A method for detecting and correcting feature misidentification on expression microarrays

• Published in: BMC genomics 2004-09, Vol.5 (1), p.64-64, Article 64
• Main Authors: Tu, I-Ping, Schaner, Marci, Diehn, Maximilian, Sikic, Branimir I, Brown, Patrick O, Botstein, David, Fero, Michael J
• Format: Article
• Language: English
• Subjects: Animals; Female; Gene Expression Profiling - standards; Gene Expression Profiling - statistics & numerical data; Gene Expression Regulation, Neoplastic - genetics; Humans; Mice; Oligonucleotide Array Sequence Analysis - standards; Oligonucleotide Array Sequence Analysis - statistics & numerical data; Ovarian Neoplasms - genetics; Software
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/1471-2164-5-64

Much of the microarray data published at Stanford is based on mouse and human arrays produced under controlled and monitored conditions at the Brown and Botstein laboratories and at the Stanford Functional Genomics Facility (SFGF). Nevertheless, as large datasets based on the Stanford Human array began to accumulate, a small but significant number of discrepancies were detected that required a serious attempt to track down the original source of error. Due to a controlled process environment, sufficient data was available to accurately track the entire process leading to up to the final expression data. In this paper, we describe our statistical methods to detect the inconsistencies in microarray data that arise from process errors, and discuss our technique to locate and fix these errors. To date, the Brown and Botstein laboratories and the Stanford Functional Genomics Facility have together produced 40,000 large-scale (10-50,000 feature) cDNA microarrays. By applying the heuristic described here, we have been able to check most of these arrays for misidentified features, and have been able to confidently apply fixes to the data where needed. Out of the 265 million features checked in our database, problems were detected and corrected on 1.3 million of them. Process errors in any genome scale high throughput production regime can lead to subsequent errors in data analysis. We show the value of tracking multi-step high throughput operations by using this knowledge to detect and correct misidentified data on gene expression microarrays.