The high-resolution and mass accuracy of Fourier transform mass spectrometry (FT-MS) has made it an increasingly popular technique for discerning the composition of soil, plant and aquatic samples containing complex mixtures of proteins, carbohydrates, lipids, lignins, hydrocarbons, phytochemicals and other compounds. Thus, there is a growing demand for informatics tools to analyze FT-MS data that will aid investigators seeking to understand the availability of carbon compounds to biotic and abiotic oxidation and to compare fundamental chemical properties of complex samples across groups. We present ftmsRanalysis, an R package which provides an extensive collection of data formatting and processing, filtering, visualization, and sample and group comparison functionalities. The package provides a suite of plotting methods and enables expedient, flexible and interactive visualization of complex datasets through functions which link to a powerful and interactive visualization user interface, Trelliscope. Example analysis using FT-MS data from a soil microbiology study demonstrates the core functionality of the package and highlights the capabilities for producing interactive visualizations.

The Multi-Source Signatures for Nuclear Programs project, part of Pacific Northwest National Laboratory's (PNNL's) Signature Discovery Initiative, seeks to computationally capture expert assessment of multi-type information to assess nuclear activities through a series of Bayesian network (BN) models. Information types may include text, sensor output, imagery, or audio/video files. The BN models incorporate knowledge from a diverse range of information sources to help assess a country's nuclear activities. The models span engineering topic areas, country-level indicators, and facility-specific characteristics. To illustrate the development, calibration, and use of BN models for multi-source assessment, we present a model that predicts a country's likelihood to participate in the international nuclear nonproliferation regime. We validate this model by examining the extent to which the model helps non-experts arrive at conclusions similar to those provided by nuclear proliferation experts. We also describe the PNNL-developed software used throughout the lifecycle of the Bayesian network model development.

Heterogeneous data sets are typically represented in different feature spaces, making it difficult to analyze relationships spanning different data sets even when they are semantically related. Data fusion via space alignment can remedy this task by integrating multiple data sets lying in different spaces into one common space. Given a set of reference correspondence data that share the same semantic meaning across different spaces, space alignment attempts to place the corresponding reference data as close together as possible, and accordingly, the entire data are aligned in a common space. Space alignment involves optimizing two potentially conflicting criteria: minimum deformation of the original relationships and maximum alignment between the different spaces. To solve this problem, we provide a novel graph embedding framework for space alignment, which converts each data set into a graph and assigns zero distance between reference correspondence pairs resulting in a single graph. We propose a graph embedding method for fusion based on nonmetric multidimensional scaling (MDS). Its criteria using the rank order rather than the distance allows nonmetric MDS to effectively handle both deformation and alignment. Experiments using parallel data sets demonstrate that our approach works well in comparison to existing methods such as constrained Laplacian eigenmaps, Procrustes analysis, and tensor decomposition. We also present standard cross-domain information retrieval tests as well as interesting visualization examples using space alignment.

Bayesian networks provide a general framework with which to model many natural phenomena. The mathematical nature of Bayesian networks enables a plethora of model validation and calibration techniques: e.g. parameter learning, structure learning, goodness of fit tests, and diagnostic checking of the model assumptions. However, they are not free of shortcomings. With regard to parameter learning, in practice it is not uncommon to find oneself lacking adequate data to reliably estimate all model parameters. In this paper we present the early development of a novel application of conjoint analysis as a method for eliciting and modeling expert opinions and for using the results in a methodology for calibrating the parameters of a Bayesian network.

SUMMARY: ELISA-BASE is an open source database for capturing, organizing and analyzing enzyme-linked immunosorbent assay (ELISA) microarray data. ELISA-BASE is an extension of the BioArray Software Environment (BASE) database system. AVAILABILITY: http://www.pnl.gov/statistics/ProMAT/ELISA-BASE.stm.

UNLABELLED: The Bayesian Estimator of Protein-Protein Association Probabilities (BEPro aff3) is a software tool for estimating probabilities of protein-protein association between bait and prey protein pairs using data from multiple-bait, multiple-replicate, protein liquid chromatography tandem mass spectrometry LC-MS/MS affinity isolation experiments. AVAILABILITY: BEPro (3) is public domain software, has been tested on WIndows XP, Linux and Mac OS, and is freely available from http://www.pnl.gov/statistics/BEPro3. SUPPLEMENTARY INFORMATION: A user guide, example dataset with analysis and additional documentation are included with the BEPro (3) download.

The Software Environment for Biological Network Inference (SEBINI) has been created to provide an interactive environment for the deployment and testing of network inference algorithms that use high-throughput expression data. Networks inferred from the SEBINI software platform can be further analyzed using the Collective Analysis of Biological Interaction Networks (CABIN), software that allows integration and analysis of protein- protein interaction and gene-to-gene regulatory evidence obtained from multiple sources. In this paper, we present a case study on the SEBINI and CABIN tools for protein-protein interaction network reconstruction. Incorporating the Bayesian Estimator of Protein-Protein Association Probabilities (BEPro) algorithm into the SEBINI toolkit, we have created a pipeline for structural inference and supplemental analysis of protein- protein interaction networks from sets of mass spectrometry bait-prey experiment data.

SUMMARY: ProMAT is a software tool for statistically analyzing data from enzyme-linked immunosorbent assay microarray experiments. The software estimates standard curves, sample protein concentrations and their uncertainties for multiple assays. ProMAT generates a set of comprehensive figures for assessing results and diagnosing process quality. The tool is available for Windows or Mac, and is distributed as open-source Java and R code. AVAILABILITY: ProMAT is available at http://www.pnl.gov/statistics/ProMAT. ProMAT requires Java version 1.5.0 and R version 1.9.1 (or more recent versions). ProMAT requires either Windows XP or Mac OS 10.4 or newer versions.

UNLABELLED: The Automated Microarray Image Analysis (AMIA) Toolbox for MATLAB is a flexible, open-source, microarray image analysis tool that allows the user to customize analyses of microarray image sets. This tool provides several methods to identify and quantify spot statistics, as well as extensive diagnostic statistics and images to evaluate data quality and array processing. The open, modular nature of AMIA provides access to implementation details and encourages modification and extension of AMIA's capabilities. AVAILABILITY: The AMIA Toolbox is freely available at http://www.pnl.gov/statistics/amia. The AMIA Toolbox requires MATLAB 6.5 (R13) (MathWorks, Inc. Natick, MA), as well as the Statistics Toolbox 4.1 and Image Processing Toolbox 4.1 for MATLAB or more recent versions. CONTACT: [email protected]

BACKGROUND: Enzyme-linked immunosorbent assay (ELISA) is a standard immunoassay to estimate a protein's concentration in a sample. Deploying ELISA in a microarray format permits simultaneous estimation of the concentrations of numerous proteins in a small sample. These estimates, however, are uncertain due to processing error and biological variability. Evaluating estimation error is critical to interpreting biological significance and improving the ELISA microarray process. Estimation error evaluation must be automated to realize a reliable high-throughput ELISA microarray system. In this paper, we present a statistical method based on propagation of error to evaluate concentration estimation errors in the ELISA microarray process. Although propagation of error is central to this method and the focus of this paper, it is most effective only when comparable data are available. Therefore, we briefly discuss the roles of experimental design, data screening, normalization, and statistical diagnostics when evaluating ELISA microarray concentration estimation errors. RESULTS: We use an ELISA microarray investigation of breast cancer biomarkers to illustrate the evaluation of concentration estimation errors. The illustration begins with a description of the design and resulting data, followed by a brief discussion of data screening and normalization. In our illustration, we fit a standard curve to the screened and normalized data, review the modeling diagnostics, and apply propagation of error. We summarize the results with a simple, three-panel diagnostic visualization featuring a scatterplot of the standard data with logistic standard curve and 95% confidence intervals, an annotated histogram of sample measurements, and a plot of the 95% concentration coefficient of variation, or relative error, as a function of concentration. CONCLUSIONS: This statistical method should be of value in the rapid evaluation and quality control of high-throughput ELISA microarray analyses. Applying propagation of error to a variety of ELISA microarray concentration estimation models is straightforward. Displaying the results in the three-panel layout succinctly summarizes both the standard and sample data while providing an informative critique of applicability of the fitted model, the uncertainty in concentration estimates, and the quality of both the experiment and the ELISA microarray process.