Large-Scale Interlaboratory Study to Develop, Analytically Validate and Apply Highly Multiplexed, Quantitative Peptide Assays to Measure Cancer-Relevant Proteins in Plasma.

There is an increasing need in biology and clinical medicine to robustly and reliably measure tens to hundreds of peptides and proteins in clinical and biological samples with high sensitivity, specificity, reproducibility, and repeatability. Previously, we demonstrated that LC-MRM-MS with isotope dilution has suitable performance for quantitative measurements of small numbers of relatively abundant proteins in human plasma and that the resulting assays can be transferred across laboratories while maintaining high reproducibility and quantitative precision. Here, we significantly extend that earlier work, demonstrating that 11 laboratories using 14 LC-MS systems can develop, determine analytical figures of merit, and apply highly multiplexed MRM-MS assays targeting 125 peptides derived from 27 cancer-relevant proteins and seven control proteins to precisely and reproducibly measure the analytes in human plasma.

Variance component analysis of a multi-site study for the reproducibility of multiple reaction monitoring measurements of peptides in human plasma.

Background: In the Addona et al. paper (Nature Biotechnology 2009), a large-scale multi-site study was performed to quantify Multiple Reaction Monitoring (MRM) measurements of proteins spiked in human plasma. The unlabeled signature peptides derived from the seven target proteins were measured at nine different concentration levels, and their isotopic counterparts were served as the internal standards.

Estimation of propensity scores using generalized additive models.

Propensity score matching is often used in observational studies to create treatment and control groups with similar distributions of observed covariates. Typically, propensity scores are estimated using logistic regressions that assume linearity between the logistic link and the predictors. We evaluate the use of generalized additive models (GAMs) for estimating propensity scores.

Adaptive image contrast enhancement using generalizations of histogram equalization.

This paper proposes a scheme for adaptive image-contrast enhancement based on a generalization of histogram equalization (HE). HE is a useful technique for improving image contrast, but its effect is too severe for many purposes. However, dramatically different results can be obtained with relatively minor modifications.

PharmID: pharmacophore identification using Gibbs sampling.

The binding of a small molecule to a protein is inherently a 3D matching problem. As crystal structures are not available for most drug targets, there is a need to be able to infer from bioassay data the key binding features of small molecules and their disposition in space, the pharmacophore. Fingerprints of 3D features and a modification of Gibbs sampling to align a set of known flexible ligands, where all compounds are active, are used to discern possible pharmacophores.

Secure analysis of distributed chemical databases without data integration.

We present a method for performing statistically valid linear regressions on the union of distributed chemical databases that preserves confidentiality of those databases. The method employs secure multi-party computation to share local sufficient statistics necessary to compute least squares estimators of regression coefficients, error variances and other quantities of interest. We illustrate our method with an example containing four companies' rather different databases.

PowerMV: a software environment for molecular viewing, descriptor generation, data analysis and hit evaluation.

Ideally, a team of biologists, medicinal chemists and information specialists will evaluate the hits from high throughput screening. In practice, it often falls to nonmedicinal chemists to make the initial evaluation of HTS hits. Chemical genetics and high content screening both rely on screening in cells or animals where the biological target may not be known.

Recursive partitioning analysis of complex disease pharmacogenetic studies. I. Motivation and overview.

Identifying genetic variation predictive of important phenotypes, including disease susceptibility, drug efficacy, and adverse events, is a challenging task, and theory and computer science work is being carried out in an attempt to tackle this issue. For many important diseases, such as diabetes, schizophrenia, and depression, the etiology is complex; either the disease is a result of several multiple mechanisms or is caused by an interaction among multiple genes or gene-environment interactions, or both. There is a need for statistical methods to deal with the large, complex data sets that will be used to disentangle these diseases.

Using recursive partitioning analysis to evaluate compound selection methods.

The design and analysis of a screening set for high throughput screening is complex. We examine three statistical strategies for compound selection, random, clustering, and space-filling. We examine two types of chemical descriptors, BCUTs and principal components of Dragon Constitutional descriptors.

Design of diverse and focused combinatorial libraries using an alternating algorithm.

There is considerable research in chemistry to develop reaction conditions so that any of a very large number of reactants will successfully form new compounds, e.g. for two components, A(i) + B(j) --> A-B(ij).

Robust singular value decomposition analysis of microarray data.

In microarray data there are a number of biological samples, each assessed for the level of gene expression for a typically large number of genes. There is a need to examine these data with statistical techniques to help discern possible patterns in the data. Our technique applies a combination of mathematical and statistical methods to progressively take the data set apart so that different aspects can be examined for both general patterns and very specific effects.

Random-walk-based estimates of transport properties in small specimens of composite materials.

A method based on random walks is developed for estimating the dc conductance and similar transport properties in small specimens of composite materials. The method is valid over a much wider range of material structures than are asymptotic methods, and requires only that the internal structure of the material be known. The error in its estimates is limited primarily by CPU speed.

Regression modeling of ordinal data with nonzero baselines.

This paper develops regression models for ordinal data with nonzero control response probabilities. The models are especially useful in dose-response studies where the spontaneous or natural response rate is nonnegligible and the dosage is logarithmic. These models generalize Abbott's formula, which has been commonly used to model binary data with nonzero background observations.

A coalescent model of ancestry for a rare allele.

In disequilibrium mapping from data on a rare allele, interest may focus on the ancestry of a random sample of current descendants of a mutation. The mutation is assumed to have been introduced into the population as a single copy a known time ago and to have reached a given copy number within the population. Theory has been developed to describe the ancestral distribution under arbitrary patterns of population expansion.

Conditional genotypic probabilities for microsatellite loci.

Modern forensic DNA profiles are constructed using microsatellites, short tandem repeats of 2-5 bases. In the absence of genetic data on a crime-specific subpopulation, one tool for evaluating profile evidence is the match probability. The match probability is the conditional probability that a random person would have the profile of interest given that the suspect has it and that these people are different members of the same subpopulation.

Adjustments for the display of quantized ion channel dwell times in histograms with logarithmic bins.

Dwell-time histograms are often plotted as part of patch-clamp investigations of ion channel currents. The advantages of plotting these histograms with a logarithmic time axis were demonstrated by, J. Physiol.

Effect of outdoor airborne particulate matter on daily death counts.

To investigate the possible relationship between airborne particulate matter and mortality, we developed regression models of daily mortality counts using meteorological covariates and measures of outdoor PM10. Our analyses included data from Cook County, Illinois, and Salt Lake County, Utah. We found no evidence that particulate matter < or = 10 microns (PM10) contributes to excess mortality in Salt Lake County, Utah.