CART variance stabilization and regularization for high-throughput genomic data.
BACKGROUND
mRNA expression data obtained from high-throughput DNA microarrays exhibit strong departures from homogeneity of variances. Often a complex relationship between mean expression value and variance is seen. Variance stabilization of such data is crucial for many types of statistical analyses, while regularization of variances (pooling of information) can greatly improve overall accuracy of test statistics.


RESULTS
A Classification and Regression Tree (CART) procedure is introduced for variance stabilization as well as regularization. The CART procedure adaptively clusters genes by variances. Using both local and cluster wide information leads to improved estimation of population variances which improves test statistics. Whereas making use of cluster wide information allows for variance stabilization of data.


BACKGROUND
Sufficient details for our CART procedure are given so that the interested reader can program the method for themselves. The algorithm is also accessible within the Java software package BAMarray(TM), which is freely available to non-commercial users at www.bamarray.com.


BACKGROUND
hemant.ishwaran@gmail.com.
Papana
Ariadni
A
Department of Statistics, Case University, 10900 Euclid Avenue Cleveland OH 44106, USA.
Ishwaran
Hemant
H
D001185:Artificial Intelligence; D016000:Cluster Analysis; D003198:Computer Simulation; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D014644:Genetic Variation / Q000235:genetics; D023281:Genomics / Q000379:methods*; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012333:RNA, Messenger / Q000235:genetics*; D012044:Regression Analysis; D017423:Sequence Analysis, RNA / Q000379:methods*
D012333:RNA, Messenger