Mathematical procedure that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components.
A statistical analytic technique used with discrete dependent variables, concerned with separating sets of observed values and allocating new values. It is sometimes used instead of regression analysis.
The systematic identification and quantitation of all the metabolic products of a cell, tissue, organ, or organism under varying conditions. The METABOLOME of a cell or organism is a dynamic collection of metabolites which represent its net response to current conditions.
A set of statistical methods used to group variables or observations into strongly inter-related subgroups. In epidemiology, it may be used to analyze a closely grouped series of events or cases of disease or other health-related phenomenon with well-defined distribution patterns in relation to time or place or both.
A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.
The statistical reproducibility of measurements (often in a clinical context), including the testing of instrumentation or techniques to obtain reproducible results. The concept includes reproducibility of physiological measurements, which may be used to develop rules to assess probability or prognosis, or response to a stimulus; reproducibility of occurrence of a condition; and reproducibility of experimental results.
A principle of estimation in which the estimates of a set of parameters in a statistical model are those quantities minimizing the sum of squared differences between the observed values of a dependent variable and the values predicted by the model.
A set of statistical methods for analyzing the correlations among several variables in order to estimate the number of fundamental dimensions that underlie the observed data and to describe and measure those dimensions. It is used frequently in the development of scoring systems for rating scales and questionnaires.
The dynamic collection of metabolites which represent a cell's or organism's net metabolic response to current conditions.
A device used to detect airborne odors, gases, flavors, volatile substances or vapors.
Assessment of psychological variables by the application of mathematical procedures.
The determination of the pattern of genes expressed at the level of GENETIC TRANSCRIPTION, under specific circumstances or in a specific cell.
Computer-based representation of physical systems and phenomena such as chemical processes.
Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.
Hybridization of a nucleic acid sample to a very large set of OLIGONUCLEOTIDE PROBES, which have been attached individually in columns and rows to a solid support, to determine a BASE SEQUENCE, or to detect variations in a gene sequence, GENE EXPRESSION, or for GENE MAPPING.
Predetermined sets of questions used to collect data - clinical data, social status, occupational group, etc. The term is often applied to a self-completed survey instrument.
Application of statistical procedures to analyze specific observed or assumed facts from a particular study.
A plant genus of the family PRIMULACEAE. It can cause CONTACT DERMATITIS. SAPONINS have been identified in the root.
In INFORMATION RETRIEVAL, machine-sensing or identification of visible patterns (shapes, forms, and configurations). (Harrod's Librarians' Glossary, 7th ed)
A set of techniques used when variation in several variables has to be studied simultaneously. In statistics, multivariate analysis is interpreted as any analytic method that allows simultaneous study of two or more dependent variables.
Computer-assisted processing of electric, ultrasonic, or electronic signals to interpret function and activity.
A mass-spectrometric technique that is used for microscopic chemical analysis. A beam of primary ions with an energy of 5-20 kiloelectronvolts (keV) bombards a small spot on the surface of the sample under ultra-high vacuum conditions. Positive and negative secondary ions sputtered from the surface are analyzed in a mass spectrometer in regards to their mass-to-charge ratio. Digital imaging can be generated from the secondary ion beams and their intensity can be measured. Ionic images can be correlated with images from light or other microscopy providing useful tools in the study of molecular and drug actions.
Methods developed to aid in the interpretation of ultrasound, radiographic images, etc., for diagnosis of disease.
Genotypic differences observed among individuals in a population.
Analysis of the intensity of Raman scattering of monochromatic light as a function of frequency of the scattered light.
A technique of inputting two-dimensional images into a computer and then enhancing or analyzing the imagery into a form that is more useful to the human observer.
A microanalytical technique combining mass spectrometry and gas chromatography for the qualitative as well as quantitative determinations of compounds.
A noninvasive technique that uses the differential absorption properties of hemoglobin and myoglobin to evaluate tissue oxygenation and indirectly can measure regional hemodynamics and blood flow. Near-infrared light (NIR) can propagate through tissues and at particular wavelengths is differentially absorbed by oxygenated vs. deoxygenated forms of hemoglobin and myoglobin. Illumination of intact tissue with NIR allows qualitative assessment of changes in the tissue concentration of these molecules. The analysis is also used to determine body composition.
The discipline studying genetic composition of populations and effects of factors such as GENETIC SELECTION, population size, MUTATION, migration, and GENETIC DRIFT on the frequencies of various GENOTYPES and PHENOTYPES using a variety of GENETIC TECHNIQUES.
A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.
Theoretical representations that simulate the behavior or activity of genetic processes or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.
Food that is grown or manufactured in accordance with nationally regulated production standards that include restrictions on the use of pesticides, non-organic fertilizers, genetic engineering, growth hormones, irradiation, antibiotics, and non-organic ingredients.
A field of biology concerned with the development of techniques for the collection and manipulation of biological data, and the use of such data to make biological discoveries or predictions. This field encompasses all computational methods and theories for solving biological problems including manipulation of models and datasets.
The outward appearance of the individual. It is the product of interactions between genes, and between the GENOTYPE and the environment.
Non-invasive method of demonstrating internal anatomy based on the principle that atomic nuclei in a strong magnetic field absorb pulses of radiofrequency energy and emit them as radiowaves which can be reconstructed into computerized images. The concept includes proton spin tomographic techniques.
Spectroscopic method of measuring the magnetic moment of elementary particles such as atomic nuclei, protons or electrons. It is employed in clinical applications such as NMR Tomography (MAGNETIC RESONANCE IMAGING).
Binary classification measures to assess test results. Sensitivity or recall rate is the proportion of true positives. Specificity is the probability of correctly determining the absence of a condition. (From Last, Dictionary of Epidemiology, 2d ed)
Statistical models in which the value of a parameter for a given value of a factor is assumed to be equal to a + bx, where a and b are constants. The models predict a linear regression.
The part of CENTRAL NERVOUS SYSTEM that is contained within the skull (CRANIUM). Arising from the NEURAL TUBE, the embryonic brain is comprised of three major parts including PROSENCEPHALON (the forebrain); MESENCEPHALON (the midbrain); and RHOMBENCEPHALON (the hindbrain). The developed brain consists of CEREBRUM; CEREBELLUM; and other structures in the BRAIN STEM.
A plant genus of the family FABACEAE. Members of this genus can cause CONTACT DERMATITIS.
The fleshy or dry ripened ovary of a plant, enclosing the seed or seeds.
Signal and data processing method that uses decomposition of wavelets to approximate, estimate, or compress signals with finite time and frequency domains. It represents a signal or data in terms of a fast decaying wavelet series from the original prototype wavelet, called the mother wavelet. This mathematical algorithm has been adopted widely in biomedical disciplines for data and signal processing in noise removal and audio/image compression (e.g., EEG and MRI).
A single nucleotide variation in a genetic sequence that occurs at appreciable frequency in the population.
The science dealing with the earth and its life, especially the description of land, sea, and air and the distribution of plant and animal life, including humanity and human industries with reference to the mutual relations of these elements. (From Webster, 3d ed)
Theory and development of COMPUTER SYSTEMS which perform tasks that normally require human intelligence. Such tasks may include speech recognition, LEARNING; VISUAL PERCEPTION; MATHEMATICAL COMPUTING; reasoning, PROBLEM SOLVING, DECISION-MAKING, and translation of language.
Products resulting from the conversion of one language to another.
A statistical technique that isolates and assesses the contributions of categorical independent variables to variation in the mean of a continuous dependent variable.
Sequential operating programs and data which instruct the functioning of a digital computer.
Learning algorithms which are a set of related supervised computer learning methods that analyze data and recognize patterns, and used for classification and regression analysis.
Reference points located by visual inspection, palpation, or computer assistance, that are useful in localizing structures on or within the human body.
Any visible result of a procedure which is caused by the procedure itself and not by the entity being analyzed. Common examples include histological structures introduced by tissue processing, radiographic images of structures that are not naturally present in living tissue, and products of chemical reactions that occur during analysis.
An analysis comparing the allele frequencies of all available (or a whole GENOME representative set of) polymorphic markers in unrelated patients with a specific symptom or disease condition, and those of healthy controls to identify markers associated with a specific disease or condition.
Regular course of eating and drinking adopted by a person or animal.
Imaging techniques used to colocalize sites of brain functions or physiological activity with brain structures.
Individuals classified according to their sex, racial origin, religion, common place of living, financial or social status, or some other cultural or behavioral attribute. (UMLS, 2003)
The relationships of groups of organisms as reflected by their genetic makeup.
Elements of limited time intervals, contributing to particular results or situations.
The sebaceous glands situated on the inner surface of the eyelids between the tarsal plates and CONJUNCTIVA.
Acquired or learned food preferences.
An analytical method used in determining the identity of a chemical based on its mass using mass analyzers/mass spectrometers.
A spectroscopic technique in which a range of wavelengths is presented simultaneously with an interferometer and the spectrum is mathematically derived from the pattern thus obtained.
The restriction of a characteristic behavior, anatomical structure or physical system, such as immune response; metabolic response, or gene or gene variant to the members of one species. It refers to that property which differentiates one species from another but it is also used for phylogenetic levels higher or lower than the species.
Procedures for finding the mathematical function which best describes the relationship between a dependent variable and one or more independent variables. In linear regression (see LINEAR MODELS) the relationship is constrained to be a straight line and LEAST-SQUARES ANALYSIS is used to determine the best fit. In logistic regression (see LOGISTIC MODELS) the dependent variable is qualitative rather than continuously variable and LIKELIHOOD FUNCTIONS are used to find the best relationship. In multiple regression, the dependent variable is considered to depend on more than a single independent variable.
Improvement of the quality of a picture by various techniques, including computer processing, digital filtering, echocardiographic techniques, light and ultrastructural MICROSCOPY, fluorescence spectrometry and microscopy, scintigraphy, and in vitro image processing at the molecular level.
Analysis based on the mathematical function first formulated by Jean-Baptiste-Joseph Fourier in 1807. The function, known as the Fourier transform, describes the sinusoidal pattern of any fluctuating pattern in the physical world in terms of its amplitude and its phase. It has broad applications in biomedicine, e.g., analysis of the x-ray crystallography data pivotal in identifying the double helical nature of DNA and in analysis of other molecules, including viruses, and the modified back-projection algorithm universally used in computerized tomography imaging, etc. (From Segen, The Dictionary of Modern Medicine, 1992)
The SKELETON of the HEAD including the FACIAL BONES and the bones enclosing the BRAIN.
The genetic constitution of the individual, comprising the ALLELES present at each GENETIC LOCUS.
One of the HISTAMINE H1 ANTAGONISTS with little sedative action. It is used in treatment of hay fever, rhinitis, allergic dermatoses, and pruritus.
Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment.
The properties, processes, and behavior of biological systems under the action of mechanical forces.
The science and art of collecting, summarizing, and analyzing data that are subject to random variation. The term is also applied to the data themselves and to the summarization of the data.
A coordinated international effort to identify and catalog patterns of linked variations (HAPLOTYPES) found in the human genome across the entire human population.
Continuous frequency distribution of infinite range. Its properties are as follows: 1, continuous, symmetrical distribution with both tails extending to infinity; 2, arithmetic mean, mode, and median identical; and 3, shape completely determined by the mean and standard deviation.
A plant species of the Salvia genus known as a spice and medicinal plant.
NMR spectroscopy on small- to medium-size biological macromolecules. This is often used for structural investigation of proteins and nucleic acids, and often involves more than one isotope.
Measurable and quantifiable biological parameters (e.g., specific enzyme concentration, specific hormone concentration, specific gene phenotype distribution in a population, presence of biological substances) which serve as indices for health- and physiology-related assessments, such as disease risk, psychiatric disorders, environmental exposure and its effects, disease diagnosis, metabolic processes, substance abuse, pregnancy, cell line development, epidemiologic studies, etc.
Studies in which the presence or absence of disease or other health-related variables are determined in each member of the study population or in a representative sample at one particular time. This contrasts with LONGITUDINAL STUDIES which are followed over a period of time.
Studies which start with the identification of persons with a disease of interest and a control (comparison, referent) group without the disease. The relationship of an attribute to the disease is examined by comparing diseased and non-diseased persons with regard to the frequency or levels of the attribute in each group.
Organic compounds that have a relatively high VAPOR PRESSURE at room temperature.
Recording of electric currents developed in the brain by means of electrodes applied to the scalp, to the surface of the brain, or placed within the substance of the brain.
The act, process, or result of passing from one place or position to another. It differs from LOCOMOTION in that locomotion is restricted to the passing of the whole body from one place to another, while movement encompasses both locomotion but also a change of the position of the whole body or any of its parts. Movement may be used with reference to humans, vertebrate and invertebrate animals, and microorganisms. Differentiate also from MOTOR ACTIVITY, movement associated with behavior.
A computer simulation developed to study the motion of molecules over a period of time.
Theoretical representations that simulate the behavior or activity of systems, processes, or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.
The production of offspring by selective mating or HYBRIDIZATION, GENETIC in animals or plants.
The process of generating three-dimensional images by electronic, photographic, or other methods. For example, three-dimensional images can be generated by assembling multiple tomographic images with the aid of a computer, while photographic 3-D images (HOLOGRAPHY) can be made by exposing film to the interference pattern created when two laser light sources shine on an object.
The pattern of GENE EXPRESSION at the level of genetic transcription in a specific organism or under specific circumstances in specific cells.
Genetic loci associated with a QUANTITATIVE TRAIT.
Electrophoresis in which various denaturant gradients are used to induce nucleic acids to melt at various stages resulting in separation of molecules based on small sequence differences including SNPs. The denaturants used include heat, formamide, and urea.
The study, control, and application of the conduction of ELECTRICITY through gases or vacuum, or through semiconducting or conducting materials. (McGraw-Hill Dictionary of Scientific and Technical Terms, 6th ed)
Complex sets of enzymatic reactions connected to each other via their product and substrate metabolites.
Extensive collections, reputedly complete, of facts and data garnered from material of a specialized subject area and made available for analysis and application. The collection can be automated by various contemporary methods for retrieval. The concept should be differentiated from DATABASES, BIBLIOGRAPHIC which is restricted to collections of bibliographic references.
A phenotypically recognizable genetic trait which can be used to identify a genetic locus, a linkage group, or a recombination event.
The monitoring of the level of toxins, chemical pollutants, microbial contaminants, or other harmful substances in the environment (soil, air, and water), workplace, or in the bodies of people and animals present in that environment.
A phase transition from liquid state to gas state, which is affected by Raoult's law. It can be accomplished by fractional distillation.
Examination of urine by chemical, physical, or microscopic means. Routine urinalysis usually includes performing chemical screening tests, determining specific gravity, observing any unusual color or odor, screening for bacteriuria, and examining the sediment microscopically.
A country spanning from central Asia to the Pacific Ocean.
A characteristic showing quantitative inheritance such as SKIN PIGMENTATION in humans. (From A Dictionary of Genetics, 4th ed)
The study, based on direct observation, use of statistical records, interviews, or experimental methods, of actual practices or the actual impact of practices or policies.
A coordinated effort of researchers to map (CHROMOSOME MAPPING) and sequence (SEQUENCE ANALYSIS, DNA) the human GENOME.
A country in western Europe bordered by the Atlantic Ocean, the English Channel, the Mediterranean Sea, and the countries of Belgium, Germany, Italy, Spain, Switzerland, the principalities of Andorra and Monaco, and by the duchy of Luxembourg. Its capital is Paris.
A meshlike structure composed of interconnecting nerve cells that are separated at the synaptic junction or joined to one another by cytoplasmic processes. In invertebrates, for example, the nerve net allows nerve impulses to spread over a wide area of the net because synapses can pass information in any direction.
Tests designed to assess neurological function associated with certain behaviors. They are used in diagnosing brain dysfunction or damage and central nervous system disorders or injury.
Fermented juice of fresh grapes or of other fruit or plant products used as a beverage.
Social and economic factors that characterize the individual or group within the social structure.
The systematic study of the complete complement of proteins (PROTEOME) of organisms.
A functional system which includes the organisms of a natural community together with their environment. (McGraw Hill Dictionary of Scientific and Technical Terms, 4th ed)
The systematic arrangement of entities in any field into categories classes based on common characteristics such as properties, morphology, subject matter, etc.
Databases devoted to knowledge about specific genes and gene products.

Feature selection for DNA methylation based cancer classification. (1/3813)

Molecular portraits, such as mRNA expression or DNA methylation patterns, have been shown to be strongly correlated with phenotypical parameters. These molecular patterns can be revealed routinely on a genomic scale. However, class prediction based on these patterns is an under-determined problem, due to the extreme high dimensionality of the data compared to the usually small number of available samples. This makes a reduction of the data dimensionality necessary. Here we demonstrate how phenotypic classes can be predicted by combining feature selection and discriminant analysis. By comparing several feature selection methods we show that the right dimension reduction strategy is of crucial importance for the classification performance. The techniques are demonstrated by methylation pattern based discrimination between acute lymphoblastic leukemia and acute myeloid leukemia.  (+info)

Separation of samples into their constituents using gene expression data. (2/3813)

Gene expression measurements are a powerful tool in molecular biology, but when applied to heterogeneous samples containing more than one cellular type the results are difficult to interpret. We present here a new approach to this problem allowing to deduce the gene expression profile of the various cellular types contained in a set of samples directly from the measurements taken on the whole sample.  (+info)

The main biological determinants of tumor line taxonomy elucidated by a principal component analysis of microarray data. (3/3813)

By using principal components analysis (PCA) we demonstrate here that the information relevant to tumor line classification linked to the activity of 1375 genes expressed in 60 tumor cell lines can be reproduced by only five independent components. These components can be interpreted as cell motility and migration, cellular trafficking and endo/exocytosis, and epithelial character. PCA, at odds with cluster analysis methods routinely used in microarray analysis, allows for the participation of individual genes to multiple biochemical pathways, while assigning to each cell line a quantitative score reflecting fundamental biological functions.  (+info)

High-resolution metabolic phenotyping of genetically and environmentally diverse potato tuber systems. Identification of phenocopies. (4/3813)

We conducted a comprehensive metabolic phenotyping of potato (Solanum tuberosum L. cv Desiree) tuber tissue that had been modified either by transgenesis or exposure to different environmental conditions using a recently developed gas chromatography-mass spectrometry profiling protocol. Applying this technique, we were able to identify and quantify the major constituent metabolites of the potato tuber within a single chromatographic run. The plant systems that we selected to profile were tuber discs incubated in varying concentrations of fructose, sucrose, and mannitol and transgenic plants impaired in their starch biosynthesis. The resultant profiles were then compared, first at the level of individual metabolites and then using the statistical tools hierarchical cluster analysis and principal component analysis. These tools allowed us to assign clusters to the individual plant systems and to determine relative distances between these clusters; furthermore, analyzing the loadings of these analyses enabled identification of the most important metabolites in the definition of these clusters. The metabolic profiles of the sugar-fed discs were dramatically different from the wild-type steady-state values. When these profiles were compared with one another and also with those we assessed in previous studies, however, we were able to evaluate potential phenocopies. These comparisons highlight the importance of such an approach in the functional and qualitative assessment of diverse systems to gain insights into important mediators of metabolism.  (+info)

Percent G+C profiling accurately reveals diet-related differences in the gastrointestinal microbial community of broiler chickens. (5/3813)

Broiler chickens from eight commercial farms in Southern Finland were analyzed for the structure of their gastrointestinal microbial community by a nonselective DNA-based method, percent G+C-based profiling. The bacteriological impact of the feed source and in-farm whole-wheat amendment of the diet was assessed by percent G+C profiling. Also, a phylogenetic 16S rRNA gene (rDNA)-based study was carried out to aid in interpretation of the percent G+C profiles. This survey showed that most of the 16S rDNA sequences found could not be assigned to any previously known bacterial genus or they represented an unknown species of one of the taxonomically heterogeneous genera, such as Ruminococcus or Clostridium. The data from bacterial community profiling were analyzed by t-test, multiple linear regression, and principal-component statistical approaches. The percent G+C profiling method with appropriate statistical analyses detected microbial community differences smaller than 10% within each 5% increment of the percent G+C profiles. Diet turned out to be the strongest determinant of the cecal bacterial community structure. Both the source of feed and local feed amendment changed the bacteriological profile significantly, whereas profiles of individual farms with identical feed regimens hardly differed from each other. This suggests that the management of typical Finnish farms is relatively uniform or that hygiene on the farm, in fact, has little impact on the structure of the cecal bacterial community. Therefore, feed compounders should have a significant role in the modulation of gut microflora and consequently in prevention of gastrointestinal disorders in farm animals.  (+info)

Independent representations of limb axis length and orientation in spinocerebellar response components. (6/3813)

Dorsal spinocerebellar tract (DSCT) neurons transmit sensory signals to the cerebellum that encode global hindlimb parameters, such as the hindlimb end-point position and its direction of movement. Here we use a population analysis approach to examine further the characteristics of DSCT neuronal responses during continuous movements of the hind foot. We used a robot to move the hind paw of anesthetized cats through the trajectories of a step or a figure-8 footpath in a parasagittal plane. Extracellular recordings from 82 cells converted to cycle histograms provided the basis for a principal-component analysis to determine the common features of the DSCT movement responses. Five principal components (PCs) accounted for about 80% of the total variance in the waveforms across units. The first two PCs accounted for about 60% of the variance and they were highly robust across samples. We examined the relationship between the responses and limb kinematic parameters by correlating the PC waveforms with waveforms of the joint angle and limb axis trajectories using multivariate linear regression models. Each PC waveform could be at least partly explained by a linear relationship to joint-angle trajectories, but except for the first PC, they required multiple angles. However, the limb axis parameters more closely related to both the first and second PC waveforms. In fact, linear regression models with limb axis length and orientation trajectories as predictors explained 94% of the variance in both PCs, and each was related to a particular linear combination of position and velocity. The first PC correlated with the limb axis orientation and orientation velocity trajectories, whereas second PC with the length and length velocity trajectories. These combinations were found to correspond to the dynamics of muscle spindle responses. The first two PCs were also most representative of the data set since about half the DSCT responses could be at least 85% accounted for by weighted linear combinations of these two PCs. Higher-order PCs were unrelated to limb axis trajectories and accounted instead for different dynamic components of the responses. The findings imply that an explicit and independent representation of the limb axis length and orientation may be present at the lowest levels of sensory processing in the spinal cord.  (+info)

Reliability, validity and psychometric properties of the Greek translation of the Zung Depression Rating Scale. (7/3813)

INTRODUCTION: The current study aimed to assess the reliability, validity and psychometric properties of the Greek translation of the Zung Depression Rating Scale (ZDRS). METHODS: The study sample included 40 depressed patients 29.65 +/- 9.38 years old and 120 normal comparison subjects 27.23 +/- 10.62 years old. In 20 of them (12 patients and 8 comparison subjects) the instrument was re-applied 1-2 days later. Translation and Back Translation was made. Clinical Diagnosis was reached by consensus of two examiners with the use of the SCAN v.2.0 and the IPDE. Statistical Analysis included ANOVA, the Pearson Product Moment Correlation Coefficient, Principal Components Analysis and Discriminant Function Analysis and the calculation of Cronbach's alpha (alpha) RESULTS: Both Sensitivity and specificity exceed 90.00 at 44/45, Chronbach's alpha for the total scale was equal to 0.09, suggesting that the scale covers a broad spectrum of symptoms. Factor analysis revealed five factors (anxiety-depression, thought content, gastrenterological symptoms, irritability and social-interpersonal functioning). The test-retest reliability was satisfactory (Pearson's R between 0.92). CONCLUSION: The ZDRS-Greek translation is both reliable and valid and is suitable for clinical and research use with satisfactory properties. Its properties are similar to those reported in the international literature, although the literature is limited. However one should always have in mind the limitations inherent in the use of self-report scales.  (+info)

Analysis of large-scale gene expression data. (8/3813)

DNA microarray technology has resulted in the generation of large complex data sets, such that the bottleneck in biological investigation has shifted from data generation, to data analysis. This review discusses some of the algorithms and tools for the analysis and organisation of microarray expression data, including clustering methods, partitioning methods, and methods for correlating expression data to other biological data.  (+info)

TY - JOUR. T1 - Quantifying Individual Brain Connectivity with Functional Principal Component Analysis for Networks. AU - Petersen, Alexander. AU - Zhao, Jianyang. AU - Carmichael, Owen. AU - Müller, Hans Georg. PY - 2016/9/1. Y1 - 2016/9/1. N2 - In typical functional connectivity studies, connections between voxels or regions in the brain are represented as edges in a network. Networks for different subjects are constructed at a given graph density and are summarized by some network measure such as path length. Examining these summary measures for many density values yields samples of connectivity curves, one for each individual. This has led to the adoption of basic tools of functional data analysis, most commonly to compare control and disease groups through the average curves in each group. Such group differences, however, neglect the variability in the sample of connectivity curves. In this article, the use of functional principal component analysis (FPCA) is demonstrated to enrich ...
Principle component analysis determines the direction of maximum variance of data for a given feature set.True or false Principle component analysis determines the direction of maximum var
One of the fundamental problems in time course gene expression data analysis is to identify genes associated with a biological process or a particular stimulus of interest, like a treatment or virus infection. Most of the existing methods for this problem are designed for data with longitudinal replicates. But in reality, many time course gene experiments have no replicates or only have a small number of independent replicates. We focus on the case without replicates and propose a new method for identifying differentially expressed genes by incorporating the functional principal component analysis (FPCA) into a hypothesis testing framework. The data-driven eigenfunctions allow a flexible and parsimonious representation of time course gene expression trajectories, leaving more degrees of freedom for the inference compared to that using a prespecified basis. Moreover, the information of all genes is borrowed for individual gene inferences. The proposed approach turns out to be more powerful in identifying
A non-iterative spatial phase-shifting algorithm based on principal component analysis (PCA) is proposed to directly extract the phase from only a single spatial carrier interferogram. Firstly, we compose a set of phase-shifted fringe patterns from the original spatial carrier interferogram shifting by one pixel their starting position. Secondly, two uncorrelated quadrature signals that correspond to the first and second principal components are extracted from the phase-shifted interferograms by the PCA algorithm. Then, the modulating phase is calculated from the arctangent function of the two quadrature signals. Meanwhile, the main factors that may influence the performance of the proposed method are analyzed and discussed, such as the level of random noise, the carrier-frequency values and the angle of carrier-frequency of fringe pattern. Numerical simulations and experiments are given to demonstrate the performance of the proposed method and the results show that the proposed method is fast, ...
Autism is often diagnosed during preschool or toddled age. This diagnosis often depends on behavioral test. It is known that individuals with autism have abnormal brain signals different from typical persons yet this difference in signals is slight that it is often difficult to distinguish from the normal. However, Electroencephalogram (EEG) signals have a lot of information which reflect the behavior of brain functions which therefore captures the marker for autism, help to early diagnose and speed the treatment. This work investigates and compares classification process for autism in open-eyed tasks and motor movement by using Principle Component Analysis (PCA) for feature extracted in Time-frequency domain to reduce data dimension. The results show that the proposed method gives accuracy in the range 90-100% for autism and normal children in motor task and around 90% to detect normal in open-eyed tasks though difficult to detect autism in this task.. ...
Time series is a series of observations over time. When there is one observation at each time instance, it is called a univariate time series (UTS), and when there are more than one observations, it is called a multivariate time series (MTS). While UTS datasets have been extensively explored, MTS datasets have not been broadly investigated. The techniques for UTS datasets, however, cannot be simply extended for MTS datasets, since multivariate time series is different from multiple univariate time series. That is, an MTS item may not be broken into multiple univariate time series and be separately analyzed, because this will result in the loss of the correlation information within the multivariate time series.; In this dissertation, we introduce a set of techniques for multivariate time series analysis based on principal component analysis (PCA). As a similarity measure for MTS datasets, we present Eros (Extended Frobenius norm). Eros computes the similarity between two MTS items by comparing ...
The aim of this study was to forecast the returns for the Stock Exchange of Thailand (SET) Index by adding some explanatory variables and stationary Autoregressive Moving-Average order p and q (ARMA (p, q)) in the mean equation of returns. In addition, we used Principal Component Analysis (PCA) to remove possible complications caused by multicollinearity. Afterwards, we forecast the volatility of the returns for the SET Index. Results showed that the ARMA (1,1), which includes multiple regression based on PCA, has the best performance. In forecasting the volatility of returns, the GARCH model performs best for one day ahead; and the EGARCH model performs best for five days, ten days and twenty-two days ahead.
When the number of training samples is limited, feature reduction plays an important role in classification of hyperspectral images. In this paper, we propose a supervised feature extraction method based on discriminant analysis (DA) which uses the first principal component (PC1) to weight the scatter matrices. The proposed method, called DA-PC1, copes with the small sample size problem and has not the limitation of linear discriminant analysis (LDA) in the number of extracted features. In DA-PC1, the dominant structure of distribution is preserved by PC1 and the class separability is increased by DA. The experimental results show the good performance of DA-PC1 compared to some state-of-the-art feature extraction methods.
Kinetic modeling using a reference region is a common method for the analysis of dynamic PET studies. Available methods for outlining regions of interest representing reference regions are usually time-consuming and difficult and tend to be subjective; therefore, MRI is used to help physicians and experts to define regions of interest with higher precision. The current work introduces a fast and automated method to delineate the reference region of images obtained from an N-methyl-(11)C-2-(4-methylaminophenyl)-6-hydroxy-benzothiazole ((11)C-PIB) PET study on Alzheimer disease patients and healthy controls using a newly introduced masked volumewise principal-component analysis.. METHODS: The analysis was performed on PET studies from 22 Alzheimer disease patients (baseline, follow-up, and test/retest studies) and 4 healthy controls, that is, a total of 26 individual scans. The second principal-component images, which illustrate the kinetic behavior of the tracer in gray matter of the cerebellar ...
Given a set of points in Euclidean space, the first principal component corresponds to a line that passes through the multidimensional mean and minimizes the sum of squares of the distances of the points from the line. The second principal component corresponds to the same concept after all correlation with the first principal component has been subtracted from the points. The singular values (in Σ) are the square roots of the eigenvalues of the matrix XTX. Each eigenvalue is proportional to the portion of the variance (more correctly of the sum of the squared distances of the points from their multidimensional mean) that is associated with each eigenvector. The sum of all the eigenvalues is equal to the sum of the squared distances of the points from their multidimensional mean. PCA essentially rotates the set of points around their mean in order to align with the principal components. This moves as much of the variance as possible (using an orthogonal transformation) into the first few ...
I think that what you describe is a standard application of multivariate functional data clustering. In the context of multivariate functional data each data unit is treated as the relation of a $d$-dimensional stochastic (often Gaussian) process $X := ( X_1, \dots , X_d )$.. Jacques & Preda (the authors of the nice survey paper you attach) have (somewhat) recently published a paper on Model-based clustering for multivariate functional data (2014) which extends their earlier work on Clustering multivariate functional data (2012). Approximately at the same time Chiou et al. also on Multivariate functional principal component analysis: A normalization approach (2014). Note that the two approach are quite different; Chious approach has a particular (very flexible) parametric association between the curve-samples while Jacques & Preda is much more data-driven.. Both of these works are based on multivariate functional principal component analysis (MvFPCA). Earlier applications where alluded in ...
Aiming at the problem that the evaluation model had proposed by researchers to evaluate the drivability of a vehicle in the process of engine start to exist poor stability and poor accuracy. In this paper, a drivability evaluation model combined with principal component analysis and support vector r
TY - GEN. T1 - A novel dimensionality reduction technique based on independent component analysis for modeling microarray gene expression data. AU - Liu, Han. AU - Kustra, Rafal. AU - Zhang, Ji. PY - 2004/12/1. Y1 - 2004/12/1. N2 - DNA microarray experiments generating thousands of gene expression measurements, are being used to gather information from tissue and cell samples regarding gene expression differences that will be useful in diagnosing disease. But one challenge of microarray studies is the fact that the number n of samples collected is relatively small compared to the number p of genes per sample which are usually in thousands. In statistical terms this very large number of predictors compared to a small number of samples or observations makes the classification problem difficult. This is known as the curse of dimensionality problem. An efficient way to solve this problem is by using dimensionality reduction techniques. Principle Component Analysis(PCA) is a leading method for ...
Background: Bacteria employ a variety of adaptation strategies during the course of chronic infections. Understanding bacterial adaptation can facilitate the identification of novel drug targets for better treatment of infectious diseases. Transcriptome profiling is a comprehensive and high-throughput approach for characterization of bacterial clinical isolates from infections. However, exploitation of the complex, noisy and high-dimensional transcriptomic dataset is difficult and often hindered by low statistical power. Results: In this study, we have applied two kinds of unsupervised analysis methods, principle component analysis (PCA) and independent component analysis (ICA), to extract and characterize the most informative features from transcriptomic dataset generated from cystic fibrosis (CF) Pseudomonas aeruginosa isolates. ICA was shown to be able to efficiently extract biological meaningful features from the transcriptomic dataset and improve clustering patterns of CF isolates. ...
NOTE: Where studies included discovery and validation cohorts, diagnostic metrics of the validation set included for analysis.. Abbreviations: EAC, esophageal adenocarcinoma; ESCC, esophageal squamo-cellular carcinoma; GAC, gastric adenocarcinoma; CRC, colorectal adenocarcinoma; UPLC-TQMS, ultra-performance liquid chromatography-triple quadrupole mass spectrometry; NMR, nuclear magnetic resonance spectroscopy; ESI-TOFMS, electrospray ionization time-of-flight mass spectrometry; RRLC, rapid relaxing liquid chromatography; GC-MS, gas chromatography mass spectrometry; HPLC, high-performance liquid chromatography; FTICR-MS, Fourier transform ion cyclotron mass spectrometry; MS/MS, tandem mass spectrometry; TQMRM, triple quadrupole multiple reaction monitoring; DI, direct ionization; SPME, solid phase microextraction; PLS-DA, partial least squares discriminant analysis; ROC, receiver operating characteristic curve; PCA, principle component analysis; OPLS-DA, orthogonal projection to latent structures ...
For anyone in need of a concise, introductory guide to principle components analysis, this book is a must. Through an effective use of simple mathematical geometrical and multiple real-life examples (such as crime statistics, indicators of drug abuse, and educational expenditures)--and by minimizing the use of matrix algebra--the reader can quickly master and put this technique to immediate use. In addition, the author shows how this technique can be used in tandem with other multivariate analysis techniques-such as multiple regression and discriminant analysis.. Flexible in his presentation, Dunteman speaks to students at differing levels, beginning or advanced, bringing them new material that is both accessible and useful.. Two of the best attributes of the book are the prolific use of good examples--primarily social science based--and the repetition basics. . . . This book is a useful addition to the work in this area.. --Issues in Researching Sexual Behavior Most academic researchers and ...
In recent years, many algorithms based on kernel principal component analysis (KPCA) have been proposed including kernel principal component regression (KPCR). KPCR can be viewed as a non-linearization of principal component regression (PCR) which uses the ordinary least squares (OLS) for estimating its regression coefficients. We use PCR to dispose the negative effects of multicollinearity in regression models. However, it is well known that the main disadvantage of OLS is its sensitiveness to the presence of outliers. Therefore, KPCR can be inappropriate to be used for data set containing outliers. In this paper, we propose a novel nonlinear robust technique using hybridization of KPCA and R-estimators. The proposed technique is compared to KPCR and gives better results than KPCR.
TY - JOUR. T1 - All sparse PCA models are wrong, but some are useful. Part I. T2 - Computation of scores, residuals and explained variance. AU - Camacho, J.. AU - Smilde, A. K.. AU - Saccenti, E.. AU - Westerhuis, J. A.. PY - 2020/1/15. Y1 - 2020/1/15. N2 - Sparse Principal Component Analysis (sPCA) is a popular matrix factorization approach based on Principal Component Analysis (PCA) that combines variance maximization and sparsity with the ultimate goal of improving data interpretation. When moving from PCA to sPCA, there are a number of implications that the practitioner needs to be aware of. A relevant one is that scores and loadings in sPCA may not be orthogonal. For this reason, the traditional way of computing scores, residuals and variance explained that is used in the classical PCA can lead to unexpected properties and therefore incorrect interpretations in sPCA. This also affects how sPCA components should be visualized. In this paper we illustrate this problem both theoretically and ...
It is important to manage leaks in water distribution systems by smart water technologies. In order to reduce the water loss, researches on the main factors of water pipe network affecting non-revenue water (NRW) are being actively carried out. In recent years, research has been conducted to estimate NRW using statistical analysis techniques such as Artificial Neural Network (ANN) and Principle Component Analysis (PCA). Research on identifying factors that affect NRW in the target area is actively underway. In this study, Principle components selected through Multiple Regression Analysis are reclassified and applied to NRW estimation using PCA-ANN. The results show that the principal components estimated through PCA are connected to the NRW estimation using ANN. The detailed NRW estimation methodology presented through the study, as a result of simulating PCA-ANN after selecting statistically significant factors by MRA, forward method showed higher NRW estimation accuracy than other MRA methods.
Background: In this paper we apply the principal-component analysis filter (Hotelling filter) to reduce noise fromdynamic positron-emission tomography (PET) patient data, for a number of different radio-tracer molecules. Wefurthermore show how preprocessing images with this filter improves parametric images created from suchdynamic sequence.We use zero-mean unit variance normalization, prior to performing a Hotelling filter on the slices of a dynamictime-series. The Scree-plot technique was used to determine which principal components to be rejected in thefilter process. This filter was applied to [11C]-acetate on heart and head-neck tumors, [18F]-FDG on liver tumors andbrain, and [11C]-Raclopride on brain. Simulations of blood and tissue regions with noise properties matched to realPET data, was used to analyze how quantitation and resolution is affected by the Hotelling filter. Summing varyingparts of a 90-frame [18F]-FDG brain scan, we created 9-frame dynamic scans with image statistics ...
Principal component analysis is a popular tool for performing dimensionality reduction in a dataset. PCA performs a linear transformation of a dataset (having possibly correlated variables) to a dimension of linearly uncorrelated variables (called principal components). This transformation aims to maximize the variance of the data. In practice, you would select a subset of the principal components to represent your dataset in a reduced dimension.. The Principal Component Analysis card provides a visual representation of a dataset in a reduced dimension.. ...
Inter-subject variability is a major hurdle for neuroimaging group-level inference, as it creates complex image patterns that are not captured by standard analysis models and jeopardizes the sensitivity of statistical procedures. A solution to this problem is to model random subjects effects by using the redundant information conveyed by multiple imaging contrasts. In this paper, we introduce a novel analysis framework, where we estimate the amount of variance that is fit by a random effects subspace learned on other images; we show that a principal component regression estimator outperforms other regression models and that it fits a significant proportion (10% to 25%) of the between-subject variability. This proves for the first time that the accumulation of contrasts in each individual can provide the basis for more sensitive neuroimaging group analyzes.
The common task in matrix completion (MC) and robust principle component analysis (RPCA) is to recover a low-rank matrix from a given data matrix. These problems gained great attention from various areas in applied sciences recently, especially after the publication of the pioneering works of Candès et al.. One fundamental result in MC and RPCA is that nuclear norm based convex optimizations lead to the exact low-rank matrix recovery under suitable conditions. In this paper, we extend this result by showing that strongly convex optimizations can guarantee the exact low-rank matrix recovery as well. The result in this paper not only provides sufficient conditions under which the strongly convex models lead to the exact low-rank matrix recovery, but also guides us on how to choose suitable parameters in practical algorithms.
A principal components analysis was carried out on male crania from the northeast quadrant of Africa and selected European and other African series. Individuals, not predefined groups, were the units of study, while nevertheless keeping group membership in evidence. The first principal component seems to largely capture size variation in crania from all of the regions. The same general morphometric trends were found to exist within the African and European crania, although there was some broad separation along a cline. Anatomically, the second principal component captures predominant trends denoting a broader to narrower nasal aperture combined with a similar shape change in the maxilla, an inverse relation between face-base lengths (projection) and base breadths, and a decrease in anterior base length relative to base breadth. The third principal component broadly describes trends within Africa and Europe: specifically, a change from a combination of a relatively narrower face and longer vault, ...
Indocyanine green (ICG) fluorescence imaging has been clinically used for noninvasive visualizations of vascular structures. We have previously developed a diagnostic system based on dynamic ICG fluorescence imaging for sensitive detection of vascular disorders. However, because high-dimensional raw data were used, the analysis of the ICG dynamics proved difficult. We used principal component analysis (PCA) in this study to extract important elements without significant loss of information. We examined ICG spatiotemporal profiles and identified critical features related to vascular disorders. PCA time courses of the first three components showed a distinct pattern in diabetic patients. Among the major components, the second principal component (PC2) represented arterial-like features. The explained variance of PC2 in diabetic patients was significantly lower than in normal controls. To visualize the spatial pattern of PCs, pixels were mapped with red, green, and blue channels. The PC2 score ...
The classical functional linear regression model (FLM) and its extensions, which are based on the assumption that all individuals are mutually independent, have been well studied and are used by many researchers. This independence assumption is sometimes violated in practice, especially when data with a network structure are collected in scientific disciplines including marketing, sociology and spatial economics. However, relatively few studies have examined the applications of FLM to data with network structures. We propose a novel spatial functional linear model (SFLM), that incorporates a spatial autoregressive parameter and a spatial weight matrix into FLM to accommodate spatial dependencies among individuals. The proposed model is relatively flexible as it takes advantage of FLM in handling high-dimensional covariates and spatial autoregressive (SAR) model in capturing network dependencies. We develop an estimation method based on functional principal component analysis (FPCA) and maximum
This chapter discusses several popular clustering functions and open source software packages in R and their feasibility of use on larger datasets. These will include the kmeans() function, the pvclust package, and the DBSCAN (density-based spatial clustering of applications with noise) package, which implement K-means, hierarchical, and density-based clustering, respectively. Dimension reduction methods such as PCA (principle component analysis) and SVD (singular value decomposition), as well as the choice of distance measure, are explored as methods to improve the performance of hierarchical and model-based clustering methods on larger datasets. These methods are illustrated through an application to a dataset of RNA-sequencing expression data for cancer patients obtained from the Cancer Genome Atlas Kidney Clear Cell Carcinoma (TCGA-KIRC) data collection from The Cancer Imaging Archive (TCIA).
In this study, comparison of academically advanced science students and gifted students in terms of attitude toward science and motivation toward science learning is aimed. The survey method was used for the data collection by the help of two different instruments: Attitude Toward Science scale and motivation toward science learning . Examination of reliability and validity of the scores on the instruments was conducted by using the principle component analysis with varimax rotation due to existence of a new group for validation of the instruments. The study involved 93 advanced science students and 12 gifted students who had higher IQ scores than 130 on WISC-R. The results of the study showed that the adapted instrument was valid and reliable to use for the measurements of motivation toward science learning in the context of advanced science classrooms. The comparisons of the groups in terms of the variables of the study showed that there is no statistically significant difference between the ...
Chaka sheep, named after Chaka Salt Lake, are adapted to a harsh, highly saline environment. They are known for their high-grade meat quality and are a valuable genetic resource in China. Furthermore, the Chaka sheep breed has been designated a geographical symbol of agricultural products by the Chinese Ministry of Agriculture. The genomes of 10 Chaka sheep were sequenced using next-generation sequencing, and compared to that of additional Chinese sheep breeds (Mongolian: Bayinbuluke and Tan; Tibetan: Oula sheep) to explore its population structure, genetic diversity and positive selection signatures. Principle component analysis and a neighbor-joining tree indicated that Chaka sheep significantly diverged from Bayinbuluke, Tan, and Oula sheep. Moreover, they were found to have descended from unique ancestors (K = 2 and K = 3) according to the structure analysis. The Chaka sheep genome demonstrated comparable genetic diversity from the other three breeds, as indicated by observed heterozygosity (Ho),
The Dniester-Carpathian region has attracted much attention from historians, linguists, and anthropologists, but remains insufficiently studied genetically. We have analyzed a set of autosomal polymorphic loci and Y-chromosome markers in six autochthonous Dniester-Carpathian population groups: 2 Moldavian, 1 Romanian, 1 Ukrainian and 2 Gagauz populations. To gain insight into the population history of the region, the data obtained in this study were compared with corresponding data for other populations of Western Eurasia. The analysis of 12 Alu human-specific polymorphisms in 513 individuals from the Dniester-Carpathian region showed a high degree of homogeneity among Dniester-Carpathian as well as southeastern European populations. The observed homogeneity suggests either a common ancestry of all southeastern European populations or a strong gene flow between them. Nevertheless, tree reconstruction and principle component analyses allow the distinction between Balkan-Carpathian (Macedonians, ...
This course is in two halves: machine learning and complex networks. We will begin with an introduction to the R language and to visualisation and exploratory data analysis. We will describe the mathematical challenges and ideas in learning from data. We will introduce unsupervised and supervised learning through theory and through application of commonly used methods (such as principle components analysis, k-nearest neighbours, support vector machines and others). Moving to complex networks, we will introduce key concepts of graph theory and discuss model graphs used to describe social and biological phenomena (including Erdos-Renyi graphs, small-world and scale-free networks). We will define basic metrics to characterise data-derived networks, and illustrate how networks can be a useful way to interpret data. This level 7 (Masters) version of the module will have additional extension material for self-study incorporated into the projects. This will require a deeper understanding of the subject ...
Active data screening is an integral part of many scientific activities, and mobile technologies have greatly facilitated this process by minimizing the reliance on large hardware instrumentation. In order to meet with the increasingly growing field of metabolomics and heavy workload of data processing, we designed the first remote metabolomic data screening platform for mobile devices. Two mobile applications (apps), XCMS Mobile and METLIN Mobile, facilitate access to XCMS and METLIN, which are the most important components in the computer-based XCMS Online platforms. These mobile apps allow for the visualization and analysis of metabolic data throughout the entire analytical process. Specifically, XCMS Mobile and METLIN Mobile provide the capabilities for remote monitoring of data processing, real time notifications for the data processing, visualization and interactive analysis of processed data (e.g., cloud plots, principle component analysis, box-plots, extracted ion chromatograms, and ...
We use spectral methods (SVD) to building statistical language models. The resulting vector models of language are then used to predict a variety of properties of words including their entity type (E.g., person, place, organization ...), their part of speech, and their meaning (or at least their word sense). Canonical Correlation Analysis, CCA, a generalization of Principle Component Analysis (PCA), gives context-oblivious vector representations of words. More sophisticated spectral methods are used to estimate Hidden Markov Models (HMMs) and generative parsing models. These methods give state estimates for words and phrases based on their contexts, and probabilites for word sequences. These again can be used to imrpove performance on many NLP tasks. Core to this work is the use of the Eigenword, a real-valued vector associated with a word that captures its meaning in the sense that distributionally similar words have similar eigenwords. Eigenwords are computed as the singular vectors of the ...
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Mass cytometry enabled the measurement of more than 30 intracellular and cell surface markers on each single cell, with an additional six channels reserved for metal barcoding for sample multiplexing.30 The convenient single tube labeling of multiplexed samples combined with semiautomatic analysis makes the technique highly efficient. We were able to recapitulate the expected hematologic response, and could also demonstrate the debulking of leukemic CD34 cells in the PB by immunophenotyping as early as one week after start of therapy. Importantly, by simultaneously probing key intracellular phosphorylation targets of the Bcr-Abl1 signaling network,32 we monitored changes in signal transduction of individual cell types for each patient undergoing TKI therapy. Unsupervised principle component analysis of these early changes in signal transduction allowed patients to be identified according to their BCR-ABL1, indicating a possible future prognostic impact of this approach.. The proportion of ...
The PM-bound metallic elements in 43 daily PM1 samples collected at Mount Tai during a summer campaign were analyzed by ICP-MS. The PM1 concentrations ranged between 11.02 and 83.71 µg m-3, with an average of 38.98 µg m-3, and were influenced by meteorological events, exhibiting an increasing trend in the early stage of rain, followed by a significant decrease denoting efficient scavenging. Higher elemental concentrations were detected at Mount Tai than at other overseas background sites. According to the enrichment factor (EF) and geo-accumulation index (Igeo) calculations, among the 16 considered elements, Mn, Al, Co, Sr, Mo, Fe, Ca, V, Ti and Ni in the PM1 were mainly of crustal origin, while Cu, Cr, As, Zn, Pb and Cd were primarily due to anthropogenic causes. Source identification via Pearson correlation analysis and principle component analysis showed that coal mining and coal burning activities, metal processing industries and vehicle emissions were common sources of heavy metals on Mount Tai;
Enhancers and promoters are cis-acting regulatory elements associated with lineage-specific gene expression. Previous studies showed that different categories of active regulatory elements are in regions of open chromatin, and each category is associated with a specific subset of post-translationally marked histones. These regulatory elements are systematically activated and repressed to promote commitment of hematopoietic stem cells along separate differentiation paths, including the closely related erythrocyte (ERY) and megakaryocyte (MK) lineages. However, the order in which these decisions are made remains unclear. To characterize the order of cell fate decisions during hematopoiesis, we collected primary cells from mouse bone marrow and isolated 10 hematopoietic populations to generate transcriptomes and genome-wide maps of chromatin accessibility and histone H3 acetylated at lysine 27 binding (H3K27ac). Principle component analysis of transcriptional and open chromatin profiles demonstrated that
BACKGROUND: Cryopreservation introduces iatrogenic damage to sperm cells due to excess production of reactive oxygen species (ROS) that can damage sperm macromolecules and alter the physiochemical properties of sperm cells. These altered properties can affect the biological potential of sperm cell towards fertility. OBJECTIVE: The study was designed to assess the role of oxidative stress in sperm DNA damage upon cryopreservation. MATERIALS AND METHODS: Semen samples (160) were classified into fertile and infertile on the basis of Computer Assisted Semen Analysis (CASA), and cryopreserved. Thawed samples were analyzed for 8OHdG marker, sperm chromatin dispersion (SCD)-based DNA fragmentation index (SCD-DFI) and ROS levels. Receiver Operating Characteristics (ROC) was performed to find the specificity and sensitivity of SCD-DFI in assessing the sperm DNA integrity. Principle component analysis (PCA) was performed to group semen parameters. RESULTS: SCD-DFI significantly correlates with 8OHdG in ...
Methods We used principal component analysis and factor analysis to evaluate the 6 hospitals management quality. 方法用主成分分析与因子分析法对6所医院的管理工作质量进行综合评价。 ...
TY - JOUR. T1 - Two-way principal component analysis for matrix-variate data, with an application to functional magnetic resonance imaging data. AU - Huang, Lei. AU - Reiss, Philip T.. AU - Xiao, Luo. AU - Zipunnikov, Vadim. AU - Lindquist, Martin. AU - Crainiceanu, Ciprian M. PY - 2017/4/1. Y1 - 2017/4/1. N2 - Many modern neuroimaging studies acquire large spatial images of the brain observed sequentially over time. Such data are often stored in the forms of matrices. To model these matrix-variate data we introduce a class of separable processes using explicit latent process modeling. To account for the size and two-way structure of the data, we extend principal component analysis to achieve dimensionality reduction at the individual level. We introduce necessary identifiability conditions for each model and develop scalable estimationprocedures.Themethodismotivatedbyandappliedtoafunctionalmagneticresonanceimaging study designed to analyze the relationship between pain and brain ...
To explore the clinical patterns of patients with IgG4-related disease (IgG4-RD) based on laboratory tests and the number of organs involved. Twenty-two baseline variables were obtained from 154 patients with IgG4-RD. Based on principal component analysis (PCA), patients with IgG4-RD were classified into different subgroups using cluster analysis. Additionally, IgG4-RD composite score (IgG4-RD CS) as a comprehensive score was calculated for each patient by principal component evaluation. Multiple linear regression was used to establish the
Implements biplot (2d and 3d) of multivariate data based on principal components analysis and diagnostic tools of the quality of the reduction.. ...
The HPPRINCOMP procedure is a high-performance procedure that performs principal component analysis. It is a high-performance version of the PRINCOMP procedure in SAS/STAT software. PROC HPPRINCOMP accepts raw data as input and can create output data sets that contain eigenvalues, eigenvectors, and standardized or unstandardized principal component scores. Principal component analysis is a multivariate technique for examining relationships among several quantitative variables. The choice between using factor analysis and using principal component analysis depends in part on your research objectives. You should use the HPPRINCOMP procedure if you are interested in summarizing data and detecting linear relationships. You can use principal component analysis to reduce the number of variables in regression, clustering, and so on. Principal component analysis was originated by Pearson (1901) and later developed by Hotelling (1933). The application of principal components is discussed by Rao (1964); ...
The present study addresses the challenge of identifying the features of the centre of pressure (CoP) trajectory that are most sensitive to postural...
Principal component analysis (PCA) has gained popularity as a method for the analysis of high-dimensional genomic data. However, it is often difficult to interpret the results because the principal components are linear combinations of all variables, and the coefficients (loadings) are typically nonzero. These nonzero values also reflect poor estimation of the true vector loadings; for example, for gene expression data, biologically we expect only a portion of the genes to be expressed in any tissue, and an even smaller fraction to be involved in a particular process. Sparse PCA methods have recently been introduced for reducing the number of nonzero coefficients, but these existing methods are not satisfactory for high-dimensional data applications because they still give too many nonzero coefficients. Here we propose a new PCA method that uses two innovations to produce an extremely sparse loading vector: (i) a random-effect model on the loadings that leads to an unbounded penalty at the origin and
Downloadable! This article documents and examines the integration of grain markets in Europe across the early modern/late modern divide and across distances and regions. It relies on principal component analysis to identify market structures. The analysis finds that a European market emerged only in the nineteenth century, but the process had earlier roots. In early modern times a fall in trading costs was followed by an increase in market efficiency. Gradually expanding processes of integration unfolded in the long-run. Early modern regional integration was widespread but uneven, with North-Western Europe reaching high levels of integration at a particularly early stage. Low-land European markets tended to be larger and better integrated than in land-locked Europe, especially within large, centralised states. In the nineteenth century, national markets grew in old states, but continental and domestic dynamics had become strictly linked.
This article describes the major statistical analyses used in a large-scale follow-up study of prelingually deaf children implanted before 5 yrs of age. The data from this longitudinal project posed a number of challenges that required a compromise among statistical sophistication, ease of interpretation, consistency with analyses used following the initial wave of data collection, and attention to limited sample size and missing data. Primary analyses were based on principal components analysis to form composite measures of highly correlated variables followed by hierarchical multiple regression to determine the contribution of predictor sets ordered to reflect important causal assumptions and conceptual questions ...
Objective: To develop a psychometric questionnaire to measure psychological barriers to insulin treatment in patients with type 2 diabetes.. Research Design and Methods: Scale development was based on principal component analyses in two cross-sectional studies of insulin-naïve patients with type 2 diabetes. The structure of the questionnaire was developed in the first sample of 448 patients and subsequently cross-validated in an independent sample of 449 patients.. Results: Analyses in the first sample yielded five components that accounted for 74.5% of the variance based on 14 items and led to the following subscales: Fear of injection and self-testing, Expectations regarding positive insulin-related outcomes, Expected hardship from insulin treatment, Stigmatization by insulin injections, and Fear of hypoglycemia. In addition, an overall sum score of all values was calculated. The structure of the questionnaire was cross-validated in the second sample with almost identical component loadings ...
SANTEE and PEEDEE will be used to run the Spectral Multidomain model to perform three-dimensional large eddy simulations (LES) of flow condition (like along the coastal region) that contains surface waves, turbulence, and currents (pictured below). A new method based on Principal Component analysis will be applied to the LES results to better quantify turbulence. This effort is led by Prof. Roi Gurka in the School of Coastal and Marine Systems Science.. Caption. Conceptual flow geometry. A propagating monochromatic surface wave with a vertically decaying induced velocity field drives, along with a pre-existing steady background current, boundary layer turbulence that extends across the water column. Red dashed lines represent the truncated top boundary of the computational LES domain, which employs periodic boundary conditions in the horizontal (the turbulence is homogeneous in the spanwise.. Publications:. To be added.. ...
Principal components analysis (PCA)33,34 was used to investigate the morphological affinities of the pre-5000 BP sample and also to characterize their primary morphological traits. All analyses were carried out on untransformed data (preserving size). The computation of the principal components (PCs) was done via the correlation matrix. PCA has the advantage of being able to reduce a large data set of (possibly) correlated variables into a (smaller) number of uncorrelated variables, the PCs. Analysing the PCs makes it easier to identify meaningful underlying variables that distinguish crania from one another. PCs may be plotted against each other, to visualize morphological relationships. Specimens that are morphologically similar occupy similar multivariate space. PCA is particularly useful in the context of this study in that it allows for the evaluation of size and size-related shape variation within the study sample. In biological studies, the first principal component commonly reflects ...
... is given by principal component analysis (PCA).[47][48] The PCA subspace spanned by the principal directions is identical to ... Principal component analysis[edit]. The relaxed solution of k. -means clustering, specified by the cluster indicators, ... Cluster analysis[edit]. Main article: Cluster analysis. In cluster analysis, the k-means algorithm can be used to partition the ... "K-means Clustering via Principal Component Analysis" (PDF). Proc. of Int'l Conf. Machine Learning (ICML 2004): 225-232.. CS1 ...
In principal component analysis[edit]. Explained variance is routinely used in principal component analysis. The relation to ... Variance-based sensitivity analysis. References[edit]. *^ Kent, J. T. (1983). "Information gain and a general measure of ... Achen, C. H. (1990). "'What Does "Explained Variance" Explain?: Reply". Political Analysis. 2 (1): 173-184. doi:10.1093/pan/2.1 ...
Principal components analysis[edit]. In principal components analysis, "Variables measured on different scales or on a common ... 2.7 Principal components analysis. *2.8 Relative importance of variables in multiple regression: Standardized regression ... Cluster analysis and multidimensional scaling[edit]. "For some multivariate techniques such as multidimensional scaling and ... Everitt, Brian; Hothorn, Torsten J (2011), An Introduction to Applied Multivariate Analysis with R, Springer, ISBN 978- ...
Principal component analysis[edit]. Main article: Principal component analysis. The relaxed solution of k. -means clustering, ... Independent component analysis[edit]. Main article: Independent component analysis. Under sparsity assumptions and when input ... Cluster analysis[edit]. Main article: Cluster analysis. In cluster analysis, the k-means algorithm can be used to partition the ... "K-means Clustering via Principal Component Analysis" (PDF). Proceedings of International Conference on Machine Learning (ICML ...
It is widely used in statistics where it is related to principal component analysis and to Correspondence analysis, and in ... In addition, multilinear principal component analysis in multilinear subspace learning involves the same mathematical ... "Singular value decomposition and principal component analysis". In D.P. Berrar; W. Dubitzky; M. Granzow. A Practical Approach ... "SIAM Journal on Matrix Analysis and Applications. 21 (4): 1253-1278. doi:10.1137/S0895479896305696. ISSN 0895-4798.. ...
It is widely used in statistics where it is related to principal component analysis and to Correspondence analysis, and in ... In addition, multilinear principal component analysis in multilinear subspace learning involves the same mathematical ... "Singular value decomposition and principal component analysis". In D.P. Berrar; W. Dubitzky; M. Granzow (eds.). A Practical ... Banerjee, Sudipto; Roy, Anindya (2014), Linear Algebra and Matrix Analysis for Statistics, Texts in Statistical Science (1st ed ...
Lloyd, Seth; Mohseni, Masoud; Rebentrost, Patrick (2014). "Quantum principal component analysis". Nature Physics. 10 (9): 631. ... Quantum learning theory pursues a mathematical analysis of the quantum generalizations of classical learning models and of the ... The most common use of the term refers to machine learning algorithms for the analysis of classical data executed on a quantum ... Quantum neural networks apply the principals quantum information and quantum computation to classical neurocomputing.[62] ...
Principal component analysis. The method of fitting a linear subspace to multivariate data by minimising the chi distances. The ... Jolliffe, I. T. (2002). Principal Component Analysis, 2nd ed. New York: Springer-Verlag. Pearson, K. (1895). "Contributions to ... These techniques, which are widely used today for statistical analysis, include the chi-squared test, standard deviation, and ... science is in reality a classification and analysis of the contents of the mind..." "In truth, the field of science is much ...
"Principal component analysis". Chemometrics and Intelligent Laboratory Systems. 2 (1-3): 37-52. doi:10.1016/0169-7439(87)80084- ...
Jolliffe, I. T. (9 May 2006). Principal Component Analysis. ISBN 9780387224404.. ... This was the result of ten years of research into the field of ridge analysis. Ridge regression was developed as a possible ...
A principal component analysis" (PDF). Growth. 24: 339-354. PMID 13790416. Archived from the original (PDF) on 2011-07-20. ... "Utah GAP analysis - painted turtle". Utah Department of Natural Resources. Retrieved 2011-02-11.. ...
Tipping, Michael E.; Bishop, Christopher M. (1999). "Probabilistic Principal Component Analysis". Journal of the Royal ...
Zou, Hui; Hastie, Trevor; Tibshirani, Robert (2006). "Sparse Principal Component Analysis". Journal of Computational and ...
Independent Component Analysis (ICA); and Principal Component Analysis (PCA). These ML computing systems can be grouped into ... and linear/nonlinear regression analysis. It is a multi-purpose computing system that can be used as an alternative to LR, NLR ...
Principal Component and Factor Analyses. PN 1996. Extensions of a Characterization of an Exponential Distribution Based on a ... Multivariate Analysis and Its Applications. PN 1988. Linear Transformations, Projection Operators and Generalized Inverses; A ... with S. Ghosh). Handbook of Statistics 13: Design and Analysis of Experiments. North Holland 1994. (Ed. with G.P. Patil). ... with Tata Subba Rao and Suhasini Subba Rao). Handbook of Statistics 30: Time Series Analysis: Methods and Applications . North ...
Sparse principal component analysis (sparse PCA) is a specialised technique used in statistical analysis and, in particular, in ... It extends the classic method of principal component analysis (PCA) for the reduction of dimensionality of data by introducing ... Iain M Johnstone; Arthur Yu Lu (2009). "On Consistency and Sparsity for Principal Components Analysis in High Dimensions". ... Hui Zou; Lingzhou Xue (2018). "A Selective Overview of Sparse Principal Component Analysis". Proceedings of the IEEE. 106 (8): ...
Mills, Peter (2012). "Principal component proxy tracer analysis". arXiv:1202.1999 [].. ...
Structured sparse principal component analysis. In Proc. AISTATS, 2009. Rosasco, Lorenzo; Poggio, Tomaso (Fall 2015). "MIT ... socio-linguistic analysis in natural language processing, and analysis of genetic expression in breast cancer. Consider the ... The functions in the space H {\displaystyle H} can be seen as the sums of two components, one in the space H A {\displaystyle H ... and a convex potentially non-differentiable component. As such, proximal gradient methods are useful for solving sparsity and ...
"Generalized principal component analysis (GPCA)". IEEE Transactions on Pattern Analysis and Machine Intelligence. 27 (12): 1945 ... Vidal, Rene (2003). Generalized Principal Component Analysis (GPCA): An Algebraic Geometric Approach to Subspace Clustering and ... Vidal, R.; Ma, Y.; Sastry, S.S. (2016). Generalized principal component analysis (GPCA). Interdisciplinary Applied Mathematics ... Much of his work in machine learning is summarized in his book Generalized Principal Component Analysis. Currently, he is ...
Jolliffe, I. T. (2002). Principal Component Analysis, 2nd ed. New York: Springer-Verlag. Box, R. A. Fisher, pp 93-166 Agresti, ... Pearson's chi-squared test and principal component analysis. In 1911 he founded the world's first university statistics ... In 1979, José-Miguel Bernardo introduced reference analysis, which offers a general applicable framework for objective analysis ... "analysis of variance". Perhaps even more important, Fisher began his systematic approach to the analysis of real data as the ...
Cluster AnalysisPrincipal Components • Factor Models C39 Other C4 Econometric and Statistical Methods: Special Topics C40 ... Oceania O1 Economic Development O10 General O11 Macroeconomic Analyses of Economic Development O12 Microeconomic Analyses of ... Prices Q12 Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets Q13 Agricultural Markets and Marketing • ... Spatial Production Analysis, and Firm Location R30 General R31 Housing Supply and Markets R32 Other Spatial Production and ...
"Robust principal component analysis for computer vision". Int. Conf. on Computer Vision (ICCV). ICCV. Vancouver, BC, USA. pp. ... and principal component analysis (PCA) [11][12] The robust formulation was hand crafted and used small spatial neighborhoods. ... which has become an important component of self-supervised training of neural networks for problems like facial analysis. ... Classical methods for analysis by synthesis formulate an objective function and then differentiate it. The OpenDR [34] ...
ISBN 978-0-12-088492-6. Reich D, Price AL, Patterson N (May 2008). "Principal component analysis of genetic data". Nature ... A principal component analysis of data from the Human Genome Diversity Project by Reich et al. detected a west-to-east gradient ... 5,000 BC). Ancient DNA analysis of these specimens indicates that they carried paternal haplotypes related to the E1b1b1b1a (E- ... 2009)-which also contains an admixture analysis chart but no cluster membership coefficients-shows little to no Sub-Saharan ...
Principal components analysis; Image statistics and measurement; A variety of supervised and unsupervised classification ... The software runs only on Microsoft Windows, although three of its four components also build and run on Linux. Goldin and ...
The same data, plus Citrus micrantha, top right (papeda). Three-dimensional projection of a Principal component analysis. ... Phylogenetic analysis suggests the species of Oxanthera from New Caledonia should be transferred to the genus Citrus.[17] ... Citrus as a Component of the Mediterranean Diet. Journal of Spatial and Organizational Dynamics - JSOD, IV(4): 289-304. ... A phylogenetic analysis of 34 chloroplast genomes elucidates the relationships between wild and domestic species within the ...
I. Basic Data and Principal Component Analysis". The Astrophysical Journal. 664 (2): 890-908. arXiv:0704.2179. Bibcode:2007ApJ ... It is one of the minor but noticeable components of the atmospheres of all the giant planets, with abundances from about 30 ppm ... NaOD Hydrogen deuteride is a minor component of naturally occurring molecular hydrogen. ...
Hubert, Mia; Rousseeuw, Peter J; Vanden Branden, Karlien (2005). "ROBPCA: A New Approach to Robust Principal Component Analysis ... and on robust principal component analysis . His 1984 paper has been reprinted in Breakthroughs in Statistics collected and ... With L. Kaufman he coined the word medoid when proposing the k-medoids method for cluster analysis, also known as Partitioning ... Kaufman, L.; Rousseeuw, P.J. (1987). "Clustering by means of Medoids". Statistical Data Analysis Based on the L1-Norm and ...
Principal geodesic analysis, a generalization of principal component analysis. Medicine[edit]. *Polyglandular autoimmune ... Programmable gate array, a semiconductor device containing programmable logic components and programmable interconnects (vast ...
Principal Chaplain Church of Scotland & Free Churches (Naval). *Principal Roman Catholic Chaplain (Naval) ... Directorate of Operational Analysis (RN). *Directorate of Naval Operations and Trade. *Directorate of Naval Operational ... components. *Naval Intelligence Division. *Naval Recruitment Training Agency. *Royal Corps of Naval Constructors ...
The components of food webs, including organisms and mineral nutrients, cross the thresholds of ecosystem boundaries. This has ... Published examples that are used in meta analysis are of variable quality with omissions. However, the number of empirical ... Complexity explains many principals pertaining to self-organization, non-linearity, interaction, cybernetic feedback, ... "the same overall structure is maintained in spite of an ongoing flow and change of components."[75]:476 The farther a living ...
A leaf (plural leaves) is a dorsiventrally flattened organ of a vascular plant and is the principal lateral appendage of the ... Analyses of vein patterns often fall into consideration of the vein orders, primary vein type, secondary vein type (major veins ... Not every species produces leaves with all of these structural components. The proximal stalk or petiole is called a stipe in ... The lamina is the expanded, flat component of the leaf and containing the chloroplasts. The sheath is a structure, typically at ...
Principal components. *Canonical correlation. *Discriminant analysis. *Cluster analysis. *Classification. *Structural equation ... This ensures that subsequent user errors cannot inadvertently perform meaningless analyses (for example correlation analysis ... Mosteller, Frederick (1977). Data analysis and regression : a second course in statistics. Reading, Mass: Addison-Wesley Pub. ... Cliff, N. (1996). Ordinal Methods for Behavioral Data Analysis. Mahwah, NJ: Lawrence Erlbaum. ISBN 0-8058-1333-0 ...
The principal role of these signaling pathways is to ensure reliable production and elimination of the CtrA protein from the ... The specific coupling between the protein components of the cell cycle control network and the downstream readout of the ... "The diversity and evolution of cell cycle regulation in alpha-proteobacteria: A comparative genomic analysis". BMC Systems ... Underlying all these operations are the mechanisms for production of protein and structural components and energy production. ...
Principal components. *Canonical correlation. *Discriminant analysis. *Cluster analysis. *Classification. *Structural equation ... Kruskal-Wallis one-way analysis of variance. Notes[edit]. *^ a b Mann, Henry B.; Whitney, Donald R. (1947). "On a Test of ... Zar, Jerrold H. (1998). Biostatistical Analysis. New Jersey: Prentice Hall International, INC. p. 147. ISBN 0-13-082390-2.. ... A thorough analysis of the statistic, which included a recurrence allowing the computation of tail probabilities for arbitrary ...
"Standard Test Method for Extracting Residue from Metallic Medical Components and Quantifying via Gravimetric Analysis". ASTM ... and which does not achieve its principal intended action in or on the human body by pharmacological, immunological or metabolic ... Standard test method for extracting residue from metallic medical components and quantifying via gravimetric analysis[50] 2. ... A biomedical equipment technician (BMET) is a vital component of the healthcare delivery system. Employed primarily by ...
and their components are related by:[40] ε. r. =. n. 2. −. κ. 2. ,. {\displaystyle \varepsilon _{\mathrm {r} }=n^{2}-\kappa ^{2 ... By fitting the theoretical model to the measured R or T, or ψ and δ using regression analysis, n and κ can be deduced. ... In this case the propagation of light cannot simply be described by refractive indices except for polarizations along principal ... Zernike phase-contrast microscopy introduces a phase shift to the low spatial frequency components of the image with a phase- ...
This offset analysis system continued for 3 years until their analysis chains were proved to produce equivalent results. Thus, ... The QBEE provides high-speed signal processing by combining pipelined components. These components are a newly developed custom ... The left frame shows the three principal cycles comprising the pp chain (ppI, ppII, and ppIII), the neutrinos sources ... The first stage data reduction processes were done for the high energy analysis and for the low energy analysis. The data ...
However, the principal cause of cascade effects is the loss of top predators as the key species. As a result of this loss, a ... Population viability analysis. *Priority effect. *Rapoport's rule. *Relative abundance distribution. *Relative species ... Ecology: Modelling ecosystems: Trophic components. General. *Abiotic component. *Abiotic stress. *Behaviour. *Biogeochemical ...
A principal component analysis of the nest site variables showed nest height, concealment, plant height and canopy cover as the ...
This method of analysis is known as partial-equilibrium analysis (supply and demand). This method aggregates (the sum of all ... In microeconomics, principal concepts include supply and demand, marginalism, rational choice theory, opportunity cost, budget ... and price inflation and subaggregates like total consumption and investment spending and their components. It also studies ... This includes standard analysis of the business cycle in macroeconomics. Analysis often revolves around causes of such price ...
Two principal varieties are used: Camellia sinensis var. sinensis, which is used for most Chinese, Formosan and Japanese teas, ... This requires longer steeping time to extract the key components, and produces a different flavor profile. Cold brews use about ... plants are not necessarily the evidence of the dualism hypothesis from the researches using the statistical cluster analysis ... but is a suspension when all of the insoluble components are considered, such as the cellulose in the tea leaves.[82] ...
"Journal of Experimental Analysis of Behaviour. 50 (3): 553-564. doi:10.1901/jeab.1988.50-553. PMC 1338917. PMID 16812572.. ... The most important components of most parrots' diets are seeds, nuts, fruit, buds, and other plant material. A few species ... The principal threats of parrots are habitat loss and degradation, hunting, and, for certain species, the wild-bird trade. ... de Kloet, RS; de Kloet SR (2005). "The evolution of the spindlin gene in birds: Sequence analysis of an intron of the spindlin ...
are orthonormal, rather than being redundant, then MP is a form of principal component analysis ... Applied and Computational Harmonic Analysis. 26 (3): 301-321. arXiv:0803.2392. doi:10.1016/j.acha.2008.07.002.. ... The main disadvantage of Fourier analysis in signal processing is that it extracts only the global features of the signals and ...
Three-dimensional projection of a principal component analysis of citrus hybrids, with citron (yellow), pomelo (blue), mandarin ... "Journal of Food and Drug Analysis. 26 (2): S61-S71. doi:10.1016/j.jfda.2018.01.009. ISSN 1021-9498. PMID 29703387.. [permanent ... "Journal of Food and Drug Analysis. 25 (1): 71-83. doi:10.1016/j.jfda.2016.11.008. ISSN 1021-9498. PMID 28911545.. [permanent ... Phylogenetic analysis suggests the species of Oxanthera from New Caledonia should be transferred to the genus Citrus.[22] ...
Sources of specific effects on the separate components". Bulgarian Journal of Agricultural Science. 7: 329-35. Archived from ... DNA analysis in February 2012 revealed that Ötzi was lactose intolerant, supporting the theory that lactose intolerance was ... The principal manifestation of lactose intolerance is an adverse reaction to products containing lactose (primarily milk), ... Genetic analysis shows lactase persistence has developed several times in different places independently in an example of ...
Principal components. *Canonical correlation. *Discriminant analysis. *Cluster analysis. *Classification. *Structural equation ... In terms of numerical analysis, isotonic regression involves finding a weighted least-squares fit x. ∈. R. n. {\displaystyle x\ ...
A few years later there was a debate between Heaviside and Peter Guthrie Tait about the relative merits of vector analysis and ... In 1857 Maxwell befriended the Reverend Daniel Dewar, who was then the Principal of Marischal.[59] Through him Maxwell met ... are composed of a number of monochromatic components which could then be recombined into white light.[102] Newton also showed ... and vector analysis became commonplace.[96] Maxwell was proven correct, and his quantitative connection between light and ...
The principal measure of diesel fuel quality is its cetane number. A cetane number is a measure of the delay of ignition of a ... During development of rocket engines in Germany during World War II J-2 Diesel fuel was used as the fuel component in several ... Chemical analysisEdit. Chemical compositionEdit. Diesel does not mix with water. ... As biodiesel contains low levels of sulfur, the emissions of sulfur oxides and sulfates, major components of acid rain, are low ...
2. Analyse the components of consciousness and develop a model that could feasibly be implemented to deploy consciousness in a ... You have said that you are forced into the anthropic principal in these circumstances when talking about consciousness because ... I am not saying that self-awareness is not capable of analysis, nor am I saying that other attributes might also be found ... and why this is thought to be an essential component of consciousness? Matt Stan 08:49, 25 Mar 2004 (UTC) ...
... by combining an analysis of local groups with an analysis of the country's financial and economic situation the World Bank ... The vice presidents of the Bank are its principal managers, in charge of regions, sectors, networks and functions. There are ... The World Bank is a component of the World Bank Group. ...
Vol 1: Translations of the principal sources with philosophical commentary. Cambridge: Cambridge University Press. pp. 25-26. ... Modern Transmutation is a large component of nuclear chemistry, and the table of nuclides is an important result and tool for ... They can be analyzed using the tools of chemical analysis, e.g. spectroscopy and chromatography. Scientists engaged in chemical ... A principal difference between solid phases is the crystal structure, or arrangement, of the atoms. Another phase commonly ...
Begley CG, Paragina S, Sporn A (March 1990). "An analysis of contact lens enzyme cleaners". 》Journal of the American Optometric ... Okada S, O'Brien JS (August 1969). "Tay-Sachs disease: generalized absence of a beta-D-N-acetylhexosaminidase component". 》 ... the principal products of its reactions, and their applications to the industrial arts]. 》Annales de chimie et de physique》. ...
Newton, Janice (1990). "Becoming 'Authentic' Australians through Music". Social Analysis: The International Journal of Social ... In the 19th century, smallpox was the principal cause of Aboriginal deaths, and vaccinations of the "native inhabitants" had ... there is also a Eurasian component that could indicate South Asian admixture or more recent European influence.[44][45] ... 2005). "Single, Rapid Coastal Settlement of Asia Revealed by Analysis of Complete Mitochondrial Genomes". Science. 308 (5724): ...
Principal components. *Canonical correlation. *Discriminant analysis. *Cluster analysis. *Classification. *Structural equation ... Related to the absolute values of vectors with normally distributed components[edit]. *Rayleigh distribution, for the ... Rice distribution, a generalization of the Rayleigh distributions for where there is a stationary background signal component. ... For generalized functions in mathematical analysis, see Distribution (mathematics). For continuous variation in biology, see ...
O Aβ é o principal compoñente das placas amiloides (depósitos extracelulares que se encontran no cerebro dos pacientes da ... Schmidt SD, Nixon RA, Mathews PM (2012). "Tissue processing prior to analysis of Alzheimer's disease associated proteins and ... "beta-Amyloid-(1-42) is a major component of cerebrovascular amyloid deposits: implications for the pathology of Alzheimer ... O Aβ é o principal constituínte do amiloide do parénquima cerebral e do amiloide vascular; contribúe a lesións ...
... principal components, and spurious significance" (PDF). Geophysical Research Letters. 32 (3): L03710. Bibcode:2005GeoRL.. ... US NRC (2001), Climate Change Science: An Analysis of Some Key Questions. A report by the Committee on the Science of Climate ... An analysis of statements on projected regional impacts in the 2007 report. A report by the Netherlands Environmental ... In addition, analysis of the Medieval Warm Period cited reconstructions by Crowley & Lowery 2000 (as cited in the TAR) and ...
In 1984, decades after having been granted monopoly power by force of law, AT&T was broken up into various components, MCI, ... The boundaries of what constitutes a market and what does not are relevant distinctions to make in economic analysis. In a ... Government regulation generally consists of regulatory commissions charged with the principal duty of setting prices.[67] ... There are four basic types of market structures in traditional economic analysis: perfect competition, monopolistic competition ...
A Tutorial on Principal Component Analysis. *A laymans introduction to principal component analysis on YouTube (a video of ... mlpack - Provides an implementation of principal component analysis in C++.. *NAG Library - Principal components analysis is ... Jackson, J.E. (1991). A Users Guide to Principal Components (Wiley).. *. Jolliffe, I. T. (1986). Principal Component Analysis ... Independent component analysis[edit]. Independent component analysis (ICA) is directed to similar problems as principal ...
Multilinear principal component analysis (MPCA) is a multilinear extension of principal component analysis (PCA). MPCA is ... such as multilinear principal component analysis (MPCA), or multilinear independent component analysis (MICA), etc. ... a b c P. M. Kroonenberg and J. de Leeuw, Principal component analysis of three-mode data by means of alternating least squares ... a b H. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, "Uncorrelated multilinear principal component analysis for ...
... techniques ofmultivariate analysis. and developed independently by Hotelling (1933) ... Principal component analysis is probably the oldest and best known of the It was first introduced by Pearson (1901), ... Eigenvalue Finite Matrix Statistica computation computer eigenvector factor analysis form principal component analysis ... Any feelings that principal component analysis is a narrow subject should soon be dispelled by the present book; indeed some ...
... principal components analysis (en); osagai nagusien bidezko analisi (eu); 主要元素分析 (zh); principal component analysis, PCA (da) ... principal component analysis (en); anàlisi de components principals (ca); Análise de compoñentes principais (gl); تحليل العنصر ... Principal component analysis (en-gb); تحلیل مؤلفه‌های اصلی (fa); 主成分分析 (zh); principal komponent-analyse (da); 主成分分析 (ja); ... principal component analysis conversion of a set of observations of possibly correlated variables into a set of values of
Principal component analysis is central to the study of multivariate data. Although one of the earliest multivariate techniques ... and principal curves.". "This book is one of the very few texts entirely devoted to principal component analysis (PCA). The ... Principal Component Analysis. Authors. * I.T. Jolliffe Series Title. Springer Series in Statistics. Copyright. 2002. Publisher ... Principal component analysis is central to the study of multivariate data. Although one of the earliest multivariate techniques ...
Principal component analysis: a review and recent developments. Philos Trans A Math Phys Eng Sci. 2016 Apr 13;374(2065): ... Principal component analysis: a review and recent developments. Philos Trans A Math Phys Eng Sci. 2016 Apr 13;374(2065): ... Principal component analysis: a review and recent developments. Philos Trans A Math Phys Eng Sci. 2016 Apr 13;374(2065): ... Principal component analysis: a review and recent developments. Philos Trans A Math Phys Eng Sci. 2016 Apr 13;374(2065): ...
... factor analysis and principal component analysis are among the islands more frequently visited by human scientists. This book ... Within the vast archipelago of data analysis tools, ... Factor analysis and principal component analysis Autori e ... A asscilfication of the goals of factor analysis and principal component analysis). A century of factor analysis and principal ... Extracting principal components: an example). Differences beetwen factor analysis and principal component analysis. (The echnt ...
We adopted principal component analysis =-=[5]-=- as a feature selector, in which the number of relevant features was reduced ... Data Size Reduction for Clustering-based Binning of ICs Using Principal Component Analysis by Ashish S. Banthia, Anura P. ... Principal Components Analysis (PCA) is the predominant linear dimensionality reduction technique, and has been widely applied ... Principal Components Analysis (PCA) is the predominant linear dimensionality reduction technique, and has been widely applied ...
Use of Principal Components Analysis in Conjunction with Other Multivariate Analysis Procedures ... For anyone in need of a concise, introductory guide to principle components analysis, this book is a must. Through an effective ... Not only does Dunteman contribute to our understanding of principal components, but he suggests several good ideas on how to ... In addition, the author shows how this technique can be used in tandem with other multivariate analysis techniques-such as ...
... we introduce Principal Components Analysis, and show how it can be used for data compression to speed up learning algorithms as ... Principal Component Analysis Algorithm. To view this video please enable JavaScript, and consider upgrading to a web browser ... In this module, we introduce Principal Components Analysis, and show how it can be used for data compression to speed up ... Principal Component Analysis Problem Formulation9:05. Principal Component Analysis Algorithm15:13 ...
... Roger Peng rpeng at Thu Apr 11 01:36:26 CEST 2002 *Previous message: [R ... It seems that you have gotten the same answer as the book, with the exception that the first, fourth and fifth components have ... Previous message: [R] Principal Component analysis question *Next message: [R] Principal Component analysis question ... Principal Component analysis question *Next message: [R] Principal Component analysis question * Messages sorted by: [ date ] ...
Pervious works mainly focus on the analysis of the existence and uniqueness. In this paper, we address its stability. We prove ... Compressive principal component pursuit (CPCP) recovers a target matrix that is a superposition of low-complexity structures ... Stable Analysis of Compressive Principal Component Pursuit. Algorithms 2017, 10, 29. AMA Style. You Q, Wan Q. Stable Analysis ... Stable Analysis of Compressive Principal Component Pursuit. Qingshan You 1,2,* and Qun Wan 2. ...
Then, the evaluation of edible quality of sorghum was based on principal component analysis and fitted with the score of ... Five principal components (PCs) with a cumulative contribution rate of 86.19% could be picked out to describe the taste, ... Principal Component Analysis. Principal component analysis (PCA) converts observations of correlated variables into a set of ... principal component analysis, cluster analysis, and discriminant analysis are the main methods used in the comprehensive ...
Principal Components Analysis. Description. Performs a principal components analysis on the given data matrix and returns the ... containing the following components: sdev. the standard deviations of the principal components (i.e., the square roots of the ... a numeric or complex matrix (or data frame) which provides the data for the principal components analysis. ... optionally, a number specifying the maximal rank, i.e., maximal number of principal components to be used. Can be set as ...
Standard deviations along each principal component \(=\sqrt{\lambda_i}\). If we keep \(k\) components, \[ R^2 = \frac{\sum_{i=1 ... Principal components of the USA, \(\approx 1977\). signif(state.pca$x[1:10, 1:2], 2). ... Principal components of the USA, \(\approx 1977\). state.pca ,- prcomp(state.x77,scale.=TRUE). ... Principal components of the USA, \(\approx 1977\). The weight/loading matrix \(\w\) gets called $rotation. (why?): ...
Buy the Hardcover Book Generalized Principal Component Analysis by René Vidal at, Canadas largest bookstore. + Get ...
... A principal component analysis can be performed via the calculations dialog which is accessed by ... Principal components analysis is a technique for examining the structure of complex data sets. The components are a set of ... Jalview can perform PCA analysis on both proteins and nucleotide sequence alignments. In both cases, components are generated ... jalview.analysis.scoremodels.ScoreMatrix.scoreGapAsAny=true jalview.analysis[email protected][ ...
We present a method for simultaneous dimension reduction and metastability analysis of high dimensional time series. The ... Hide Markov Model Independent Component Analysis Dimension Reduction Independent Component Analysis Hide State These keywords ... Monahan, A.: Nonlinear principal component analysis by neural networks: Theory and application to the lorenz system. J. Climate ... The approach is based on the combination of hidden Markov models (HMMs) and principal component analysis. We derive optimal ...
... analysis, and applications of antennas, along with theoretical and practical studies relating the propagation of ... In this paper, Principal Component Analysis technique is applied on the signal measured by an ultra wide-band radar to compute ... The Principal Component Analysis is applied on the signal reflected by the thorax and the obtained breath frequencies are ... In particular, the Principal Component Analysis (PCA) technique is applied to the UWB reflected signals. The PCA technique is ...
Figure 2: Principal component analysis graphs for different combinations of muscle type images and characteristics.. From: ... Figure 2: Principal component analysis graphs for different combinations of muscle type images and characteristics. , ...
Principal Components Analysis and Factor Analysis SERGIO M. FOCARDI, PhD Partner, The Intertek Group FRANK J. FABOZZI, PhD, CFA ... can be determined with factor analysis or principal component analysis. Principal component analysis identifies the largest ... Principal component analysis (PCA) and factor analysis are statistical tools that allow a modeler to (1) reduce the number of ... Factor analysis can be used to identify the structure of the latent factors. ...
Right click on the Principal Component Analysis icon in the Apps Gallery window, and choose Show Samples Folder from the short- ... Click the Principal Component Analysis icon in the Apps Gallery window to open the dialog. ... This tool is an enhanced version of the built-in Principal Component Analysis tool available in OriginPro.. This version offers ... Number of Components to Extract is used to control output of loadings, scores and their plots. Standardize Scores option will ...
In the Select Principal Components to Plot group, set Principal Component for X Axis to 1, and set Principal Component for Y ... To determine the number of principal components to be retained, we should first run Principal Component Analysis and then ... Principal Component Analysis is an appropriate tool for removing the collinearity. *The main component variables are defined as ... Select the entire worksheet and then select Statistics: Multivariate Analysis: Principal Component Analysis. ...
Principal Component Analysis. We can think of dimensionality reduction as a way of compressing data with some loss, similar to ... Principal Component Analysis (PCA) is one of the most fundamental dimensionality reduction techniques that are used in machine ... This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a ... our original data point onto the ith basis vector that spans our principal subspace. ...
Principal components analysis is a standard, but usually terrible, technique for visualising complex data. Were using network ... Moving beyond principal components analysis. Nick Burns, 2019-10-11 (first published: 2017-09-19) ... Principal components analysis (PCA) is one of those basic statistical methods that (I think) could really transform your ... According to the results of the principal components analysis, PC1 captures a massive 88% of the variance in this dataset. ...
Principal Component Analysis: Mathematical procedure that transforms a number of possibly correlated variables into a smaller ...
Advanced strategies for Metabolomic Data Analysis - Free download as Powerpoint Presentation (.ppt), PDF File (.pdf), Text File ... Principal Components Analysis (PCA) unsupervised maximize variance (X) Partial Least Squares Projection to Latent Structures ( ... Non-supervised dimensional reduction technique Principal Components (PCs) projection of the data which maximize variance ... Multivariate Analysis. Simultaneous analysis of many variables. Visualization Clustering Projection Modeling Networks ...
BARBOSA, L. et al. Identification of informative performance traits in swine using principal component analysis. Arq. Bras. Med ... Using principal component analysis, records of 435 animals from an F2 swine population were used to identity independent and ... Six principal components expressed variation lower than 0.7 (eigen values lower than 0.7) suggesting that six variables could ... Keywords : swine; multivariate analysis; correlation; discard; growth; litter size; performance. · abstract in Portuguese · ...
Principal Components Analysis (PCA), introduced by Karl Pearson in 1901, is a simple example of a low rank model. It finds a ... Beyond Principal Components Analysis (PCA). Using low rank models to understand big data ... This framework makes it easier to use low rank models in everyday data analysis workflows. ...
  • Abstract factors, also called unidentified or latent factors, can be determined with factor analysis or principal component analysis. (
  • abstract = "A methodology based on Principal Component Analysis (PCA) and clustering is evaluated for process monitoring and process analysis of a pilot-scale SBR removing nitrogen and phosphorus. (
  • The coordinates for the whole PCA space, or just the current view may also be exported as CSV files for visualization in another program or further analysis. (
  • In: Principal Manifolds for Data Visualization and Dimension Reduction. (
  • In section 2 we discuss applications of SVD to gene expression analysis, including specific methods for SVD-based visualization of gene expression data, and use of SVD in detection of weak expression patterns. (
  • This book provides a solid practical guidance to summarize, visualize and interpret the most important information in a large multivariate data sets, using principal component methods (PCMs) in R. The visualization is based on the factoextra R package that we developed for creating easily beautiful ggplot2-based graphs from the output of PCMs. (
  • The visualization of the SOM-analysis on the other hand allows a straightforward interpretation of the dataset structure in which even non-linear relationships between variables can be identified. (
  • Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. (
  • [4] The results of a PCA are usually discussed in terms of component scores , sometimes called factor scores (the transformed variable values corresponding to a particular data point), and loadings (the weight by which each standardized original variable should be multiplied to get the component score). (
  • [5] If component scores are standardized to unit variance, loadings must contain the data variance in them (and that is the magnitude of eigenvalues). (
  • Number of Components to Extract is used to control output of loadings, scores and their plots. (
  • The biplot shows both the loadings and the scores for two selected components in parallel. (
  • In the loading plot, we can see that Red Meat, Eggs, Milk, and White Meat have similar heavy loadings for principal component 1. (
  • Colormap indicates principal component coefficients or loadings. (
  • Rotation contains the principal component loadings matrix values which explains /proportion of each variable along each principal component. (
  • However, it is often difficult to interpret the results because the principal components are linear combinations of all variables, and the coefficients (loadings) are typically nonzero. (
  • the columns of Z = UD = XV are the principal component scores, and the columns of the p × p matrix V are the corresponding loadings. (
  • Each principal component in (2) is a linear combination of p variables, where the loadings are typically nonzero so that PCA results are often difficult to interpret. (
  • Factor analysis typically incorporates more domain specific assumptions about the underlying structure and solves eigenvectors of a slightly different matrix. (
  • From either objective, it can be shown that the principal components are eigenvectors of the data's covariance matrix. (
  • PCA transforms high-dimensional input vectors into uncorrelated principal components (PCs) by calculating the eigenvectors of the covariance matrix of original inputs. (
  • What to do in PCA when one variable has similar values in several principal component eigenvectors? (
  • From an empirical perspective, we evaluate this algorithm as an unsupervised feature selection strategy in three application domains of modern statistical data analysis: finance, document-term data, and genetics. (
  • For sparse matrices, use Lanczos methods , and for dense matrices, random-projection methods can be used. (
  • The second algorithm is based on h-NLPCA, a nonlinear generalization of standard PCA, which utilizes an autoassociative network, and constrains the nonlinear components to have the same hierarchical order as the linear components in standard PCA. (
  • Non-Linear Principal Component AnalysissThe non-linear principal component analysis (NLPCA) algorithm developed by =-=[97]-=- is generallysconsidered as a non-linear generalization of standard linear PCA and was successfully applied insatmospheric and oceanic sciences [98-102]. (
  • The sensor responses were evaluated by principal component analysis (PCA) and then with hybrid neural network (PCANN optimized by genetic algorithm). (
  • In this paper, we proposed the application of a frequency-based algorithm derived from principal component analysis (PCA), and demonstrated improved efficacy for early seizure detection in a pilocarpine-induced epilepsy rat model. (
  • Principal component analysis, a data-reduction algorithm, generates a set of principal components that are independent, linear combinations of the original dataset. (
  • We develop a stable computing algorithm by modifying nonlinear iterative partial least square (NIPALS) algorithm, and illustrate the method with an analysis of the NCI cancer dataset that contains 21,225 genes. (
  • Reference : Comparison of Kohonen's Self-Organizing Map algorithm and principal component analysi. (
  • In this study, principal component analysis is compared to Kohonen's self-organizing map (SOM) algorithm. (
  • In the second step, the principal scores and the Q-statistics resulting from the MPCA model are fed to the LAMDA clustering algorithm. (
  • A fast algorithm for robust principal components based on projection pursuit. (
  • Finding such new variables, the principal components, reduces to solving an eigenvalue/eigenvector problem, and the new variables are defined by the dataset at hand, not a priori, hence making PCA an adaptive data analysis technique. (
  • According to the results of the principal components analysis, PC1 captures a massive 88% of the variance in this dataset. (
  • Det första pappret jämför prestationen av maximum likelihood estimatorn och Krzanowskis estimator för CPC-modellen för två verkliga dataset och i en Monte Carlo-simuleringstudie. (
  • In the above image, u1 & u2 are principal components wherein u1 accounts for highest variance in the dataset and u2 accounts for next highest variance and is orthogonal to u1. (
  • An analysis of the changes in blood pressure using normalization and principal component analysis techniques was performed using an existing electronic dataset of intra-arterial pediatric blood pressure values during anesthesia. (
  • A principal-component analysis (PCA) was carried out on a dataset of peanut and flour spectra. (
  • In PCA a linear dimensionality reduction of the original, high dimensional dataset is carried out in order to identify orthogonal directions (principal components) of maximum variance in the dataset based on linear combinations of correlated variables. (
  • The dimensionality of the land-cover dataset was reduced using principal component analysis, and eight urban classes were derived by cluster analysis using an agglomeration technique on the extracted components. (
  • Principal component analysis (PCA) aims at estimating the direction of maximal variability of a high-dimensional dataset. (
  • Principal component analysis ( PCA ) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components . (
  • This transformation is defined in such a way that the first principal component has the largest possible variance (that is, accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it is orthogonal to the preceding components. (
  • they are the cosines of orthogonal rotation of variables into principal components or back. (
  • The principal components of a collection of points in a real coordinate space are a sequence of p {\displaystyle p} unit vectors, where the i th {\displaystyle i^{\text{th}}} vector is the direction of a line that best fits the data while being orthogonal to the first i − 1 {\displaystyle i-1} vectors. (
  • The i th {\displaystyle i^{\text{th}}} principal component can be taken as a direction orthogonal to the first i − 1 {\displaystyle i-1} principal components that maximizes the variance of the projected data. (
  • PCA is defined as an orthogonal linear transformation that transforms the data to a new coordinate system such that the greatest variance by some scalar projection of the data comes to lie on the first coordinate (called the first principal component), the second greatest variance on the second coordinate, and so on. (
  • that each principal component is orthogonal to one another. (
  • PCA is a mathematical procedure that uses an orthogonal transformation to convert a set of possibly correlated variables into a smaller or equal number of uncorrelated variables called principal components. (
  • Technically speaking, PCA uses orthogonal projection of highly correlated variables to a set of values of linearly uncorrelated variables called principal components. (
  • Each succeeding component in turn has the highest variance using the features that are less correlated with the first principal component and that are orthogonal to the preceding component. (
  • variables into few orthogonal components defined at where the data 'stretch' the most, rendering a simplified overview. (
  • In other words it discovers the largest principal component of a set of mean-centred samples, along with the score (the magnitude of the principal component) for each sample, and the residual of each sample that is orthogonal to the principal component. (
  • the standard deviations of the principal components (i.e., the square roots of the eigenvalues of the covariance/correlation matrix, though the calculation is actually done with the singular values of the data matrix). (
  • Principal component analysis identifies the largest eigenvalues of the variance-covariance matrix or the correlation matrix. (
  • In the Eigenvalues of the Correlation Matrix table, we can see that the first four principal components explain 86% of the variance and the remaining components each contribute 5% or less. (
  • The number of components depends on the "elbow" point at which the remaining eigenvalues are relatively small and all about the same size. (
  • Gilles Raîche in RMT 19:1 p. 102 reports eigenvalues in the range 1.4 to 2.1 for the first component in a PCA of inter-item correlations of standardized residuals of Rasch-fitting data. (
  • The Figures shows the eigenvalues sizes of the first components (contrasts) in a PCA of the standardized-residual item-correlation matrices. (
  • PCA is the simplest of the true eigenvector -based multivariate analyses. (
  • Computation of the principal components reduces to the solution of an eigenvalue-eigenvector problem for a positive-semidefinite symmetrie matrix. (
  • PCA is the simplest of the true eigenvector-based multivariate analyses and is closely related to factor analysis. (
  • In both cases, components are generated by an eigenvector decomposition of the matrix formed from the sum of substitution matrix scores at each aligned position between each pair of sequences - computed with one of the available score matrices, such as BLOSUM62 , PAM250 , or the simple single nucleotide substitution matrix . (
  • Initially, the display shows the first three components of the similarity space, but any eigenvector can be used by changing the selected dimension for the x, y, or z axis through each ones menu located below the 3d display. (
  • PCA is closely related to factor analysis . (
  • Within the vast archipelago of data analysis tools, factor analysis and principal component analysis are among the islands more frequently visited by human scientists. (
  • Factor analysis can be used to identify the structure of the latent factors. (
  • Principal component analysis (PCA) and factor analysis are statistical tools that allow a modeler to (1) reduce the number of variables in a model (i.e., to reduce the dimensionality), and (2) identify if there is structure in the relationships between variables (i.e., to classify variables). (
  • In this entry, we explain PCA and factor analysis. (
  • Coding and most applications where Factor Analysis and PCA is currently used. (
  • Principal Component Analysis (PCA) and Factor Analysis (FA) to reduce dimensionality. (
  • Multinomial - multivariate normal - Wishart and Hotellings T2distributions shall be studied in detail.Important applied multivariate data analysis concepts of principal component analysis, profile analysis - multivariate analysis of variance - cluster analysis - discriminant analysis and classification - factor analysis and canonical correlations analysis. (
  • In part III, you'll learn advanced methods for analyzing a data set containing a mix of variables (continuous and categorical) structured or not into groups: Factor Analysis of Mixed Data (FAMD) and Multiple Factor Analysis (MFA). (
  • Thus, the principal components are often computed by eigendecomposition of the data covariance matrix or singular value decomposition of the data matrix. (
  • This chapter describes gene expression analysis by Singular Value Decomposition (SVD), emphasizing initial characterization of the data. (
  • In addition to a broader utility in analysis methods, singular value decomposition (SVD) and principal component analysis (PCA) can be valuable tools in obtaining such a characterization. (
  • Principal component analysis (PCA) or its equivalent singular-value decomposition (SVD) is widely used for the analysis of high-dimensional data. (
  • Monahan, A.: Nonlinear principal component analysis by neural networks: Theory and application to the lorenz system. (
  • Citation Query Nonlinear principal component analysis: Neural network models and applications. (
  • Similar constraints arise in a number of applications, ranging from analysis of gene expression data to spike sorting in neural signal processing. (
  • The central idea of principal component analysis is to reduce the dimen- sionality of a data set in which there are a large number of interrelated variables, while retaining as much as possible of the variation present in the data set. (
  • This reduction is achieved by transforming to a new set of variables, the principal components, which are uncorrelated, and which are ordered so that the first few retain most of the variation present in all of the original variables. (
  • Some of these newer parts include the expanded discussion of ordination and scaling methods (e.g., biplots), selection of the number of components to retain, canonical correlation for comparing groups of variables, independent correlation analysis for non-normal data, and principal curves. (
  • a logical value indicating whether the variables should be scaled to have unit variance before the analysis takes place. (
  • The main component variables are defined as linear combinations of the original variables. (
  • The Loading Plot reveals the relationships between variables in the space of the first two components. (
  • Using principal component analysis, records of 435 animals from an F2 swine population were used to identity independent and informative variables of economically important performance. (
  • Six principal components expressed variation lower than 0.7 (eigen values lower than 0.7) suggesting that six variables could be discarded with little information loss. (
  • The number of principal components is less than or equal to the number of original variables. (
  • Its counterpart, the partial least squares (PLS), is a supervised method and will perform the same sort of covariance decomposition, albeit building a user-defined number of components (frequently designated as latent variables) that minimize the SSE from predicting a specified outcome with an ordinary least squares (OLS). (
  • In order to provide a deeper understanding of the workings of principal components, four data sets were constructed by taking linear combinations of values of two uncorrelated variables to form the X-variates for the principal component analysis. (
  • Thus, a principal component analysis (PCA) is applied considering the amplitudes, in the respective frequency accused by the FFT method, as variables. (
  • Also, the transformation of variables into apicultural indexes for later use in the analysis of principal components proved to be efficient to draw a beekeeping profile. (
  • This article proposes parametric bootstrap methods for hypothesis testing of principal components when variables are standardized. (
  • The main purpose of this article, therefore, is to fill this yawning gap and provide a solution on how to statistically test significance of principal components when variables are standardized. (
  • Mathematical procedure that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components. (
  • What PCA does is to discover new variables, called "Principal Components" (PCs), which account for the majority of the variability in the data. (
  • Principal component analysis (PCA) finds a smaller set of synthetic variables that capture the maximum variance in an original data set. (
  • Part II describes classical principal component methods to analyze data sets containing, predominantly, either continuous or categorical variables. (
  • These methods include: Principal Component Analysis (PCA, for continuous variables), simple correspondence analysis (CA, for large contingency tables formed by two categorical variables) and Multiple CA (MCA, for a data set with more than 2 categorical variables). (
  • Part IV covers hierarchical clustering on principal components (HCPC), which is useful for performing clustering with a data set containing only categorical variables or with a mixed data of categorical and continuous variables. (
  • Data from 492 participants were daily clinical manifestations, weekly copro-parasitological diagnosis, and housing characteristics and assets owned (48 variables), and these data were used to construct a global wealth index using principal component analysis. (
  • Principal component analysis (PCA) is a potential solution for dealing with high correlation where many correlated variables may be reduced to two or three principal components, allowing for visualisation of the merits and demerits of alternatives in scatter diagrams or bar charts. (
  • For 200 items (variables) and 1000 persons (subjects), that software reports that the first PCA component in the random-normal deviates has an eigenvalue of 2.05 which accords with the findings above. (
  • Principal component analysis (PCA) is a common approach considered to not only classify and quantify these yield curve movements but also to further manage the risk arising from groups of highly correlated market variables. (
  • In simple terms, the technique takes historical data on changes in market variables and attempts to define a set of uncorrelated components or factors that can effectively explain these movements in the most economical manner (Hull 2012). (
  • Exploratory data analysis is mostly focussed on finding related variables and groupings of similar observations. (
  • Projections of the original data in the subspace defined by the principal components can be used to identify groups in the data and to reveal relationships between variables (Davis, 1986). (
  • The main advantage of PCA is the mathematical quantification of correlation between variables and the expression of the original data in the subspace defined by the principal components. (
  • After applying PCA, all the different principal components are uncorrelated, even if some of the original variables are highly correlated. (
  • Evaluation methods include sensory evaluation and instrumental analysis. (
  • In the field of multivariate statistics, kernel principal component analysis (kernel PCA) is an extension of principal component analysis (PCA) using techniques of kernel methods. (
  • Principal components analysis (PCA) is one of those basic statistical methods that (I think) could really transform your classic-style dashboards. (
  • What distinguishes ICA from other methods is that it looks for components that are both statistically independent, and nonGaussian. (
  • The advantage of NIPALS over more traditional methods, like SVD, is that it is memory efficient, and can complete early if only a small number of principal components are needed. (
  • Auto-Associative models cover a large class of methods used in data analysis. (
  • The goal of this chapter is to provide precise explanations of the use of SVD and PCA for gene expression analysis, illustrating methods using simple examples. (
  • Our discussion in section 3 gives some general advice on the use of SVD analysis on gene expression data, and includes references to specific published SVD-based methods for gene expression analysis. (
  • SVR uses the structural risk minimization principal for function estimation while the traditional methods implement empirical risk minimization principal. (
  • Many methods have been proposed for determining how many principal components to retain in the model, but most of these assume non-standardized data. (
  • In a simulation study, the proposed parametric bootstrap methods for standardized data were compared with parallel analysis for PCA and methods using the Tracy-Widom distribution. (
  • Several procedures (Jolliffe 2002 ) have been proposed for determining how many principal components to retain: the popular Kaiser ( 1960 ) rule and the scree plot (Cattell 1966 ), resampling methods (Peres-Neto et al. (
  • METHODS: The analysis was performed on PET studies from 22 Alzheimer disease patients (baseline, follow-up, and test/retest studies) and 4 healthy controls, that is, a total of 26 individual scans. (
  • Using these values, the quantitative methods of normalization and principal component analysis allow the identification of statistically significant changes. (
  • Denaturing high‐performance liquid chromatography resultant chromatograms are suitable for feature extraction analysis with multivariate methods such as principal component analysis. (
  • When genotypes are dense, GRAF-pop is comparable in quality and running time to existing ancestry inference methods EIGENSTRAT, FastPCA, and FlashPCA2, all of which rely on principal components analysis (PCA). (
  • Principal component coefficients of the first three principal components. (
  • Principal component coefficients represent the relative "weight" of each original variable (relative cerebellar regional volume) in each principal component. (
  • Considering the nonlinearity between the various objectives, grey relational analysis (GRA) was first performed to transform these indexes into the corresponding grey relational coefficients, and then kernel principal component analysis (KPCA) was applied to extract the kernel principal components and determine the corresponding weights which showed their relative importance. (
  • However, LASSO and EN may not be satisfactory either, because it can still gives too many nonzero coefficients. (
  • PCA is mostly used as a tool in exploratory data analysis and for making predictive models . (
  • Analysis and interpretation of these datasets starts with an exploratory data analysis in order to summarize the available data, extract useful information and formulate hypotheses for further research. (
  • Recently, Krauledat [1] applied principal component analysis (PCA) to session-wise average covariance matrices and discussed session-to-session variability of BCI signals. (
  • We can usually get further insight by showing the first eigen vectors of the eigen matrices as scalp maps (see Fig. 1).The data of the analysis were recorded in a one-day session from 1 healthy BCI-novice user. (
  • Projection-pursuit approach to robust dispersion matrices and principal components: primary theory and Monte Carlo. (
  • In this work, principal component analysis was applied to analyze the chromatograms from 3 different genes. (
  • Principal Component Analysis: How many most variable genes should I take? (
  • For your main statistical analyses, I think that you should just leave everything in (at least initially) and then, afterwards, make a decision about removing genes if you feel that it is necessary. (
  • Among the differentially expressed genes, cosinor analysis identified seasonal rhythmicity for the observed changes in blood gene expression, consistent with studies in humans. (
  • The principal vectors are often interpretable and convey important information about the data. (
  • This in turn imposes constraints on these vectors: for instance, in some cases the principal directions are known to be non-negative or sparse. (
  • NIPALS -- Nonlinear Iterative Partial Least Squares , is a method for iteratively finding the left singular vectors of a large matrix. (
  • Principal Component Analysis is useful for reducing and interpreting large multivariate data sets with underlying linear structures, and for discovering previously unsuspected relationships. (
  • 1988), spectral decomposition in noise and vibration, and empirical modal analysis in structural dynamics. (
  • Principal component analysis is the empirical manifestation of the eigen value-decomposition of a correlation or covariance matrix. (
  • We present a method for simultaneous dimension reduction and metastability analysis of high dimensional time series. (
  • Principal component analysis (PCA) is one of the useful statistical tools for analyzing multivariate data and recently high-dimensional data in genetics and genomics. (
  • Principal component analysis (PCA) has gained popularity as a method for the analysis of high-dimensional genomic data. (
  • In the Settings tab, set Number of Components to Extract to 4 . (
  • In this paper, an SVR based forecasting model is proposed which first uses the principal component analysis (PCA) to extract the low-dimensional and efficient feature information, and then uses the independent component analysis (ICA) to preprocess the extracted features to nullify the influence of noise in the features. (
  • In addition, we describe the precise relation between SVD analysis and Principal Component Analysis (PCA) when PCA is calculated using the covariance matrix, enabling our descriptions to apply equally well to either method. (
  • In addition, we describe the mathematical relation between SVD analysis and Principal Component Analysis (PCA) when PCA is calculated using the covariance matrix, enabling our descriptions to apply equally well to either method. (
  • Some scholars have made comprehensive analysis of cereal quality by means of instrument and physicochemical characteristics that include the texture tester and the rapid viscosity analyser (RVA). (
  • In addition, physicochemical components are often used to analyse the quality of rice taste. (
  • Traditionally multivariate statistical techniques like principal component analysis (PCA) are used for this purpose. (
  • Johnson, R.A. and Wichern, D.W. (1992) Applied Multivariate Statistical Analysis (Third Edition). (
  • Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, increasing interpretability but at the same time minimizing information loss. (
  • Principal component analysis aims at reducing the dimensionality of such datasets by projecting samples in a few directions of maximum variability. (
  • Thus, the definition and computation of principal components are straightforward but, as will be seen, this apparently simple technique has a wide variety of different applications, as weIl as a number of different deri- vations. (
  • Principal Components Analysis (PCA) is the predominant linear dimensionality reduction technique, and has been widely applied on datasets in all scientific domains. (
  • In addition, the author shows how this technique can be used in tandem with other multivariate analysis techniques-such as multiple regression and discriminant analysis. (
  • Not only does Dunteman contribute to our understanding of principal components, but he suggests several good ideas on how to make wider and better use of the technique. (
  • Principal components analysis is a technique for examining the structure of complex data sets. (
  • In this paper, Principal Component Analysis technique is applied on the signal measured by an ultra wide-band radar to compute the breath and heart rate of volunteers. (
  • In this blog, we will discuss about principal component analysis, a popular dimensionality reduction technique. (
  • PCA (Principal Component Analysis) is one of the most wildly used dimension reduction technique, which is often applied to identify patterns in complex data of high dimension [1]. (
  • As an important data analysis technique for reducing dimensionality of complex process data , Principal Component Analysis (PCA) has been widely used in pharmaceutical PAT applications. (
  • We investigated the process of adoption of beekeeping practices of 28 beekeepers and the quality of the honey produced by them in the Western region of Paraná, using the technique of Principal Components Analysis after the construction of apicultural indexes. (
  • The findings show that the principal component analysis technique is able to effectively classify and quantify the movements of yield curves across both markets in terms of three main factors, namely level, slope and curvature shifts. (
  • Regression analysis of the major principal components was used to identify those correlated with posttraumatic headache. (
  • A principal component analysis can be performed via the calculations dialog which is accessed by selecting Calculate→Calculate Tree or PCA... . (
  • Click the Principal Component Analysis icon in the Apps Gallery window to open the dialog. (
  • In the Plots tab of the dialog, users can choose whether they want to create a scree plot or a component diagram. (
  • It is commonly used for dimensionality reduction by projecting each data point onto only the first few principal components to obtain lower-dimensional data while preserving as much of the data's variation as possible. (
  • Interpreting principal component analyses of spatial population genetic variation. (
  • pioneered the use of principal component analysis (PCA) in population genetics and used PCA to produce maps summarizing human genetic variation across continental regions. (
  • The sets of measurements that differ the most should lie at either end of this principal axis, and the other axes correspond to less extreme patterns of variation in the data set. (
  • The figure illustrates the first principal component of the direction of main variation for each class. (
  • The first components are often interesting, since these typically account for a large proportion of the total variation, but the last components are usually discarded, since these may reflect noise rather than systematic pattern. (
  • Principal component analysis for selection of optimal SNP-sets that capture intragenic genetic variation. (
  • Principal components analysis revealed moderate clustering by sex associated with the variation among global gene expression profiles (PC1, 22 % of variance). (
  • But there is a bunch of detail that is hidden in this plot and which is also hidden by the results of the principal components analysis. (
  • Projection or mapping of analysis results into a biological context. (
  • The results of principal component analysis reduce these parameters to two and three components (PC) for Tabriz and Ardebil respectively. (
  • The results of evapotranspiration modeling show that the coefficient of determination between daily reference evapotranspiration and principal components (PC) for calibration and verification periods are 0.53 and 0.69 for Tabriz and 071 and 0.73 for Ardebil, respectively. (
  • A question of crucial importance for interpretation of results is how many principal components should be utilized. (
  • Analysing the original data in TNT produced little difference in the results, but using the principal components as continuous characters resulted in alternative positions for Caseopsis agilis , Ennatosaurus tecton and Caseoides sanangeloensis . (
  • Results of the analysis were used to study the role of the chromosome traits on principal components. (
  • The results of the range analysis show that the depth of cut has the most significant effect, followed by the feed rate and type of inserts. (
  • SVD and PCA are common techniques for analysis of multivariate data, and gene expression data are well suited to analysis using SVD/PCA. (
  • A summary of previous applications is presented in order to suggest directions for SVD analysis of gene expression data. (
  • Some examples are given of previous applications of SVD to analysis of gene expression data. (
  • also referred to as Principal Coordinates Analysis, PCoA) and discriminant analysis based on a fingerprint data set in BioNumerics version 7. (
  • According to this fact that the estimation of potential evapotranspiration needs lots of meteorological parameters, the aim of this research is to obtain a simple equation for estimating of evapotranspiration, using principal component analysis in Ardabil and Tabriz. (
  • A natural question is: does this task become easier, and estimation more accurate, when we exploit additional knowledge on the principal vector? (
  • This paper is focused on the development of a damage detection indicator that combines a data driven baseline model (reference pattern obtained from the healthy structure) based on principal component analysis (PCA) and multivariate hypothesis testing. (
  • Circa 2001, Vasilescu reframed the data analysis, recognition and synthesis problems as multilinear tensor problems based on the insight that most observed data are the compositional consequence of several causal factors of data formation, and are well suited for multi-modal data tensor analysis. (
  • Their success is due both to the century-old status they have reached, and to the fact that they are easy to apply nowadays, thanks, in particular, to the availability of dedicated data analysis software packages. (
  • This course will teach you how to perform data analysis using MongoDB's powerful Aggregation Framework. (
  • Advanced techniques for metabolomic data analysis. (
  • This framework makes it easier to use low rank models in everyday data analysis workflows. (
  • TREIMAN, D. J., D. D. JOHNSTON and T. J. GRITES (2009) Quantitative data analysis : doing social research to test ideas, 1st ed. (
  • This training course introduces the important ideas in statistics and multivariate data analysis. (
  • This course is especially suitable for non-statistician who needs to perform hands on data analysis. (
  • in A Practical Approach to Microarray Data Analysis . (
  • EH contributed to data analysis and interpretation, and wrote the manuscript. (
  • The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible. (
  • Cluster analysis or clustering is the assignment of a set of observations into subsets (called clusters) so that observations in the same cluster are similar in some sense. (
  • Cluster analysis is the assignment of a set of objects into one or more clusters based on object similarity. (
  • NMath Stats includes a variety of techniques for performing cluster analysis, which we will explore in a series of p. (
  • I took this course and the Intro to Cluster Analysis on consecutive days. (
  • I attended both the PCA and cluster analysis session followed by workshop. (
  • The Principal Component Analysis is applied on the signal reflected by the thorax and the obtained breath frequencies are compared against measures acquired by a piezoelectric belt, a widely used commercial system for respiratory activity monitoring. (
  • Widely used Principal Component Analysis (PCA) addresses this problem by computing a low dimensional data embedding that maximally explain variance of the data. (
  • Principal components analysis (PCA) and independent-component analysis (ICA) are widely used transforms to perform various tasks. (
  • Principal component analysis of genetic data. (
  • Selection of genetic markers for association analyses, using linkage disequilibrium and haplotypes. (
  • Note: the resultant components of pca object from the above code corresponds to the standard deviations and Rotation. (
  • PCA is also related to canonical correlation analysis (CCA) . (
  • Uncertainties associated with this agglomerative classification were investigated in detail using fuzzy-type analyses. (
  • To determine whether multivariate, functional principal component analysis of the size and shape of retinal pigment epithelial (RPE) cell morphology allows discrimination of mouse RPE genotypes and age. (
  • PCA was performed on the relative regional volumes and inter-diagnosis differences in principal components were assessed using Hotelling's T-squared distribution test. (
  • Our method uses principal components analysis to explicitly model ancestry differences between cases and controls. (
  • In the current investigation, we aimed to visualize the differences in fermentative flavors in various beers by principal component analysis (PCA). (
  • The differences are judged to be due to the reduced redundancy of the characters, the smaller number of principal components not overwhelming the discrete characters and the use of a scaling method which allows principal components with a higher variance to have a greater influence on the analysis. (
  • In fact, 78 components are enough to get representativity greater than 90% against the original data that had the observations distributed in 6599 different amplitudes in frequency domain. (
  • The researcher may wonder how many principal components are statistically significant. (
  • Find the matrix W so that for any i j , the components y i and y j are uncorrelated, and the transformed components g(y i ) and h(y j ) are uncorrelated, where g and h are some suitable nonlinear functions. (
  • Jalview can perform PCA analysis on both proteins and nucleotide sequence alignments. (
  • Candidate gene association studies often utilize one single nucleotide polymorphism (SNP) for analysis, with an initial report typically not being replicated by subsequent studies. (
  • By chance, the first ever TD column was about one of the most important tools in chemometrics, Principal Component Analysis (PCA). (
  • A fast method for robust principal components with applications to chemometrics. (
  • In this module, we introduce Principal Components Analysis, and show how it can be used for data compression to speed up learning algorithms as well as for visualizations of complex datasets. (
  • en] Groundwater monitoring networks typically yield large, multivariate datasets. (
  • SIAM Journal of Matrix Analysis and Applications 21 (4), 1324-1342. (
  • Compressive principal component pursuit (CPCP) recovers a target matrix that is a superposition of low-complexity structures from a small set of linear measurements. (
  • a numeric or complex matrix (or data frame) which provides the data for the principal components analysis. (
  • The File menu allows the view to be saved ( File→Save submenu) as an EPS or PNG image or printed, and the original alignment data and matrix resulting from its PCA analysis to be retrieved. (
  • 2 (t)x n (t), where t is the time or sample index, assume that they are generated as a linear mixture of independent components: y=Wx, where W is some unknown matrix. (
  • Principal Components Regression: Recap of Part 2 Recall that the least squares solution to the multiple linear problem is given by (1) And that problems occurred finding when the matrix (2) was close to being singular. (
  • Second, prior to the phylogenetic analysis, the continuous characters were subjected to a log‐ratio principal component analysis, and then the principal components were included in the character matrix rather than the raw ratios. (
  • Immediate-release and controlled-release theophylline tablets were manufactured and subjected to NIR diffuse reflectance spectroscopy analysis. (