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Comments on the article `A Systematic Approach to Planning for Designed Industrial Experiments,' by David E. Coleman and Douglas C. Montgomery, published in the February 1993 issue of `Technometrics.' Application of the authors' planning process in a research and development setting; Whether planning should be isolated from design and problem solving.

This article discusses a study of cell images in cell culture biology from an object-oriented point of view. The motivation of this research is to develop a statistical approach to cell image analysis that better supports the automated development of stem cell growth media. A major hurdle in this process is the need for human expertise, based on studying cells under the microscope, to make decisions about the next step of the cell culture process. We aim to use digital imaging technology coupled with statistical analysis to tackle this important problem. The discussion in this article highlights a common critical issue: choice of data objects. Instead of conventionally treating either the individual cells or the wells (a container in which the cells are grown) as data objects, a new type of data object is proposed, that is the union of a well with its corresponding set of cells. The image data analysis suggests that the cell–well unions can be a better choice of data objects than the cells or the wells alone. The data are available in the online supplementary materials. [ABSTRACT FROM AUTHOR]

In the biomedical products industry, measures of the quality of individual clinical specimens or manufacturing production units are often available in the form of high-dimensional data such as continuous recordings obtained from an analytical instrument. These recordings are then examined by experts in the field who extract certain features and use these to classify individuals. To formalize and quantify this procedure, an approach for extracting features from recordings based on nonparametric regression is described. These features are then used to build a classification model that incorporates the knowledge of the expert. The procedure is illustrated with the problem of grading of syringes from associated friction profile data. Features of the syringe friction profiles used in the classification are extracted via smoothing splines, and grades of the syringes are assigned by an expert tribologist. A nonlinear classification model is constructed to predict syringe grades based on the extracted features. The classification model males it possible to grade syringes automatically without expert inspection. Using leave-one-out cross-validation, the prediction accuracy of the classification model is found to be about the same as the accuracy obtained from the expert. [ABSTRACT FROM AUTHOR]

Background: Recent advances in automation technologies have enabled the use of flow cytometry for high throughput screening, generating large complex data sets often in clinical trials or drug discovery settings. However, data management and data analysis methods have not advanced sufficiently far from the initial small-scale studies to support modeling in the presence of multiple covariates. Results: We developed a set of flexible open source computational tools in the R package flowCore to facilitate the analysis of these complex data. A key component of which is having suitable data structures that support the application of similar operations to a collection of samples or a clinical cohort. In addition, our software constitutes a shared and extensible research platform that enables collaboration between bioinformaticians, computer scientists, statisticians, biologists and clinicians. This platform will foster the development of novel analytic methods for flow cytometry. Conclusion: The software has been applied in the analysis of various data sets and its data structures have proven to be highly efficient in capturing and organizing the analytic work flow. Finally, a number of additional Bioconductor packages successfully build on the infrastructure provided by flowCore, open new avenues for flow data analysis. [ABSTRACT FROM AUTHOR]

Background: Diminished IL-2 production and lack of effector differentiation have been reported for HIV-specific T cells. In this study, we examined the prevalence of these phenomena using 8-color cytokine flow cytometry, and tested the hypothesis that these two findings were causally related. We analyzed cytokine profiles and memory/effector phenotypes of HIV-specific and CMV-specific T cells using short-term in vitro stimulation with HIV or CMV peptide pools. Nineteen HIV-positive subjects with progressive disease and twenty healthy, HIV-negative subjects were examined. Results: Among HIV-infected subjects, there were significantly fewer CD8+ IL-2+ T cells responding to HIV compared to CMV, with no significant difference in CD4+ IL-2+ T cells. The majority of CMV-specific T cells in both HIV-negative and HIV-positive subjects appeared to be terminally differentiated effector cells (CD8+ CD27- CD28- CD45RA+ or CD8+ CD27- CD28- CD45RA-). In HIV-positive subjects, the most common phenotype of HIV-specific T cells was intermediate in differentiation (CD8+ CD27+ CD28- CD45RA-). These differences were statistically significant, both as absolute cell frequencies and as percentages. There was a significant correlation between the absolute number of HIV-specific CD8+ IL-2+ T cells and HIV-specific CD8+ CD27- CD28- CD45RA+ terminal effector cells. Conclusion: IL-2 production from antigen-specific CD8+ T cells correlates with effector cell differentiation of those cells. [ABSTRACT FROM AUTHOR]

In this report, we describe a new flow cytometry technique termed flow cytometric high-content screening (FC-HCS) which involves semi-automated processing and analysis of multiparameter flow cytometry samples. As a first test of the FC-HCS technique, we used it to screen a 2000-compound library, called the National Cancer Institute (NCI) Diversity Set, to identify agents that would enhance the anti-lymphoma activity of the therapeutic monoclonal antibody rituximab. FC-HCS identified 15 compounds from the Diversity Set that significantly enhanced the ability of rituximab to inhibit cell cycle progression and induce apoptosis in lymphoma cells. The validity of the screening results was confirmed for several compounds using additional assays of cell proliferation, apoptosis and cell growth. The FC-HCS technique was relatively simple and reliable and could process up to 1000 samples/day on a single flow cytometer. The FC-HCS technique may be useful for a variety of applications including drug discovery, immunologic monitoring of patients, functional genomics studies and tissue engineering efforts. [Copyright &y& Elsevier]

Background: In mitogen-stimulated lymphocyte responses (sLR), cytokines and cell-surface receptors in peripheral human blood lymphocytes (PBL) are sensitive to cyclosporine (CsA), and can predict its in vivo effect with pharmacodynamic (PD) modeling. This is not known for multiple-agent combinations. Methods: Twenty-five concentration mixtures of CsA (0–1200 ng/ml) plus sirolimus (SRL, 0–30 ng/ml) were added to whole blood from five normal human subjects (NHS) for effect on a limited array of six targets. Effect–concentration relationships were analyzed with Emax PD equations, and expressed as the range of concentration mixtures associated with one-half of maximal inhibitory effect (EC50) on a model biomarker target. This predicted range was examined to see whether it contained representative concentration mixtures of these two drugs, which were associated with a stable post-transplant outcome in a logistic regression model of 1039 clinical trial patients. Results: PD analyses suggested that in NHS samples containing CsA+SRL (n=5), (1) PMA–Ionomycin-stimulated T-cell expression of intracellular IL-2, TNF-α, and IFN-γ was inhibited by CsA, and minimally by SRL, and (2) the two agents inhibited pokeweed mitogen (PWM)-stimulated B-cell expression of CD54 and CD95, but not CD86 (ICAM-1, Fas antigen, and B7.2), synergistically. With CD54 as the model biomarker, contour plots also predicted a wide range of concentration mixtures of the two agents across which an EC50 could be predicted for CsA+SRL in a population (e.g., CsA-72 ng/ml+SRL 15 ng/ml, n=5), as well as in the individual subject (e.g., CsA-0 ng/ml+SRL-13.75 ng/ml in NHS-D310). Logistic regression analysis of clinical outcomes in 1039 patients suggested that the concentration mixture of CsA≅50–150 ng/ml+SRL≅10 ng/ml was associated with a stable post-transplant course. The EC50 contour plot for CD54 suggested a nearly identical CsA concentration of 120 ng/ml in the presence of 10 ng/ml of sirolimus. Conclusions: Our data suggest that pharmacodynamic evaluation of immunosuppressive agents with biomarkers may be an efficient process with which to characterize immunosuppressive effect of combination agents in individual patients and in patient populations. [Copyright &y& Elsevier]

Summary: R/DWD is an extensible package for classification. It is built based on a recently developed powerful classification method called distance weighted discrimination (DWD). DWD is related to, and has been shown to be superior to, the support vector machine in situations that are fundamental to bioinformatics, such as very high dimensional data. DWD has proven to be very useful for several fundamental bioinformatics tasks, including classification, data visualization and removal of biases, such as batch effects. Earlier DWD implementations, however, relied on Matlab, which is not free and requires a license. The major contribution of the R/DWD package is an implementation that is completely in R and thus can be used without any requirements for licensing or software purchase. In addition, R/DWD also provides efficient solvers for second-order-cone-programming and quadratic programming.Availability and implementation: The package is freely available from cran.r-project.org.Contact: hanwen.huang@uth.tmc.edu; Perry_Haaland@bd.comSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]