Data Quality Assessment of Ungated Flow Cytometry Data in High

Background: The recent development of semi-automated techniques for staining and analyzing flow cytometry samples has presented new challenges. Quality control and quality assessment are critical when developing new high throughput technologies and their associated information services. Our experience suggests that significant bottlenecks remain in the development of high throughput flow cytometry methods for data analysis and display. Especially, data quality control and quality assessment are crucial steps in processing and analyzing high throughput flow cytometry data. Methods: We propose a variety of graphical exploratory data analytic tools for exploring ungated flow cytometry data. We have implemented a number of specialized functions and methods in the Bioconductor package rflowcyt. We demonstrate the use of these approaches by investigating two independent sets of high throughput flow cytometry data. Results: We found that graphical representations can reveal substantial non-biological differences in samples. Empirical Cumulative Distribution Function and summary scatterplots were especially useful in the rapid identification of problems not identified by manual review. Conclusions: Graphical exploratory data analytic tools are quick and useful means of assessing data quality. We propose that the described visualizations should be used as quality assessment tools and where possible, be used for quality control.

Reproducible Research: A Bioinformatics Case Study

While scientific research and the methodologies involved have gone through substantial technological evolution the technology involved in the publication of the results of these endeavors has remained relatively stagnant. Publication is largely done in the same manner today as it was fifty years ago. Many journals have adopted electronic formats, however, their orientation and style is little different from a printed document. The documents tend to be static and take little advantage of computational resources that might be available. Recent work, Gentleman and Temple Lang (2004), suggests a methodology and basic infrastructure that can be used to publish documents in a substantially different way. Their approach is suitable for the publication of papers whose message relies on computation. Stated quite simply, Gentleman and Temple Lang propose a paradigm where documents are mixtures of code and text. Such documents may be self-contained or they may be a component of a compendium which provides the infrastructure needed to provide access to data and supporting software. These documents, or compendiums, can be processed in a number of different ways. One transformation will be to replace the code with its output -- thereby providing the familiar, but limited, static document. In this paper we apply these concepts to a seminal paper in bioinformatics, namely The Molecular Classification of Cancer, Golub et al. (1999). The authors of that paper have generously provided data and other information that have allowed us to largely reproduce their results. Rather than reproduce this paper exactly we demonstrate that such a reproduction is possible and instead concentrate on demonstrating the usefulness of the compendium concept itself.