Homogeneous Subpopulations Research Articles

Mixtures of logistic normal multinomial regression models for microbiome data

In the realm of bioinformatics, we frequently encounter discrete data, particularly microbiome taxa count data obtained through 16S rRNA sequencing. These microbiome datasets are commonly characterized by their high dimensionality and the ability to provide insights solely into relative abundance, necessitating their classification as compositional data. Analyzing such data presents challenges due to their confinement within a simplex. Additionally, microbiome taxa counts are subject to influence by various biological and environmental factors like age, gender, and diet. Thus, we have developed a novel approach involving regression-based mixtures of logistic normal multinomial models for clustering microbiome data. These models effectively categorize samples into more homogeneous subpopulations, enabling the exploration of relationships between bacterial abundance and biological or environmental covariates within each identified group. To enhance the accuracy and efficiency of parameter estimation, we employ a robust framework based on variational Gaussian approximations (VGA). Our proposed method's effectiveness is demonstrated through its application to simulated and real datasets.

Assessing heterogeneity in treatment initiation guidelines in longitudinal randomized controlled trials

Treatment initiation guidelines are essential in healthcare, dictating when patients begin therapy. These guidelines are typically assessed through randomized controlled trials (RCTs) to measure their average effect on a population. However, this method may not fully account for patient heterogeneity. We introduce a refined analysis methodology that accounts for diverse times to treatment initiation (TTI) arising from these guidelines. We offer a more detailed perspective on the guidelines’ impact by analyzing homogeneous subpopulations based on their TTI. We develop a longitudinal regression model with smooth time functions to capture dynamic changes in average guideline effects on subpopulations (AGES). A unique weighting mechanism creates pseudo-subpopulations from RCT data, enabling consistent and precise estimation of smooth functions. The efficacy of our approach is validated through theoretical and numerical studies, underscoring its capacity to provide insightful statistical inferences. We exemplify the utility of our methodology by applying it to an RCT of the World Health Organization (WHO) guideline for adults with HIV. This analysis promises to enhance the evaluation of treatment initiation guidelines, leading to more personalized and efficient patient care.

Estimation in a general mixture of Markov jump processes

AbstractWe propose a general mixture of Markov jump processes. The key novel feature of the proposed mixture is that the generator matrices of the Markov processes comprising the mixture are entirely unconstrained. The Markov processes are mixed with distributions that depend on the initial state of the mixture process. The maximum likelihood (ML) estimates of the mixture's parameters are obtained from continuous realizations of the mixture process and their standard errors from an explicit form of the observed Fisher information matrix, which simplifies the Louis (Journal of the Royal Statistical Society Series B, 44:226–233, 1982) general formula for the same matrix. The asymptotic properties of the ML estimators are also derived. A simulation study verifies the estimates' accuracy. The proposed mixture provides an exploratory tool for identifying the homogeneous subpopulations in a heterogeneous population. This is illustrated with an application to a medical dataset.

Reinforcement Learning in Latent Heterogeneous Environments

Reinforcement Learning holds great promise for data-driven decision-making in various social contexts, including healthcare, education, and business. However, classical methods that focus on the mean of the total return may yield misleading results when dealing with heterogeneous populations typically found in large-scale datasets. To address this issue, we introduce the K-Value Heterogeneous Markov Decision Process, a framework designed to handle sequential decision problems with latent population heterogeneity. Within this framework, we propose auto-clustered policy evaluation for estimating the value of a given policy and auto-clustered policy iteration for estimating the optimal policy within a parametric policy class. Our auto-clustered algorithms can automatically identify homogeneous subpopulations while simultaneously estimating the action value function and the optimal policy for each subgroup. We establish convergence rates and construct confidence intervals for the estimators. Simulation results support our theoretical findings, and an empirical study conducted on a real medical dataset confirms the presence of value heterogeneity and validates the advantages of our novel approach. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Ragas: integration and enhanced visualization for single cell subcluster analysis.

Subcluster analysis is a powerful means to improve clustering and characterization of single cell RNA-Seq data. However, there are no existing tools to systematically integrate results from multiple subclusters, which creates hurdles for accurate data quantification, visualization, and interpretation in downstream analysis. To address this issue, we developed Ragas, an R package that integrates multi-level subclustering objects for streamlined analysis and visualization. A new data structure was implemented to seamlessly connect and assemble miscellaneous single cell analyses from different levels of subclustering, along with several new or enhanced visualization functions. Moreover, a re-projection algorithm was developed to integrate nearest-neighbor graphs from multiple subclusters in order to maximize their separability on the combined cell embeddings, which significantly improved the presentation of rare and homogeneous subpopulations. The Ragas package and its documentation can be accessed through https://github.com/jig4003/Ragas and its source code is also available at https://zenodo.org/records/11244921.

Parsimonious ultrametric Gaussian mixture models

Gaussian mixture models represent a conceptually and mathematically elegant class of models for casting the density of a heterogeneous population where the observed data is collected from a population composed of a finite set of G homogeneous subpopulations with a Gaussian distribution. A limitation of these models is that they suffer from the curse of dimensionality, and the number of parameters becomes easily extremely large in the presence of high-dimensional data. In this paper, we propose a class of parsimonious Gaussian mixture models with constrained extended ultrametric covariance structures that are capable of exploring hierarchical relations among variables. The proposal shows to require a reduced number of parameters to be fit and includes constrained covariance structures across and within components that further reduce the number of parameters of the model.

Abstract 7362: Integrated analysis of scRNAseq and spatial omics in brain metastatic lung cancer tumor tissue

Abstract Spatial omics technologies have made significant advances in recent years, improving our understanding of the spatial localization of cells and intercellular interactions. Intercellular interactions are essential for maintaining proper tissue function and homeostasis, and dysregulation of these interactions can contribute to various diseases, including cancer and autoimmune disorders. Ligand proteins, one of the signaling molecules responsible for cell-cell interactions, bind to cell surface receptors and trigger intracellular signaling pathways. The effective range of ligand signaling, i.e. the extent to which a cell spatially and directly transmits information via the ligand, has not been systematically studied, although it may determine the occurrence of cell-cell interactions. The authors have previously developed methodologies combining spatial transcriptomics and single-cell RNA sequencing to analyze intercellular interactions at the single-cell level in spatial dimensions. Additionally, we have devised a technique to model the spatial diffusion of ligands from spatial transcriptomics and estimate the spatial range of ligand action. In this study, we used these developed methods to elucidate events in the tumor microenvironment of brain metastatic lung cancer tissue. Specifically, an integrated analysis of metastatic brain tumor samples was performed using visium and single-cell data from the same patient. The results suggest the presence of a heterogeneous microglial subpopulation, including clusters enriched in cytokines and chemokines for angiogenesis and wound healing, clusters associated with immune tolerance via IL-10 signaling, clusters enriched in gene sets related to phagocytosis, antigen processing, and antigen presentation, and clusters related to clearance of pathogens and residues. The presence of homogeneous microglial subpopulations was suggested. Furthermore, these subpopulations exhibited different spatial distributions, suggesting that they interact with surrounding cancer cells through different mechanisms. Citation Format: Haruka Hirose, Takahiro Tsuji, Yasuhiro Kojima, Akihiko Yoshizawa, Yoshiki Arakawa, Hiroaki Ozasa, Hiroaki Wake, Teppei Shimamura. Integrated analysis of scRNAseq and spatial omics in brain metastatic lung cancer tumor tissue [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 7362.

Subepithelial Stromal Cells: Their Roles and Interactions with Intestinal Epithelial Cells during Gut Mucosal Homeostasis and Regeneration.

Intestinal epithelial cell activities during homeostasis and regeneration are well described, but their potential interactions with stromal cells remain unresolved. Exploring the functions of these heterogeneous intestinal mesenchymal stromal cells (iMSCs) remains challenging. This difficulty is due to the lack of specific markers for most functionally homogenous subpopulations. In recent years, however, novel clustering techniques such as single-cell RNA sequencing (scRNA-seq), fluorescence-activated cell sorting (FACS), confocal microscope, and computational remodeling of intestinal anatomy have helped identify and characterize some specific iMSC subsets. These methods help researchers learn more about the localization and functions of iMSC populations during intestinal morphogenic and homeostatic conditions. Consequently, it is imperative to understand the cellular pathways that regulate their activation and how they interact with surrounding cellular components, particularly during intestinal epithelial regeneration after mucosal injury. This review provides insights into the spatial distribution and functions of identified iMSC subtypes. It focuses on their involvement in intestinal morphogenesis, homeostasis, and regeneration. We reviewed related signaling mechanisms implicated during epithelial and subepithelial stromal cell crosstalk. Future research should focus on elucidating the molecular intermediates of these regulatory pathways to open a new frontier for potential therapeutic targets that can alleviate intestinal mucosa-related injuries.

Behavioral selection in structured populations

The multilevel model of behavioral selection (MLBS) by Borgstede and Eggert (Behav Process 186:104370. 10.1016/j.beproc.2021.104370, 2021) provides a formal framework that integrates reinforcement learning with natural selection using an extended Price equation. However, the MLBS is so far only formulated for homogeneous populations, thereby excluding all sources of variation between individuals. This limitation is of primary theoretical concern because any application of the MLBS to real data requires to account for variation between individuals. In this paper, I extend the MLBS to account for inter-individual variation by dividing the population into homogeneous sub-populations and including class-specific reproductive values as weighting factors for an individual’s evolutionary fitness. The resulting formalism closes the gap between the theoretical underpinnings of behavioral selection and the application of the theory to empirical data, which naturally includes inter-individual variation. Furthermore, the extended MLBS is used to establish an explicit connection between the dynamics of learning and the maximization of individual fitness. These results expand the scope of the MLBS as a general theoretical framework for the quantitative analysis of learning and evolution.

A latent class linear mixed model for monotonic continuous processes measured with error.

Motivated by measurement errors in radiographic diagnosis of osteoarthritis, we propose a Bayesian approach to identify latent classes in a model with continuous response subject to a monotonic, that is, non-decreasing or non-increasing, process with measurement error. A latent class linear mixed model has been introduced to consider measurement error while the monotonic process is accounted for via truncated normal distributions. The main purpose is to classify the response trajectories through the latent classes to better describe the disease progression within homogeneous subpopulations.

Sequential estimation for mixture of regression models for heterogeneous population

Heterogeneity among patients commonly exists in clinical studies and leads to challenges in medical research. It is widely accepted that there exist various sub-types in the population and they are distinct from each other. The approach of identifying the sub-types and thus tailoring disease prevention and treatment is known as precision medicine. The mixture model is a classical statistical model to cluster the heterogeneous population into homogeneous sub-populations. However, for the highly heterogeneous population with multiple components, its parameter estimation and clustering results may be ambiguous due to the dependence of the EM algorithm on the initial values. For sub-typing purposes, the finite mixture of regression models with concomitant variables is considered and a novel statistical method is proposed to identify the main components with large proportions in the mixture sequentially. Compared to existing typical statistical inferences, the new method not only requires no pre-specification on the number of components for model fitting, but also provides more reliable parameter estimation and clustering results. Simulation studies demonstrated the superiority of the proposed method. Real data analysis on the drug response prediction illustrated its reliability in the parameter estimation and capability to identify the important subgroup.

Analysis of immunoglobulin/T-cell receptor repertoires by high-throughput RNA sequencing reveals a continuous dynamic of positive clonal selection in follicular lymphoma.

Follicular lymphoma (FL) course is highly variable, making its clinical management challenging. In this incurable and recurring pathology, the interval between relapses tends to decrease while aggressiveness increases, sometimes resulting in the transformation to higher-grade lymphoma. These evolutions are particularly difficult to anticipate, resulting from complex clonal evolutions where multiple subclones compete and thrive due to their capacity to proliferate and resist therapies. Here, to apprehend further these processes, we used a high-throughput RNA sequencing approach to address simultaneously the B-cell immunoglobulin repertoires and T-cell immunoglobulin repertoires repertoires of lymphoma cells and their lymphoid microenvironment in a large cohort of 131 FL1/2-3A patients. Our data confirm the existence of a high degree of intra-clonal heterogeneity in this pathology, resulting from ongoing somatic hyper-mutation and class switch recombination. Through the evaluation of the Simpson ecological-diversity index, we show that the contribution of the cancerous cells increases during the course of the disease to the detriment of the reactive compartment, a phenomenon accompanied by a concomitant decrease in the diversity of the tumoral population. Clonal evolution in FL thus contrasts with many tumors, where clonal heterogeneity steadily increases over time and participates in treatment evasion. In this pathology, the selection of lymphoma subclones with proliferative advantages progressively outweighs clonal diversification, ultimately leading in extreme cases to transformation to high-grade lymphoma resulting from the rapid emergence of homogeneous subpopulations.

Isolation of Homogeneous Sub-populations of Spermatocytes from Mouse Testis.

Mammalian meiosis is a highly specialized cell division process, resulting in the production of genetically unique haploid cells. However, the molecular mechanisms governing meiosis remain largely unknown, primarily due to the difficulty in isolating pure sub-populations of spermatocytes. Definitive molecular, biochemical, and functional investigations of the meiosis process require the isolation of these individual homogeneous sub-populations of spermatocytes. Here, we present an approach that enables the purification of homogeneous spermatocytes from mouse testis at desired sub-stages. This approach consists of two strategic steps. The first is to synchronize spermatogenesis, aiming to minimize the diversity and complexity of testicular germ cells. The second involves utilizing mouse models with germ cell-specific fluorescent markers to differentiate the desired subtype from other cells in the testis. By employing fluorescence-activated cell sorting (FACS), this approach yields highly pure populations of spermatocytes at each sub-stage. When combined with other massively parallel sequencing techniques and in vitro cell culture methods, this approach will enhance our comprehension of the molecular mechanisms underlying mammalian meiosis and promote in vitro gametogenesis.

Heuristic algorithm for univariate stratification problem

In sampling theory, stratification corresponds to a technique used in surveys, which allows segmenting a population into homogeneous subpopulations (strata) to produce statistics with a higher level of precision. In particular, this article proposes a heuristic to solve the univariate stratification problem – widely studied in the literature. One of its versions sets the number of strata and the precision level and seeks to determine the limits that define such strata to minimize the sample size allocated to the strata. A heuristic-based on a stochastic optimization method and an exact optimization method was developed to achieve this goal. The performance of this heuristic was evaluated through computational experiments, considering its application in various populations used in other works in the literature, based on 20 scenarios that combine different numbers of strata and levels of precision. From the analysis of the obtained results, it is possible to verify that the heuristic had a performance superior to four algorithms in the literature in more than 94% of the cases, particularly concerning the known algorithm of Lavallée–Hidiroglou.

Inflammation in the pathogenesis of depression: a disorder of neuroimmune origin.

There are several hypotheses concerning the underlying pathophysiological mechanisms of major depression, which centre largely around adaptive changes in neuronal transmission and plasticity, neurogenesis, and circuit and regional connectivity. The immune and endocrine systems are commonly implicated in driving these changes. An intricate interaction of stress hormones, innate immune cells and the actions of soluble mediators of immunity within the nervous system is described as being associated with the symptoms of depression. Bridging endocrine and immune processes to neurotransmission and signalling within key cortical and limbic brain circuits are critical to understanding depression as a disorder of neuroimmune origins. Emergent areas of research include a growing recognition of the adaptive immune system, advances in neuroimaging techniques and mechanistic insights gained from transgenic animals. Elucidation of glial-neuronal interactions is providing additional avenues into promising areas of research, the development of clinically relevant disease models and the discovery of novel therapies. This narrative review focuses on molecular and cellular mechanisms that are influenced by inflammation and stress. The aim of this review is to provide an overview of our current understanding of depression as a disorder of neuroimmune origin, focusing on neuroendocrine and neuroimmune dysregulation in depression pathophysiology. Advances in current understanding lie in pursuit of relevant biomarkers, as the potential of biomarker signatures to improve clinical outcomes is yet to be fully realised. Further investigations to expand biomarker panels including integration with neuroimaging, utilising individual symptoms to stratify patients into more homogenous subpopulations and targeting the immune system for new treatment approaches will help to address current unmet clinical need.

Immune mechanisms shape the clonal landscape during early progression of prostate cancer.

Understanding the role of the immune microenvironment in modulating intratumor heterogeneity is essential for effective cancer therapies. Using multicolor lineage tracing in genetically engineered mouse models and single-cell transcriptomics, we show that slowly progressing tumors contain a multiclonal landscape of relatively homogeneous subpopulations within a well-organized tumor microenvironment. In more advanced and aggressive tumors, however, the multiclonal landscape develops into competing dominant and minor clones accompanied by a disordered microenvironment. We demonstrate that this dominant/minor landscape is associated with differential immunoediting, in which minor clones are marked by an increased expression of IFNγ-response genes and the Tcell-activating chemokines Cxcl9 and Cxcl11. Furthermore, immunomodulation of the IFNγ pathway can rescue minor clones from elimination. Notably, the immune-specific gene signature of minor clones exhibits a prognostic value for biochemical recurrence-free survival in human prostate cancer. These findings suggest new immunotherapy approaches for modulating clonal fitness and tumor progression in prostate cancer.

RWD102 Cluster Algorithm for Credible Subgroup Identification in Patients with Erectile Dysfunction Receiving Tadalafil: A Real-World Data Study

This non-comparative, retrospective study aimed to characterize the profiles of erectile disfunction (ED) patients prescribed with tadalafil by clustering the homogenous subpopulations with different ED presentations and risks.

A Methodology for Air Temperature Extrema Characterization Pertinent to Improving the Accuracy of Climatological Analyses

The suggested methodology for the characterization of temperature extrema presents a multistep preprocessing procedure intended to derive extrema time series of correctly identified and thermally defined daily air temperature extrema pairs. The underlying conceptual framework for this approach was developed in response to the existing gaps in the current state of daily extrema identification and the development of extrema-based synthetic air temperature time series. A code consisting of a series of algorithms was developed to establish four-parameter criteria for a more accurate representation of daily variability that allows easy replication of temperature distribution based on the correct characterization of daily temperature patterns. The first preprocessing step consists of subjecting the high-frequency temperature time series to a theoretical diurnal observing window that imposes latitudinally and seasonally crafted limits for the individual identification of daily minima and maxima. The following pre-processing step involves the supplementation of air temperature extrema with the information on the occurrence of extrema timing deemed as vital information for the reconstruction of the temperature time series. The subsequent step involves the application of an innovative temperature pattern recognition algorithm that identifies physically homogeneous air temperature populations based on the information obtained in previous steps. The last step involves the use of a metric for the assessment of extrema temperature and timing parameters’ susceptibility to climate change. The application of the presented procedure to high-frequency temperature data yields two strains of physically homogeneous extrema time series with the preserved characteristics of the overall temperature variability. In the present form, individual elements of this methodology are applicable for correcting historical sampling and air temperature averaging biases, improving the reproducibility of daily air temperature variation, and enhancing the performance of temperature index formulae based on daily temperature extrema. The objective of this analysis is the eventual implementation of the presented methodology into the practice of systematic temperature extrema identification and preprocessing of temperature time series for the configuration of physically homogeneous air temperature subpopulations.

Motor and psychiatric features in idiopathic blepharospasm: A data-driven cluster analysis

IntroductionIdiopathic blepharospasm is a clinically heterogeneous dystonia also characterized by non motor symptoms. MethodsWe used a k-means cluster analysis to assess 188 patients with idiopathic blepharospasm in order to identify relatively homogeneous subpopulations of patients, using a set of motor and psychiatric variables to generate the cluster solution. ResultsBlepharospasm patients reached higher scores on scales assessing depressive- and anxiety-related disorders than healthy/disease controls. Cluster analysis suggested the existence of three groups of patients that differed by type of spasms, overall motor severity, and presence/severity of psychiatric problems. The greater severity of motor symptoms was observed in Group 1, the least severity in Group 3, while the severity of blepharospasm in Group 2 was between that observed in Groups 1 and 3. The three motor subtypes also differed by psychiatric features: the lowest severity of psychiatric symptoms was observed in the group with least severe motor symptoms (group 3), while the highest psychiatric severity scores were observed in group 2 that carried intermediate motor severity rather than in the group with more severe motor symptoms (group 1). The three groups did not differ by disease duration, age of onset, sex or other clinical features. ConclusionsThe present study suggests that blepharospasm patients may be classified in different subtypes according to the type of spasms, overall motor severity and presence/severity of depressive symptoms and anxiety.

Niewydolność serca z zachowaną frakcją wyrzutową lewej komory — rozpoznawanie i leczenie

Heart faillure with preserved ejection fraction (HFpEF) is now the most common form of HF and accounts for about half the cases of the disease due to population ageing and the increasing prevalence of obesity, diabetes and high blood pressure. HFpEF is considered to be a systemic syndrome with diverse fanotypes, various pathophysiologies, and multiple comorbidities. The diagnosis of HFpEF is challenging and ultimately refers to the concept of heart failure as a clinical syndrome characterised by symptoms associated with a reduced ability of the heart to adequately pump blood at normal filling pressures during diastolic conditions. Previous clinical studies have been largely ineffective in determining effective HFpEF treatments, but there is evidence of the use of diuretics, mineral corticosteroid antagonists and lifestyle interventions. Based on the results of EMPEROR-PRESERVED study sodium-glucose cotransporter 2 inhibitors are recommended in HFpEF The pathophysiological diversity of HFpEF imaging is considerable and studies are currently being conducted to evaluate the optimal methods for classifying patients into phenotypically homogeneous subpopulations to allow better individualisation of treatment.