Gaining weights... and feeling good about it!
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.
Wit
Ernst
E
Medical Statistics Unit, Department of Mathematics and Statistics, Lancaster University, LA1 1AE, U.K. e.wit@lancaster.ac.uk
Purutcuoglu
Vilda
V
O'Donovan
Lucy
L
Zhu
Ximin
X
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
D012333:RNA, Messenger