Uniform Manifold Approximation And Projection Research Articles

Signature analysis of high-throughput transcriptomics screening data for mechanistic inference and chemical grouping.

High-throughput transcriptomics (HTTr) uses gene expression profiling to characterize the biological activity of chemicals in in vitro cell-based test systems. As an extension of a previous study testing 44 chemicals, HTTr was used to screen an additional 1,751 unique chemicals from the EPA's ToxCast collection in MCF7 cells using 8 concentrations and an exposure duration of 6 h. We hypothesized that concentration-response modeling of signature scores could be used to identify putative molecular targets and cluster chemicals with similar bioactivity. Clustering and enrichment analyses were conducted based on signature catalog annotations and ToxPrint chemotypes to facilitate molecular target prediction and grouping of chemicals with similar bioactivity profiles. Enrichment analysis based on signature catalog annotation identified known mechanisms of action (MeOAs) associated with well-studied chemicals and generated putative MeOAs for other active chemicals. Chemicals with predicted MeOAs included those targeting estrogen receptor (ER), glucocorticoid receptor (GR), retinoic acid receptor (RAR), the NRF2/KEAP/ARE pathway, AP-1 activation, and others. Using reference chemicals for ER modulation, the study demonstrated that HTTr in MCF7 cells was able to stratify chemicals in terms of agonist potency, distinguish ER agonists from antagonists, and cluster chemicals with similar activities as predicted by the ToxCast ER Pathway model. Uniform manifold approximation and projection (UMAP) embedding of signature-level results identified novel ER modulators with no ToxCast ER Pathway model predictions. Finally, UMAP combined with ToxPrint chemotype enrichment was used to explore the biological activity of structurally related chemicals. The study demonstrates that HTTr can be used to inform chemical risk assessment by determining in vitro points of departure, predicting chemicals' MeOA and grouping chemicals with similar bioactivity profiles.

Surface-Enhanced Raman Scattering Combined with Machine Learning for Rapid and Sensitive Detection of Anti-SARS-CoV-2 IgG

This work reports an efficient method to detect SARS-CoV-2 antibodies in blood samples based on SERS combined with a machine learning tool. For this purpose, gold nanoparticles directly conjugated with spike protein were used in human blood samples to identify anti-SARS-CoV-2 antibodies. The comprehensive database utilized Raman spectra from all 594 blood serum samples. Machine learning investigations were carried out using the Scikit-Learn library and were implemented in Python, and the characteristics of Raman spectra of positive and negative SARS-CoV-2 samples were extracted using the Uniform Manifold Approximation and Projection (UMAP) technique. The machine learning models used were k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Decision Trees (DTs), logistic regression (LR), and Light Gradient Boosting Machine (LightGBM). The kNN model led to a sensitivity of 0.943, specificity of 0.9275, and accuracy of 0.9377. This study showed that combining Raman spectroscopy and a machine algorithm can be an effective diagnostic method. Furthermore, we highlighted the advantages and disadvantages of each algorithm, providing valuable information for future research.

Clustering of artificial intelligence derived plaque features for risk stratification for future atrial fibrillation development

Abstract Introduction We previously demonstrated that artificial intelligence (AI)-derived coronary artery plaque features are associated with future development of atrial fibrillation (AF) among patients undergoing CCTA(1). Our results suggest that phenotyping a large dataset of plaque features using data science techniques may reveal associations not visible from univariate analysis. Purpose Use data science analysis of AI-CT plaque characteristics to identify subpopulations at higher risk of AF. Methods We applied dimensionality reduction and visual cluster identification to phenotype our population of patients based on their AI-CT plaque features. We used a single center CCTA registry of patients with prior clinically-indicated CCTA (N=2700) from 2017-2020. Cases of new AF were defined by onset of AF on ECG or telemetry after CCTA via EMR search. Patients with AF prior to CCTA, history of mitral valve surgery, or postoperative AF were excluded. Controls were selected 1:1 by nearest neighbor propensity matching (R. v. 4.0.3.) based on age, sex, tobacco use, HTN, HLD, and diabetes. Atherosclerotic plaques were quantitatively analyzed using a commercially available FDA-approved AI coronary analysis software yielding stenosis area and diameter, remodeling index, volume of calcified/non-calcified/low-density plaques, and number of 2 or more high risk plaques. Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction with cosine distance metric was used to reduce the plaque feature space from 80 dimensions (8 AI measurements per each of 10 anatomical vessel segments in all patients) to 2 dimensions on a scatter plot. Two non-contiguous clusters (i.e. plaque phenotypes) were identified based on visual analysis of UMAP feature reduction (Figure 1). Results 94 cases comprised the study cohort (47 cases and 47 controls). Mean age was 67±11 years. 38% were female. Mean CADRADS score was 1.8±1.52 and mean CAC was 325±622. Mean time from CT to AF was 281±300 days. The majority of AF patients (67%) fell into Cluster 1 (N=36), while Cluster 2 (N=58) contained most of the control subjects (75%). Treating Cluster 1 as a predictor of AF, the PPV and specificity of the UMAP reduction was 67% and 75% respectively, with NPV of 60% and sensitivity of 51% (Table 1). Left atrial volume index (LAVI) was significantly different between the 2 clusters, with mean 56.15±12.42 ml/m2 in Cluster 1 and 46.96±16.03 in Cluster 2 (p=0.0046). LAVI was also significantly different between control subjects in Cluster 1 vs 2, mean 54.84±11.6 vs 41.62±7.92 (p<0.001). Conclusions We demonstrated that UMAP-derived clustering of AI-CT plaque characteristics identifies novel phenotypes for future risk of AF among patients undergoing CCTA. Further, control patients in Cluster 1 were found to have a significantly higher LAVI compared to control subjects in Cluster 2, which supports that membership in Cluster 1 may increase risk of future development of AF.

Distributed Photovoltaic Communication Anomaly Detection Based on Spatiotemporal Feature Collaborative Modeling

As distributed photovoltaic (PV) technology rapidly develops and is widely applied, the methods of cyberattacks are continuously evolving, posing increasingly severe threats to the communication networks of distributed PV systems. Recent studies have shown that the Transformer model, which effectively integrates global information and handles long-distance dependencies, has garnered significant attention. Based on this, our research proposes a model named STformer, which is applied to the task of attack detection in distributed PV communication. Specifically, we propose a temporal attention mechanism and a variable attention mechanism. The temporal attention mechanism focuses on capturing subtle changes and trends in data sequences over time, ensuring a highly sensitive recognition of patterns inherent in time-series data. In contrast, the variable attention mechanism analyzes the intrinsic relationships and interactions between different variables, uncovering critical correlations that may indicate abnormal behavior or potential attacks. Additionally, we incorporate the Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction technique. This technique not only helps reduce computational complexity but, in certain cases, can enhance anomaly detection performance. Finally, compared to classical and advanced methods, STformer demonstrates satisfactory performance in simulation experiments.

Elucidation of endogenous and exogenous chemicals in maternal serum using high-resolution mass spectrometry

The increasing exposure to environmental chemicals calls for comprehensive non-targeted analysis to detect unrecognized substances in human samples. We examined human serum samples to classify compounds as endogenous or exogenous using public databases and to explore the relationships between exposure markers and metabolic patterns. Serum samples from 84 pregnant women at 32 weeks gestation were analyzed using LC-QToFMS. Using the PubChemLite for Exposomics database, we annotated and classified 106 compounds (51 endogenous, 55 exogenous). The compound patterns were analyzed using three dimensional reduction methods: Principal Component Analysis (PCA), regularized Generalized Canonical Correlation Analysis (rGCCA), and Uniform Manifold Approximation and Projection (UMAP). OPTICS clustering applied to these methods revealed two distinct clusters, with 89 % of significant compounds overlapping between clusters. The detected exogenous compounds included dietary substances, phthalates, nitrogenous compounds, and parabens. Pathway enrichment analysis showed that chemical exposure was linked to changes in amino acid metabolism, protein and mineral transport, and energy metabolism. While we found associations between exposure and metabolite changes, we could not establish causality. Our approach of analyzing both exogenous and endogenous chemicals from the same dataset using PubChemLite database presents a new method for exposome research, despite limitations in sample size and peak annotation accuracy. These findings contribute to understanding multiple chemical exposures and their metabolic effects in human biomonitoring.

Optimizing Methane Uptake on N/O Functionalized Graphene via DFT, Machine Learning, and Uniform Manifold Approximation and Projection (UMAP) Techniques

Optimizing Methane Uptake on N/O Functionalized Graphene via DFT, Machine Learning, and Uniform Manifold Approximation and Projection (UMAP) Techniques

A Real-Time System Status Evaluation Method for Passive UHF RFID Robots in Dynamic Scenarios

In dynamic scenarios, the status of a Radio Frequency Identification (RFID) system fluctuates with environmental changes. The key to improving system efficiency lies in the real-time monitoring and evaluation of the system status, along with adaptive adjustments to the system parameters and read algorithms. This paper focuses on the status changes in RFID systems in dynamic scenarios, aiming to enhance system robustness and reading performance, ensuring high link quality, reasonable resource scheduling, and real-time status evaluation under varying conditions. This paper comprehensively considers the system parameter settings in dynamic scenarios, integrating the interaction model between readers and tags. The system’s real-time status is evaluated from both the physical layer and the Medium Access Control (MAC) layer perspectives. For the physical layer, a link quality evaluation model based on Uniform Manifold Approximation and Projection (UMAP) and K-Means clustering is proposed from the link quality. For the MAC layer, a multi-criteria decision-making evaluation model based on combined weighting and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is proposed, which comprehensively considers both subjective and objective factors, utilizing the TOPSIS algorithm for an accurate evaluation of the MAC layer system status. For the RFID system, this paper proposes a real-time status evaluation model based on the Classification and Regression Tree (CART), which synthesizes the evaluation results of the physical layer and MAC layer. Finally, engineering tests and verification were conducted on the RFID robot system in mobile scenarios. The results showed that the clustering average silhouette coefficient of the physical layer link quality evaluation model based on K-Means was 0.70184, indicating a relatively good clustering effect. The system status evaluation model of the MAC layer, based on the combined weighting-TOPSIS method, demonstrated good flexibility and generalization. The real-time status evaluation model of the RFID system, based on CART, achieved a classification accuracy of 98.3%, with an algorithm runtime of 0.003 s. Compared with other algorithms, it had a higher classification accuracy and shorter runtime, making it well suited for the real-time evaluation of the RFID robot system’s status in dynamic scenarios.

Wide Area VISTA Extra-galactic Survey (WAVES): Unsupervised star-galaxy separation on the WAVES-Wide photometric input catalogue using UMAP and hdbscan

Abstract Star-galaxy separation is a crucial step in creating target catalogues for extragalactic spectroscopic surveys. A classifier biased towards inclusivity risks including high numbers of stars, wasting fibre hours, while a more conservative classifier might overlook galaxies, compromising completeness and hence survey objectives. To avoid bias introduced by a training set in supervised methods, we employ an unsupervised machine learning approach. Using photometry from the Wide Area VISTA Extragalactic Survey (WAVES)-Wide catalogue comprising 9-band u - Ks data, we create a feature space with colours, fluxes, and apparent size information extracted by ProFound. We apply the non-linear dimensionality reduction method UMAP (Uniform Manifold Approximation and Projection) combined with the classifier hdbscan to classify stars and galaxies. Our method is verified against a baseline colour and morphological method using a truth catalogue from Gaia, SDSS, GAMA, and DESI. We correctly identify 99.75% of galaxies within the AB magnitude limit of Z = 21.2, with an F1 score of 0.9971 ± 0.0018 across the entire ground truth sample, compared to 0.9879 ± 0.0088 from the baseline method. Our method’s higher purity (0.9967 ± 0.0021) compared to the baseline (0.9795 ± 0.0172) increases efficiency, identifying 11% fewer galaxy or ambiguous sources, saving approximately 70,000 fibre hours on the 4MOST instrument. We achieve reliable classification statistics for challenging sources including quasars, compact galaxies, and low surface brightness galaxies, retrieving 92.7%, 84.6%, and 99.5% of them respectively. Angular clustering analysis validates our classifications, showing consistency with expected galaxy clustering, regardless of the baseline classification.

Subclonal Populations Detected by Flow Cytometry in Chronic Lymphocytic Leukemia

Abstract Introduction Chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) is a mature, small B-cell lymphoma that typically co-expresses CD5 and CD23. Molecular-targeted therapies have produced remarkable therapeutic benefits in CLL, yet many patients are resistant to treatment. This study aims to identify subpopulations in CLL by conventional flow cytometry and clustering analysis. Methods One-hundred-and-forty-one CLL cases were taken from a public database and analyzed by uniform manifold approximation and projection (UMAP). Differences in size, granularity, and intensities of CD19, CD20, CD79a, CD5, CD23, CD10, and FMC7 were plotted with BD FACSDiva Software. Eighteen cases with a new diagnosis of CLL at our institution between January 1, 2021 to December 31, 2022 were analyzed. Only peripheral blood samples were included. Markers including forward scatter, side scatter, and fluorescent intensities of CD19, CD20, CD5, CD23, CD10, surface kappa, and surface lambda were used to identify discrete CLL clusters in UMAP plots. Results: Public Dataset Clustering analysis of conventional flow cytometry data from 141 cases from a public database reveals discrete populations of cells exhibiting varying sizes, granularity and fluorescent intensities. Among the 141 cases, 33 (23.4%) had 1 cluster, 64 (45.4%) with 2 clusters, 42 (29.8%) with 3 clusters and 2 (1.4%) with 4 clusters. Among the cases with 2 clusters, a smaller population of cells deviated from the main neoplastic population. This smaller population had a higher forward and side scatter, and brighter intensities in CD45 and CD19, consistent with prolymphocytes. Among the cases with 3 clusters, a third population recurrently had a smaller size by forward scatter. A few cases additionally had varying intensities by CD23 and CD19. Among the 2 cases with 4 clusters, only slight deviations by UMAP were seen and may be artifactual. Institutional Dataset Clustering analysis of 18 cases at our institution revealed complex patterns of cell clusters exhibiting varying sizes, granularity and surface receptor expression intensity. Among the 18 cases, 1 (5.6%) had 1 cluster, 1 (5.6%) with 2 clusters, 10 (55.6%) with 3 clusters, 3 (16.7%) with 4 clusters, and 3 (16.7%) with 5 clusters. Similar to the public dataset, the third population, in addition to the predominant population and prolymphocytes, consistently varied by lower forward scatter. Additional clusters, such as a population with varying CD23 signal intensities, were identified in some cases. Conclusions In our study, multiple clonal subsets in CLL/SLL are detected by conventional flow cytometry analysis. These subclonal populations may result in therapy resistance for these patients, and identifying them by flow cytometry analysis may provide additional prognostic information in the clinical setting. Furthermore, the ability to detect these subclonal populations may allow for isolation of specific clones for future investigation in research settings.

Advanced classification of hot subdwarf binaries using artificial intelligence techniques and Gaia DR3 data

Hot subdwarf stars are compact blue evolved objects, burning helium in their cores surrounded by a tiny hydrogen envelope. In the Hertzsprung-Russell Diagram they are located by the blue end of the Horizontal Branch. Most models agree on a quite probable common envelope binary evolution scenario in the Red Giant phase. However, the current binarity rate for these objects is yet unsolved, but key, question in this field. This study aims to develop a novel classification method for identifying hot subdwarf binaries within large datasets using Artificial Intelligence techniques and data from the third Gaia data release (GDR3). The results will be compared with those obtained previously using Virtual Observatory techniques on coincident samples. The methods used for hot subdwarf binary classification include supervised and unsupervised machine learning techniques. Specifically, we have used Support Vector Machines (SVM) to classify 3084 hot subdwarf stars based on their colour-magnitude properties. Among these, 2815 objects have Gaia DR3 BP/RP spectra, which were classified using Self-Organizing Maps (SOM) and Convolutional Neural Networks (CNN). In order to ensure spectral quality, previously to SOM and CNN classification, our 2815 BP/RP set were pre-analysed with two different approaches: the cosine similarity technique and the Uniform Manifold Approximation and Projection (UMAP) technique. Additional analysis onto a golden sample of 88 well-defined objects, is also presented. The findings demonstrate a high agreement level (sim 70-90<!PCT!>) with the classifications from the Virtual Observatory Sed Analyzer (VOSA) tool. This shows that the SVM, SOM, and CNN methods effectively classify sources with an accuracy comparable to human inspection or non-AI techniques. Notably, SVM in a radial basis function achieves 70.97<!PCT!> reproducibility for binary targets using photometry, and CNN reaches 84.94<!PCT!> for binary detection using spectroscopy. We also found that the single--binary differences are especially observable on the infrared flux in our Gaia DR3 BP/BR spectra, at wavelengths larger than $ sim $700 nm. We find that all the methods used are in fairly good agreement and are particularly effective to discern between single and binary systems. The agreement is also consistent with the results previously obtained with VOSA. In global terms, considering all quality metrics, CNN is the method that provides the best accuracy. The methods also appear effective for detecting peculiarities in the spectra. While promising, challenges in dealing with uncertain compositions highlight the need for caution, suggesting further research is needed to refine techniques and enhance automated classification reliability, particularly for large-scale surveys.

Single-cell analysis reveals alternations between the aged and young mice prostates

BackgroundAging of the male prostate is an inevitable process in which the prostate undergoes hyperplasia, and this growth may lead to compression of the urethra, resulting in voiding dysfunction and associated symptoms, and an increased risk of prostate cancer. Despite the significance of prostate aging, the molecular mechanisms involved are still not fully understood.MethodsProstate split by lobes from young (2 months) and aged (24 months) mice were collected for single-cell RNA sequencing (scRNA-seq) analysis. Tissues from both anterior prostate (AP) and ventral/dorsal/lateral prostate (VDLP) were included in the study. Data analysis included unsupervised clustering using the uniform manifold approximation and projection (UMAP) algorithm to identify distinct cell types based on marker gene expression. Differential gene expression analysis was performed to identify age-related changes in gene expression across different cell types. Functional enrichment analysis was conducted to elucidate biological pathways associated with differentially expressed genes. Additionally, cellular interactions and developmental trajectories were analyzed to characterize cellular dynamics during prostate aging.ResultsThe single-cell transcriptome analysis of the mouse prostate during aging revealed heterogeneity across various cell types and their changes during the aging process. We found a significant increase in the proportion of mesenchymal and immune cells in aged mice. Our study unveiled alterations in genes and pathways associated with cellular senescence, oxidative stress, and regeneration in epithelial cells. Furthermore, we observed that basal cells may undergo epithelial-mesenchymal transition (EMT) to become mesenchymal cells, particularly prominent in aged mice. Additionally, immune cells, notably macrophages and T cells, exhibited a heightened inflammatory response in aged mice.ConclusionIn summary, our study provides a comparative analysis of the single-cell transcriptome of the aged and young mice prostates, elucidating cellular and molecular changes between the aged and young mice prostates.

Heart Disease Prediction Using CNN with Various Feature Selection Approaches

Objectives: To evaluate the performance of CNN models with feature selection methods like Relief, Uniform Manifold Approximation and Projection (UMAP), and Linear discriminant Analysis (LDA) for forecasting heart diseases. Methods: The present research for heart disease prediction compares the performance of feature selection algorithms like ReliefF, Uniform Manifold Approximation and Projection (UMAP), and Linear discriminant Analysis (LDA) with Convolution Neural networks (CNN) for prediction. The study is conducted in a dataset with 303 records collected from patients with 14 attributes. It is also validated with the publicly available Cleveland dataset (Kaggle). The software environment used for implementation is Jupyter Notebook, which uses Python. The dataset consists of 303 records collected from the patients with 14 attributes. It is validated with the publicly available Cleveland dataset. Findings: The study examines how these feature selection techniques affect CNN's accuracy. According to experimental findings, the CNN-UMAP hybrid model outperforms with an accuracy of 91.88%, precision of 0.89, and recall of 0.85 compared to ReliefF and LDA, UMAP shows up as the most successful feature selection method among the studied techniques when utilized alongside CNNs. Novelty: ReliefF, UMAP, and LDA allow CNN to learn more significant and discriminative features by minimizing the dimensions of the input data while preserving its underlying pattern. Previous studies also attempted to identify the key contributing characteristics to heart disease prediction, but less emphasis was placed on these feature selection methods in determining the effectiveness of the features for heart disease prediction. Keywords: Heart Disease, Feature Selection Methods, Convolutional Neural Network, ReliefF, UMAP and LDA

Sifting the debris: Patterns in the SNR population with unsupervised ML methods

Context. Supernova remnants (SNRs) carry vast amounts of mechanical and radiative energy that heavily influence the structural, dynamical, and chemical evolution of galaxies. To this day, more than 300 SNRs have been discovered in the Milky Way, exhibiting a wide variety of observational features. However, existing classification schemes are mainly based on their radio morphology. Aims. In this work, we introduce a novel unsupervised deep learning pipeline to analyse a representative subsample of the Galactic SNR population (~50% of the total) with the aim of finding a connection between their multi-wavelength features and their physical properties. Methods. The pipeline involves two stages: (1) a representation learning stage, consisting of a convolutional autoencoder that feeds on imagery from infrared and radio continuum surveys (WISE 22 μm, Hi-GAL 70 μm and SMGPS 30 cm) and produces a compact representation in a lower-dimensionality latent space; and (2) a clustering stage that seeks meaningful clusters in the latent space that can be linked to the physical properties of the SNRs and their surroundings. Results. Our results suggest that this approach, when combined with an intermediate uniform manifold approximation and projection (UMAP) reprojection of the autoencoded embeddings into a more clusterable manifold, enables us to find reliable clusters. Despite a large number of sources being classified as outliers, most clusters relate to the presence of distinctive features, such as the distribution of infrared emission, the presence of radio shells and pulsar wind nebulae, and the existence of dust filaments.

Machine learning for anxiety and depression profiling and risk assessment in the aftermath of an emergency

Background & objectivesMental health disorders pose an increasing public health challenge worsened by the COVID-19 pandemic. The pandemic highlighted gaps in preparedness, emphasizing the need for early identification of at-risk groups and targeted interventions. This study aims to develop a risk assessment tool for anxiety, depression, and self-perceived stress using machine learning (ML) and explainable AI to identify key risk factors and stratify the population into meaningful risk profiles. MethodsWe utilized a cohort of 9291 individuals from Northern Spain, with extensive post-COVID-19 mental health surveys. ML classification algorithms predicted depression, anxiety, and self-reported stress in three classes: healthy, mild, and severe outcomes. A novel combination of SHAP (SHapley Additive exPlanations) and UMAP (Uniform Manifold Approximation and Projection) was employed to interpret model predictions and facilitate the identification of high-risk phenotypic clusters. ResultsThe mean macro-averaged one-vs-one AUROC was 0.77 (± 0.01) for depression, 0.72 (± 0.01) for anxiety, and 0.73 (± 0.02) for self-perceived stress. Key risk factors included poor self-reported health, chronic mental health conditions, and poor social support. High-risk profiles, such as women with reduced sleep hours, were identified for self-perceived stress. Binary classification of healthy vs. at-risk classes yielded F1-Scores over 0.70. ConclusionsCombining SHAP with UMAP for risk profile stratification offers valuable insights for developing effective interventions and shaping public health policies. This data-driven approach to mental health preparedness, when validated in real-world scenarios, can significantly address the mental health impact of public health crises like COVID-19.

Mugen-UMAP: UMAP visualization and clustering of mutated genes in single-cell DNA sequencing data

BackgroundThe application of Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction and visualization has revolutionized the analysis of single-cell RNA expression and population genetics. However, its potential in single-cell DNA sequencing data analysis, particularly for visualizing gene mutation information, has not been fully explored.ResultsWe introduce Mugen-UMAP, a novel Python-based program that extends UMAP’s utility to single-cell DNA sequencing data. This innovative tool provides a comprehensive pipeline for processing gene annotation files of single-cell somatic single-nucleotide variants and metadata to the visualization of UMAP projections for identifying clusters, along with various statistical analyses. Employing Mugen-UMAP, we analyzed whole-exome sequencing data from 365 single-cell samples across 12 non-small cell lung cancer (NSCLC) patients, revealing distinct clusters associated with histological subtypes of NSCLC. Moreover, to demonstrate the general utility of Mugen-UMAP, we applied the program to 9 additional single-cell WES datasets from various cancer types, uncovering interesting patterns of cell clusters that warrant further investigation. In summary, Mugen-UMAP provides a quick and effective visualization method to uncover cell cluster patterns based on the gene mutation information from single-cell DNA sequencing data.ConclusionsThe application of Mugen-UMAP demonstrates its capacity to provide valuable insights into the visualization and interpretation of single-cell DNA sequencing data. Mugen-UMAP can be found at https://github.com/tengchn/Mugen-UMAP

Exploring copper metabolism-induced cell death in gastric cancer: a single-cell RNA sequencing study and prognostic model development

BackgroundGastric cancer (GC) is the third leading cause of cancer-related deaths globally. Despite advancements in treatment, the overall 5-year survival rate remains below 30%, particularly in advanced stages. Copper metabolism, vital for various cellular processes, has been linked to cancer progression, but its role in GC, especially at the single-cell level, is not well understood.ObjectiveThis study aims to investigate copper metabolism in GC by integrating single-cell RNA sequencing (scRNA-seq) data and developing a prognostic model based on copper metabolism-related gene (CMRG) expression. The study explores how copper metabolism affects the tumor microenvironment and identifies potential therapeutic targets.MethodsscRNA-seq data from gastric cancer and normal tissues were analyzed using the Seurat package. Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) were used for dimensionality reduction and clustering. Non-negative matrix factorization (NMF) was employed for T cell subpopulation analysis. A high-dimensional weighted gene co-expression network analysis (HdWGCNA) identified key molecular features. LASSO regression and Random Survival Forest (RSF) techniques were used to create and validate a prognostic model. Survival analysis, immune microenvironment assessment, and drug sensitivity analysis were conducted.ResultsSixteen cell clusters and nine distinct cell types were identified, with T cells showing significant roles in cell communication. The NMF analysis of CD8 +T cells revealed five copper metabolism-related subtypes. The prognostic model based on nine CMRGs indicated significant survival differences between high- and low-risk groups. High-risk patients showed shorter survival times, increased immune cell infiltration, and altered immune responses. Drug sensitivity analysis suggested higher efficacy of certain drugs in high-CMRG patients.

Machine learning embedded hybrid MCDM model to mitigate decision uncertainty in transport safety planning for OAS countries

Providing defensible decisions is a prerequisite for methodologies of multi-criteria decision-making (MCDM) activities, and this is especially true for socio-economic analysis in public sector. This study proposes an all-in-one MCDM model with machine learning algorithms. The model integrates the method based on the removal effects of criteria (MEREC), combined compromise solution (CoCoSo), and density-based spatial clustering of applications with noise (DBSCAN), i.e., MEREC–CoCoSo–DBSCAN. In particular, the uniform manifold approximation and projection (UMAP) is implanted in DBSCAN to reduce the data dimensionality, and the k-nearest neighbors (KNN) algorithm is embedded to determine the inflection points (ɛ) and minPts in the data. This counters the inherent model failure of DBSCAN in dealing with high-dimensional data and eliminates the requirement for manual intervention in the model procedure, thereby fully avoiding potential human error and automating the computing process. A case study on benchmarking transport safety systems for member countries of the Organization of American States (OAS) demonstrates the reliability, adaptability, and efficiency of the proposed model. It moreover reflects its feasibility in resolving real-life socio-economic issues by offering valuable insights and potential solutions in economic investment and funding allocation in regard to transport safety strategy. Overall, this study provides government officials, managers, and policymakers with a valuable tool for handling MCDM activities in socio-economic development with considerable practicality and credibility.

Signature Genes Selection and Functional Analysis of Astrocytoma Phenotypes: A Comparative Study.

Novel cancer biomarkers discoveries are driven by the application of omics technologies. The vast quantity of highly dimensional data necessitates the implementation of feature selection. The mathematical basis of different selection methods varies considerably, which may influence subsequent inferences. In the study, feature selection and classification methods were employed to identify six signature gene sets of grade 2 and 3 astrocytoma samples from the Rembrandt repository. Subsequently, the impact of these variables on classification and further discovery of biological patterns was analysed. Principal component analysis (PCA), uniform manifold approximation and projection (UMAP), and hierarchical clustering revealed that the data set (10,096 genes) exhibited a high degree of noise, feature redundancy, and lack of distinct patterns. The application of feature selection methods resulted in a reduction in the number of genes to between 28 and 128. Notably, no single gene was selected by all of the methods tested. Selection led to an increase in classification accuracy and noise reduction. Significant differences in the Gene Ontology terms were discovered, with only 13 terms overlapping. One selection method did not result in any enriched terms. KEGG pathway analysis revealed only one pathway in common (cell cycle), while the two methods did not yield any enriched pathways. The results demonstrated a significant difference in outcomes when classification-type algorithms were utilised in comparison to mixed types (selection and classification). This may result in the inadvertent omission of biological phenomena, while simultaneously achieving enhanced classification outcomes.

Machine learning for automated classification of lung collagen in a urethane-induced lung injury mouse model.

Dysregulation of lung tissue collagen level plays a vital role in understanding how lung diseases progress. However, traditional scoring methods rely on manual histopathological examination introducing subjectivity and inconsistency into the assessment process. These methods are further hampered by inter-observer variability, lack of quantification, and their time-consuming nature. To mitigate these drawbacks, we propose a machine learning-driven framework for automated scoring of lung collagen content. Our study begins with the collection of a lung slide image dataset from adult female mice using second harmonic generation (SHG) microscopy. In our proposed approach, first, we manually extracted features based on the 46 statistical parameters of fibrillar collagen. Subsequently, we pre-processed the images and utilized a pre-trained VGG16 model to uncover hidden features from pre-processed images. We then combined both image and statistical features to train various machine learning and deep neural network models for classification tasks. We employed advanced unsupervised techniques like K-means, principal component analysis (PCA), t-distributed stochastic neighbour embedding (t-SNE), and uniform manifold approximation and projection (UMAP) to conduct thorough image analysis for lung collagen content. Also, the evaluation of the trained models using the collagen data includes both binary and multi-label classification to predict lung cancer in a urethane-induced mouse model. Experimental validation of our proposed approach demonstrates promising results. We obtained an average accuracy of 83% and an area under the receiver operating characteristic curve (ROC AUC) values of 0.96 through the use of a support vector machine (SVM) model for binary categorization tasks. For multi-label classification tasks, to quantify the structural alteration of collagen, we attained an average accuracy of 73% and ROC AUC values of 1.0, 0.38, 0.95, and 0.86 for control, baseline, treatment_1, and treatment_2 groups, respectively. Our findings provide significant potential for enhancing diagnostic accuracy, understanding disease mechanisms, and improving clinical practice using machine learning and deep learning models.

Reliability of Serum-Derived Connectome Indicators in Identifying Cirrhosis.

Patients with cirrhosis face a heightened risk of complications, underscoring the importance of identification. We have developed a Connectome strategy that combines metabolites with peptide spectral matching (PSM) in proteomics to integrate metabolomics and proteomics, identifying specific metabolites bound to blood proteins in cirrhosis using open search proteomics methods. Analysis methods including Partial Least Squares Discriminant Analysis (PLS-DA), Uniform Manifold Approximation and Projection (UMAP), and hierarchical clustering were used to distinguish significant differences among the Cirrhosis group, Chronic Hepatitis B (CHB) group, and Healthy group. In this study, we identified 81 cirrhosis-associated connectomes and established an effective model distinctly distinguishing cirrhosis from chronic hepatitis B and healthy samples, confirmed by PLS-DA, hierarchical clustering analysis, and UMAP analysis, and further validated using six new cirrhosis samples. We established a Unified Indicator for Identifying cirrhosis, including tyrosine, Unnamed_189.2, thiazolidine, etc., which not only enables accurate identification of cirrhosis groups but was also further validated using six new cirrhosis samples and extensively supported by other cirrhosis research data (PXD035024). Our study reveals that characteristic cirrhosis connectomes can reliably distinguish cirrhosis from CHB and healthy groups. The established unified cirrhotic indicator facilitates the identification of cirrhosis cases in both this study and additional research data.