Proposed Principal Component Analysis Research Articles

Projection in genomic analysis: A theoretical basis to rationalize tensor decomposition and principal component analysis as feature selection tools.

Identifying differentially expressed genes is difficult because of the small number of available samples compared with the large number of genes. Conventional gene selection methods employing statistical tests have the critical problem of heavy dependence of P-values on sample size. Although the recently proposed principal component analysis (PCA) and tensor decomposition (TD)-based unsupervised feature extraction (FE) has often outperformed these statistical test-based methods, the reason why they worked so well is unclear. In this study, we aim to understand this reason in the context of projection pursuit (PP) that was proposed a long time ago to solve the problem of dimensions; we can relate the space spanned by singular value vectors with that spanned by the optimal cluster centroids obtained from K-means. Thus, the success of PCA- and TD-based unsupervised FE can be understood by this equivalence. In addition to this, empirical threshold adjusted P-values of 0.01 assuming the null hypothesis that singular value vectors attributed to genes obey the Gaussian distribution empirically corresponds to threshold-adjusted P-values of 0.1 when the null distribution is generated by gene order shuffling. For this purpose, we newly applied PP to the three data sets to which PCA and TD based unsupervised FE were previously applied; these data sets treated two topics, biomarker identification for kidney cancers (the first two) and the drug discovery for COVID-19 (the thrid one). Then we found the coincidence between PP and PCA or TD based unsupervised FE is pretty well. Shuffling procedures described above are also successfully applied to these three data sets. These findings thus rationalize the success of PCA- and TD-based unsupervised FE for the first time.

A Holistic Strategy for Classification of Sleep Stages with EEG.

Manual sleep stage scoring is usually implemented with the help of sleep specialists by means of visual inspection of the neurophysiological signals of the patient. As it is a very hectic task to perform, automated sleep stage classification systems were developed in the past, and advancements are being made consistently by researchers. The various stages of sleep are identified by these automated sleep stage classification systems, and it is quite an important step to assist doctors for the diagnosis of sleep-related disorders. In this work, a holistic strategy named as clustering and dimensionality reduction with feature extraction cum selection for classification along with deep learning (CDFCD) is proposed for the classification of sleep stages with EEG signals. Though the methodology follows a similar structural flow as proposed in the past works, many advanced and novel techniques are proposed under each category in this work flow. Initially, clustering is applied with the help of hierarchical clustering, spectral clustering, and the proposed principal component analysis (PCA)-based subspace clustering. Then the dimensionality of it is reduced with the help of the proposed singular value decomposition (SVD)-based spectral algorithm and the standard variational Bayesian matrix factorization (VBMF) technique. Then the features are extracted and selected with the two novel proposed techniques, such as the sparse group lasso technique with dual-level implementation (SGL-DLI) and the ridge regression technique with limiting weight scheme (RR-LWS). Finally, the classification happens with the less explored multiclass Gaussian process classification (MGC), the proposed random arbitrary collective classification (RACC), and the deep learning technique using long short-term memory (LSTM) along with other conventional machine learning techniques. This methodology is validated on the sleep EDF database, and the results obtained with this methodology have surpassed the results of the previous studies in terms of the obtained classification accuracy reporting a high accuracy of 93.51% even for the six-classes classification problem.

Identification of genes associated with altered gene expression and m6A profiles during hypoxia using tensor decomposition based unsupervised feature extraction

Although hypoxia is a critical factor that can drive the progression of various diseases, the mechanism underlying hypoxia itself remains unclear. Recently, m6A has been proposed as an important factor driving hypoxia. Despite successful analyses, potential genes were not selected with statistical significance but were selected based solely on fold changes. Because the number of genes is large while the number of samples is small, it was impossible to select genes using conventional feature selection methods with statistical significance. In this study, we applied the recently proposed principal component analysis (PCA), tensor decomposition (TD), and kernel tensor decomposition (KTD)-based unsupervised feature extraction (FE) to a hypoxia data set. We found that PCA, TD, and KTD-based unsupervised FE could successfully identify a limited number of genes associated with altered gene expression and m6A profiles, as well as the enrichment of hypoxia-related biological terms, with improved statistical significance.

A retrieval system to analyse dermatological lesions using feature ortho-normalisation

ABSTRACTAn information retrieval system is proposed as an assistance tool for diagnosing the skin lesion using Content-Based Image Retrieval approach. Efficiency of the retrieval system is deliberated in terms of the most relevant retrieval of images from database. The proposed diagnostic assistive model retrieves the skin lesion images and its disease category, case history, symptoms and treatment plan. This retrieval process is made from a dermatology database by the way of visual features in the input image such as shape, texture and colour. The author’s proposed principal component analysis (PCA) feature projection technique is to discriminate the features by projecting them onto a feature subspace. While projecting the features onto a feature subspace features are normalised orthogonally. So the proposed methodology is used to improve the classification by the way of discriminate the features, in-turn it focus the retrieval of comprehensive reference sources, so that the diagnosis accuracy of the dermatologists are also improved. Receiver-operating characteristic curve is used to analyse the proposed computer-aided diagnosis (CAD) method, while analysis we attained high contribution to detect the skin lesions. Totally 1450 images are experimented and the system produced the 99.09% specificity, 96.69% sensitivity and 98.3% accuracy. When compared with other works this system of assessment shows high retrieval and diagnosis concert.

Tensor decomposition-based and principal-component-analysis-based unsupervised feature extraction applied to the gene expression and methylation profiles in the brains of social insects with multiple castes

BackgroundEven though coexistence of multiple phenotypes sharing the same genomic background is interesting, it remains incompletely understood. Epigenomic profiles may represent key factors, with unknown contributions to the development of multiple phenotypes, and social-insect castes are a good model for elucidation of the underlying mechanisms. Nonetheless, previous studies have failed to identify genes associated with aberrant gene expression and methylation profiles because of the lack of suitable methodology that can address this problem properly.MethodsA recently proposed principal component analysis (PCA)-based and tensor decomposition (TD)-based unsupervised feature extraction (FE) can solve this problem because these two approaches can deal with gene expression and methylation profiles even when a small number of samples is available.ResultsPCA-based and TD-based unsupervised FE methods were applied to the analysis of gene expression and methylation profiles in the brains of two social insects, Polistes canadensis and Dinoponera quadriceps. Genes associated with differential expression and methylation between castes were identified, and analysis of enrichment of Gene Ontology terms confirmed reliability of the obtained sets of genes from the biological standpoint.ConclusionsBiologically relevant genes, shown to be associated with significant differential gene expression and methylation between castes, were identified here for the first time. The identification of these genes may help understand the mechanisms underlying epigenetic control of development of multiple phenotypes under the same genomic conditions.

PCA/SVM-Based Method for Pattern Detection in a Multisensor System

This paper presents a multivariate analysis framework for pattern detection in a multisensor system; the proposed principal component analysis (PCA)/support vector machine- (SVM-) based supervision scheme can identify patterns in the multisensory system. Although the PCA and SVM are commonly used in pattern recognition, an effective methodology using the PCA/SVM for multisensory system remains unexplored. Pattern detection in a multisensor system has long been a challenge. For example, object inspections in multisensor systems are difficult to perform because inspectors might fail to use multiple sensing devices when concurrently detecting different patterns. Therefore, to resolve this issue, this study proposes a novel framework for establishing indicators and corresponding thresholds to identify patterns in the system; it employs a feature-based scheme that integrates principal component analysis (PCA) with an SVM for effectively detecting patterns in the system. Experiments were conducted using a tactile and optical measurement system. The experimental results demonstrated that the proposed method can effectively identify patterns in multisensor systems by using a feature-based algorithm that combines PCA and SVM classification for detecting various patterns. Moreover, the proposed framework established alarm indicators and corresponding thresholds that can be used for pattern detection.

Characterization of Developmental Immature Fiber (im) Mutant and Texas Marker-1 (TM-1) Cotton Fibers Using Attenuated Total Reflection Fourier Transform Infrared (ATR FT-IR) Spectroscopy

The immature fiber ( im) mutant is one type of cotton fiber mutant with unique characteristics of non-fluffy cotton bolls. Compared to its near-isogenic wild type Texas Marker-1 (TM-1), im fiber has a thin secondary cell wall and is less mature. In this work, we applied the previously proposed principal component analysis (PCA) and simple algorithms to analyze the attenuated total reflection Fourier transform infrared (ATR FT-IR) spectra of developmental im and TM-1 fibers. The results from these approaches could not effectively and consistently indicate the inherent difference between TM-1 and im fibers at the same developmental stage. The difference between TM-1 and corresponding im fibers was detected when comparing the normalized intensity variations of the 730 cm-1 bands. The 730 cm-1 band intensities in developmental im fibers are temporally lower than those in developmental TM-1 fibers although they became similar when the TM-1 and im fibers are fully mature. The observation might imply the likelihood of temporal reduction of amorphous regions in developmental im fibers rather than in developmental TM-1 fibers.

AXES ROTATION FOR MAXIMIZATION OF INDIVIDUAL DIFFERENCES IN INDIVIDUAL SCALES METHOD

This study examines the methodology of "individual scaling method" in order to value each person's vocabulary and viewpoints. "Individual scales" refers to evaluation items made by subjects' own terms. Thus, these scales are different from person to person. In the previous report Part1, we proposed principal component analysis (PCA) method for evaluation data measured by individual scales. In this PCA method, evaluation object is regarded as observations, and individual scales of all subjects is regarded as variables. And, individual difference in vocabulary and viewpoint is described as difference in distribution of factor loadings vectors in principal component space. However, suitable method for analyzing individual differences that are described as such has not yet been proposed because of so-called "rotational indeterminacy" related problem. In this paper, first, when analyzing individual differences based on proposed PCA method, we discuss the problems caused by so-called "rotational indeterminacy". Next, as a solution to this problem, the new method of orthogonal rotation is proposed. This rotation method has the following features. ·By the criterion in this method, R-squares matrix of [each person × rotated axes] is directed to simple structure. ·By this rotation, individual differences in semantic space is maximized. R-squares is measured by personal-PCR (principal component regression analysis), i.e. PCA is performed by each subject, and then by using their each PCA scores as explanatory variables, multiple regression analysis is performed by each subject. Thus, the number of personal PCA scores is an important option in the analysis. The criteria to be maximized , we propose the following three. These are another important option. a) Sum of variances of columns of R-squares matrix b) Sum of variances of columns of Normalized R-squares matrix divided by row sum. c) Simply, variance of R-squares When replacing R-squares matrix to factor loadings squares matrix, each of the criteria is equivalent to the following well-known criteria. a) Row varimax criterion, b) Normal varimax criterion, c) Quartimax criterion As a case study, the proposed rotation method was applied on evaluation data of townscape in Kanda area. The results obtained and discussion are as follows . 1) In this case of data, the results by criterion c) seems to be most useful. 2) It's recommended to use personal PCA scores of slightly many numbers. In this case, six score is necessary. 3) Rotated axes are interpreted as follows; R1: History and taste, R2: Disorderly-Orderly, R3: Harmony and familiar, R4: Enjoying 4) Among these axes, R3 and R4 are large individual differences of R-squares. About these individual differences, the following tendencies were found. (1) Correlation between R-squares of R3 and R-squares of R4 is negative. Thus, terms to express R3 and terms to express R4, there seem to be not so many people with the both of vocabulary. (2) Subjects majoring in architecture have larger R-squares of R3 than other majors, and have smaller R-squares of R4 than other majors. Furthermore, from the viewpoint of validity of R-square as indicator of explanatory power, diagnostics of the number of personal PCA scores was discussed. Finally, it was noted that there are cases not to require rotation proposed in this paper. And some cases of analysis on such data were presented.

Principal component analysis based unsupervised feature extraction applied to budding yeast temporally periodic gene expression.

BackgroundThe recently proposed principal component analysis (PCA) based unsupervised feature extraction (FE) has successfully been applied to various bioinformatics problems ranging from biomarker identification to the screening of disease causing genes using gene expression/epigenetic profiles. However, the conditions required for its successful use and the mechanisms involved in how it outperforms other supervised methods is unknown, because PCA based unsupervised FE has only been applied to challenging (i.e. not well known) problems.ResultsIn this study, PCA based unsupervised FE was applied to an extensively studied organism, i.e., budding yeast. When applied to two gene expression profiles expected to be temporally periodic, yeast metabolic cycle (YMC) and yeast cell division cycle (YCDC), PCA based unsupervised FE outperformed simple but powerful conventional methods, with sinusoidal fitting with regards to several aspects: (i) feasible biological term enrichment without assuming periodicity for YMC; (ii) identification of periodic profiles whose period was half as long as the cell division cycle for YMC; and (iii) the identification of no more than 37 genes associated with the enrichment of biological terms related to cell division cycle for the integrated analysis of seven YCDC profiles, for which sinusoidal fittings failed. The explantation for differences between methods used and the necessary conditions required were determined by comparing PCA based unsupervised FE with fittings to various periodic (artificial, thus pre-defined) profiles. Furthermore, four popular unsupervised clustering algorithms applied to YMC were not as successful as PCA based unsupervised FE.ConclusionsPCA based unsupervised FE is a useful and effective unsupervised method to investigate YMC and YCDC. This study identified why the unsupervised method without pre-judged criteria outperformed supervised methods requiring human defined criteria.Electronic supplementary materialThe online version of this article (doi:10.1186/s13040-016-0101-9) contains supplementary material, which is available to authorized users.

Identification of More Feasible MicroRNA-mRNA Interactions within Multiple Cancers Using Principal Component Analysis Based Unsupervised Feature Extraction.

MicroRNA(miRNA)–mRNA interactions are important for understanding many biological processes, including development, differentiation and disease progression, but their identification is highly context-dependent. When computationally derived from sequence information alone, the identification should be verified by integrated analyses of mRNA and miRNA expression. The drawback of this strategy is the vast number of identified interactions, which prevents an experimental or detailed investigation of each pair. In this paper, we overcome this difficulty by the recently proposed principal component analysis (PCA)-based unsupervised feature extraction (FE), which reduces the number of identified miRNA–mRNA interactions that properly discriminate between patients and healthy controls without losing biological feasibility. The approach is applied to six cancers: hepatocellular carcinoma, non-small cell lung cancer, esophageal squamous cell carcinoma, prostate cancer, colorectal/colon cancer and breast cancer. In PCA-based unsupervised FE, the significance does not depend on the number of samples (as in the standard case) but on the number of features, which approximates the number of miRNAs/mRNAs. To our knowledge, we have newly identified miRNA–mRNA interactions in multiple cancers based on a single common (universal) criterion. Moreover, the number of identified interactions was sufficiently small to be sequentially curated by literature searches.

Identification of aberrant gene expression associated with aberrant promoter methylation in primordial germ cells between E13 and E16 rat F3 generation vinclozolin lineage.

BackgroundTransgenerational epigenetics (TGE) are currently considered important in disease, but the mechanisms involved are not yet fully understood. TGE abnormalities expected to cause disease are likely to be initiated during development and to be mediated by aberrant gene expression associated with aberrant promoter methylation that is heritable between generations. However, because methylation is removed and then re-established during development, it is not easy to identify promoter methylation abnormalities by comparing normal lineages with those expected to exhibit TGE abnormalities.MethodsThis study applied the recently proposed principal component analysis (PCA)-based unsupervised feature extraction to previously reported and publically available gene expression/promoter methylation profiles of rat primordial germ cells, between E13 and E16 of the F3 generation vinclozolin lineage that are expected to exhibit TGE abnormalities, to identify multiple genes that exhibited aberrant gene expression/promoter methylation during development.ResultsThe biological feasibility of the identified genes were tested via enrichment analyses of various biological concepts including pathway analysis, gene ontology terms and protein-protein interactions. All validations suggested superiority of the proposed method over three conventional and popular supervised methods that employed t test, limma and significance analysis of microarrays, respectively. The identified genes were globally related to tumors, the prostate, kidney, testis and the immune system and were previously reported to be related to various diseases caused by TGE.ConclusionsAmong the genes reported by PCA-based unsupervised feature extraction, we propose that chemokine signaling pathways and leucine rich repeat proteins are key factors that initiate transgenerational epigenetic-mediated diseases, because multiple genes included in these two categories were identified in this study.

A MANUFACTURING FAILURE ROOT CAUSE ANALYSIS IN IMBALANCE DATA SET USING PCA WEIGHTED ASSOCIATION RULE MINING

Root cause analysis is key issue for manufacturing processes. It has been a very challenging problem due to the increasing level of complexity and huge number of operational aspects in manufacturing systems. Association rule mining (ARM) which aids in root cause analysis was introduced to extract interesting correlations, frequent patterns, associations or casual structures among items in the transactional database. Although ARM was proven outstanding in many application domains, not many researches were focusing on solving rare items problem in imbalance dataset. The existence of imbalanced dataset in manufacturing environment make the classical ARM fails to extract interesting pattern in an efficient way. Weighted association rule mining (WARM) overcomes the rare items problem by assigning weights to items. The goal of using weighted support is to make use of the weight in the mining process and priorities the selection of the selection of targeted itemsets according to their significance, rather than frequency alone. However, the development of a suitable weight assignment scheme has been an important issue. In this research, we proposed principal component analysis (PCA) to automate the weight in WARM. The result shows that PCA-WARM is capable in capturing pattern from the data of industrial process. These patterns are proven able to explain industrial failure.

Detection of delamination in laminated composites with limited measurements combining PCA and dynamic QPSO

This paper presents an output only damage diagnostic algorithm based on frequency response functions and the principal components for health monitoring of laminated composite structures. The principal components evaluated from frequency response data, are employed as dynamical invariants to handle the effects of operational/environmental variability on the dynamic response of the structure. Finite element models of a laminated composite beam and plate are used to generate vibration data for healthy and damaged structures. Three numerical examples include a laminated composite beam, cantilever plate made of carbon–epoxy and a laminated composite simply supported plate. Varied levels of delamination of laminated composite plies and matrix cracking at varied locations in the plies are simulated at different spatial locations of the structure. Numerical investigations have been carried out to identify the spatial location of damage using the proposed principal component analysis (PCA) based algorithm. In order to limit the number of sensors on the structure, an optimal sensor placement algorithm based on PCA is employed in the present work and the effectiveness of the proposed algorithm with a limited number of sensors is also investigated. Finally, the inverse problem associated with the detection of delamination and matrix cracking is formulated as an optimization problem and is solved using the newly developed dynamic quantum particle swarm optimization (DQPSO) algorithm. Studies carried out and presented in this paper clearly indicate that the proposed SHM scheme can robustly identify the instant of damage, spatial location, the extent of delamination and matrix cracking even with limited sensor measurements and also with noisy data.

Artificial neural networks based on principal component analysis input selection for clinical pattern recognition analysis

Two clinical data sets were applied for pattern recognition in order to discover the correlation between urinary nucleoside profiles and tumours. One data set contains 168 clinical urinary samples, of which 84 specimens are from female thyroid cancer patients (malignant tumour group), and the other samples were collected from healthy women (normal group). However, 168 clinical urinary samples comprised the second data set, too. In all the specimens, each number of the samples for both uterine cervical cancer patients (malignant tumour group) and healthy females (normal group) is 60, and the other 48 samples were collected from uterine myoma patients (benign tumour group). For the two data sets, the separation and quantitative determination of the clinical urinary nucleosides were performed by capillary electrophoresis (CE). The pattern recognition was achieved applying multiple layer perceptron artificial neural networks (MLP ANN) based on conjugate gradient descent training algorithm. Moreover, applying the proposed principal component analysis (PCA) input selection scheme to MLP ANN, the accuracy rate of the pattern recognition was improved to some extent (or without any deterioration) even by much simpler structure of MLP ANN. The study showed that MLP ANN based on PCA input selection was a promising tool for pattern recognition.

Multi-response optimization using weighted principal component

Taguchi method is a very popular offline quality design. However, it cannot solve the multi-response problem which occurs often in today’s society. Research shows that the multi-response problem is still an issue with the Taguchi method. Researchers have tried to find a series of theories and methods in seeking a combination of factors/levels to achieve the situation of optimal multi-response instead of using engineers’ judgement to make a decision in the Taguchi method. In 1997, Su et al. submitted the multivariate method, and in 2000 Antony proposed principal component analysis (PCA), to solve this problem. But with the PCA method, there are still two main shortcomings. In this study, the weighted principal components (WPC) method is proposed to overcome these two shortcomings, and three cases in their papers will be illustrated and compared in the application of WPC method. The result shows that the WPC method offers significant improvements in quality.

The exponential stability of the invariant-norm PCA algorithm

This brief investigates a recently proposed principal component analysis (PCA) algorithm. We prove that that the solutions of the corresponding differential equations converge to the principal eigenvectors of the autocorrelation matrix and calculate an exponential decaying bound for the error.