Blind source separation and the analysis of microarray data.

We develop an approach for the exploratory analysis of gene expression data, based upon blind source separation techniques. This approach exploits higher-order statistics to identify a linear model for (logarithms of) expression profiles, described as linear combinations of "independent sources." As a result, it yields "elementary expression patterns" (the "sources"), which may be interpreted as potential regulation pathways. Further analysis of the so-obtained sources show that they are generally characterized by a small number of specific coexpressed or antiexpressed genes. In addition, the projections of the expression profiles onto the estimated sources often provides significant clustering of conditions. The algorithm relies on a large number of runs of "independent component analysis" with random initializations, followed by a search of "consensus sources." It then provides estimates for independent sources, together with an assessment of their robustness. The results obtained on two datasets (namely, breast cancer data and Bacillus subtilis sulfur metabolism data) show that some of the obtained gene families correspond to well known families of coregulated genes, which validates the proposed approach.     

Blind noise reduction for multisensory signals using ICA and subspace filtering, with application to EEG analysis

 In many applications of signal processing, especially in communications and biomedicine, preprocessing is necessary to remove noise from data recorded by multiple sensors. Typically, each sensor or electrode measures the noisy mixture of original source signals. In this paper a noise reduction technique using independent component analysis (ICA) and subspace filtering is presented. In this approach we apply subspace filtering not to the observed raw data but to a demixed version of these data obtained by ICA. Finite impulse response filters are employed whose vectors are parameters estimated based on signal subspace extraction. ICA allows us to filter independent components. After the noise is removed we reconstruct the enhanced independent components to obtain clean original signals; i.e., we project the data to sensor level. Simulations as well as real application results for EEG-signal noise elimination are included to show the validity and effectiveness of the proposed approach. Received: 6 November 2000 / Accepted in revised form: 12 November 2001     

Blind source separation methods for deconvolution of complex signals in cancer biology

Two blind source separation methods (Independent Component Analysis and Non-negative Matrix Factorization), developed initially for signal processing in engineering, found recently a number of applications in analysis of large-scale data in molecular biology. In this short review, we present the common idea behind these methods, describe ways of implementing and applying them and point out to the advantages compared to more traditional statistical approaches. We focus more specifically on the analysis of gene expression in cancer. The review is finalized by listing available software implementations for the methods described.     

A new method for the extraction of fetal ECG from the dependent abdominal signals using blind source separation and adaptive noise cancellation techniques

Background

The electrocardiogram (ECG) is a diagnostic tool that records the electrical activity of the heart, and depicts it as a series of graph-like tracings, or waves. Being able to interpret these details allows diagnosis of a wide range of heart problems. Fetal electrocardiogram (FECG) extraction has an important impact in medical diagnostics during the mother pregnancy period. Since the observed FECG signals are often mixed with the maternal ECG (MECG) and the noise induced by the movement of electrodes or by mother motion, the separation process of the ECG signal sources from the observed data becomes quite complicated. One of its complexity is when the ECG sources are dependent, thus, in this paper we introduce a new approach of blind source separation (BSS) in the noisy context for both independent and dependent ECG signal source. This approach consist in denoising the observed ECG signals using a bilateral total variation (BTV) filter; then minimizing the Kullbak-Leibler divergence between copula densities to separate the FECG signal from the MECG one.

Results

We present simulation results illustrating the performance of our proposed method. We will consider many examples of independent/dependent source component signals. The results will be compared with those of the classical method called independent component analysis (ICA) under the same conditions. The accuracy of source estimation is evaluated through a criterion, called again the signal-to-noise-ratio (SNR). The first experiment shows that our proposed method gives accurate estimation of sources in the standard case of independent components, with performance around 27 dB in term of SNR. In the second experiment, we show the capability of the proposed algorithm to successfully separate two noisy mixtures of dependent source components - with classical criterion devoted to the independent case - fails, and that our method is able to deal with the dependent case with good performance.

Conclusions

In this work, we focus specifically on the separation of the ECG signal sources taken from skin two electrodes located on a pregnant woman’s body. The ECG separation is interpreted as a noisy linear BSS problem with instantaneous mixtures. Firstly, a denoising step is required to reduce the noise due to motion artifacts using a BTV filter as a very effective one-pass filter for denoising. Then, we use the Kullbak-Leibler divergence between copula densities to separate the fetal heart rate from the mother one, for both independent and dependent cases.

     

Advances in blind source separation (BSS) and independent component analysis (ICA) for nonlinear mixtures

In this paper, we review recent advances in blind source separation (BSS) and independent component analysis (ICA) for nonlinear mixing models. After a general introduction to BSS and ICA, we discuss in more detail uniqueness and separability issues, presenting some new results. A fundamental difficulty in the nonlinear BSS problem and even more so in the nonlinear ICA problem is that they provide non-unique solutions without extra constraints, which are often implemented by using a suitable regularization. In this paper, we explore two possible approaches. The first one is based on structural constraints. Especially, post-nonlinear mixtures are an important special case, where a nonlinearity is applied to linear mixtures. For such mixtures, the ambiguities are essentially the same as for the linear ICA or BSS problems. The second approach uses Bayesian inference methods for estimating the best statistical parameters, under almost unconstrained models in which priors can be easily added. In the later part of this paper, various separation techniques proposed for post-nonlinear mixtures and general nonlinear mixtures are reviewed.     

Efficiency of neural transmission as a function of synaptic noise, threshold, and source characteristics

There has been a growing interest in the estimation of information carried by a single neuron and multiple single units or population of neurons to specific stimuli. In this paper we analyze, inspired by article of Levy and Baxter (2002), the efficiency of a neuronal communication by considering dendrosomatic summation as a Shannon-type channel (1948) and by considering such uncertain synaptic transmission as part of the dendrosomatic computation. Specifically, we study Mutual Information between input and output signals for different types of neuronal network architectures by applying efficient entropy estimators. We analyze the influence of the following quantities affecting transmission abilities of neurons: synaptic failure, activation threshold, firing rate and type of the input source. We observed a number of surprising non-intuitive effects. It turns out that, especially for lower activation thresholds, significant synaptic noise can lead even to twofold increase of the transmission efficiency. Moreover, the efficiency turns out to be a non-monotonic function of the activation threshold. We find a universal value of threshold for which a local maximum of Mutual Information is achieved for most of the neuronal architectures, regardless of the type of the source (correlated and non-correlated). Additionally, to reach the global maximum the optimal firing rates must increase with the threshold. This effect is particularly visible for lower firing rates. For higher firing rates the influence of synaptic noise on the transmission efficiency is more advantageous. Noise is an inherent component of communication in biological systems, hence, based on our analysis, we conjecture that the neuronal architecture was adjusted to make more effective use of this attribute.     

Comparative analysis of changes in short EEG segments during music perception based on event-related synchronization/desynchronization and wavelet synchronicity

The goal of the pilot study was to analyze the characteristics of changes in short EEG segments recorded from 32 sites during perception of musical melodies by healthy subjects depending on logical (recognition) and emotional (pleasant/unpleasant) estimation of the melody. For this purpose, the changes in event-related synchronization/desynchronization and the indices of wavelet synchrony of EEG responses were compared in 31 healthy subjects (18 to 60 years old). It has been shown that melody recognition during logical estimation of music is accompanied by event-related desynchronization in the left frontal-parietal-temporal area. Emotional estimation of a melody is characterized by event-related synchronization in the left frontal-temporal area for pleasant melodies, desynchronization in the temporal area for unpleasant melodies, and desynchronization in the occipital area for melodies inducing no emotional response. The analysis of EEG wavelet synchronization characterizing reactive changes in the interaction between cortical areas shows that the most distinct topographic differences are associated with the type of music processing: logical (familiar/unfamiliar) or emotional (pleasant/unpleasant). The changes in interhemispheric connections between the associative cortical areas (central, frontal, temporal) are greater during emotional estimation, while the changes in inter- and intrahemispheric connections between the projection areas of the acoustic analyzer (temporal area) are greater during logical estimation. It is assumed that the revealed event-related synchronization/desynchronization is most likely to reflect the activation component of musical fragment estimation, whereas wavelet analysis provides insight into the character of musical stimulus processing.     

Fourier and wavelet transform analysis, a tool for visualizing regular patterns in DNA sequences

A correlation function that compares each base in a DNA sequence to its various neighbours and which is subsequently processed by Fourier and wavelet transforms has been developed. The procedure has been applied to sequences from the human chromosome 22, to nef genes from various HIV clones and to myosin heavy chain DNA. It permits to readily visualize regular features in DNA which are related to the stability of heteroduplexes formed upon strand slippage.     

复杂疾病驱使的融合SDA-SVM集成基因挖掘方法

提出了一种新颖的复杂疾病驱使的融合SDA-SVM(Stepwise Discriminant Analysis-Support Vector Machine,SDA-SVM)技术的集成基因挖掘方法。该集成方法融合逐步判别分析和支持向量机的优点,能够有效地进行复杂疾病相关基因的深度挖掘,使得挖掘出的基因能够较好地识别疾病类型和亚型。通过将该方法应用于一套弥散性大B细胞淋巴瘤DNA表达谱数据,并与其它基因挖掘方法对比,结果表明该方法挖掘出的基因具有较高的疾病相关性和较强的疾病类型识别能力。     

Automatic physiological waveform processing for FMRI noise correction and analysis

Functional MRI resting state and connectivity studies of brain focus on neural fluctuations at low frequencies which share power with physiological fluctuations originating from lung and heart. Due to the lack of automated software to process physiological signals collected at high magnetic fields, a gap exists in the processing pathway between the acquisition of physiological data and its use in fMRI software for both physiological noise correction and functional analyses of brain activation and connectivity. To fill this gap, we developed an open source, physiological signal processing program, called PhysioNoise, in the python language. We tested its automated processing algorithms and dynamic signal visualization on resting monkey cardiac and respiratory waveforms. PhysioNoise consistently identifies physiological fluctuations for fMRI noise correction and also generates covariates for subsequent analyses of brain activation and connectivity.     

The effects of morphine on the relationship between fetal EEG, breathing and blood pressure signals using fast wavelet transform

In this study, we introduce the fast wavelet transform (WT) as a method for investigating the effects of morphine on the electroencephalogram (EEG), respiratory activity and blood pressure in fetal lambs. Morphine was infused intravenously at 25 mg/h. The EEG, respiratory activity and blood pressure signals were analyzed using WT. We performed wavelet decomposition for five sets of parameters D _2j where -1 < j 5. The five series WTs represent the detail signal bandwidths: 1, 16–32 Hz; 2, 8–16 Hz; 3, 4–8 Hz; 4, 2–4 Hz; 5, 1–2 Hz. Before injection of the high-dose morphine, power in the EEG was high in all six frequency bandwidths. The respiratory and blood pressure signals showed common frequency components with respect to time and were coincident with the low-voltage fast activity (LVFA) EEG signal. Respiratory activity was observed during only some of the LVFA periods, and was completely absent during high-voltage slow activity (HVSA) EEG. The respiratory signal showed dominant power in the fourth wavelet band, and less power in the third and fifth bands. The blood pressure signal was also characterized by dominant power in the fourth wavelet band. This power was significantly increased during periods of respiratory activity. There was a strong relationship between fetal EEG, blood pressure and breathing movements. However, the injection of high-dose morphine resulted in a disruption of the normal cyclic pattern between the two EEG states and a significant increase in power in the first wavelet band. In addition, the high-dose drug resulted in a significant increase in the power of respiratory signal in the fourth and fifth wavelet bands, while power was reduced in the third wavelet band. Breathing activity was also continuous after the drug. The high-dose morphine also caused a temporary power shift from the third wavelet band to the fourth wavelet band for the 30-min period after injection of drug. Finally, high-dose morphine completely destroyed the correlation between EEG, breathing and blood pressure signals.     

Topography, independent component analysis and dipole source analysis of movement related potentials

The objective of this study was to test, in single subjects, the hypothesis that the signs of voluntary movement-related neural activity would first appear in the prefrontal region, then move to both the medial frontal and posterior parietal regions, progress to the medial primary motor area, lateralize to the contralateral primary motor area and finally involve the cerebellum (where feedback-initiated error signals are computed). Six subjects performed voluntary finger movements while DC coupled EEG was recorded from 64 scalp electrodes. Event-related potentials (ERPs) averaged on the movements were analysed both before and after independent component analysis (ICA) combined with dipole source analysis (DSA) of the independent components. Both a simple topographic analysis of undecomposed ERPs and the ICA/DSA analysis suggested that the original hypothesis was inadequate. The major departure from its predictions was that, while activity over many brain regions did appear at the expected times, it also appeared at unexpected times. Overall, the results suggest that the neuroscientific ‘standard model’, in which neural activity occurs sequentially in a series of discrete local areas each specialized for a particular function, may reflect the true situation less well than models in which large areas of brain shift simultaneously into and out of common activity states. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.

A classification approach for genotyping viral sequences based on multidimensional scaling and linear discriminant analysis

Background  

Accurate classification into genotypes is critical in understanding evolution of divergent viruses. Here we report a new approach, MuLDAS, which classifies a query sequence based on the statistical genotype models learned from the known sequences. Thus, MuLDAS utilizes full spectra of well characterized sequences as references, typically of an order of hundreds, in order to estimate the significance of each genotype assignment.     

Techniques of preparing plant material for chromatographic separation and analysis

This paper discusses preparation techniques of samples of plant material for chromatographic analysis. Individual steps of the procedures used in sample preparation, including sample collection from the environment or from tissue cultures, drying, comminution, homogenization, leaching, extraction, distillation and condensation, analyte enrichment, and obtaining the final extracts for chromatographic analysis are discussed. The techniques most often used for isolation of analytes from homogenized plant material, i.e., Soxhlet extraction, ultrasonic solvent extraction (sonication), accelerated solvent extraction, microwave-assisted extraction, supercritical-fluid extraction, steam distillation, as well as membrane processes are emphasized. Sorptive methods of sample enrichment and removal of interferences, i.e., solid-phase extraction, and solid-phase micro-extraction are also discussed.     

A novel and effective separation method for single mitochondria analysis

To investigate the set of mtDNA molecules contained in small biological structures, powerful techniques for separation are required. We tested flow cytometry (FCM¹), laser capture microdissection (LCM²) and a method using optical tweezers (OT³) in combination with a 1μ-Ibidi-Slide with regard to their ability to deposit single mitochondrial particles. The success of separation was determined by real-time quantitative PCR (qPCR⁴) and sequencing analysis.OT revealed the highest potential for the separation and deposition of single mitochondrial particles. The study presents a novel setup for effective separation of single mitochondrial particles, which is crucial for the analysis of single mitochondria.