ProCAT: a data analysis approach for protein microarrays
1 Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA
2 Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06511, USA
3 Department of Computer Science, Yale University, New Haven, CT 06511, USA
4 Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT 06511, USA
Genome Biology 2006, 7:R110 doi:10.1186/gb-2006-7-11-r110Published: 16 November 2006
Protein microarrays provide a versatile method for the analysis of many protein biochemical activities. Existing DNA microarray analytical methods do not translate to protein microarrays due to differences between the technologies. Here we report a new approach, ProCAT, which corrects for background bias and spatial artifacts, identifies significant signals, filters nonspecific spots, and normalizes the resulting signal to protein abundance. ProCAT provides a powerful and flexible new approach for analyzing many types of protein microarrays.