicate, but current methods for assessing differential expression are not able to make full use of the resulting information. The usual practice is to average the duplicate or triplicate results for each probe before assessing differential expression. This results in the loss of valuable information about genewise variability.

Results: A method is proposed for extracting more information from within-array replicate spots in microarray experiments by estimating the strength of the correlation between them. The method involves fitting separate linear models to the expression data for each gene but with a common value for the between-replicate correlation. The method greatly improves the precision with which the genewise variances are estimated and thereby improves inference methods designed to identify differentially expressed genes. The method may be combined with empirical Bayes methods for moderating the genewise variances between genes. The method is validated using data from a microarray experiment involving calibration and ratio control spots in conjunction with spiked-in RNA. Comparing results for calibration and ratio control spots shows that the common correlation method results in substantially better discrimination of differentially expressed genes from those which are not. The spike-in experiment also confirms that the results may be further improved by empirical Bayes smoothing of the variances when the sample size is small.

Availability: The methodology is implemented in the limma software package for R, available from the CRAN repository

Publication types

  • Comparative Study
  • Evaluation Study
  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Algorithms*
  • Gene Expression Profiling / methods*
  • Genetic Variation / genetics*
  • In Situ Hybridization, Fluorescence / methods*
  • Linear Models
  • Models, Genetic*
  • Models, Statistical
  • Oligonucleotide Array Sequence Analysis / methods*
  • RNA, Messenger / genetics*
  • Software


  • RNA, Messenger