Home Methods Translational Research and The Rich Analytical Environment
Translational Research and The Rich Analytical Environment Print

Conceptualizing, managing, and analyzing the data generated in complex translational research is a daunting undertaking.  We have the experience, strategies, and tools to tackle these challenges.

A conceptual overview of complex translational experiments is illustrated below.

The figure shows one subject who donates samples at multiple points in time.  Each sample can be divided into multiple aliquots, each of which might be analyzed with a different technology, such as flow cytometry, microarray gene expression, or Luminex.  Each technology is accompanied by technology-specific software that extracts features from the instrument output.  In a microarray experiment, the extraction software translates the images into numbers, and may perform additional data normalization and quality assurance.  In flow cytometry, the extraction involves manual or automated gating, and the export of summary statistics describing gated populations of cells.  At each sampling point, a variety of clinical data may be collected.

The figure is intentionally simplified, as it does not illustrate multiple patients or their attributes (such as age, gender, race, and genetic background), multiple tissue types (e.g. blood, tumor, or cerebrospinal fluid), or multiple ex-vivo sample preparations, such as stimulation with cytokines or antigens, or multiple incubations times. Some of these complicating dimensions in are described in “MIFlowCyt: the minimum information about a Flow Cytometry Experiment” (Lee et al. 2008).

Additionally, to be able to fully analyze this heterogeneous experimental data, it must be integrated into a rich analytical environment that supports thorough and flexible analysis of the overall experiment.  Such a system is illustrated below.

The integration is accomplished using standard practices of data warehousing as discussed by Ralph Kimball in The Data Warehouse Toolkit (2008) and Siebert (2006).  Taken together, these figures illustrate that feature extraction (e.g. gating, in the case of FCM) and analysis of experimental results are distinct activities with a well defined separation of concerns.  Creating the rich analytical environment and analyzing experimental results are our expertise.

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