Dataset Information

TAMEE: data management and analysis for tissue microarrays.

ABSTRACT:

Background

With the introduction of tissue microarrays (TMAs) researchers can investigate gene and protein expression in tissues on a high-throughput scale. TMAs generate a wealth of data calling for extended, high level data management. Enhanced data analysis and systematic data management are required for traceability and reproducibility of experiments and provision of results in a timely and reliable fashion. Robust and scalable applications have to be utilized, which allow secure data access, manipulation and evaluation for researchers from different laboratories.

Results

TAMEE (Tissue Array Management and Evaluation Environment) is a web-based database application for the management and analysis of data resulting from the production and application of TMAs. It facilitates storage of production and experimental parameters, of images generated throughout the TMA workflow, and of results from core evaluation. Database content consistency is achieved using structured classifications of parameters. This allows the extraction of high quality results for subsequent biologically-relevant data analyses. Tissue cores in the images of stained tissue sections are automatically located and extracted and can be evaluated using a set of predefined analysis algorithms. Additional evaluation algorithms can be easily integrated into the application via a plug-in interface. Downstream analysis of results is facilitated via a flexible query generator.

Conclusion

We have developed an integrated system tailored to the specific needs of research projects using high density TMAs. It covers the complete workflow of TMA production, experimental use and subsequent analysis. The system is freely available for academic and non-profit institutions from http://genome.tugraz.at/Software/TAMEE.

SUBMITTER: Thallinger GG

PROVIDER: S-EPMC1838435 | biostudies-literature | 2007 Mar

REPOSITORIES: biostudies-literature

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Publications

TAMEE: data management and analysis for tissue microarrays.

Thallinger Gerhard G GG Baumgartner Kerstin K Pirklbauer Martin M Uray Martina M Pauritsch Elke E Mehes Gabor G Buck Charles R CR Zatloukal Kurt K Trajanoski Zlatko Z

BMC bioinformatics 20070307

<h4>Background</h4>With the introduction of tissue microarrays (TMAs) researchers can investigate gene and protein expression in tissues on a high-throughput scale. TMAs generate a wealth of data calling for extended, high level data management. Enhanced data analysis and systematic data management are required for traceability and reproducibility of experiments and provision of results in a timely and reliable fashion. Robust and scalable applications have to be utilized, which allow secure dat ...[more]

PMID: 17343750

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Project description:BackgroundA major challenge in studies of etiologic heterogeneity in breast cancer has been the limited throughput, accuracy, and reproducibility of measuring tissue markers. Computerized image analysis systems may help address these concerns, but published reports of their use are limited. We assessed agreement between automated and pathologist scores of a diverse set of immunohistochemical assays done on breast cancer tissue microarrays (TMA).MethodsTMAs of 440 breast cancers previously stained for estrogen receptor (ER)-alpha, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), ER-beta, and aromatase were independently scored by two pathologists and three automated systems (TMALab II, TMAx, and Ariol). Agreement between automated and pathologist scores of negative/positive was measured using the area under the receiver operating characteristics curve (AUC) and weighted kappa statistics for categorical scores. We also investigated the correlation between immunohistochemical scores and mRNA expression levels.ResultsAgreement between pathologist and automated negative/positive and categorical scores was excellent for ER-alpha and PR (AUC range = 0.98-0.99; kappa range = 0.86-0.91). Lower levels of agreement were seen for ER-beta categorical scores (AUC = 0.99-1.0; kappa = 0.80-0.86) and both negative/positive and categorical scores for aromatase (AUC = 0.85-0.96; kappa = 0.41-0.67) and HER2 (AUC = 0.94-0.97; kappa = 0.53-0.72). For ER-alpha and PR, there was a strong correlation between mRNA levels and automated (rho = 0.67-0.74) and pathologist immunohistochemical scores (rho = 0.67-0.77). HER2 mRNA levels were more strongly correlated with pathologist (rho = 0.63) than automated immunohistochemical scores (rho = 0.41-0.49).ConclusionsAutomated analysis of immunohistochemical markers is a promising approach for scoring large numbers of breast cancer tissues in epidemiologic investigations. This would facilitate studies of etiologic heterogeneity, which ultimately may allow improved risk prediction and better prevention approaches.

Project description:BackgroundA nearly complete collection of gene-deletion mutants (96% of annotated open reading frames) of the yeast Saccharomyces cerevisiae has been systematically constructed. Tag microarrays are widely used to measure the fitness of each mutant in a mutant mixture. The tag array experiments can have a complex experimental design, such as time course measurements and drug treatment with multiple dosages.ResultsTagSmart is a web application for analysis and visualization of Saccharomyces cerevisiae mutant fitness data measured by tag microarrays. It implements a robust statistical approach to assess the concentration differences among S. cerevisiae mutant strains. It also provides an interactive environment for data analysis and visualization. TagSmart has the following advantages over previously described analysis procedures: 1) it is user-friendly software rather than merely a description of analytical procedure; 2) It can handle complicated experimental designs, such as multiple time points and treatment with multiple dosages; 3) it has higher sensitivity and specificity; 4) It allows users to mask out "bad" tags in the analysis. Two biological tests were performed to illustrate the performance of TagSmart. First, we generated titration mixtures of mutant strains, in which the relative concentration of each strain was controlled. We used tag microarrays to measure the numbers of tag copies in each titration mixture. The data was analyzed with TagSmart and the result showed high precision and recall. Second, TagSmart was applied to a dataset in which heterozygous deletion strain mixture pools were treated with a new drug, Cincreasin. TagSmart identified 53 mutant strains as sensitive to Cincreasin treatment. We individually tested each identified mutant, and found 52 out of the 53 predicted mutants were indeed sensitive to Cincreasin.ConclusionTagSmart is provided "as is" to analyze tag array data produced by Affymetrix and Agilent arrays. TagSmart web application is assessable by Windows, Mac, and Linux users. It also has a downloadable version for execution on PCs running Windows. TagSmart is available for academic use at: http://biocomp.bioen.uiuc.edu/tagsmart.

Dataset Information

TAMEE: data management and analysis for tissue microarrays.

Background

Results

Conclusion

Publications

TAMEE: data management and analysis for tissue microarrays.

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