Etsuko Inoue

Normalization of biological expression data based on selection of a stable element set

Normalization of biological expression data is the process to remove several experimental or technical bias from the data in order to perform accurate analysis. Recently computational analysis of expression data is widely applied since very large number of genes or proteins can be examined simultaneously. In this paper we proposed a new normalization method for expression data which is based on selection of a stable element set. Our idea is that using a part of genes or proteins which is relatively stably expressed leads more accurate normalization. We formulate the problem and give the algorithm to solve it in practical time. Through evaluation with artificial and real data, we found that our method outperforms global scaling, and global scaling tend to over-correct the bias.