PMart Web Application: Marketplace for Interactive Analysis of Panomics Data.

PMart is a web-based tool for reproducible quality control, exploratory data analysis, statistical analysis, and interactive visualization of 'omics data, based on the functionality of the pmartR R package. The newly improved user interface supports more 'omics data types, additional statistical capabilities, and enhanced options for creating downloadable graphics. PMart supports the analysis of label-free and isobaric-labeled (e.g., TMT, iTRAQ) proteomics, nuclear magnetic resonance (NMR) and mass-spectrometry (MS)-based metabolomics, MS-based lipidomics, and ribonucleic acid sequencing (RNA-seq) transcriptomics data. At the end of a PMart session, a report is available that summarizes the processing steps performed and includes the pmartR R package functions used to execute the data processing. In addition, built-in safeguards in the backend code prevent users from utilizing methods that are inappropriate based on omics data type. PMart is a user-friendly interface for conducting exploratory data analysis and statistical comparisons of omics data without programming.

PSpecteR: A User-Friendly and Interactive Application for Visualizing Top-Down and Bottom-Up Proteomics Data in R.

Visual examination of mass spectrometry data is necessary to assess data quality and to facilitate data exploration. Graphics provide the means to evaluate spectral properties, test alternative peptide/protein sequence matches, prepare annotated spectra for publication, and fine-tune parameters during wet lab procedures. Visual inspection of LC-MS data is constrained by proteomics visualization software designed for particular workflows or vendor-specific tools without open-source code. We built PSpecteR, an open-source and interactive R Shiny web application for visualization of LC-MS data, with support for several steps of proteomics data processing, including reading various mass spectrometry files, running open-source database search engines, labeling spectra with fragmentation patterns, testing post-translational modifications, plotting where identified fragments map to reference sequences, and visualizing algorithmic output and metadata. All figures, tables, and spectra are exportable within one easy-to-use graphical user interface. Our current software provides a flexible and modern R framework to support fast implementation of additional features. The open-source code is readily available (https://github.com/EMSL-Computing/PSpecteR), and a PSpecteR Docker container (https://hub.docker.com/r/emslcomputing/pspecter) is available for easy local installation.

ftmsRanalysis: An R package for exploratory data analysis and interactive visualization of FT-MS data.

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.

P-Mart: Interactive Analysis of Ion Abundance Global Proteomics Data.

The use of mass-spectrometry-based techniques for global protein profiling of biomedical or environmental experiments has become a major focus in research centered on biomarker discovery; however, one of the most important issues recently highlighted in the new era of omics data generation is the ability to perform analyses in a robust and reproducible manner. This has been hypothesized to be one of the issues hindering the ability of clinical proteomics to successfully identify clinical diagnostic and prognostic biomarkers of disease. P-Mart ( https://pmart.labworks.org ) is a new interactive web-based software environment that enables domain scientists to perform quality-control processing, statistics, and exploration of large-complex proteomics data sets without requiring statistical programming. P-Mart is developed in a manner that allows researchers to perform analyses via a series of modules, explore the results using interactive visualization, and finalize the analyses with a collection of output files documenting all stages of the analysis and a report to allow reproduction of the analysis.

Degree of anxiety in food allergic children in a tertiary care center.

BACKGROUND: The link between internalizing psychiatric disorders, such as anxiety and depression, and allergic diseases has attracted a high level of interest from psychiatrists and immunologists. Recent studies have found increased anxiety in children with asthma, but findings in children with food allergy (FA) have been inconsistent. OBJECTIVE: It was hypothesized that children with FA would score significantly higher on a standardized anxiety screen than general pediatric (GP) patients but not as high as patients with diagnosed anxiety disorders. METHODS: A total of 114 patients aged 8 to 16 years (37 with confirmed anxiety disorder from a pediatric psychiatry clinic, 40 with confirmed FA from a pediatric allergy clinic, and 43 well-care patients from a GP clinic) and their mothers completed the Screen for Child Anxiety Related Emotional Disorders (SCARED). RESULTS: Children and mothers in the allergy group did not report increased levels of anxiety in children on total SCARED scores or subscales compared with children and mothers from the GP group. There was a trend toward increased panic disorder symptoms reported in children by mothers of children in the allergy group, but this finding did not reach statistical significance. CONCLUSION: Children with FA did not have increased anxiety; however, there was a trend for mothers of children with allergies to report more symptoms of panic disorder in their children. It remains important to screen families for anxiety-related symptoms and refer them to mental health services when indicated.

Theory and practice of using cell strainers to sort Caenorhabditis elegans by size.

The nematode Caenorhabditis elegans is a model organism widely used in basic, translational, and industrial research. C. elegans development is characterized by five morphologically distinct stages, including four larval stages and the adult stage. Stages differ in a variety of aspects including size, gene expression, physiology, and behavior. Enrichment for a particular developmental stage is often the first step in experimental design. When many hundreds of worms are required, the standard methods of enrichment are to grow a synchronized population of hatchlings for a fixed time, or to sort a mixed population of worms according to size. Current size-sorting methods have higher throughput than synchronization and avoid its use of harsh chemicals. However, these size-sorting methods currently require expensive instrumentation or custom microfluidic devices, both of which are unavailable to the majority C. elegans laboratories. Accordingly, there is a need for inexpensive, accessible sorting strategies. We investigated the use of low-cost, commercially available cell strainers to filter C. elegans by size. We found that the probability of recovery after filtration as a function of body size for cell strainers of three different mesh sizes is well described by logistic functions. Application of these functions to predict filtration outcomes revealed non-ideal properties of filtration of worms by cell strainers that nevertheless enhanced filtration outcomes. Further, we found that serial filtration using a pair of strainers that have different mesh sizes can be used to enrich for particular larval stages with a purity close to that of synchronization, the most widely used enrichment method. Throughput of the cell strainer method, up to 14,000 worms per minute, greatly exceeds that of other enrichment methods. We conclude that size sorting by cell strainers is a useful addition to the array of existing methods for enrichment of particular developmental stages in C. elegans.

The conserved endocannabinoid anandamide modulates olfactory sensitivity to induce hedonic feeding in C. elegans.

The ability of cannabis to increase food consumption has been known for centuries. In addition to producing hyperphagia, cannabinoids can amplify existing preferences for calorically dense, palatable food sources, a phenomenon called hedonic amplification of feeding. These effects result from the action of plant-derived cannabinoids that mimic endogenous ligands called endocannabinoids. The high degree of conservation of cannabinoid signaling at the molecular level across the animal kingdom suggests hedonic feeding may also be widely conserved. Here, we show that exposure of Caenorhabditis elegans to anandamide, an endocannabinoid common to nematodes and mammals, shifts both appetitive and consummatory responses toward nutritionally superior food, an effect analogous to hedonic feeding. We find that anandamide's effect on feeding requires the C. elegans cannabinoid receptor NPR-19 but can also be mediated by the human CB1 cannabinoid receptor, indicating functional conservation between the nematode and mammalian endocannabinoid systems for the regulation of food preferences. Furthermore, anandamide has reciprocal effects on appetitive and consummatory responses to food, increasing and decreasing responses to inferior and superior foods, respectively. Anandamide's behavioral effects require the AWC chemosensory neurons, and anandamide renders these neurons more sensitive to superior foods and less sensitive to inferior foods, mirroring the reciprocal effects seen at the behavioral level. Our findings reveal a surprising degree of functional conservation in the effects of endocannabinoids on hedonic feeding across species and establish a new system to investigate the cellular and molecular basis of endocannabinoid system function in the regulation of food choice.

Intact maternal buffering of stress response in infant rats despite altered responsivity towards maternal olfactory cues in the valproic acid model of autism-like behavior.

Disrupted processing of social cues and altered social behaviors are among the core symptoms of autism spectrum disorders (ASDs), and they emerge as early as the first year of life. These differences in sensory abilities may affect the ability of children with ASDs to securely attach to a caregiver and experience caregiver buffering of stress. Prenatal exposure to valproic acid (VPA) has been used to model some aspects of ASDs in rodents. Here, we asked whether prenatal VPA exposure altered infant rats' behavioral responsivity to maternal olfactory cues in an Odor Preference Test (OPT) and affected maternal buffering of infants' stress responsivity to shock. In the odor preference test, 1-week old rats treated with VPA during pregnancy appeared to have impaired social recognition and/or may be less motivated to approach social odors in early infancy. These effects were particularly prominent in female pups. In 2-week old rats, VPA-exposed pups and saline-exposed pups showed similar preferences for home cage bedding. Although VPA-exposed pups may initially have a deficit in this attachment-related behavior they do recover typical responses to home cage bedding in later infancy. Both control and VPA-exposed pups showed robust stress hormone responses to repeated shocks, an effect which was blocked when a calm mother was present during shock exposure. No sex differences in the effect of maternal presence on the stress response to shock and no interactions between sex and prenatal drug exposure were observed. Although VPA-exposed pups may show impaired responsivity to maternal cues in early infancy, maternal presence is still capable of regulating the stress response in VPA-exposed pups. In this study we demonstrate the importance of utilizing multiple batteries of tests in assessing behavior, dissecting the behavior on one test into different components. Our results inform about the underlying behavioral characteristics of some of the ASD phenotypes, including sex differences reported by clinical studies, and could shed light on potential opportunities for intervention.

An internal calibration method for protein-array studies.

Nuisance factors in a protein-array study add obfuscating variation to spot intensity measurements, diminishing the accuracy and precision of protein concentration predictions. The effects of nuisance factors may be reduced by design of experiments, and by estimating and then subtracting nuisance effects. Estimated nuisance effects also inform about the quality of the study and suggest refinements for future studies.We demonstrate a method to reduce nuisance effects by incorporating a non-interfering internal calibration in the study design and its complemental analysis of variance. We illustrate this method by applying a chip-level internal calibration in a biomarker discovery study. The variability of sample intensity estimates was reduced 16% to 92% with a median of 58%; confidence interval widths were reduced 8% to 70% with a median of 35%. Calibration diagnostics revealed processing nuisance trends potentially related to spot print order and chip location on a slide. The accuracy and precision of a protein-array study may be increased by incorporating a non-interfering internal calibration. Internal calibration modeling diagnostics improve confidence in study results and suggest process steps that may need refinement. Though developed for our protein-array studies, this internal calibration method is applicable to other targeted array-based studies.

ELISA-BASE: an integrated bioinformatics tool for analyzing and tracking ELISA microarray data.

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.

A Bayesian estimator of protein-protein association probabilities.

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.

Predicting protein concentrations with ELISA microarray assays, monotonic splines and Monte Carlo simulation.

Making sound proteomic inferences using ELISA microarray assay requires both an accurate prediction of protein concentration and a credible estimate of its error. We present a method using monotonic spline statistical models (MS), penalized constrained least squares fitting (PCLS) and Monte Carlo simulation (MC) to predict ELISA microarray protein concentrations and estimate their prediction errors. We contrast the MSMC (monotone spline Monte Carlo) method with a LNLS (logistic nonlinear least squares) method using simulated and real ELISA microarray data sets.MSMC rendered good fits in almost all tests, including those with left and/or right clipped standard curves. MS predictions were nominally more accurate; especially at the extremes of the prediction curve. MC provided credible asymmetric prediction intervals for both MS and LN fits that were superior to LNLS propagation-of-error intervals in achieving the target statistical confidence. MSMC was more reliable when automated prediction across simultaneous assays was applied routinely with minimal user guidance.

P-MartCancer-Interactive Online Software to Enable Analysis of Shotgun Cancer Proteomic Datasets.

P-MartCancer is an interactive web-based software environment that enables statistical analyses of peptide or protein data, quantitated from mass spectrometry-based global proteomics experiments, without requiring in-depth knowledge of statistical programming. P-MartCancer offers a series of statistical modules associated with quality assessment, peptide and protein statistics, protein quantification, and exploratory data analyses driven by the user via customized workflows and interactive visualization. Currently, P-MartCancer offers access and the capability to analyze multiple cancer proteomic datasets generated through the Clinical Proteomics Tumor Analysis Consortium at the peptide, gene, and protein levels. P-MartCancer is deployed as a web service (https://pmart.labworks.org/cptac.html), alternatively available via Docker Hub (https://hub.docker.com/r/pnnl/pmart-web/). Cancer Res; 77(21); e47-50. ©2017 AACR.

ELISA microarray technology as a high-throughput system for cancer biomarker validation.

A large gap currently exists between the ability to discover potential biomarkers and the ability to assess the real value of these proteins for cancer screening. One major challenge in biomarker validation is the inherent variability in biomarker levels. This variability stems from the diversity across the human population and the considerable molecular heterogeneity between individual tumors, even those that originate from a single tissue. An additional challenge with cancer screening is that most cancers are rare in the general population, meaning that assay specificity must be very high. Otherwise, the number of false positives will be much greater than the number of true positives. Due to these challenges associated with biomarker validation, it is necessary to analyze thousands of samples in order to obtain a clear idea of the utility of a screening assay. Enzyme-linked immunosorbent assay (ELISA) microarray technology can simultaneously quantify levels of multiple proteins and, thus, has the potential to accelerate validation of protein biomarkers for clinical use. This review will discuss current ELISA microarray technology and potential advances that could help to achieve the reproducibility and throughput that are required to evaluate cancer biomarkers.

Evaluating concentration estimation errors in ELISA microarray experiments.

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.

Direct comparison of the psychometric properties of multiple interview and patient-rated assessments of suicidal ideation and behavior in an adult psychiatric inpatient sample.

OBJECTIVE: Compare the accuracy, agreement, internal consistency, and interrater reliability of 3 interviews to assess suicidal ideation and behavior in accordance with US Food and Drug Administration guidance about reporting categories. METHOD: Adults admitted to a psychiatric inpatient unit (N = 199) completed 3 assessments of past month and lifetime suicidal ideation and behavior-the Columbia Suicide Severity Rating Scale (C-SSRS), the Suicide Tracking Scale (STS), and the Sheehan Suicidality Tracking Scale (S-STS)-in randomized, counterbalanced order. "Missing gold standard" latent class analyses defined categories for ideation and behavior. Analyses also evaluated the S-STS mapping to C-SSRS categories. Three trained judges re-rated 89 randomly selected interview videotapes. Cohen κ, the primary outcome measure, quantified agreement above chance. Data were collected between November 2011 and June 2013. RESULTS: All 3 assessments showed excellent accuracy for suicidal ideation (κ = 0.72 to 1.00) and attempts (κ = 0.82 to 0.95) calibrated against latent classes. Interrater agreement ranged from κ = 0.52 to 1.00. Interrater agreement about more granular C-SSRS categories varied more widely (κ = 0.48 to 1.00), and the C-SSRS and S-STS assigned significantly different numbers of cases to many categories. Cronbach α was < 0.55 for the C-SSRS ideation and between 0.78 and 0.92 for the other scales. CONCLUSIONS: All 3 assessments showed good accuracy for broad categories of suicidal ideation and behavior. More granular, specific categories usually were rated reliably, but the C-SSRS and S-STS differed significantly in regard to which patients were assigned to these subcategories. Using any of these interviews would improve reliability over unstructured assessment in evaluating suicidal ideation and behavior. Clinical predictive validity of these interviews, and particularly the more granular categories, remains to be shown.

Analysis of high-throughput ELISA microarray data.

Our research group develops analytical methods and software for the high-throughput analysis of quantitative enzyme-linked immunosorbent assay (ELISA) microarrays. ELISA microarrays differ from DNA microarrays in several fundamental aspects and most algorithms for analysis of DNA microarray data are not applicable to ELISA microarrays. In this review, we provide an overview of the steps involved in ELISA microarray data analysis and how the statistically sound algorithms we have developed provide an integrated software suite to address the needs of each data-processing step. The algorithms discussed are available in a set of open-source software tools (http://www.pnl.gov/statistics/ProMAT).

An analysis pipeline for the inference of protein-protein interaction networks.

We present a platform for the reconstruction of protein-protein interaction networks inferred from Mass Spectrometry (MS) bait-prey data. The Software Environment for Biological Network Inference (SEBINI), an environment for the deployment of network inference algorithms that use high-throughput data, forms the platform core. Among the many algorithms available in SEBINI is the Bayesian Estimator of Probabilities of Protein-Protein Associations (BEPro3) algorithm, which is used to infer interaction networks from such MS affinity isolation data. Also, the pipeline incorporates the Collective Analysis of Biological Interaction Networks (CABIN) software. We have thus created a structured workflow for protein-protein network inference and supplemental analysis.

Evaluation of surface chemistries for antibody microarrays.

Antibody microarrays are an emerging technology that promises to be a powerful tool for the detection of disease biomarkers. The current technology for protein microarrays has been derived primarily from DNA microarrays and is not fully characterized for use with proteins. For example, there are a myriad of surface chemistries that are commercially available for antibody microarrays, but there are no rigorous studies that compare these different surfaces. Therefore, we have used a sandwich enzyme-linked immunosorbent assay (ELISA) microarray platform to analyze 17 different commercially available slide types. Full standard curves were generated for 23 different assays. We found that this approach provides a rigorous and quantitative system for comparing the different slide types based on spot size and morphology, slide noise, spot background, lower limit of detection, and reproducibility. These studies demonstrate that the properties of the slide surface affect the activity of immobilized antibodies and the quality of data produced. Although many slide types produce useful data, glass slides coated with aldehyde silane, poly-l-lysine, or aminosilane (with or without activation with a crosslinker) consistently produce superior results in the sandwich ELISA microarray analyses we performed.

Statistically inferring protein-protein associations with affinity isolation LC-MS/MS assays.

Affinity isolation of protein complexes followed by protein identification by LC-MS/MS is an increasingly popular approach for mapping protein interactions. However, systematic and random assay errors from multiple sources must be considered to confidently infer authentic protein-protein interactions. To address this issue, we developed a general, robust statistical method for inferring authentic interactions from protein prey-by-bait frequency tables using a binomial-based likelihood ratio test (LRT) coupled with Bayes' Odds estimation. We then applied our LRT-Bayes' algorithm experimentally using data from protein complexes isolated from Rhodopseudomonas palustris. Our algorithm, in conjunction with the experimental protocol, inferred with high confidence authentic interacting proteins from abundant, stable complexes, but few or no authentic interactions for lower-abundance complexes. The algorithm can discriminate against a background of prey proteins that are detected in association with a large number of baits as an artifact of the measurement. We conclude that the experimental protocol including the LRT-Bayes' algorithm produces results with high confidence but moderate sensitivity. We also found that Monte Carlo simulation is a feasible tool for checking modeling assumptions, estimating parameters, and evaluating the significance of results in protein association studies.