github link
Accession IconGSE16458

A simple method to integrate different versions of Affymetrix microarrays using duplicate samples

Organism Icon Rattus norvegicus
Sample Icon 48 Downloadable Samples
Technology Badge Icon Affymetrix Rat Expression 230A Array (rae230a)

Submitter Supplied Information

The size and scope of microarray experiments continue to increase. However, datasets generated on different platforms or at different centres contain biases. Improved techniques are needed to remove platform- and batch-specific biases. One experimental control is the replicate hybridization of a subset of samples at each site or on each platform to learn the relationship between the two platforms. To date, no algorithm exists to specifically use this type of control. LTR is a linear-modelling-based algorithm that learns the relationship between different microarray batches from replicate hybridizations. LTR was tested on a new benchmark dataset of 20 samples hybridized to different Affymetrix microarray platforms. Before LTR, the two platforms were significantly different; application of LTR removed this bias. LTR was tested with six separate data pre-processing algorithms, and its effectiveness was independent of the pre-processing algorithm. Sample-size experiments indicate that just three replicate hybridizations can significantly reduce bias. An R library implementing LTR is available.
PubMed ID
Total Samples
Submitter’s Institution
Alternate Accession IDs


Show of 0 Total Samples
Accession Code
Processing Information
Additional Metadata
No rows found