A conditional density error model for the statistical analysis of microarray data.
BACKGROUND
In many microarray experiments, relatively few intra- and inter-array replicate measurements are made due to significant cost limitations and sample availability. Compounding this problem is a lack of robust statistical methods for analyzing gene expression data with limited experimental replicates. As a result, the interpretation of the results of these experiments are difficult with little understanding of the probability of type I and type II errors.


RESULTS
The variability in a series of replicate microarray measurements was modelled using a combination of parametric and non-parametric methods. A 3-dimensional surface was created for the conditional distribution of the variability given the mean signal intensity in both the Cy3 and Cy5 channels. The results were used as the basis for developing statistical methods for analyzing gene expression data with limited experimental replicates.


BACKGROUND
The statistical analysis scripts are available upon request.
Love
Brad
B
Aeomica, 928 East Arques Avenue, Sunnyvale, CA 94085, USA. drblove@yahoo.com
Rank
David R
DR
Penn
Sharron G
SG
Jenkins
David A
DA
Thomas
Russell S
RS
D003198:Computer Simulation; D004247:DNA / Q000235:genetics*; D004261:DNA Replication / Q000235:genetics; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods / Q000706:statistics & numerical data; D005786:Gene Expression Regulation; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000706:statistics & numerical data; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
D004247:DNA