title: SED, a normalization free method for DNA microarray data analysis.
abstract: BACKGROUND
  Analysis of DNA microarray data usually begins with a normalization step where intensities of different arrays are adjusted to the same scale so that the intensity levels from different arrays can be compared with one other. Both simple total array intensity-based as well as more complex "local intensity level" dependent normalization methods have been developed, some of which are widely used. Much less developed methods for microarray data analysis include those that bypass the normalization step and therefore yield results that are not confounded by potential normalization errors.
  
  
  RESULTS
  Instead of focusing on the raw intensity levels, we developed a new method for microarray data analysis that maps each gene's expression intensity level to a high dimensional space of SEDs (Signs of Expression Difference), the signs of the expression intensity difference between a given gene and every other gene on the array. Since SED are unchanged under any monotonic transformation of intensity levels, the SED based method is normalization free. When tested on a multi-class tumor classification problem, simple Naive Bayes and Nearest Neighbor methods using the SED approach gave results comparable with normalized intensity-based algorithms. Furthermore, a high percentage of classifiers based on a single gene's SED gave good classification results, suggesting that SED does capture essential information from the intensity levels.
  
  
  CONCLUSIONS
  The results of testing this new method on multi-class tumor classification problems suggests that the SED-based, normalization-free method of microarray data analysis is feasible and promising.
authors.affiliation: Oscient Pharmaceuticals Corporation, 100 Beaver St, Waltham, Massachusetts 02453, USA. hw14@columbia.edu
authors.forename: Huajun
authors.initials: H
authors.lastname: Wang
authors.forename: Hui
authors.initials: H
authors.lastname: Huang
mesh_terms: D016543:Central Nervous System Neoplasms / Q000145:classification; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods* / Q000706:statistics & numerical data*; D015972:Gene Expression Regulation, Neoplastic / Q000235:genetics; D052138:Genes, Neoplasm / Q000235:genetics; D006801:Humans; D007938:Leukemia / Q000145:classification; D008223:Lymphoma / Q000145:classification; D008297:Male; D009369:Neoplasms / Q000145:classification / Q000235:genetics; D016011:Normal Distribution; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000706:statistics & numerical data*; D010051:Ovarian Neoplasms / Q000145:classification; D011471:Prostatic Neoplasms / Q000145:classification