Asymmetric microarray data produces gene lists highly predictive of research literature on multiple cancer types

Background: Much of the public access cancer microarray data is asymmetric, belonging to datasets containing no samples from normal tissue. Asymmetric data cannot be used in standard meta-analysis approaches (such as the inverse variance method) to obtain large sample sizes for statistical power enrichment. Noting that plenty of normal tissue microarray samples exist in studies not involving cancer, we investigated the viability and accuracy of an integrated microarray analysis approach based on significance analysis of microarrays (merged SAM) using a collection of data from separate diseased and normal samples. Results: We focused on five solid cancer types (colon, kidney, liver, lung, and pancreas), where available microarray data allowed us to compare meta-analysis and integrated approaches. Our results from the merged SAM significantly overlapped gene lists from the validated inverse-variance method. Both meta-analysis and merged SAM approaches successfully captured the aberrance...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Noor B. Dawany, Aydin Tozeren
• Keywords: BMCBI 2010, Cancer, Meta-analysis, Microarray Data