title: Gaining weights... and feeling good about it!
abstract: Two problems that dog current microarrays analyses are (i) the relatively arbitrary nature of data preprocessing and (ii) the inability to incorporate spot quality information in inference except by all-or-nothing spot filtering. In this chapter we propose an approach based on using weights to overcome these two problems. The first approach uses weighted p-values to make inference robust to normalization and the second approach uses weighted spot intensity values to improve inference without any filtering.
authors.affiliation: Medical Statistics Unit, Department of Mathematics and Statistics, Lancaster University, LA1 1AE, U.K. e.wit@lancaster.ac.uk
authors.forename: Ernst
authors.initials: E
authors.lastname: Wit
authors.forename: Vilda
authors.initials: V
authors.lastname: Purutcuoglu
authors.forename: Lucy
authors.initials: L
authors.lastname: O'Donovan
authors.forename: Ximin
authors.initials: X
authors.lastname: Zhu
mesh_terms: D000465:Algorithms; D000818:Animals; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D051379:Mice; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated; D012333:RNA, Messenger / Q000378:metabolism; D012984:Software
chemical_list: D012333:RNA, Messenger