Analysis of cardiac left-ventricular volume based on time warping averaging

Extensive evaluation of the generalized relevance network approach to inferring gene regulatory networks

Background

The generalized relevance network approach to network inference reconstructs network links based on the strength of associations between data in individual network nodes. It can reconstruct undirected networks, i.e., relevance networks, sensu stricto, as well as directed networks, referred to as causal relevance networks. The generalized approach allows the use of an arbitrary measure of pairwise association between nodes, an arbitrary scoring scheme that transforms the associations into weights of the network links, and a method for inferring the directions of the links. While this makes the approach powerful and flexible, it introduces the challenge of finding a combination of components that would perform well on a given inference task.

Results

We address this challenge by performing an extensive empirical analysis of the performance of 114 variants of the generalized relevance network approach on 47 tasks of gene network inference from time-series data and 39 tasks of gene network inference from steady-state data. We compare the different variants in a multi-objective manner, considering their ranking in terms of different performance metrics. The results suggest a set of recommendations that provide guidance for selecting an appropriate variant of the approach in different data settings.

Conclusions

The association measures based on correlation, combined with a particular scoring scheme of asymmetric weighting, lead to optimal performance of the relevance network approach in the general case. In the two special cases of inference tasks involving short time-series data and/or large networks, association measures based on identifying qualitative trends in the time series are more appropriate.

Evaluation of respiratory- and postural-induced changes on the ballistocardiogram signal by time warping averaging

OBJECTIVE

The aim of this work was to evaluate the potential changes in the ballistocardiogram (BCG) signal induced by different respiratory patterns and posture, by using the dynamic time warping (DTW) technique.

APPROACH

BCG signals were recorded in a group of 20 healthy volunteers, simultaneously with an electrocardiogram (ECG). Two recordings, one in a supine (SUP) and one in a standing (ST) position, including spontaneous breathing, two 1 min apneas (at full and empty-lungs, respectively) and 30 s of Valsalva, were analyzed. A warped averaged waveform was computed for each phase, from which amplitude and temporal parameters were extracted to characterize each condition.

MAIN RESULTS

Variations were found in both amplitude and duration of BCG-derived parameters among manoeuvres, especially when compared to spontaneous breathing, suggesting a complex interaction between intra-thoracic pressure changes acting on venous return, together with the autonomic nervous system modulation on heart rate. The effect of a hydrostatic pressure gradient elicited by postural conditions was also evident.

SIGNIFICANCE

Posture and respiratory manoeuvres affect the BCG signal in different ways, probably as a result of changes induced in preload and afterload. This supports the need to define separate normality ranges for each posture and/or breathing conditions, as well as the importance of applying specific manoeuvres to highlight any pathological response in the computed BCG parameters.

Evaluation of ensemble averaging methods in 3D ballistocardiography

Ballistocardiography (BCG) is a non-invasive technique which measures the acceleration of a body induced by cardiovascular activity, namely the force exerted by the beating heart. Measuring a BCG in a gravity-free environment provides ideal conditions where the subject is completely decoupled from its environment. Furthermore, because gravity constrains the motion in two dimensions, the non-negligible accelerations taking place in the third dimension are lost. In every experimental situation, the measured BCG signal contains artifacts pertaining to different causes. One of them is the undesirable involuntary movements of the subject. Ensemble averaging (EA) tackles the issue of constructing a typical one cardiac cycle BCG signal which best represents a longer recording. The present work compares state-of-the-art EA methods and proposes two novel techniques, one taking into account the ECG sub-intervals and the other one based on Dynamic Time Warping. The effects of lung volume are also assessed.

Unraveling gene regulatory networks from time-resolved gene expression data - a measures comparison study

BACKGROUND

Inferring regulatory interactions between genes from transcriptomics time-resolved data, yielding reverse engineered gene regulatory networks, is of paramount importance to systems biology and bioinformatics studies. Accurate methods to address this problem can ultimately provide a deeper insight into the complexity, behavior, and functions of the underlying biological systems. However, the large number of interacting genes coupled with short and often noisy time-resolved read-outs of the system renders the reverse engineering a challenging task. Therefore, the development and assessment of methods which are computationally efficient, robust against noise, applicable to short time series data, and preferably capable of reconstructing the directionality of the regulatory interactions remains a pressing research problem with valuable applications.

RESULTS

Here we perform the largest systematic analysis of a set of similarity measures and scoring schemes within the scope of the relevance network approach which are commonly used for gene regulatory network reconstruction from time series data. In addition, we define and analyze several novel measures and schemes which are particularly suitable for short transcriptomics time series. We also compare the considered 21 measures and 6 scoring schemes according to their ability to correctly reconstruct such networks from short time series data by calculating summary statistics based on the corresponding specificity and sensitivity. Our results demonstrate that rank and symbol based measures have the highest performance in inferring regulatory interactions. In addition, the proposed scoring scheme by asymmetric weighting has shown to be valuable in reducing the number of false positive interactions. On the other hand, Granger causality as well as information-theoretic measures, frequently used in inference of regulatory networks, show low performance on the short time series analyzed in this study.

CONCLUSIONS

Our study is intended to serve as a guide for choosing a particular combination of similarity measures and scoring schemes suitable for reconstruction of gene regulatory networks from short time series data. We show that further improvement of algorithms for reverse engineering can be obtained if one considers measures that are rooted in the study of symbolic dynamics or ranks, in contrast to the application of common similarity measures which do not consider the temporal character of the employed data. Moreover, we establish that the asymmetric weighting scoring scheme together with symbol based measures (for low noise level) and rank based measures (for high noise level) are the most suitable choices.

Robust projective filtering of time-warped ECG beats

In this paper a new version, more immune to noise, of the nonlinear projective filtering is presented. The method employs an algorithm of robust principal component analysis to signal subspaces construction and as a result it achieves high performance in real electromyographic noise environment. Two aspects of the method's action are investigated: its ability to suppress noise and its influence on the precision of the QT interval measurement. Then the method influence on evaluation of the beat-to-beat variability of the repolarization duration is presented. A comparison to the previous versions of the nonlinear projective filtering and to the classical linear one is carried out.

BTW: a web server for Boltzmann time warping of gene expression time series

Dynamic time warping (DTW) is a well-known quadratic time algorithm to determine the smallest distance and optimal alignment between two numerical sequences, possibly of different length. Originally developed for speech recognition, this method has been used in data mining, medicine and bioinformatics. For gene expression time series data, time warping distance is arguably a more flexible tool to determine genes having similar temporal expression, hence possibly related biological function, than either Euclidean distance or correlation coefficient—especially since time warping accommodates sequences of different length. The BTW web server allows a user to upload two tab-separated text files A,B of gene expression data, each possibly having a different number of time intervals of different durations. BTW then computes time warping distance between each gene of A with each gene of B, using a recently developed symmetric algorithm which additionally computes the Boltzmann partition function and outputs Boltzmann pair probabilities. The Boltzmann pair probabilities, not available with any other existent software, suggest possible biological significance of certain positions in an optimal time warping alignment. Availability: .

Quantification of left ventricular modification in weightlessness conditions from the spatio-temporal analysis of 2D echocardiographic images

Two-dimensional echocardiography (2DE) performed during flights with a parabolic trajectory to simulate weightlessness provides a unique means to study left ventricular (LV) modifications to prevent post-flight orthostatic intolerance in astronauts. However, conventional analysis of 2DE is based on manual tracings and depends on experience. Accordingly, the aim was objectively to quantify, from 2DE images, the LV modifications related to different gravity levels, by applying a semi-automated level-set border detection technique. The algorithm validation was performed by the comparison of manual tracing results, obtained by two independent observers with 20 images, with the semi-automated measurements. To quantify LV modifications, three consecutive cardiac cycles were analysed for each gravity phase (1 Gz, 1.8 Gz, 0 Gz). The level-set procedure was applied frame-by-frame to detect the LV endocardial contours and obtain LV area against time curves, from which end-diastolic (EDA) and end-systolic (ESA) areas were computed and averaged to compensate for respiratory variations. Linear regression (y = 0.91x + 1.47, r = 0.99, SEE:0.80cm2) and Bland-Altman analysis (bias = -0.58 cm2, 95% limits of agreement= +/- 2.14cm2) showed excellent correlation between the semi-automatic and manually traced values. Inter-observer variability was 5.4%, and the inter-technique variability was 4.1%. Modifications in LV dimensions during the parabola were found: compared with 1 Gz values, EDA and ESA were significantly reduced at 1.8 Gz by 8.8 +/- 5.5% and 12.1 +/- 10.1%, respectively, whereas, during 0 Gz, EDA and ESA increased by 13.3 +/- 7.3% and 11.6 +/- 5.1%, respectively, owing to abrupt changes in venous return. The proposed method resulted in fast and reliable estimations of LV dimensions, whose changes caused by different gravity conditions were objectively quantified.