Faculty Collaboration Database

Quantitative quality control in microarray experiments and the application in data filtering, normalization and false positive rate prediction. Bioinformatics 2003 Jul 22;19(11):1341-7

Date

07/23/2003

Pubmed ID

12874045

DOI

10.1093/bioinformatics/btg154

Scopus ID

2-s2.0-0041592552 (requires institutional sign-in at Scopus site)   70 Citations

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.

Author List

Wang X, Hessner MJ, Wu Y, Pati N, Ghosh S

Author

Martin J. Hessner PhD Professor in the Pediatrics department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Algorithms
Computer Simulation
False Positive Reactions
Gene Expression Profiling
Models, Genetic
Models, Statistical
Oligonucleotide Array Sequence Analysis
Quality Control
Reproducibility of Results
Sensitivity and Specificity
Signal Processing, Computer-Assisted
Statistics as Topic