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MetICA: independent component analysis for high-resolution mass-spectrometry based non-targeted metabolomics

BMC BIOINFORMATICS 2016年第1期17卷 1-14页

作者： Liu, Youzhong Smirnov, Kirill Lucio, Marianna Gougeon, Regis D. Alexandre, Herve Schmitt-Kopplin, Philippe Helmholtz Zentrum Munchen Dept Environm Sci Res Unit Analyt BioGeoChem Ingolstadter Landstr 1 D-85758 Neuherberg Germany UMR PAM Univ Bourgogne Inst Univ Vigne & Vin Agrosup Dijon Rue Claude LadreyBP 27877 Dijon France Tech Univ Munich Chair Analyt Food Chem Alte Akad 1085354 Freising Weihenstephan Germany

Background: Interpreting non-targeted metabolomics data remains a challenging task. Signals from non-targeted metabolomics studies stem from a combination of biological causes, complex interactions between them and experimental bias/noise. The resulting data matrix usually contain huge number of variables and only few samples, and classical techniques using nonlinear mapping could result in computational complexity and overfitting. independent component analysis (ICA) as a linear method could potentially bring more meaningful results than Principal component analysis (PCA). However, a major problem with most ICA algorithms is the output variations between different runs and the result of a single ICA run should be interpreted with reserve. Results: ICA was applied to simulated and experimental mass spectrometry (MS)-based non-targeted metabolomics data, under the hypothesis that underlying sources are mutually independent. Inspired from the Icasso algorithm, a new ICA method, MetICA was developed to handle the instability of ICA on complex datasets. Like the original Icasso algorithm, MetICA evaluated the algorithmic and statistical reliability of ICA runs. In addition, MetICA suggests two ways to select the optimal number of model components and gives an order of interpretation for the components obtained. Conclusions: Correlating the components obtained with prior biological knowledge allows understanding how nontargeted metabolomics data reflect biological nature and technical phenomena. We could also extract mass signals related to this information. This novel approach provides meaningful components due to their independent nature. Furthermore, it provides an innovative concept on which to base model selection: that of optimizing the number of reliable components instead of trying to fit the data. The current version of MetICA is available at https://***/daniellyz/MetICA.

关键词： independent component analysis independent component analysis algorithm algorithm Input independent component analysis Method FastICA algorithm

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Application of independent component analysis to microarrays

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GENOME BIOLOGY 2003年第11期4卷 R76-R76页

作者： Lee, SI Batzoglou, S Stanford Univ Dept Comp Sci Stanford CA 94305 USA Stanford Univ Dept Elect Engn Stanford CA 94305 USA

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.

关键词： independent component analysis Blind Source Separation independent component analysis algorithm independent component analysis Method independent component analysis Model

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Embedded Power Quality Monitoring System based on independent component analysis and SVMs

Embedded Power Quality Monitoring System based on Independen...

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International Joint Conference on Neural Networks (IJCNN)

作者： Ruiz-Llata, Marta Guarnizo, Guillermo Boya, Carlos Univ Carlos III Madrid Dept Elect Technol Madrid 28911 Spain

ISBN: (纸本)9781424496365

On-line identification and classification of voltage and current disturbances in power systems are important tasks in the power quality monitoring and protection of power systems. Some power quality disturbances are non-stationary and transitory while other are steady-state variations that distort the voltage signal. One, two or more different power quality disturbances may appear at the same time. In this paper we propose a power quality monitoring system that employs independent component analysis algorithm that is able to decouple multiple simultaneous power quality disturbances, and Support Vector Machines for identification the occurrence of a disturbance. We also show the first steps towards embedding the proposed system on an FPGA for online power quality monitoring.

关键词： Harmonic analysis Kernel Monitoring Power harmonic filters Power quality SVM Support vector machines Transient analysis current disturbance embedded power quality monitoring system field programmable gate arrays independent component analysis independent component analysis algorithm power engineering computing power quality disturbance power supply quality power system faults power system identification power system measurement power system protection power system protection support vector machine support vector machines voltage disturbance voltage signal distortion

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A mixture model with a reference-based automatic selection of components for disease classification from protein and/or gene expression levels

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BMC BIOINFORMATICS 2011年第1期12卷 1-18页

作者： Kopriva, Ivica Filipovic, Marko Rudjer Boskovic Inst Div Laser & Atom R&D Zagreb 10000 Croatia

Background: Bioinformatics data analysis is often using linear mixture model representing samples as additive mixture of components. Properly constrained blind matrix factorization methods extract those components using mixture samples only. However, automatic selection of extracted components to be retained for classification analysis remains an open issue. Results: The method proposed here is applied to well-studied protein and genomic datasets of ovarian, prostate and colon cancers to extract components for disease prediction. It achieves average sensitivities of: 96.2 (sd = 2.7%), 97.6% (sd = 2.8%) and 90.8% (sd = 5.5%) and average specificities of: 93.6% (sd = 4.1%), 99% (sd = 2.2%) and 79.4% (sd = 9.8%) in 100 independent two-fold cross-validations. Conclusions: We propose an additive mixture model of a sample for feature extraction using, in principle, sparseness constrained factorization on a sample-by-sample basis. As opposed to that, existing methods factorize complete dataset simultaneously. The sample model is composed of a reference sample representing control and/or case (disease) groups and a test sample. Each sample is decomposed into two or more components that are selected automatically (without using label information) as control specific, case specific and not differentially expressed (neutral). The number of components is determined by cross-validation. Automatic assignment of features (m/z ratios or genes) to particular component is based on thresholds estimated from each sample directly. Due to the locality of decomposition, the strength of the expression of each feature across the samples can vary. Yet, they will still be allocated to the related disease and/or control specific component. Since label information is not used in the selection process, case and control specific components can be used for classification. That is not the case with standard factorization methods. Moreover, the component selected by proposed method as disease specif

关键词： independent component analysis Support Vector Machine Classifier Nonnegative Matrix Factorization Sparseness Constraint independent component analysis algorithm

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Research of rectal dynamic function diagnosis based on FastICA-STFT

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2018年第8期12卷 965-969页

作者： Zan, Peng Liu, Yankai Chang, Meihan Shanghai Univ Sch Mechatron Engn & Automat Dept Automat Shanghai 200072 Peoples R China Shanghai Key Lab Power Stn Automat Technol Shanghai 200072 Peoples R China

Aiming at the lack of signal preprocessing in current rectal signal research, a new 15th-order Newton iterative fast independent component analysis (FastICA) algorithm is proposed to separate the signal. To solve the incomplete problem of the single channel rectal pressure signal, C-C phase space reconstruction technology is used for multi-dimensional signal reconstruction. In order to achieve the diagnosis of rectal dynamic after artificial anal sphincter implantation, the preprocessed rectal signal component is subjected to short-time Fourier transforms for time-frequency analysis. This constitutes a complete rectal dynamic function diagnosis process. The non-stationary simulation signal is constructed to verify the preprocessing method. A complete diagnostic experiment is performed on two rectal signals of different gut dynamic. Simulation results show that the proposed method can realise the preprocessing of non-stationary signals. Through the rectal dynamic function experiment based FastICA-STFT, two different rectal signals are distinguished, and the results are consistent with the clinical diagnosis, which has achieved the diagnosis of rectal motility. This study will provide support for the recovery of rectal dynamic function in patients with artificial anal sphincter implants.

关键词： Newton method time-frequency analysis patient diagnosis Fourier transforms independent component analysis wavelet transforms signal reconstruction fault diagnosis blind source separation different rectal signals clinical diagnosis rectal motility artificial anal sphincter implants FastICA-STFT current rectal signal research 15th-order Newton independent component analysis algorithm incomplete problem single channel rectal pressure signal C-C phase space reconstruction technology multidimensional signal reconstruction artificial anal sphincter implantation preprocessed rectal signal component short-time Fourier time-frequency analysis complete rectal dynamic function diagnosis process nonstationary simulation signal preprocessing method complete diagnostic experiment different gut dynamic nonstationary signals rectal dynamic function experiment

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A Multiple Access Scheme for the Uplink of MIMO Systems

A Multiple Access Scheme for the Uplink of MIMO Systems

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6th International Conference on Wireless Communications, Networking and Mobile Computing (WICOM)

作者： Yang, Pinlu Hu, Aiqun Southeast Univ Sch Informat Sci & Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781424437092

In order to efficiently use the common channel resources in multi-user multiple-input and multiple-output (MIMO) systems, an independent component based multiple access (ICMA) scheme is introduced. In ICMA, the signals generated by different users are distinguished only from their independence, where neither non-interfering subchannels nor orthogonality constraints are needed. The scheme is based on independent component analysis (ICA) algorithm, which is a fundamental technique for blind source separation. The performance of the proposed receiver is compared with zero forcing (ZF) receiver and minimum mean-square error (MMSE) receiver. Simulation results show that ICMA receiver performs better than ZF receiver, and worse than MMSE receiver. However, in ICMA, there is no need of synchronization and channal estimation, which is required in ZF and MMSE. The efficiencies of the scheme are observed.

关键词： ICMA receiver MIMO communication MIMO systems MMSE receiver ZF receiver blind source separation blind source separation common channel resources independent component analysis independent component analysis algorithm independent component based multiple access scheme minimum mean-square error receiver multi-access systems multiuser systems radio receivers wireless channels zero forcing receiver

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