Automated Analysis of Single-Molecule Photobleaching Data by Statistical Modeling of Spot Populations

The number of fluorophores within a molecule complex can be revealed by single-molecule photobleaching imaging. A widely applied strategy to analyze intensity traces over time is the quantification of photobleaching step counts. However, several factors can limit and bias the detection of photobleaching steps, including noise, high numbers of fluorophores, and the possibility that several photobleaching events occur almost simultaneously. In this study, we propose a new approach, to our knowledge, to determine the fluorophore number that correlates the intensity decay of a population of molecule complexes with the decay of the number of visible complexes. We validated our approach using single and fourfold Atto-labeled DNA strands. As an example we estimated the subunit stoichiometry of soluble CD95L using GFP fusion proteins. To assess the precision of our method we performed in silico experiments showing that the estimates are not biased for experimentally observed intensity fluctuations and that the relative precision remains constant with increasing number of fluorophores. In case of fractional fluorescent labeling, our simulations predicted that the fluorophore number estimate corresponds to the product of the true fluorophore number with the labeling fraction. Our method, denoted by spot number and intensity correlation (SONIC), is fully automated, robust to noise, and does not require the counting of photobleaching events.     

Statistical Analysis of Nonrectangular Family Data

MINQUE (Minimum Norm Quadratic Unbiased Estimators) theory is applied to the problem of estimation of variance components in family data (siblings) with variable family size. Using this approach, the traditional iterative maximum likelihood estimators are shown to be asymptotically normal, even though the data come from non-identical parent distributions. Asymptotic expressions are also obtained for the variance of the MINQUE estimators which hold even if the data are decidedly non-normal (e.g. a mixture of normals). In the case of normal data, exact small-sample variance estimates are derived. Simulations demonstrate the fast rate of convergence to asymptotic properties as the number of families increases. These desirable qualities suggest that the easy to compute MINQUE class of estimators may provide a useful alternative method for modelling familial aggregation.     

Effects on Household Labor of Temporary Out-migration by Male Household Heads in Nicaragua and Peru: an Analysis of Spot-check Time Allocation Data Using Mixed-effects Models

When adult males are temporarily away from the household, observational evidence suggests cross-cultural and intra-cultural variation in the effects of their absence on the labor of other household members. In subsistence-based economies, we predict that other adolescent or older members will work more in essential production activities that otherwise would be performed by the missing men. We test this hypothesis using spot-check time allocation datasets from rural Nicaragua and Peru and the methodology of mixed-effects statistical models. In Nicaragua, we find that the absence of male household heads rarely necessitates substitute labor by household co-residents, apparently because men typically time their absences to coincide with the non-peak agricultural season. In Peru, the absence of male household heads results in increased men’s work by co-residents only under unusual circumstances, as households apparently rely on other strategies to mitigate for the loss of labor. In addition to the comparative empirical analysis of the two cases, we show how mixed-effects models allow for individual heterogeneity and data structures that confound more familiar statistical techniques and occasionally produce spurious results. Mixed-effects modeling techniques will be necessary if we are to realize the analytic potential of the extensive, standardized time allocation datasets gathered by anthropologists.     

Statistical Object Data Analysis of Taxonomic Trees from Human Microbiome Data

Human microbiome research characterizes the microbial content of samples from human habitats to learn how interactions between bacteria and their host might impact human health. In this work a novel parametric statistical inference method based on object-oriented data analysis (OODA) for analyzing HMP data is proposed. OODA is an emerging area of statistical inference where the goal is to apply statistical methods to objects such as functions, images, and graphs or trees. The data objects that pertain to this work are taxonomic trees of bacteria built from analysis of 16S rRNA gene sequences (e.g. using RDP); there is one such object for each biological sample analyzed. Our goal is to model and formally compare a set of trees. The contribution of our work is threefold: first, a weighted tree structure to analyze RDP data is introduced; second, using a probability measure to model a set of taxonomic trees, we introduce an approximate MLE procedure for estimating model parameters and we derive LRT statistics for comparing the distributions of two metagenomic populations; and third the Jumpstart HMP data is analyzed using the proposed model providing novel insights and future directions of analysis.     

Characterization of DNA-Binding Proteins Using Multiplexed Competitor EMSA

We describe a low-cost high-throughput technique to characterize nuclear protein DNA-binding interactions. This technique, known as Multiplexed Competitor Electrophoretic Mobility Shift Assay, uses a series of multiplexed oligonucleotide DNA consensus competitors, in combination with a standard electrophoretic mobility shift assay procedure, to efficiently characterize DNA-binding proteins. We show utility for the method to identify a previously unreported hepatocyte nuclear factor-3 site created in intron 8 of the lipoprotein lipase gene by a common single-nucleotide polymorphism (rs327).     

Multiplexed Fluorescent Microarray for Human Salivary Protein Analysis Using Polymer Microspheres and Fiber-optic Bundles

Herein, we describe a protocol for simultaneously measuring six proteins in saliva using a fiber-optic microsphere-based antibody array. The immuno-array technology employed combines the advantages of microsphere-based suspension array fabrication with the use of fluorescence microscopy. As described in the video protocol, commercially available 4.5 μm polymer microspheres were encoded into seven different types, differentiated by the concentration of two fluorescent dyes physically trapped inside the microspheres. The encoded microspheres containing surface carboxyl groups were modified with monoclonal capture antibodies through EDC/NHS coupling chemistry. To assemble the protein microarray, the different types of encoded and functionalized microspheres were mixed and randomly deposited in 4.5 μm microwells, which were chemically etched at the proximal end of a fiber-optic bundle. The fiber-optic bundle was used as both a carrier and for imaging the microspheres. Once assembled, the microarray was used to capture proteins in the saliva supernatant collected from the clinic. The detection was based on a sandwich immunoassay using a mixture of biotinylated detection antibodies for different analytes with a streptavidin-conjugated fluorescent probe, R-phycoerythrin. The microarray was imaged by fluorescence microscopy in three different channels, two for microsphere registration and one for the assay signal. The fluorescence micrographs were then decoded and analyzed using a homemade algorithm in MATLAB.     

Joint Modeling of Multivariate Longitudinal Data and Competing Risks Using Multiphase Sub-models

In many clinical studies that involve follow-up, it is common to observe one or more sequences of longitudinal measurements, as well as one or more time to event outcomes. A competing risks situation arises when the probability of occurrence of one event is altered/hindered by another time to event. Recently, there has been much attention paid to the joint analysis of a single longitudinal response and a single time to event outcome, when the missing data mechanism in the longitudinal process is non-ignorable. We, in this paper, propose an extension where multiple longitudinal responses are jointly modeled with competing risks (multiple time to events). Our shared parameter joint model consists of a system of multiphase non-linear mixed effects sub-models for the multiple longitudinal responses, and a system of cause-specific non-proportional hazards frailty sub-models for competing risks, with associations among multiple longitudinal responses and competing risks modeled using latent parameters. The joint model is applied to a data set of patients who are on mechanical circulatory support and are awaiting heart transplant, using readily available software. While on the mechanical circulatory support, patient liver and renal functions may worsen and these in turn may influence one of the two possible competing outcomes: (i) death before transplant; (ii) transplant. In one application, we propose a system of multiphase cause-specific non-proportional hazard sub-model where frailty can be time varying. Performance under different scenarios was assessed using simulation studies. By using the proposed joint modeling of the multiphase sub-models, one can identify: (i) non-linear trends in multiple longitudinal outcomes; (ii) time-varying hazards and cumulative incidence functions of the competing risks; (iii) identify risk factors for the both types of outcomes, where the effect may or may not change with time; and (iv) assess the association between multiple longitudinal and competing risks outcomes, where the association may or may not change with time.     

Statistical Analysis of Data from Gene Frequency Perturbation Experiments

Data from gene frequency perturbation experiments are being used to resolve the selectionistneutralist debate over the evolutionary significance of electrophoretic enzyme variation. Previous statistical analyses of such data have encountered the major difficulty that the effective population size is not known. In this article, we give the statistical theory for fitting various selection models, and we evaluate the known asymptotic results by simulation. We also show that there is further information in data from these experiments besides the estimates of the selection parameters, namely estimates of the bounds for the effective population sizes.     

General Statistical Issues in the Analysis of Pedigree Data

When analysing pedigree data, it is just as important to detect deficiencies in the fitting and application of the current model as to be able to specify the model, estimate its parameters and test appropriate hypotheses. Some of the statistical issues that arise in the fitting and application of genetic models to pedigree data are discussed in this paper. A particular set of data and a non-controversial characteristic have been chosen to highlight these issues.     

成年Beagle犬生物学指标及心电图检测值统计分析

目的 测定正常Beagle犬生理指标,比较不同性别间各参数的差异,以建立健康成年Beagle犬相关生理学参数的基础正常值.方法 根据国家(成都)中药安全性评价中心制定的标准操作规程(SOP)并参照国内外相关资料,对200只4～8月龄Beagle犬的心电图、血压、呼吸、血液学及血液生化等指标进行检测.结果 200只犬均为窦性心律,雌性犬心率较雄性快(P<0.01);血液学指标中RBC、Hb及HCT值雌性均低于雄性,差异有统计学意义(P<0.01);血生化指标中雌性犬高于雄性的是BUN(P<0.05);雌性犬低于雄性的是TP、ALB、GLU、TC及K+的含量(P<0.05或P<0.01).结论 在本实验室条件下,雌雄Beagle犬之间部分指标有统计学差异,本结果可为Beagle犬的实验提供正常值参考.     

Spatial Normalization of Reverse Phase Protein Array Data

Reverse phase protein arrays (RPPA) are an efficient, high-throughput, cost-effective method for the quantification of specific proteins in complex biological samples. The quality of RPPA data may be affected by various sources of error. One of these, spatial variation, is caused by uneven exposure of different parts of an RPPA slide to the reagents used in protein detection. We present a method for the determination and correction of systematic spatial variation in RPPA slides using positive control spots printed on each slide. The method uses a simple bi-linear interpolation technique to obtain a surface representing the spatial variation occurring across the dimensions of a slide. This surface is used to calculate correction factors that can normalize the relative protein concentrations of the samples on each slide. The adoption of the method results in increased agreement between technical and biological replicates of various tumor and cell-line derived samples. Further, in data from a study of the melanoma cell-line SKMEL-133, several slides that had previously been rejected because they had a coefficient of variation (CV) greater than 15%, are rescued by reduction of CV below this threshold in each case. The method is implemented in the R statistical programing language. It is compatible with MicroVigene and SuperCurve, packages commonly used in RPPA data analysis. The method is made available, along with suggestions for implementation, at http://bitbucket.org/rppa_preprocess/rppa_preprocess/src.     

Isobaric Labeling and Data Normalization without Requiring Protein Quantitation

Isobaric multiplexed quantitative proteomics can complement high-resolution sample isolation techniques. Here, we report a simple workflow exponentially modified protein abundance index (emPAI)-MW deconvolution (EMMOL) for normalizing isobaric reporter ratios within and between experiments, where small or unknown amounts of protein are used. EMMOL deconvolutes the isobaric tags for relative and absolute quantification (iTRAQ) data to yield the quantity of each protein of each sample in the pool, a new approach that enables the comparison of many samples without including a channel of reference standard. Moreover, EMMOL allows using a sufficient quantity of control sample to facilitate the peptide fractionation (isoelectric-focusing was used in this report), and mass spectrometry MS/MS sequencing yet relies on the broad dynamic range of iTRAQ quantitation to compare relative protein abundance. We demonstrated EMMOL by comparing four pooled samples with 20-fold range differences in protein abundance and performed data normalization without using prior knowledge of the amounts of proteins in each sample, simulating an iTRAQ experiment without protein quantitation prior to labeling. We used emPAI,¹ the target protein MW, and the iTRAQ reporter ratios to calculate the amount of each protein in each of the four channels. Importantly, the EMMOL-delineated proteomes from separate iTRAQ experiments can be assorted for comparison without using a reference sample. We observed no compression of expression in iTRAQ ratios over a 20-fold range for all protein abundances. To complement this ability to analyze minute samples, we report an optimized iTRAQ labeling protocol for using 5 μg protein as the starting material.