High-dimensional Data Analysis Research Articles

Pilot-Study to Explore Metabolic Signature of Type 2 Diabetes: A Pipeline of Tree-Based Machine Learning and Bioinformatics Techniques for Biomarkers Discovery.

This study aims to identify unique metabolomics biomarkers associated with Type 2 Diabetes (T2D) and develop an accurate diagnostics model using tree-based machine learning (ML) algorithms integrated with bioinformatics techniques. Univariate and multivariate analyses such as fold change, a receiver operating characteristic curve (ROC), and Partial Least-Squares Discriminant Analysis (PLS-DA) were used to identify biomarker metabolites that showed significant concentration in T2D patients. Three tree-based algorithms [eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Adaptive Boosting (AdaBoost)] that demonstrated robustness in high-dimensional data analysis were used to create a diagnostic model for T2D. As a result of the biomarker discovery process validated with three different approaches, Pyruvate, D-Rhamnose, AMP, pipecolate, Tetradecenoic acid, Tetradecanoic acid, Dodecanediothioic acid, Prostaglandin E3/D3 (isobars), ADP and Hexadecenoic acid were determined as potential biomarkers for T2D. Our results showed that the XGBoost model [accuracy = 0.831, F1-score = 0.845, sensitivity = 0.882, specificity = 0.774, positive predictive value (PPV) = 0.811, negative-PV (NPV) = 0.857 and Area under the ROC curve (AUC) = 0.887] had the slight highest performance measures. ML integrated with bioinformatics techniques offers accurate and positive T2D candidate biomarker discovery. The XGBoost model can successfully distinguish T2D based on metabolites.

Resource-efficient quantum principal component analysis

Principal component analysis (PCA) is an important dimensionality reduction method in machine learning and data analysis. Recently, the quantum version of PCA has been established to diagonalize quantum states. Although these quantum algorithms promise quantum advantages, they require substantial resources beyond the reach of state-of-the-art quantum technologies. This work aims to reduce resource requirements and improve the efficiency of quantum PCA. Assuming that the quantum state is accessed through a purified quantum query model and a sampling model, we propose quantum algorithms that use minimal resource requirements for ancillary qubits to reveal properties of eigenvectors and eigenvalues of a state. In particular, we show that estimating eigenvalue λ with error ε and success probability larger than λ(1−η) requests a query complexity O~(ϵ−1) and a sample complexity O~(ϵ−2η−1) , respectively. To our knowledge, our result is the first quantum speedup that achieves asymptotic linear scaling in 1/ϵ for quantum PCA. As applications, we discuss estimating the minimum relative entropy of entanglement of bipartite pure-states and performing quantum state discrimination tasks. We show that quantum speedups are maintained when the pure state has a low Schmidt number and states of discrimination have a low rank. This study opens up a new quantum PCA method for high-dimensional quantum data analysis and discusses its application in quantum information processing tasks.

Multivariate Time Series Change-Point Detection with a Novel Pearson-like Scaled Bregman Divergence

Change-point detection is a challenging problem that has a number of applications across various real-world domains. The primary objective of CPD is to identify specific time points where the underlying system undergoes transitions between different states, each characterized by its distinct data distribution. Precise identification of change points in time series omics data can provide insights into the dynamic and temporal characteristics inherent to complex biological systems. Many change-point detection methods have traditionally focused on the direct estimation of data distributions. However, these approaches become unrealistic in high-dimensional data analysis. Density ratio methods have emerged as promising approaches for change-point detection since estimating density ratios is easier than directly estimating individual densities. Nevertheless, the divergence measures used in these methods may suffer from numerical instability during computation. Additionally, the most popular α-relative Pearson divergence cannot measure the dissimilarity between two distributions of data but a mixture of distributions. To overcome the limitations of existing density ratio-based methods, we propose a novel approach called the Pearson-like scaled-Bregman divergence-based (PLsBD) density ratio estimation method for change-point detection. Our theoretical studies derive an analytical expression for the Pearson-like scaled Bregman divergence using a mixture measure. We integrate the PLsBD with a kernel regression model and apply a random sampling strategy to identify change points in both synthetic data and real-world high-dimensional genomics data of Drosophila. Our PLsBD method demonstrates superior performance compared to many other change-point detection methods.

Application of non parametric Bayesian methods in high dimensional data

With the development of technology and the widespread collection of data, high-dimensional data analysis has become a research hotspot in many fields. Traditional parameter methods often face problems such as dimensional disasters in high-dimensional data analysis. Non parametric methods have broad application prospects in high-dimensional data because they do not rely on specific parameter distribution assumptions. The Bayesian rule is more suitable for dealing with noise and outliers in high-dimensional data because it takes uncertainty into account. Therefore, it is of great significance to combine non parametric methods with Bayesian methods for application research in high-dimensional data analysis. In this paper, the nonparametric Bayesian method was applied to the analysis of high-dimensional data, and the Dirichlet process Mixture model was used to cluster high-dimensional data. The regression analysis of high-dimensional data was carried out through the prediction model of nonparametric Bayesian regression. In this paper, the nonparametric Bayesian method based on Bayesian sparse linear model was used for feature selection of high-dimensional data. In order to determine the superiority of nonparametric Bayesian methods in high-dimensional data analysis, this paper conducted experiments on nonparametric Bayesian methods and traditional parametric methods in high-dimensional data analysis from five aspects of cluster analysis, classification analysis, regression analysis, feature selection and anomaly detection, and evaluated them through multiple indicators. This article explored the application of non parametric Bayesian methods in high-dimensional data analysis from these aspects through simulation experiments. The experimental results show that the clustering accuracy of the non parametric Bayesian clustering algorithm was 0.93, and the accuracy of the non parametric Bayesian classification algorithm was between 0.93 and 0.99; the coefficient of determination of nonparametric Bayesian regression algorithm was 0.98; the F1 values of non parametric Bayesian methods in anomaly detection ranged from 0.86 to 0.91, which was superior to traditional methods. Non parametric Bayesian methods have broad application prospects in high-dimensional data analysis, and can be applied in multiple fields such as clustering, classification, regression, etc.

Exploring local interpretability in dimensionality reduction: Analysis and use cases

Dimensionality reduction is a crucial area in artificial intelligence that enables the visualization and analysis of high-dimensional data. The main use of dimensionality reduction is to lower the dimensional complexity of data, improving the performance of machine learning models. Non-linear dimensionality reduction approaches, which provide higher quality representations than linear ones, lack interpretability, prohibiting their application in tasks requiring interpretability. This paper presents LXDR (Local eXplanation of Dimensionality Reduction), a local, model-agnostic technique that can be applied to any DR technique. LXDR trains linear models around a neighborhood of a specific instance and provides local interpretations using a variety of neighborhood generation techniques. Variations of the proposed technique are also introduced. The effectiveness of LXDR’s interpretations is evaluated by quantitative and qualitative experiments, as well as demonstrations of its practical implementation in diverse use cases. The experiments emphasize the importance of interpretability in dimensionality reduction and how LXDR reinforces it.

MetaGate: Interactive analysis of high-dimensional cytometry data with metadata integration

MetaGate: Interactive analysis of high-dimensional cytometry data with metadata integration

Supervised Rank Aggregation (SRA): A Novel Rank Aggregation Approach for Ensemble-based Feature Selection

Background: Feature selection (FS) is critical for high dimensional data analysis. Ensemble based feature selection (EFS) is a commonly used approach to develop FS techniques. Rank aggregation (RA) is an essential step in EFS where results from multiple models are pooled to estimate feature importance. However, the literature primarily relies on static rule-based methods to perform this step which may not always provide an optimal feature set. The objective of this study is to improve the EFS performance using dynamic learning in RA step. Method: This study proposes a novel Supervised Rank Aggregation (SRA) approach to allow RA step to dynamically learn and adapt the model aggregation rules to obtain feature importance.Method: This study proposes a novel Supervised Rank Aggregation (SRA) approach to allow RA step to dynamically learn and adapt the model aggregation rules to obtain feature importance. Results: We evaluate the performance of the algorithm using simulation studies and implement it into real research studies, and compare its performance with various existing RA methods. The proposed SRA method provides better or at par performance in terms of feature selection and predictive performance of the model compared to existing methods. Conclusion: SRA method provides an alternative to the existing approaches of RA for EFS. While the current study is limited to the continuous cross-sectional outcome, other endpoints such as longitudinal, categorical, and time-to-event data could also be used.

Unravelling the complexity of environmental exposures and health: A novel exposome-centered framework for occupational and environmental epidemiology

The exposome-centered approach marks a significant shift in occupational and environmental epidemiology, prioritizing a holistic evaluation of environmental exposures throughout an individual's life to elucidate their effects on health. Unlike traditional methods that single out environmental factors for their direct health impacts, this approach seeks an integrated analysis of all non-genetic influences, encompassing specific and general external environments and internal responses. While acknowledging environmental factors' pivotal role in chronic diseases and global mortality, existing methods struggle with precisely measuring and analyzing the broad spectrum of exposures and their combined effects. The exposome framework addresses these gaps through advanced omics technologies, refined exposure assessment models, and comprehensive data analytics. Yet, its application faces hurdles like the requirement for substantial infrastructure, cross-disciplinary training, and new statistical techniques for high-dimensional data analysis. Recent endeavors in applying the exposome concept show its promise in improving our understanding of environmental and occupational health, underlining its importance in advancing epidemiological research and public health. This abstract condenses the rationale, challenges, and ongoing efforts to incorporate the exposome into epidemiol-ogical studies, emphasizing its potential in enhancing the field.

Single-Cell Informatics for Tumor Microenvironment and Immunotherapy.

Cancer comprises malignant cells surrounded by the tumor microenvironment (TME), a dynamic ecosystem composed of heterogeneous cell populations that exert unique influences on tumor development. The immune community within the TME plays a substantial role in tumorigenesis and tumor evolution. The innate and adaptive immune cells "talk" to the tumor through ligand-receptor interactions and signaling molecules, forming a complex communication network to influence the cellular and molecular basis of cancer. Such intricate intratumoral immune composition and interactions foster the application of immunotherapies, which empower the immune system against cancer to elicit durable long-term responses in cancer patients. Single-cell technologies have allowed for the dissection and characterization of the TME to an unprecedented level, while recent advancements in bioinformatics tools have expanded the horizon and depth of high-dimensional single-cell data analysis. This review will unravel the intertwined networks between malignancy and immunity, explore the utilization of computational tools for a deeper understanding of tumor-immune communications, and discuss the application of these approaches to aid in diagnosis or treatment decision making in the clinical setting, as well as the current challenges faced by the researchers with their potential future improvements.

Variable selection using data splitting and projection for principal fitted component models in high dimension

Sufficient dimension reduction (SDR) is such an effective way to detect nonlinear relationship between response variable and covariates by reducing the dimensionality of covariates without information loss. The principal fitted component (PFC) model is a way to implement SDR using some class of basis functions, however the PFC model is not efficient when there are many irrelevant or noisy covariates. There have been a few studies on the selection of variables in the PFC model via penalized regression or sequential likelihood ratio test. A novel variable selection technique in the PFC model has been proposed by incorporating a recent development in multiple testing such as mirror statistics and random data splitting. It is highlighted how we construct a mirror statistic in the PFC model using the idea of projection of coefficients to the other space generated from data splitting. The proposed method is superior to some existing methods in terms of false discovery rate (FDR) control and applicability to high-dimensional cases. In particular, the proposed method outperforms other methods as the number of covariates tends to be getting larger, which would be appealing in high dimensional data analysis. Simulation studies and analyses of real data sets have been conducted to show the finite sample performance and the gain that it yields compared to existing methods.

SIMEXBoost: An R package for Analysis of High-Dimensional Error-Prone Data Based on Boosting Method

SIMEXBoost: An R package for Analysis of High-Dimensional Error-Prone Data Based on Boosting Method

The influence of the land-to-sea macroevolutionary transition on vertebral column disparification in Pinnipedia.

The repeated returns of vertebrates to the marine ecosystems since the Triassic serve as an evolutionary model to understand macroevolutionary change. Here we investigate the effects of the land-to-sea transition on disparity and constraint of the vertebral column in aquatic carnivorans (Carnivora; Pinnipedia) to assess how their functional diversity and evolutionary innovations influenced major radiations of crown pinnipeds. We use three-dimensional geometric morphometrics and multivariate analysis for high-dimensional data under a phylogenetic framework to quantify vertebral size and shape in living and extinct pinnipeds. Our analysis demonstrates an important shift in vertebral column evolution by 10-12 million years ago, from an unconstrained to a constrained evolutionary scenario, a point of time that coincides with the major radiation of crown pinnipeds. Moreover, we also demonstrate that the axial skeleton of phocids and otariids followed a different path of morphological evolution that was probably driven by their specialized locomotor strategies. Despite this, we found a significant effect of habitat preference (coastal versus pelagic) on vertebral morphology of crown taxa regardless of the family they belong. In summary, our analysis provides insights into how the land-to-sea transition influenced the complex evolutionary history of pinniped vertebral morphology.

Survival Analysis Based Qos Recommendation for Bus Transportation using Deep Learning

Public transportation play an important role in metropolitan cities.Bus services are an important part of public transportation systems, which are frequently chosen because they are convenient, economical, environmentally beneficial, and less taxing on the infrastructure. The temporal patterns of bus transportation incidents are examined in this study using survival analysis, with a focus on the amount of time until certain events like delays, malfunctions, or safe arrivals at destinations. High-dimensional data is being generated at an increasing rate due to technological advancements. As a result, it is quite difficult to analyze such a dataset adequately. Algorithms for machine learning (ML) have become popular tools for modeling complex and nonlinear interactions in a variety of real-world applications, including as the analysis of high-dimensional survival data. Multilayer Deep Neural Network (DNN) models have achieved impressive results recently. Thus, using Keras and TensorFlow, a Cox-based DNN prediction survival model (DNNSurv model) was created. Its findings, however, were limited to survival datasets with large sample sizes or high dimensionality. Using high dimensional survival data, the proposed work will evaluate the DNNSurv model's prediction ability and compare it to a well-known machine learning survival prediction model (random survival forest). The Proposed Work also offers the best settings for a number of hyperparameters, including tuning parameter selection, for this reason. The suggested approach showed through data analysis that, when compared to the ML model, the DNNSurv model performed better overall in terms of the two primary survival prediction evaluation measures (i.e., the concordance index and the time-dependent AUC).

Abstract 3858: Peripheral CD38+ effector memory (EM) T cells are dynamic biomarkers of response to neoadjuvant immune checkpoint blockade (NeoICB) in patients (pts) with resectable dedifferentiated liposarcoma (DDLPS) and undifferentiated pleomorphic sarcoma (UPS)

Abstract Introduction: NeoICB is a promising approach for pts with DDLPS and UPS. However, biomarkers to identify which pts benefit from this treatment and understanding the mechanisms of response and resistance to ICB remains a challenge. Methods: We conducted a randomized phase 2 trial of neoICB (anti-PD1 +/- CTLA4) in pts with resectable DDLPS (n=17) and UPS (n=10). Pts with UPS received concurrent neoICB and radiation. The primary endpoint of the trial was pathological response; 3 DDLPS and 9 UPS pts were responders (R) and 14 DDLPS and 1 UPS pts were non-responders (NR). Peripheral blood mononuclear cells (PBMCs) were collected at baseline, prior to cycle 2 of neoICB (C2D1), and at the time of surgery (SURG). We performed mass cytometry (CyTOF) to identify immune cells associated with response and their dynamic changes with neoICB. We designed a custom 41-parameter panel using CD45 live cell barcoding with 31 markers for immune profiling, of which 23 were selected to detect key cell lineage and 8 to detect signaling markers. Batch correction and high dimensional data analyses were performed using CyCombine and CyTOF workflow R packages. Longitudinal comparisons between immune cell populations were conducted using Mann-Whitney U Test. Results: Of 79 available PBMC samples from 27 pts, 63 passed quality control after thawing (≥30% viability) and data clean-up (at least 10,000 events). Data was down-sampled to 5000 events for analysis. Of 19 immune cell types identified, monocytes were the most common followed by CD4+ and CD8+ T cells. There was an early increase in CD38+ effector/effector memory (EM) T cells at C2D1 in pts with DDLPS (CD8+ p=0.005, CD4+ p<0.001) and a similar increase observed in pts with UPS (CD8+ C2D1 p=NS, SURG p=0.034; CD4+ C2D1 p=0.024, SURG p=0.06) and these cells had higher Ki67 expression than CD38- T cells (p<0.005). At baseline, R pts with DDLPS had higher fractions of CD38+ EM CD8+ T cells than NR pts (p=0.044). Analysis in the UPS cohort was limited by the low number of NR pts. Higher expression of TIGIT was observed on CD38+ EM CD8+ T cells compared to CD38- counterparts at all time points. Conclusion: Peripheral CD38+ EM CD8+ T cells are associated with response to neoICB in pts with DDLPS. Longitudinal peripheral immune profiling reveals an increase in CD38+ EM CD8+ and CD4+ T cells with neoICB in pts with DDLPS and UPS. These cells are proliferative and express higher levels of TIGIT, which could be a future target for therapeutic exploration. Importantly, changes in the peripheral immune populations may precede changes in the tumor microenvironment (TME) as preliminary analyses of the TME did not identify early (C2D1) changes. TME imaging mass cytometry analyses are currently underway to associate peripheral and intratumor changes in immune populations. Citation Format: Elise F. Nassif, Hsinyi Lu, Michael Stanford, Karen Millerchip, Diana Romero, Jared Malke, Brenna Matejka, Jeffrey E. Gershenwald, Susmita Kumari, Duncan H. Mak, Angelique J. Lin, Jared K. Burks, Wei-Lien Wang, Alexander J. Lazar, Neeta Somaiah, Cara Haymaker, Christina L. Roland, Emily Z. Keung. Peripheral CD38+ effector memory (EM) T cells are dynamic biomarkers of response to neoadjuvant immune checkpoint blockade (NeoICB) in patients (pts) with resectable dedifferentiated liposarcoma (DDLPS) and undifferentiated pleomorphic sarcoma (UPS) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3858.

Neural network big data fusion in remote sensing image processing technology

Abstract Remote sensing (RS) image processing has made significant progress in the past few years, but it still faces some problems such as the difficulty in processing large-scale RS image data, difficulty in recognizing complex background, and low accuracy and efficiency of processing. In order to improve the existing problems in RS image processing, this study dealt with ConvNext-convolutional neural network (CNN) and big data (BD) in parallel. Moreover, it combined the existing RS image processing with the high dimensional analysis of data and other technologies. In this process, the parallel processing of large data and high-dimensional data analysis technology improves the difficulty and low efficiency of large-scale RS image data processing in the preprocessing stage. The ConvNext-CNN optimizes the two modules of feature extraction and object detection in RS image processing, which improves the difficult problem of complex background recognition and improves the accuracy of RS image processing. At the same time, the performance of RS image processing technology after neural networks (NNs) and BD fusion and traditional RS image processing technology in many aspects are analyzed by experiments. In this study, traditional RS image processing and RS image processing combined with NN and BD were used to process 2,328 sample datasets. The image processing accuracy and recall rate of traditional RS image processing technology were 81 and 82%, respectively, and the F1 score was about 0.81 (F1 value is the reconciled average of accuracy and recall, a metric that combines accuracy and recall to evaluate the quality of the results, a higher F1 value indicates a better overall performance of the retrieval system). The accuracy rate and recall rate of RS image processing technology, which integrates NN and BD, were 97 and 98%, respectively, and its F1 score was about 0.97. After analyzing the process of these experiments and the final output results, it can be determined that the RS image processing technology combined with NN and BD can improve the problems of large-scale data processing difficulty, recognition difficulty under complex background, low processing accuracy and efficiency. In this study, the RS image processing technology combined with NN and BD has stronger adaptability with the help of NN and BD technology, and can adjust parameters and can be applied in more tasks.

Variable selection and subgroup analysis for high-dimensional censored data

This paper proposes a penalized method for high-dimensional variable selection and subgroup identification in the Tobit model. Based on Olsen's [(1978). Note on the uniqueness of the maximum likelihood estimator for the Tobit model. Econometrica: Journal of the Econometric Society, 46(5), 1211–1215. https://doi.org/10.2307/1911445] convex reparameterization of the Tobit negative log-likelihood, we develop an efficient algorithm for minimizing the objective function by combining the alternating direction method of multipliers (ADMM) and generalised coordinate descent (GCD). We also establish the oracle properties of our proposed estimator under some mild regularity conditions. Furthermore, extensive simulations and an empirical data study are conducted to demonstrate the performance of the proposed approach.

Double sparsity garrotized kernel machine in high-dimensional partially linear model

Obtaining excellent prediction accuracy in the high-dimensional partially linear model is particularly important. However, it is difficult to achieve due to the complex relationship between nonparametric covariates and the response. Irrelevant covariates and unimportant data also commonly attenuate the prediction performance of the model. Further, high-dimensional data analysis is challenging for modern statistical studies. To overcome these difficulties, we propose the Double Sparsity Garrotized Kernel Machine (DSGKM) method with an efficient algorithm and its adjusted version for prediction. Specifically, we estimate the nonparametric components using the kernel machine technique, and impose L 1 -norm penalties simultaneously to select relevant covariates and retain the representative data in the final model. Besides, the convergence analysis of the adjusted algorithm is conducted. The advantages of our method are: (i) to sufficiently capture the complex relationship between nonparametric covariates and the response; (ii) to identify relevant covariates and select representative data; and (iii) to achieve higher computational efficiency, especially the situations when both parametric and nonparametric components are high-dimensional. Results on both simulated and real data show that the proposed method outperforms existing methods, even when outliers exist.

Covariance Matrix Estimation for High-Throughput Biomedical Data with Interconnected Communities

Estimating a covariance matrix is central to high-dimensional data analysis. Empirical analyses of high-dimensional biomedical data, including genomics, proteomics, microbiome, and neuroimaging, among others, consistently reveal strong modularity in the dependence patterns. In these analyses, intercorrelated high-dimensional biomedical features often form communities or modules that can be interconnected with others. While the interconnected community structure has been extensively studied in biomedical research (e.g., gene co-expression networks), its potential to assist in the estimation of covariance matrices remains largely unexplored. To address this gap, we propose a procedure that leverages the commonly observed interconnected community structure in high-dimensional biomedical data to estimate large covariance and precision matrices. We derive the uniformly minimum variance unbiased estimators for covariance and precision matrices in closed forms and provide theoretical results on their asymptotic properties. Our proposed method enhances the accuracy of covariance- and precision-matrix estimation and demonstrates superior performance compared to the competing methods in both simulations and real data analyses.

MMLF: Multi-Metric Latent Feature Analysis for High-Dimensional and Incomplete Data

MMLF: Multi-Metric Latent Feature Analysis for High-Dimensional and Incomplete Data

Supervised Clustering of Persian Handwritten Images Using Regularization and Dimension Reduction Methods

Clustering, as a fundamental exploratory data technique, not only is used to discover patterns and structures in complex datasets but also is utilized to group variables in high-dimensional data analysis. Dimension reduction through clustering helps identify important variables and reduce data dimensions without losing significant information. High-dimensional image datasets, such as Persian handwritten images, have numerous pixels, making statistical inference difficult. Such high-dimensionality property pose challenges for analysis and processing, requiring specialized techniques like clustering to extract information. Incorporating response variable information enhances clustering analysis, transforming it into a supervised method. This article evaluates a supervised clustering approach using Ridge and Lasso penalties, comparing them in analyzing a real dataset while identifying important variables. We demonstrate that despite choosing a small number of variables as important variables, Lasso penalty performs relatively well in predicting the labels of new observations for this multi-class dataset.