WinCD: Windows Software for Complex Demodulation

We describe WinCD, a program for extracting quantitative information about periodicity in time-series data using the method of complex demodulation (CD). The method is particularly well suited for the analysis of the effects of variables that may produce changes in biological rhythms, such as sleep deprivation, adaptation to changes in work schedules, time zone displacements, and various sorts of pathology. WinCD enables exploratory analysis of time series data by providing graphical displays of raw and processed time series, as well as numerous options for viewing and saving quantitative data. We describe WinCD operations and examples of the use of the program.     

Detecting Change in Biological Rhythms: A Multivariate Permutation Test Approach to Fourier‐Transformed Data

Treatment‐related changes in neurobiological rhythms are of increasing interest to psychologists, psychiatrists, and biological rhythms researchers. New methods for analyzing change in rhythms are needed, as most common methods disregard the rich complexity of biological processes. Large time series data sets reflect the intricacies of underlying neurobiological processes, but can be difficult to analyze. We propose the use of Fourier methods with multivariate permutation test (MPT) methods for analyzing change in rhythms from time series data. To validate the use of MPT for Fourier‐transformed data, we performed Monte Carlo simulations and compared statistical power and family‐wise error for MPT to Bonferroni‐corrected and uncorrected methods. Results show that MPT provides greater statistical power than Bonferroni‐corrected tests, while appropriately controlling family‐wise error. We applied this method to human, pre‐ and post‐treatment, serially‐sampled neurotransmitter data to confirm the utility of this method using real data. Together, Fourier with MPT methods provides a statistically powerful approach for detecting change in biological rhythms from time series data.     

Modelling regulatory pathways in E. coli from time series expression profiles

MOTIVATION: Cells continuously reprogram their gene expression network as they move through the cell cycle or sense changes in their environment. In order to understand the regulation of cells, time series expression profiles provide a more complete picture than single time point expression profiles. Few analysis techniques, however, are well suited to modelling such time series data. RESULTS: We describe an approach that naturally handles time series data with the capabilities of modelling causality, feedback loops, and environmental or hidden variables using a Dynamic Bayesian network. We also present a novel way of combining prior biological knowledge and current observations to improve the quality of analysis and to model interactions between sets of genes rather than individual genes. Our approach is evaluated on time series expression data measured in response to physiological changes that affect tryptophan metabolism in E. coli. Results indicate that this approach is capable of finding correlations between sets of related genes.     

Analysis of Non-stationary Data for Heart-rate Fluctuations in Terms of Drift and Diffusion Coefficients

We describe a method for analyzing the stochasticity in non-stationary data for the beat-to-beat fluctuations in the heart rates of healthy subjects, as well as those with congestive heart failure. The method analyzes the return time series of the data as a Markov process, and computes the Markov time scale, i.e., the time scale over which the data are a Markov process. We also construct an effective stochastic continuum equation for the return series. We show that the drift and diffusion coefficients, as well as the amplitude of the return time series for healthy subjects are distinct from those with CHF. Thus, the method may potentially provide a diagnostic tool for distinguishing healthy subjects from those with congestive heart failure, as it can distinguish small differences between the data for the two classes of subjects in terms of well-defined and physically-motivated quantities. PACS: 05.10.Gg 05.40.-a, 05.45.Tp, 87.19.Hh     

A primer on the study of transitory dynamics in ecological series using the scale-dependent correlation analysis

Here we describe a practical, step-by-step primer to scale-dependent correlation (SDC) analysis. The analysis of transitory processes is an important but often neglected topic in ecological studies because only a few statistical techniques appear to detect temporary features accurately enough. We introduce here the SDC analysis, a statistical and graphical method to study transitory processes at any temporal or spatial scale. SDC analysis, thanks to the combination of conventional procedures and simple well-known statistical techniques, becomes an improved time-domain analogue of wavelet analysis. We use several simple synthetic series to describe the method, a more complex example, full of transitory features, to compare SDC and wavelet analysis, and finally we analyze some selected ecological series to illustrate the methodology. The SDC analysis of time series of copepod abundances in the North Sea indicates that ENSO primarily is the main climatic driver of short-term changes in population dynamics. SDC also uncovers some long-term, unexpected features in the population. Similarly, the SDC analysis of Nicholsons blowflies data locates where the proposed models fail and provides new insights about the mechanism that drives the apparent vanishing of the population cycle during the second half of the series.     

Quantifying Intracellular Particle Flows by DIC Object Tracking

Label-free imaging techniques such as differential interference contrast (DIC) allow the observation of cells and large subcellular structures in their native, unperturbed states with minimal exposure to light. The development of robust computational image-analysis routines is vital to quantitative label-free imaging. The reliability of quantitative analysis of time-series microscopy data based on single-particle tracking relies on accurately detecting objects as distinct from the background, i.e., segmentation. Typical approaches to segmenting DIC images either involve converting images to those resembling phase contrast, mimicking the optics of DIC object formation, or using the morphological properties of objects. Here, we describe MATLAB based, single-particle tracking tool with a GUI for mobility analysis of objects from in vitro and in vivo DIC time-series microscopy. The tool integrates contrast enhancement with multiple modified Gaussian filters, automated threshold detection for segmentation and minimal distance-based two-dimensional single-particle tracking. We compare the relative performance of multiple filters and demonstrate the utility of the tool for DIC object tracking (DICOT). We quantify subcellular dynamics of a time series of Caenorhabditis elegans embryos in the one-celled stage by detecting birefringent yolk granules in the cytoplasm with high precision. The resulting two-dimensional map of oscillatory dynamics of granules quantifies the cytoplasmic flows driven by anaphasic spindle oscillations. The frequency of oscillations across the anterior-posterior (A-P) and transverse axes of the embryo correspond well with the reported frequency of spindle oscillations. We validate the quantitative accuracy of our method by tracking the in vitro diffusive mobility of micron-sized beads in glycerol solutions. Estimates of the diffusion coefficients of the granules are used to measure the viscosity of a dilution series of glycerol. Thus, our computational method is likely to be useful for both intracellular mobility and in vitro microrheology.     

Simultaneous intracellular recording and calcium imaging in single neurons of hippocampal slices

Fluorescent Ca2+ indicator dyes can be introduced into cells through the same microelectrode used for intracellular voltage recording. Simultaneous measurement of cell membrane potential and intracellular Ca2+ concentration can be very helpful in interpreting the mechanisms of Ca2+ increases. This chapter describes fluorescence image acquisition using a CCD camera and a computer program that also records a synchronized membrane potential trace. The same program allows for preliminary data analysis. More elaborate analyses can be accomplished with commercial programs. We also describe quantitative evaluations of sources of error in the use of the statistic deltaF/F as an indicator of Ca2+ concentration. Especially important errors to minimize are changes in background fluorescence and inappropriate autofluorescence corrections. Some improvement of fluorescence images of cells deep within slices may be accomplished by masking. One method is described for making a mask based on the raw fluorescence image. With another method, highly detailed cell morphologies may be conveyed by using masks based on neurobiotin injections and camera lucida drawings.     

Bayesian correlation between temperature and blossom onset data

The recent quantification of changes in time series of phenology data with Bayesian methods has provided compelling evidence for changes during the last 20 years. In this paper we correlate the phenological observations with spring temperature time series. We provide quantitative answers to the question whether changes in temperature and phenological time series should be regarded as coherent or independent. For the three considered species snowdrops, cherry and lime tree we find factors of 1.05, 2.19 and 3.26, respectively, in favor of coherence. The functional behavior and the trend in the temperature time series are presented. They amount to 0.15°C yr^?1 for the January–March average, 0.09°C yr^?1 for February–April and 0.1°C yr^?1 for March–May in 2002. In addition, we compare blossom trends for the coherent and independent hypotheses and find that the transition from trend values slightly positive before 1970 to strongly negative at present becomes sharper as the temperature data are included in the analysis.     

Ecogeographic patterns of rabies in southern Ontario based on time series analysis

We describe a method based on time series analysis that divided the rabies enzootic area of southern Ontario into 13 regions using data collected at the township level, the smallest available geographical unit for Ontario (Canada). The intent was to discover ecogeographic patterns if such existed. For the period 1957-89, the quarterly time series of fox rabies cases for each of the 423 townships in the study area was correlated with the time series of its adjacent neighbors. Townships were then linked to adjacent townships provided the pair-wise correlations had significant correlation coefficients. This procedure produced 13 clusters that remained stable when additional lead/lag relationships between townships were examined. Furthermore, those clusters, which we then termed "rabies units," had different behaviors in terms of species distribution, persistence, and periodicity. Time series in adjacent units were not synchronous. We discuss how our findings influenced the rabies control program in Ontario, how they relate to recent findings about the distribution of fox rabies virus subtypes, and how they lend support for the role of metapopulation structulre in persistence of disease.     

The chi square periodogram: its utility for analysis of circadian rhythms

It is proposed that chi-square statistic be employed in constructing periodograms for the analysis of hourly time series data obtained in studies of circadian rhythmicity. We show that even for relatively short (10 day) time series, the integral-valued chi-square periodogram can distinguish circadian-periodic from random series at a level of significance of about 0·01. In addition, we describe the effects of serial correlation and examine the resolving power of the method for two periodic components in the circadian range. We suggest how the method can be most profitably employed in the analysis of event-recorder data for detection of rhythmicity in the range 14 to 34 h., and for the estimation of period to ±0·2 h.     

Quantitative analysis of DNA methylation after whole bisulfitome amplification of a minute amount of DNA from body fluids

Cell-free circulating DNA isolated from the plasma of individuals with cancer has been shown to harbor cancer-associated changes in DNA methylation, and thus it represents an attractive target for biomarker discovery. However, the reliable detection of DNA methylation changes in body fluids has proven to be technically challenging. Here we describe a novel combination of methods that allows quantitative and sensitive detection of DNA methylation in minute amounts of DNA present in body fluids (quantitative Methylation Analysis of Minute DNA amounts after whole Bisulfitome Amplification, qMAMBA). This method involves genome-wide amplification of bisulphite-modified DNA template followed by quantitative methylation detection using pyrosequencing and allows analysis of multiple genes from a small amount of starting DNA. To validate our method we used qMAMBA assays for four genes and LINE1 repetitive sequences combined with plasma DNA samples as a model system. qMAMBA offered high efficacy in the analysis of methylation levels and patterns in plasma samples with extremely small amounts of DNA and low concentrations of methylated alleles. Therefore, qMAMBA will facilitate methylation studies aiming to discover epigenetic biomarkers, and should prove particularly valuable in profiling a large sample series of body fluids from molecular epidemiology studies as well as in tracking disease in early diagnostics.     

Epistat : a computer program for identifying and testing interactions between pairs of quantitative trait loci

 We describe a computer program, Epistat, which combines statistical methods and color-graphic displays to facilitate the analysis of interactions between pairs of quantitative trait loci (QTLs). Epistat organizes genetic-mapping data and quantitative-trait values into graphic displays which illustrate the individual effects of single loci as well as the interactions between any two loci. Keyboard commands allow the user to search the data set for individual QTLs and to test for interactions between QTLs. For a given trait, the program displays the effects of the alleles at each of two loci on the quantitative-trait value, as well as the effects of the interactions between these alleles. Loglikelihood ratios are used to compare the likelihood of explaining the effects by null, additive, or epistatic models. Examples of interactions in soybean are presented for near-infrared transmittance (NIT), seed number, and reproductive period. Epistat has been used to find numerous interactions between QTLs in soybean in which trait variation at one locus is conditional upon a specific allele at another. Received: 16 January 1996 / Accepted: 27 September 1996     

Optimization of the isotope-coded affinity tag-labeling procedure for quantitative proteome analysis

The combination of isotope coded affinity tag (ICAT) reagents and tandem mass spectrometry constitutes a new method for quantitative proteomics. It involves the site-specific, covalent labeling of proteins with isotopically normal or heavy ICAT reagents, proteolysis of the combined, labeled protein mixture, followed by the isolation and mass spectrometric analysis of the labeled peptides. The method critically depends on labeling protocols that are specific, quantitative, general, robust, and reproducible. Here we describe the systematic evaluation of important parameters of the labeling protocol and describe optimized labeling conditions. The tested factors include the ICAT reagent concentration, the influence of the protein, SDS, and urea concentrations on the labeling reaction, and the reaction time. We demonstrate that using the optimized conditions specific and quantitative labeling was achieved on standard proteins as well as in complex protein mixtures such as a yeast cell lysate.     

A method to determine rates and patterns of variability in ecological communities

It is well known that ecological communities are spatially and temporally dynamic. Quantifying temporal variability in ecological communities is challenging, however, especially for time-series data sets of less than 40 measurement intervals. In this paper, we describe a method to quantify temporal variability in multispecies communities over time frames of 10–40 measurement intervals. Our approach is a community-level extension of autocorrelation analysis, but we use Euclidean distance to measure similarity of community samples at increasing time lags rather than the correlation coefficient. Regressing Euclidean distances versus increasing time lags yields a measure of the rate and nature of community change over time. We demonstrate the method with empirical data sets from shortgrass steppe, old-field succession and zooplankton dynamics in lakes, and we investigate properties of the analysis using simulation models. Results indicate that time-lag analysis provides a useful quantitative measurement of the rate and pattern of temporal dynamics in communities over time frames that are too short for more traditional autocorrelation approaches.     

Quantitative trait associated microarray gene expression data analysis

Selection on phenotypes may cause genetic change. To understand the relationship between phenotype and gene expression from an evolutionary viewpoint, it is important to study the concordance between gene expression and profiles of phenotypes. In this study, we use a novel method of clustering to identify genes whose expression profiles are related to a quantitative phenotype. Cluster analysis of gene expression data aims at classifying genes into several different groups based on the similarity of their expression profiles across multiple conditions. The hope is that genes that are classified into the same clusters may share underlying regulatory elements or may be a part of the same metabolic pathways. Current methods for examining the association between phenotype and gene expression are limited to linear association measured by the correlation between individual gene expression values and phenotype. Genes may be associated with the phenotype in a nonlinear fashion. In addition, groups of genes that share a particular pattern in their relationship to phenotype may be of evolutionary interest. In this study, we develop a method to group genes based on orthogonal polynomials under a multivariate Gaussian mixture model. The effect of each expressed gene on the phenotype is partitioned into a cluster mean and a random deviation from the mean. Genes can also be clustered based on a time series. Parameters are estimated using the expectation-maximization algorithm and implemented in SAS. The method is verified with simulated data and demonstrated with experimental data from 2 studies, one clusters with respect to severity of disease in Alzheimer's patients and another clusters data for a rat fracture healing study over time. We find significant evidence of nonlinear associations in both studies and successfully describe these patterns with our method. We give detailed instructions and provide a working program that allows others to directly implement this method in their own analyses.     

Heart‐beat‐phase‐coherent Doppler optical coherence tomography for measuring pulsatile ocular blood flow

We introduce a Doppler OCT (DOCT) platform that is fully synchronized with the heart‐beat via a pulse oximeter. The system allows reconstructing heart‐beat‐phase‐coherent quantitative DOCT volumes. The method is to acquire a series of DOCT volumes and to record the pulse in parallel. The heartbeat data is used for triggering the start of each DOCT volume acquisition. The recorded volume series is registered to the level of capillaries using a cross‐volume registration. The information of the pulse phase is used to rearrange the tomograms in time, to obtain a series of phase coherent DOCT volumes over a pulse. We present Doppler angle independent quantitative evaluation of the absolute pulsatile blood flow within individual retinal vessels as well as of the total retinal blood flow over a full heartbeat cycle. (© 2013 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)