CDS: a fold-change based statistical test for concomitant identification of distinctness and similarity in gene expression analysis.

The problem of identifying differential activity such as in gene expression is a major defeat in biostatistics and bioinformatics. Equally important, however much less frequently studied, is the question of similar activity from one biological condition to another. The fold-change, or ratio, is usually considered a relevant criterion for stating difference and similarity between measurements.

Rapid assessment of age-related differences in standing balance.

As life expectancy continues to rise, in the future there will be an increasing number of older people prone to falling. Accordingly, there is an urgent need for comprehensive testing of older individuals to collect data and to identify possible risk factors for falling. Here we use a low-cost force platform to rapidly assess deficits in balance under various conditions.

Quality assessment of transcriptome data using intrinsic statistical properties.

In view of potential application to biomedical diagnosis, tight transcriptome data quality control is compulsory. Usually, quality control is achieved using labeling and hybridization controls added at different stages throughout the processing of the biologic RNA samples. These control measures, however, only reflect the performance of the individual technical manipulations during the entire process and have no bearing as to the continued integrity of the RNA sample itself.

Generation of synthetic transcriptome data with defined statistical properties for the development and testing of new analysis methods.

We have previously developed a combined signal/variance distribution model that accounts for the particular statistical properties of datasets generated on the Applied Biosystems AB1700 transcriptome system. Here we show that this model can be efficiently used to generate synthetic datasets with statistical properties virtually identical to those of the actual data by aid of the JAVA application ace.map creator 1.

High-sensitivity transcriptome data structure and implications for analysis and biologic interpretation.

Novel microarray technologies such as the AB1700 platform from Applied Biosystems promise significant increases in the signal dynamic range and a higher sensitivity for weakly expressed transcripts. We have compared a representative set of AB1700 data with a similarly representative Affymetrix HG-U133A dataset. The AB1700 design extends the signal dynamic detection range at the lower bound by one order of magnitude.

Normalization using weighted negative second order exponential error functions (NeONORM) provides robustness against asymmetries in comparative transcriptome profiles and avoids false calls.

Studies on high-throughput global gene expression using microarray technology have generated ever larger amounts of systematic transcriptome data. A major challenge in exploiting these heterogeneous datasets is how to normalize the expression profiles by inter-assay methods. Different non-linear and linear normalization methods have been developed, which essentially rely on the hypothesis that the true or perceived logarithmic fold-change distributions between two different assays are symmetric in nature.

Avoiding inconsistencies over time and tracking difficulties in Applied Biosystems AB1700/Panther probe-to-gene annotations.

Background: Significant inconsistencies between probe-to-gene annotations between different releases of probe set identifiers by commercial microarray platform solutions have been reported. Such inconsistencies lead to misleading or ambiguous interpretation of published gene expression results.

Results: We report here similar inconsistencies in the probe-to-gene annotation of Applied Biosystems AB1700 data, demonstrating that this is not an isolated concern.