Dimensionality Reduction Approach Research Articles

Subspace‐based distributed target detection method with small training data samples

AbstractDetecting distributed targets precisely in homogeneous environments has been a hot topic in radar signal processing. Generally, distributed targets are often modelled with subspace models of unknown coordinates, and clutter is modelled as the complex Gaussian distribution with zero mean and unknown covariance matrix, while covariance matrix is estimated with a set of training data without the target signal. However, in practice, the complexity of the external environment makes the training data that satisfy the condition of independent homogeneous distribution less available. Therefore, it is assumed that the covariance matrix of the clutter is persymmetric structure and the approach of dimensionality reduction using subspace transformations is introduced, two detectors based upon generalised likelihood ratio test criterion and Wald test criterion in homogeneous environments are proposed. Theoretical analyses indicate the constant false alarm rate characteristics of the two proposed detectors for unknown clutter covariance matrices. Simulation analyses indicate that the proposed detector works well even with fewer training data samples, and its detection performance outperforms that of existing contrast detectors.

Forecasting Heart Disease Risk with a Stacking-Based Ensemble Machine Learning Method

As one of the main causes of sickness and mortality, heart disease, also known as cardiovascular disease, must be detected early in order to be prevented and treated. The rapid development of computer technology presents an opportunity for the cross-combination of medicine and informatics. A novel stacking model called SDKABL is presented in this work. It uses three classifiers, namely K-Nearest Neighbor (KNN), Decision Tree (DT), and Support Vector Machine (SVM) at the base layer and the Bidirectional Long Short-Term Memory based on Attention Mechanisms (ABiLSTM) model at the meta layer for the ultimate prediction. For lowering the temporal complexity and enhancing the model’s accuracy, the dimensionality reduction approach is seen to be crucial. Principal Component Analysis (PCA) was utilized in SDKABL to minimize dimensionality and facilitate feature fusion. Using several performance measures, including precision, F1-score, accuracy, recall, and Receiver Operating Characteristic (ROC) score, the performance of SDKABL was compared to that of other independent classifiers. The experimental findings demonstrate that our proposed model combining individual classifiers with the stacking method helps improve the prediction model’s accuracy.

Analyzing the Influence of Telematics-Based Pricing Strategies on Traditional Rating Factors in Auto Insurance Rate Regulation

This study examines how telematics variables such as annual percentage driven, total miles driven, and driving patterns influence the distributional behaviour of conventional rating factors when incorporated into predictive models for capturing auto insurance risk in rate regulation. To effectively manage the complexity inherent in telematics data, we advocate for the adoption of non-negative sparse principal component analysis (NSPCA) as a structured approach for data dimensionality reduction. By emphasizing sparsity and non-negativity constraints, NSPCA enhances the interpretability and predictive power of models concerning both loss severity and claim counts. This methodological innovation aims to advance statistical analyses within insurance pricing frameworks, ensuring the robustness of predictive models and providing insights crucial for rate regulation strategies specific to the auto insurance sector. Results show that, to enhance auto insurance risk pricing models, it is essential to address data dimension reduction challenges when integrating telematics data variables. Our findings underscore that integrating telematics variables into predictive models maintains the integrity of risk relativity estimates associated with traditional policy variables.

Spatial context non-uniformly modulates inter-laminar information flow in the primary visual cortex.

Our visual experience is a result of the concerted activity of neuronal ensembles in the sensory hierarchy. Yet, how the spatial organization of objects influences this activity remains poorly understood. We investigate how inter-laminar information flow within the primary visual cortex (V1) is affected by visual stimuli in isolation or with flankers at spatial configurations that are known to cause non-uniform degradation of perception. By employing dimensionality reduction approaches to simultaneous, layer-specific population recordings, we establish that information propagation between cortical layers occurs along a structurally stable communication subspace. The spatial configuration of contextual stimuli differentially modulates inter-laminar communication efficacy, the balance of feedforward and effective feedback signaling, and contextual signaling in the superficial layers. Remarkably, these modulations mirror the spatially non-uniform aspects of perceptual degradation. Our results suggest a model of retinotopically non-uniform cortical connectivity in the output layers of V1 that influences information flow in the sensory hierarchy.

Fractal-based supervised approach for dimensionality reduction of hyperspectral images

Dimensionality reduction is one of the most challenging and crucial issues apart from data mining, security, and scalability, which have retained much traction due to the ever-growing need to analyze the large volumes of data generated daily. Fractal Dimension (FD) has been successfully used to characterize data sets and has found relevant applications in dimension reduction. This paper presents an application of the FD Reduction (FDR) Algorithm on geospatial hyperspectral data, examining its usefulness for data sets with a relatively high embedding dimension. We examine the algorithm at two levels. First is the conventional FDR approach (unsupervised) at the image level. Alternatively, we propose a pixel-level supervised approach for band reduction based on time-series complexity analysis. Techniques for determining an optimal intrinsic dimension for the dataset using these two techniques are examined. We also develop a parallel GPU-based implementation for the unsupervised image-level FDR algorithm, reducing the run-time by nearly 10 times. Furthermore, both approaches use a support vector machine classifier to compare the classification performance of the original and reduced image obtained.

A Mode Evolution Metric to Extract Reaction Coordinates for Biomolecular Conformational Transitions.

The complex, multidimensional energy landscape of biomolecules makes the extraction of suitable, nonintuitive collective variables (CVs) that describe their conformational transitions challenging. At present, dimensionality reduction approaches and machine learning (ML) schemes are employed to obtain CVs from molecular dynamics (MD)/Monte Carlo (MC) trajectories or structural databanks for biomolecules. However, minimum sampling conditions to generate reliable CVs that accurately describe the underlying energy landscape remain unclear. Here, we address this issue by developing a Mode evolution Metric (MeM) to extract CVs that can pinpoint new states and describe local transitions in the vicinity of a reference minimum from nonequilibrated MD/MC trajectories. We present a general mathematical formulation of MeM for both statistical dimensionality reduction and machine learning approaches. Application of MeM to MC trajectories of model potential energy landscapes and MD trajectories of solvated alanine dipeptide reveals that the principal components which locate new states in the vicinity of a reference minimum emerge well before the trajectories locally equilibrate between the associated states. Finally, we demonstrate a possible application of MeM in designing efficient biased sampling schemes to construct accurate energy landscape slices that link transitions between states. MeM can help speed up the search for new minima around a biomolecular conformational state and enable the accurate estimation of thermodynamics for states lying on the energy landscape and the description of associated transitions.

Identifying heterogeneous subgroups of systemic autoimmune diseases by applying a joint dimension reduction and clustering approach to immunomarkers

BackgroundThe high complexity of systemic autoimmune diseases (SADs) has hindered precise management. This study aims to investigate heterogeneity in SADs.MethodsWe applied a joint cluster analysis, which jointed multiple correspondence analysis and k-means, to immunomarkers and measured the heterogeneity of clusters by examining differences in immunomarkers and clinical manifestations. The electronic health records of patients who received an antinuclear antibody test and were diagnosed with SADs, namely systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), and Sjögren’s syndrome (SS), were retrieved between 2001 and 2016 from hospitals in Taiwan.ResultsWith distinctive patterns of immunomarkers, a total of 11,923 patients with the three SADs were grouped into six clusters. None of the clusters was composed only of a single SAD, and these clusters demonstrated considerable differences in clinical manifestation. Both patients with SLE and SS had a more dispersed distribution in the six clusters. Among patients with SLE, the occurrence of renal compromise was higher in Clusters 3 and 6 (52% and 51%) than in the other clusters (p < 0.001). Cluster 3 also had a high proportion of patients with discoid lupus (60%) than did Cluster 6 (39%; p < 0.001). Patients with SS in Cluster 3 were the most distinctive because of the high occurrence of immunity disorders (63%) and other and unspecified benign neoplasm (58%) with statistical significance compared with the other clusters (all p < 0.05).ConclusionsThe immunomarker-driven clustering method could recognise more clinically relevant subgroups of the SADs and would provide a more precise diagnosis basis.

Potential immunomodulatory effects of CAS+IMD monoclonal antibody cocktail in hospitalized patients with COVID-19

Passive administration of SARS-CoV-2 neutralizing monoclonal antibodies (mAbs), such as CAS+IMD (Casirivimab+Imdevimab) antibody cocktail demonstrated beneficial effects on clinical outcomes in hospitalized patients with COVID-19 who were seronegative at baseline and outpatients. However, little is known about their impact on the host immunophenotypes. We conducted an immunoprofiling study in 46 patients from a single site of a multi-site trial of CAS+IMD in hospitalized patients. We collected longitudinal samples during October 2020∼April 2021, prior to the emergence of the Delta and Omicron variants and the use of COVID-19 vaccines. All collected samples were analyzed without exclusion and post-hoc statistical analysis was performed. We examined the dynamic interplay of CAS+IMD with host immunity applying dimensional reduction approach on plasma proteomics and high dimensional flow cytometry data. Using an unbiased clustering method, we identified unique immunophenotypes associated with acute inflammation and disease resolution. Compared to placebo group, administration of CAS+IMD accelerated the transition from an acute inflammatory immunophenotype, to a less inflammatory or "resolving" immunophenotype, as characterized by reduced tissue injury, proinflammatory markers and restored lymphocyte/monocyte imbalance independent of baseline serostatus. Moreover, CAS+IMD did not impair the magnitude or the quality of host T cell immunity against SARS-CoV-2 spike protein. Our results identified immunophenotypic changes indicative of a possible SARS-CoV-2 neutralizing antibodies-induced anti-inflammatory effect, without an evident impairment of cellular antiviral immunity, suggesting that further studies of Mabs effects on SAS-CoV-2 or other viral mediated inflammation are warranted. Regeneron Pharmaceuticals Inc and federal funds from the Department of Health and Human Services; Administration for Strategic Preparedness and Response; Biomedical Advanced Research and Development Authority, under OT number: HHSO100201700020C.

Integration of Hydrogen-Deuterium Exchange Mass Spectrometry with Molecular Dynamics Simulations and Ensemble Reweighting Enables High Resolution Protein-Ligand Modeling.

Hydrogen-Deuterium exchange mass spectrometry's (HDX-MS) utility in identifying and characterizing protein-small molecule interaction sites has been established. The regions that are seen to be protected from exchange upon ligand binding indicate regions that may be interacting with the ligand, giving a qualitative understanding of the ligand binding pocket. However, quantitatively deriving an accurate high-resolution structure of the protein-ligand complex from the HDX-MS data remains a challenge, often limiting its use in applications such as small molecule drug design. Recent efforts have focused on the development of methods to quantitatively model Hydrogen-Deuterium exchange (HDX) data from computationally modeled structures to garner atomic level insights from peptide-level resolution HDX-MS. One such method, HDX ensemble reweighting (HDXer), employs maximum entropy reweighting of simulated HDX data to experimental HDX-MS to model structural ensembles. In this study, we implement and validate a workflow which quantitatively leverages HDX-MS data to accurately model protein-small molecule ligand interactions. To that end, we employ a strategy combining computational protein-ligand docking, molecular dynamics simulations, HDXer, and dimensional reduction and clustering approaches to extract high-resolution drug binding poses that most accurately conform with HDX-MS data. We apply this workflow to model the interaction of ERK2 and FosA with small molecule compounds and inhibitors they are known to bind. In five out of six of the protein-ligand pairs tested, the HDX derived protein-ligand complexes result in a ligand root-mean-square deviation (RMSD) within 2.5 Å of the known crystal structure ligand.

Parsimonious Tensor Dimension Reduction

Tensor data is emerging in many scientific applications, such as multi-tissue transcriptomics. In such cases, the covariates for each individual are no longer a vector. To apply traditional vector-based methods to this type of data, we need to either do the vectorization or analyze data marginally, which suffers a significant information loss. We propose a novel parsimonious tensor dimension reduction (pTDR) approach to directly link the response and tensor covariate through an unknown function g. In pTDR, the response variable, continuous or discrete, depends on K rank-one projections of the covariates, with the projections estimated via a sequential iterative dimension reduction algorithm. We further propose an asymptotic sequential statistical test to select the correct number of rank-one tensors. In contrast to the classic low-rank tensor regression, pTDR model is not restricted to the linear relationship between response and covariates. We apply pTDR to two modern genomic studies. We find that the gene expression of multiple tissues has a stronger association with aging and obesity than was apparent using previous approaches. Numerical results demonstrate the advantages of pTDR over competitors in terms of prediction accuracy and computing efficiency. Our software is publicly available on GitHub (https://github.com/BioAlgs/pTDR).

New forest-based approaches for sufficient dimension reduction

New forest-based approaches for sufficient dimension reduction

First-order electroweak phase transition at finite density

We study the Electroweak phase transition with the Standard Model effective field theory at finite temperature and finite density. Utilizing the dimensional reduction approach, we construct the tree dimensional thermal effective field theory at finite density and investigate the phase transition dynamics. We evaluate how the results depend on the renormalization scale and the chemical potential. Our results show that, with the tree dimensional thermal effective potential at 2-loop level, we can effectively reduce the theoretical uncertainty in the calculations of the phase transition parameters due to the renormalization scale dependence, and the new physics scale is restricted to be Λ ≲ (770 − 800) GeV by the baryon number washout avoidance condition. Meanwhile, the presence of the chemical potential would affect the phase transition parameter and make the constraints from the baryon number washout avoidance condition more strict, especially for weaker first-order phase transition scenarios at higher new physics scales.

Exploratory factor analysis for machining error data of compressor blades based on dimensionality-reduced statistical analysis method

Abstract Various machining errors inevitably occur on aero-engine compressor blades, including leading-edge contour error, trailing-edge contour error, camber contour error, and more. The current complexity surrounding the numerous ma-chining error types and their obscure interrelationships imposes immense effort for aerodynamic analysis and hin-ders overall error control. Thus, elucidating error correlations to achieve error dimensionality reduction is imperative. This study pioneers a dimensionality reduction approach via factor analysis to conduct a comprehensive statistical analysis of 13 types of blade machining errors. The proposed technique can categorize the 13 errors into three groups, each dominated by a distinct common factor. To validate the accuracy of the extracted factors, the factor scores of the three identified latent factors are computed. Cluster analysis is then performed on the factor scores, and the clustering results are visualized by t-Distributed Stochastic Neighbor Embedding (t-SNE). Furthermore, boot-strap resampling establishes the 95% confidence intervals for the factor scores. Capitalizing on the grouping struc-ture uncovered by factor analysis, multiple linear regression models are built for the errors within each group, and then, a preliminary conjecture is made about the potential key error types for each group of errors based on the re-gression coefficients. This hypothesis is then evidenced by the statistical analysis of cross-section profile error data of 28 blades. The present work can not only optimize machining processes but also relax tolerance requirements and diminish the effort of aerodynamic analysis.

GWAS-Identified Loci are Associated with Obesity and Type 2 Diabetes Mellitus in Patients with Severe COVID-19.

Comorbidities such as obesity and type 2 diabetes mellitus (T2DM) have emerged as critical risk factors exacerbating the severity and mortality of COVID-19. Meanwhile, numerous genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with increased susceptibility to severe COVID-19. This study investigated whether SNPs previously identified by GWAS as risk factors for severe COVID-19 also correlate with common comorbidities-obesity and T2DM-in hospitalized patients with severe COVID-19. DNA samples from 199 hospitalized COVID-19 patients were genotyped using probe-based PCR for 10 GWAS SNPs previously implicated in severe COVID-19 outcomes (rs143334143 CCHCR1, rs111837807 CCHCR1, rs17078346 SLC6A20-LZTFL1, rs17713054 SLC6A20-LZTFL1, rs7949972 ELF5, rs61882275 ELF5, rs12585036 ATP11A, rs67579710 THBS3, THBS3-AS1, rs12610495 DPP9, rs9636867 IFNAR2). The analysis revealed significant associations between certain SNPs and the increased risk of obesity and T2DM in severe COVID-19 patients. Specifically, rs17713054 SLC6A20-LZTFL1 (risk allele A; odds ratio (OR) = 2.34, 95% confidence interval (CI) = 1.24-4.4, p = 0.007) and rs7949972 ELF5 SNP (risk allele T; OR = 1.79, 95% CI = 1.11-2.91, p = 0.015) were associated with increased risk of obesity. SNP rs9636867 IFNAR2 was associated with a higher risk of T2DM (risk allele G, OR = 8.28, 95% CI = 1.69-40.64, p = 0.027). Using the model-based multifactor dimensionality reduction (MB-MDR) approach, the six most significant gene-gene interaction patterns associated with obesity in severe COVID-19 patients were identified and included five polymorphic loci: rs7949972, rs17713054, rs61882275, rs12585036, and rs143334143, participating in two or more of the most significant G-G interactions (pperm < 0.05). In total, the best models of G-G interactions associated with T2DM in patients with severe COVID-19 included eight polymorphic loci, six of which, rs7949972, rs61882275, rs12585036, rs143334143, rs67579710, and rs12610495, were involved in two or more of the most significant G-G interactions. Our study provides novel insights into the genetic associations between GWAS-identified SNPs and the risk of obesity and T2DM in patients with severe COVID-19.

Streamlining NMR Chemical Shift Predictions for Intrinsically Disordered Proteins: Design of Ensembles with Dimensionality Reduction and Clustering.

By merging advanced dimensionality reduction (DR) and clustering algorithm (CA) techniques, our study advances the sampling procedure for predicting NMR chemical shifts (CS) in intrinsically disordered proteins (IDPs), making a significant leap forward in the field of protein analysis/modeling. We enhance NMR CS sampling by generating clustered ensembles that accurately reflect the different properties and phenomena encapsulated by the IDP trajectories. This investigation critically assessed different rapid CS predictors, both neural network (e.g., Sparta+ and ShiftX2) and database-driven (ProCS-15), and highlighted the need for more advanced quantum calculations and the subsequent need for more tractable-sized conformational ensembles. Although neural network CS predictors outperformed ProCS-15 for all atoms, all tools showed poor agreement with HN CSs, and the neural network CS predictors were unable to capture the influence of phosphorylated residues, highly relevant for IDPs. This study also addressed the limitations of using direct clustering with collective variables, such as the widespread implementation of the GROMOS algorithm. Clustered ensembles (CEs) produced by this algorithm showed poor performance with chemical shifts compared to sequential ensembles (SEs) of similar size. Instead, we implement a multiscale DR and CA approach and explore the challenges and limitations of applying these algorithms to obtain more robust and tractable CEs. The novel feature of this investigation is the use of solvent-accessible surface area (SASA) as one of the fingerprints for DR alongside previously investigated α carbon distance/angles or ϕ/ψ dihedral angles. The ensembles produced with SASA tSNE DR produced CEs better aligned with the experimental CS of between 0.17 and 0.36 r2 (0.18-0.26 ppm) depending on the system and replicate. Furthermore, this technique produced CEs with better agreement than traditional SEs in 85.7% of all ensemble sizes. This study investigates the quality of ensembles produced based on different input features, comparing latent spaces produced by linear vs nonlinear DR techniques and a novel integrated silhouette score scanning protocol for tSNE DR.

Operant ethanol self-administration behaviors do not predict sex differences in continuous access home cage drinking

Understanding sex differences in disease prevalence is critical to public health, particularly in the context of alcohol use disorder (AUD). The goal of this study was to understand sex differences in ethanol drinking behavior and define the precise conditions under which sex differences emerge. Consistent with prior work, C57BL/6J females drank more than males under continuous access two-bottle choice conditions. However, using ethanol self-administration - where an operant response results in access to an ethanol sipper for a fixed time period - we found no sex differences in operant response rates or ethanol consumption (volume per body weight consumed, as well as lick behavior). This remained true across a wide range of parameters including acquisition, when the ethanol sipper access period was manipulated, and when the concentration of the ethanol available was scaled. The only sex differences observed were in total ethanol consumption, which was explained by differences in body weight between males and females, rather than by sex differences in motivation to drink. Using dimensionality reduction approaches, we found that drinking behavior in the operant context did not cluster by sex, but rather clustered by high and low drinking phenotypes. Interestingly, these high and low drinking phenotypes in the operant context showed no correlation with those same categorizations in the home cage context within the same animals. These data underscore the complexity of sex differences in ethanol consumption, highlighting the important role that drinking conditions/context plays in the expression of these differences.

Association between MAPK and PI3K/Akt signaling pathway-related gene polymorphisms and migraine.

The causes of migraine remain unclear. Evidence suggests that the MAPK and PI3K/Akt signaling pathways play a role in migraine pathogenesis. However, studies on genetic polymorphisms in the two pathways associated with migraine are still limited. This study included 226 migraineurs and 452 age- and sex-matched nonmigraine control individuals. Genotyping of 31 Single Nucleotide Polymorphisms (SNPs) in 21 genes was performed. The relationship between migraine and gene polymorphisms was analyzed by using logistic regression. SNP-SNP interactions were examined by a generalized multifactor dimension reduction (GMDR) approach. The possible role of SNPs was evaluated with gene expression data from the GTEx database. The RASGRP2-rs2230414 GT genotype was associated with decreased migraine risk compared with the wild-type GG genotype [ORadj (95% CI): 0.674(0.458-0.989)]. PIK3R1-rs3730089 was associated with migraine in the recessive model [ORadj (95% CI): 1.446(1.004-2.083)]. The CACNA1H-rs61734410 CT genotype was associated with migraine risk [ORadj (95% CI): 1.561(1.068-2.281)]. One significant two-way SNP-SNP interaction was found (PRKCA rs2228945-BDNF rs6265) (p = 0.0107). Significant eQTL and sQTL signals were observed for the SNP rs2230414. This is the first study to systematically reveal significant associations between MAPK and PI3K/Akt signaling pathway-related gene polymorphisms and migraine risk.

Genetic variations in the antioxidant genes and their role in modulating susceptibility towards chronic obstructive pulmonary disease in the North Indian population

BackgroundChronic Obstructive Pulmonary Disease (COPD) is a persistent inflammatory lung condition characterized by an obstruction in removing oxygen from the lungs. Oxidant and antioxidant imbalance have long been hallmarks of COPD development, where the amount of antioxidants produced is less than that of oxidants. Here, polymorphism in the antioxidant enzymes like Catalase, Superoxide dismutase and Glutathione peroxidase plays an essential role in regulating the levels of oxidants. Methods1000 subjects, including 500 COPD cases and 500 controls, have been recruited and genotyped to assess the correlation between COPD and the particular SNPS of antioxidant genes. Logistic regression was used to compute odds ratios (ORs) and 95 % confidence intervals (CIs) to assess the association between SNPs and COPD risk. The relationship between spirometry value and COPD for all SNPs has been analyzed using Kruskal Wallis's. Haplotype analysis has also been performed. The effect of SNP interactions on COPD risk was assessed through the Multifactor Dimensionality Reduction (MDR) approach, a nonparametric test for overcoming some of the limitations of the logistic regression for detecting and characterizing SNP interactions. ResultsOur findings indicated a strong association between COPD and the variations in the CAT rs7943316 (OR = 0.61, Pc = 0.0001), SOD2 rs4880 (OR = 2.07, Pc = 0.0006), and GPx rs1050450 (OR = 0.60, Pc = 0.0018). Furthermore, SOD2 rs4880 was associated with forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1) of COPD patients. Our study found that the triple combination of SOD1 (rs2234694), SOD1 (rs36232792) and SOD2 (rs4880) was found to be elevating the risk of COPD (OR = 2.83, Pc = 0.006). SOD2 rs4880 and GPx rs1050450 are also linked to cough and mucus production. The Haplotype study reveals a substantial relationship between CAT (rs7943316 and rs1001179) and SOD (rs2234694 and rs4880), which increases the risk of COPD. The three-locus model (CAT rs794331, CAT rs1101179, and GPx rs1050450) was the most effective for COPD risk assessment based on the MDR findings, which were statistically significant (p < 0.0001). ConclusionThis study shows that rs7943316, rs4880, and rs1050450 are associated with the risk of COPD in the north Indian population and have the potential to enhance our knowledge of COPD at the molecular level, which in turn might pave the way for earlier detection, treatment, and preventive efforts.

Integrative, high-resolution analysis of single cells across experimental conditions with PARAFAC2.

Effective tools for exploration and analysis are needed to extract insights from large-scale single-cell measurement data. However, current techniques for handling single-cell studies performed across experimental conditions (e.g., samples, perturbations, or patients) require restrictive assumptions, lack flexibility, or do not adequately deconvolute condition-to-condition variation from cell-to-cell variation. Here, we report that the tensor decomposition method PARAFAC2 (Pf2) enables the dimensionality reduction of single-cell data across conditions. We demonstrate these benefits across two distinct contexts of single-cell RNA-sequencing (scRNA-seq) experiments of peripheral immune cells: pharmacologic drug perturbations and systemic lupus erythematosus (SLE) patient samples. By isolating relevant gene modules across cells and conditions, Pf2 enables straightforward associations of gene variation patterns across specific patients or perturbations while connecting each coordinated change to certain cells without pre-defining cell types. The theoretical grounding of Pf2 suggests a unified framework for many modeling tasks associated with single-cell data. Thus, Pf2 provides an intuitive universal dimensionality reduction approach for multi-sample single-cell studies across diverse biological contexts.

Single-Trial Representations of Decision-Related Variables by Decomposed Frontal Corticostriatal Ensemble Activity.

The frontal cortex-striatum circuit plays a pivotal role in adaptive goal-directed behaviors. However, it remains unclear how decision-related signals are mediated through cross-regional transmission between the medial frontal cortex and the striatum by neuronal ensembles in making decision based on outcomes of past action. Here, we analyzed neuronal ensemble activity obtained through simultaneous multiunit recordings in the secondary motor cortex (M2) and dorsal striatum (DS) in rats performing an outcome-based left-or-right choice task. By adopting tensor component analysis (TCA), a single-trial-based unsupervised dimensionality reduction approach, for concatenated ensembles of M2 and DS neurons, we identified distinct three spatiotemporal neural dynamics (TCA components) at the single-trial level specific to task-relevant variables. Choice-position-selective neural dynamics reflected the positions chosen and was correlated with the trial-to-trial fluctuation of behavioral variables. Intriguingly, choice-pattern-selective neural dynamics distinguished whether the incoming choice was a repetition or a switch from the previous choice before a response choice. Other neural dynamics was selective to outcome and increased within-trial activity following response. Our results demonstrate how the concatenated ensembles of M2 and DS process distinct features of decision-related signals at various points in time. Thereby, the M2 and DS collaboratively monitor action outcomes and determine the subsequent choice, whether to repeat or switch, for action selection.