Libaffy: software for processing Affymetrix GeneChip data.

SUMMARY: Affymetrix GeneChip microarrays are increasingly used in gene expression studies and in greater number. A software library was developed that supports Affymetrix file formats and implements two popular summary algorithms (MAS5.0 and RMA). The library is modular in design for integration into larger systems and processing pipelines. Additionally, a graphical interface (GENE) was developed to allow end-user access to the functionality within the library. AVAILABILITY: libaffy is free to use under the GNU GPL license. The source code and Windows binaries can be freely accessed from the website http://src.moffitt.usf.edu/libaffy. Additional API documentation and user manual are available.

Development and application of a novel metric to assess effectiveness of biomedical data.

OBJECTIVE: Design a metric to assess the comparative effectiveness of biomedical data elements within a study that incorporates their statistical relatedness to a given outcome variable as well as a measurement of the quality of their underlying data. MATERIALS AND METHODS: The cohort consisted of 874 patients with adenocarcinoma of the lung, each with 47 clinical data elements. The p value for each element was calculated using the Cox proportional hazard univariable regression model with overall survival as the endpoint. An attribute or A-score was calculated by quantification of an element's four quality attributes; Completeness, Comprehensiveness, Consistency and Overall-cost. An effectiveness or E-score was obtained by calculating the conditional probabilities of the p-value and A-score within the given data set with their product equaling the effectiveness score (E-score). RESULTS: The E-score metric provided information about the utility of an element beyond an outcome-related p value ranking. E-scores for elements age-at-diagnosis, gender and tobacco-use showed utility above what their respective p values alone would indicate due to their relative ease of acquisition, that is, higher A-scores. Conversely, elements surgery-site, histologic-type and pathological-TNM stage were down-ranked in comparison to their p values based on lower A-scores caused by significantly higher acquisition costs. CONCLUSIONS: A novel metric termed E-score was developed which incorporates standard statistics with data quality metrics and was tested on elements from a large lung cohort. Results show that an element's underlying data quality is an important consideration in addition to p value correlation to outcome when determining the element's clinical or research utility in a study.

Tissue-specific RMA models to incrementally normalize Affymetrix GeneChip data.

Gene expression classifiers have been used to predict diagnosis of disease, patient prognosis and patient response to therapy. Although there have been remarkable successes in this area, a particular goal of personalized medicine is the ability predict a response from a single gene expression microarray. One aspect of this problem is the normalization of microarrays. Affymetrix GeneChip microarrays are typically processed using model-based algorithms that require all of the data in order to adequately estimate the model. We experiment with the RMA normalization procedure in an incremental fashion, adding new chips to an existing normalization model. Focusing on tissue-specific normalization models, we generate datasets that have very small differences from a batch normalization. Through several large datasets of patient samples, we provide evidence that RMA models of normalization converge to a common model in homogenous samples. These results offer the promise of maintaining large data warehouses of patient microarray samples without the requirement of constant renormalization.