title: Quantitative quality control in microarray experiments and the application in data filtering, normalization and false positive rate prediction.
abstract: Data preprocessing including proper normalization and adequate quality control before complex data mining is crucial for studies using the cDNA microarray technology. We have developed a simple procedure that integrates data filtering and normalization with quantitative quality control of microarray experiments. Previously we have shown that data variability in a microarray experiment can be very well captured by a quality score q(com) that is defined for every spot, and the ratio distribution depends on q(com). Utilizing this knowledge, our data-filtering scheme allows the investigator to decide on the filtering stringency according to desired data variability, and our normalization procedure corrects the q(com)-dependent dye biases in terms of both the location and the spread of the ratio distribution. In addition, we propose a statistical model for false positive rate determination based on the design and the quality of a microarray experiment. The model predicts that a lower limit of 0.5 for the replicate concordance rate is needed in order to be certain of true positives. Our work demonstrates the importance and advantages of having a quantitative quality control scheme for microarrays.
authors.affiliation: Max McGee National Research Center for Juvenile Diabetes, Department of Pediatrics, Medical College and Children's Hospital of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA. xujing@mcw.edu
authors.forename: Xujing
authors.initials: X
authors.lastname: Wang
authors.forename: Martin J
authors.initials: MJ
authors.lastname: Hessner
authors.forename: Yan
authors.initials: Y
authors.lastname: Wu
authors.forename: Nirupma
authors.initials: N
authors.lastname: Pati
authors.forename: Soumitra
authors.initials: S
authors.lastname: Ghosh
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005189:False Positive Reactions; D020869:Gene Expression Profiling / Q000379:methods* / Q000592:standards; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000592:standards; D011786:Quality Control; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013223:Statistics as Topic