Linear modes of gene expression determined by independent component analysis

MOTIVATION: The expression of genes is controlled by specific combinations of cellular variables. We applied Independent Component Analysis (ICA) to gene expression data, deriving a linear model based on hidden variables, which we term 'expression modes'. The expression of each gene is a linear function of the expression modes, where, according to the ICA model, the linear influences of different modes show a minimal statistical dependence, and their distributions deviate sharply from the normal distribution. RESULTS: Studying cell cycle-related gene expression in yeast, we found that the dominant expression modes could be related to distinct biological functions, such as phases of the cell cycle or the mating response. Analysis of human lymphocytes revealed modes that were related to characteristic differences between cell types. With both data sets, the linear influences of the dominant modes showed distributions with large tails, indicating the existence of specifically up- and downregulated target genes. The expression modes and their influences can be used to visualize the samples and genes in low-dimensional spaces. A projection to expression modes helps to highlight particular biological functions, to reduce noise, and to compress the data in a biologically sensible way.     

Study of local EEG specificities in children with mental development disorders using independent component analysis

Baseline EEGs in the frequency range of 3–13 Hz in children with mental disorders of perinatal origin during wakefulness with the eyes open were analyzed using independent component analysis. In cases of severe mental retardation, a significant increase in the power density spectra of the θ band was revealed in the left-sided frontotemporal and right-sided temporal cortices, which allows us to consider these regions to be putative sources of slow activity and markers for a lesion or immaturity in the fronto-thalamic system, as well as for the temporal areas responsible for the auditory analysis and synthesis of speech signals and the integration of audio-visual information.     

Metabolite fingerprinting: detecting biological features by independent component analysis

MOTIVATION: Metabolite fingerprinting is a technology for providing information from spectra of total compositions of metabolites. Here, spectra acquisitions by microchip-based nanoflow-direct-infusion QTOF mass spectrometry, a simple and high throughput technique, is tested for its informative power. As a simple test case we are using Arabidopsis thaliana crosses. The question is how metabolite fingerprinting reflects the biological background. In many applications the classical principal component analysis (PCA) is used for detecting relevant information. Here a modern alternative is introduced-the independent component analysis (ICA). Due to its independence condition, ICA is more suitable for our questions than PCA. However, ICA has not been developed for a small number of high-dimensional samples, therefore a strategy is needed to overcome this limitation. RESULTS: To apply ICA successfully it is essential first to reduce the high dimension of the dataset, by using PCA. The number of principal components determines the quality of ICA significantly, therefore we propose a criterion for estimating the optimal dimension automatically. The kurtosis measure is used to order the extracted components to our interest. Applied to our A. thaliana data, ICA detects three relevant factors, two biological and one technical, and clearly outperforms the PCA.     

Application of independent component analysis to microarrays

We apply linear and nonlinear independent component analysis (ICA) to project microarray data into statistically independent components that correspond to putative biological processes, and to cluster genes according to over- or under-expression in each component. We test the statistical significance of enrichment of gene annotations within clusters. ICA outperforms other leading methods, such as principal component analysis, k-means clustering and the Plaid model, in constructing functionally coherent clusters on microarray datasets from Saccharomyces cerevisiae, Caenorhabditis elegans and human.     

Comparison between principal component analysis and independent component analysis in electroencephalograms modelling

Principal Component Analysis (PCA) is a classical technique in statistical data analysis, feature extraction and data reduction, aiming at explaining observed signals as a linear combination of orthogonal principal components. Independent Component Analysis (ICA) is a technique of array processing and data analysis, aiming at recovering unobserved signals or 'sources' from observed mixtures, exploiting only the assumption of mutual independence between the signals. The separation of the sources by ICA has great potential in applications such as the separation of sound signals (like voices mixed in simultaneous multiple records, for example), in telecommunication or in the treatment of medical signals. However, ICA is not yet often used by statisticians. In this paper, we shall present ICA in a statistical framework and compare this method with PCA for electroencephalograms (EEG) analysis.We shall see that ICA provides a more useful data representation than PCA, for instance, for the representation of a particular characteristic of the EEG named event-related potential (ERP).     

Mining HIV dynamics using independent component analysis

MOTIVATION: We implement a data mining technique based on the method of Independent Component Analysis (ICA) to generate reliable independent data sets for different HIV therapies. We show that this technique takes advantage of the ICA power to eliminate the noise generated by artificial interaction of HIV system dynamics. Moreover, the incorporation of the actual laboratory data sets into the analysis phase offers a powerful advantage when compared with other mathematical procedures that consider the general behavior of HIV dynamics. RESULTS: The ICA algorithm has been used to generate different patterns of the HIV dynamics under different therapy conditions. The Kohonen Map has been used to eliminate redundant noise in each pattern to produce a reliable data set for the simulation phase. We show that under potent antiretroviral drugs, the value of the CD4+ cells in infected persons decreases gradually by about 11% every 100 days and the levels of the CD8+ cells increase gradually by about 2% every 100 days. AVAILABILITY: Executable code and data libraries are available by contacting the corresponding author. IMPLEMENTATION: Mathematica 4 has been used to simulate the suggested model. A Pentium III or higher platform is recommended.     

Fine-scale genetic mapping using independent component analysis

The aim of genetic mapping is to locate the loci responsible for specific traits such as complex diseases. These traits are normally caused by mutations at multiple loci of unknown locations and interactions. In this work, we model the biological system that relates DNA polymorphisms with complex traits as a linear mixing process. Given this model, we propose a new fine-scale genetic mapping method based on independent component analysis. The proposed method outputs both independent associated groups of SNPs in addition to specific associated SNPs with the phenotype. It is applied to a clinical data set for the Schizophrenia disease with 368 individuals and 42 SNPs. It is also applied to a simulation study to investigate in more depth its performance. The obtained results demonstrate the novel characteristics of the proposed method compared to other genetic mapping methods. Finally, we study the robustness of the proposed method with missing genotype values and limited sample sizes.     

Can back-projection fully resolve polarity indeterminacy of independent component analysis in study of event-related potential?

In the study of event-related potentials (ERPs) using independent component analysis (ICA), it is a traditional way to project the extracted ERP component back to electrodes for correcting its scaling (magnitude and polarity) indeterminacy. However, ICA tends to be locally optimized in practice, and then, the back-projection of a component estimated by the ICA can possibly not fully correct its polarity at every electrode. We demonstrate this phenomenon from the view of the theoretical analysis and numerical simulations and suggest checking and modifying the abnormal polarity of the projected component in the electrode field before further analysis. Moreover, when several components are to be projected, instead of the parallel projection of those components simultaneously, the sequential projection of component by component permits the correction of the abnormal polarity of a certain projected component at a certain electrode, which can improve the accuracy of the back-projection. Furthermore, after one extracted component by the ICA is projected back to electrodes under the global optimization, we cannot achieve the real source yet, but the determined scaled source, i.e., the multiplication between the real source and the mapping coefficient from the source to the point at the scalp.     

Model-free functional MRI analysis using topographic independent component analysis

Data-driven fMRI analysis techniques include independent component analysis (ICA) and different types of clustering in the temporal domain. Since each of these methods has its particular strengths, it is natural to look for an approach that unifies Kohonen's self-organizing map and ICA. This is given by the topographic independent component analysis. While achieved by a slight modification of the ICA model, it can be at the same time used to define a topographic order (clusters) between the components, and thus has the usual computational advantages associated with topographic maps. In this contribution, we can show that when applied to fMRI analysis it outperforms FastICA.     

Investigations into resting-state connectivity using independent component analysis

Inferring resting-state connectivity patterns from functional magnetic resonance imaging (fMRI) data is a challenging task for any analytical technique. In this paper, we review a probabilistic independent component analysis (PICA) approach, optimized for the analysis of fMRI data, and discuss the role which this exploratory technique can take in scientific investigations into the structure of these effects. We apply PICA to fMRI data acquired at rest, in order to characterize the spatio-temporal structure of such data, and demonstrate that this is an effective and robust tool for the identification of low-frequency resting-state patterns from data acquired at various different spatial and temporal resolutions. We show that these networks exhibit high spatial consistency across subjects and closely resemble discrete cortical functional networks such as visual cortical areas or sensory-motor cortex.     

Gene expression data classification using consensus independent component analysis

We propose a new method for tumor classification from gene expression data, which mainly contains three steps. Firstly, the original DNA microarray gene expression data are modeled by independent component analysis （ICA）. Secondly, the most discriminant eigenassays extracted by ICA are selected by the sequential floating forward selection technique. Finally, support vector machine is used to classify the modeling data. To show the validity of the proposed method, we applied it to classify three DNA microarray datasets involving various human normal and tumor tissue samples. The experimental results show that the method is efficient and feasible.     

Biotransformation of plantain pseudostem fibres using local enzyme sources; analysis of their potential as commercial poultry feed

Plantain pseudo-stem fibres (PPS) were valorized in this study by subjecting to simultaneous saccharification and fermentation (SSF) and afterwards, their complex carbohydrates and monosaccharides, mycotoxins, protein qualities, and free radical scavenging potentials were compared to those of commercial poultry feeds (CPF). The SSF of PPS was achieved using digestive juice of the snail; Archachatina marginata, and yeast, while standard methods like HPLC-UV, HPLC-DAD, monosaccharides and mycotoxin kits, and UV-VIS spectrophotometry were used for analysis. The cellulose, hemicellulose, pectin, lignin, extractives, and acetyl contents of PPS were significantly (p?<?.05) reduced when subjected to SSF. Glucose (41.1%), galactose (11.2%), mannose (1.7%), and fucose (1.8%) contents of the SSF-PPS were higher than those of the PPS and CPF while CPF showed higher contents of arabinose (8.2%), fructose (18.3%), and rhamnose (1.7%). No mycotoxin was detected in the PPS, while all aflatoxins (B₁, B₂, G₁, and G₂), citrinin, fumonisin B₁, and B₂, ochratoxin A and B contents of SSF-PPS were equivalent to those for CPF. Patulin (5.52×10^?4?µg/kg) and zearalenone (7.76×10^?6?µg/kg) contents of the SSF-PPS were lower than those for CPF (1.50×10^?3?µg/kg and 1.13×10^?5?µg/kg respectively). The total amino acids (TAA), total non-essential and essential amino acids (TNEAA and TEAA), total basic and branched chain amino acids (TBAA and TBCAA) of the SSF-PPS were higher than those of the PPS and CPF while the free radical scavenging potentials of the SSF-PPS were mostly concentration dependent, and showed significantly higher ABTS, DPPH, Ferric, OH, lipid peroxide, and superoxide radical scavenging potentials than the standards used. This study has shown that the valorization of the agricultural residue using SSF, improves carbohydrate, protein, mycotoxins, and in-vitro antioxidant properties suitable enough for poultry feeding.     

基于时间聚类分析和独立成分分析的癫痫fMRI盲分析方法

提出了一种基于时间聚类分析和独立成分分析的癫痫fMRI数据盲分析方法,并将两种方法有效联合,提取发作间期的癫痫fMRI激活时空信息.该方法首先由时间聚类分析得到与激活相关的时间峰度特征曲线,以此特征作为时间参考信息;再由空间独立成分分析分解fMRI信号得到空间独立成分;最后将每个独立成分所对应的时间曲线与参考曲线做相关分析提取相应脑激活图.提出的方法无需任何关于癫痫fMRI的先验假设信息,有效解决了独立成分的排序问题,实现了对数据的盲分析.仿真试验结果阐明了这一方法的有效性及可靠性,对癫痫数据的试验结果显示空间定位准确性优于统计参数图方法.     

Mapping gene expression quantitative trait loci by singular value decomposition and independent component analysis

Background  

The combination of gene expression profiling with linkage analysis has become a powerful paradigm for mapping gene expression quantitative trait loci (eQTL). To date, most studies have searched for eQTL by analyzing gene expression traits one at a time. As thousands of expression traits are typically analyzed, this can reduce power because of the need to correct for the number of hypothesis tests performed. In addition, gene expression traits exhibit a complex correlation structure, which is ignored when analyzing traits individually.     

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.

Nonlinear dynamics of the EEG separated by independent component analysis after sound and light stimulation

 The electroencephalogram (EEG) is a multiscaled signal consisting of several time-series components each with different dominant frequency ranges and different origins. Nonlinear measures of the EEG reflect the complexity of the overall EEG, but not of each component in it. The aim of this study is to examine effect of the sound and light (SL) stimulation on the complexity of each component of the EEG. We used independent component analysis to obtain independent components of the EEG. The first positive Lyapunov exponent (L1) was estimated as a nonlinear measure of complexity. Twelve subjects were administered photic and auditory stimuli with a frequency of 10 Hz, which corresponded to the alpha frequency of the EEG, by a sound and light entrainment device. We compared the L1 values of the EEGs and their independent components between baseline and after the SL stimulation. We detected that the L1 values of the EEG decreased after the SL stimulation in all channels except C3 and F4, indicating that the complexity of the EEG decreased. We showed that alpha components increased in proportion but decreased in complexity after the SL stimulation. The beta independent components were found to decrease in proportion and complexity. These results suggest that decreased complexity of the EEG after the SL stimulation may be principally caused by decreased complexity and increased proportion of the alpha independent components. We showed also that theta components increased in complexity after the SL stimulation. We propose that nonlinear dynamical analysis combined with independent component analysis may be helpful in understanding the temporal characteristics of the EEG, which cannot be detected by conventional linear or nonlinear methods. Received: 12 March 2001 / Accepted in revised form: 27 November 2001     

Simulation of retinal ganglion cell response using fast independent component analysis

Advances in neurobiology suggest that neuronal response of the primary visual cortex to natural stimuli may be attributed to sparse approximation of images, encoding stimuli to activate specific neurons although the underlying mechanisms are still unclear. The responses of retinal ganglion cells (RGCs) to natural and random checkerboard stimuli were simulated using fast independent component analysis. The neuronal response to stimuli was measured using kurtosis and Treves–Rolls sparseness, and the kurtosis, lifetime and population sparseness were analyzed. RGCs exhibited significant lifetime sparseness in response to natural stimuli and random checkerboard stimuli. About 65 and 72% of RGCs do not fire all the time in response to natural and random checkerboard stimuli, respectively. Both kurtosis of single neurons and lifetime response of single neurons values were larger in the case of natural than in random checkerboard stimuli. The population of RGCs fire much less in response to random checkerboard stimuli than natural stimuli. However, kurtosis of population sparseness and population response of the entire neurons were larger with natural than random checkerboard stimuli. RGCs fire more sparsely in response to natural stimuli. Individual neurons fire at a low rate, while the occasional “burst” of neuronal population transmits information efficiently.