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Journal article
Asymmetric microarray data produces gene lists highly predictive of research literature on multiple cancer types
BMC bioinformatics, v 11(1), pp 483-483
27 Sep 2010
PMID: 20875095
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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 aberrances in the cell cycle that commonly occur in the different cancer types. However, the integrated SAM analysis replicated the known cancer literature (excluding microarray studies) with much more accuracy than the meta-analysis.
Conclusion: The merged SAM test is a powerful, robust approach for combining data from similar platforms and for analyzing asymmetric datasets, including those with only normal or only cancer samples that cannot be utilized by meta-analysis methods. The integrated SAM approach can also be used in comparing global gene expression between various subtypes of cancer arising from the same tissue.
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Details
- Title
- Asymmetric microarray data produces gene lists highly predictive of research literature on multiple cancer types
- Creators
- Noor B. Dawany - Drexel UniversityAydin Tozeren - Drexel University
- Publication Details
- BMC bioinformatics, v 11(1), pp 483-483
- Publisher
- Springer Nature
- Number of pages
- 14
- Grant note
- BioAdvance funds Calhoun fellowship
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- [Retired Faculty]
- Web of Science ID
- WOS:000282657300001
- Scopus ID
- 2-s2.0-77957114898
- Other Identifier
- 991019168023604721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Biochemical Research Methods
- Biotechnology & Applied Microbiology
- Mathematical & Computational Biology