Multivariate Setting Research Articles

Improving data interpretability with new differential sample variance gene set tests

BackgroundGene set analysis methods have played a major role in generating biological interpretations of omics data such as gene expression datasets. However, most methods focus on detecting homogenous pattern changes in mean expression while methods detecting pattern changes in variance remain poorly explored. While a few studies attempted to use gene-level variance analysis, such approach remains under-utilized. When comparing two phenotypes, gene sets with distinct changes in subgroups under one phenotype are overlooked by available methods although they reflect meaningful biological differences between two phenotypes. Multivariate sample-level variance analysis methods are needed to detect such pattern changes.ResultsWe used ranking schemes based on minimum spanning tree to generalize the Cramer-Von Mises and Anderson–Darling univariate statistics into multivariate gene set analysis methods to detect differential sample variance or mean. We characterized the detection power and Type I error rate of these methods in addition to two methods developed earlier using simulation results with different parameters. We applied the developed methods to microarray gene expression dataset of prednisolone-resistant and prednisolone-sensitive children diagnosed with B-lineage acute lymphoblastic leukemia and bulk RNA-sequencing gene expression dataset of benign hyperplastic polyps and potentially malignant sessile serrated adenoma/polyps. One or both of the two compared phenotypes in each of these datasets have distinct molecular subtypes that contribute to within phenotype variability and to heterogeneous differences between two compared phenotypes. Our results show that methods designed to detect differential sample variance provide meaningful biological interpretations by detecting specific hallmark gene sets associated with the two compared phenotypes as documented in available literature.ConclusionsThe results of this study demonstrate the usefulness of methods designed to detect differential sample variance in providing biological interpretations when biologically relevant but heterogeneous changes between two phenotypes are prevalent in specific signaling pathways. Software implementation of the methods is available with detailed documentation from Bioconductor package GSAR. The available methods are applicable to gene expression datasets in a normalized matrix form and could be used with other omics datasets in a normalized matrix form with available collection of feature sets.

Joint paranormality: bridging the gap between hyponormal and normaloid tuples

This paper introduces the concept of jointly paranormal tuples of operators and establishes key inequalities and properties of this class. This extends the notion of paranormal operators to a multivariable operator theory setting. Among other things, we show that if a tuple is jointly paranormal, then its powers are also jointly paranormal. Additionally, we provide two proofs that jointly hyponormal commuting tuples are jointly paranormal. Finally, we also prove that every jointly paranormal tuple of commuting operators is normaloid, consequently offering an alternative proof to the known inclusion between jointly hyponormal and normaloid tuples.

Quantile-Parameterized Distributions for Expert Knowledge Elicitation

This paper provides a comprehensive overview of quantile-parameterized distributions (QPDs) as a tool for capturing expert predictions and parametric judgments. We survey a range of methods for constructing distributions that are parameterized by a set of quantile-probability pairs and describe an approach to generalizing them to enhance their tail flexibility. Furthermore, we explore the extension of QPDs to the multivariate setting, surveying the approaches to construct bivariate distributions, which can be adopted to obtain distributions with quantile-parameterized margins. Through this review and synthesis of the previously proposed methods, we aim to enhance the understanding and utilization of QPDs in various domains. Funding: U. Sahlin was funded by the Crafoord Foundation [ref 20200626]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/deca.2024.0219 .

Effects of childhood and adult height on later life cardiovascular disease risk estimated through Mendelian randomization.

Taller individuals are at elevated and protected risk of various cardiovascular disease endpoints. Whether this is due to a direct consequence of their height during childhood, a long-term effect of remaining tall throughout the lifecourse, or confounding by other factors, is unknown. We sought to address this by harnessing human genetic data from the UK Biobank to separate the independent effects of childhood and adulthood height using an approach known as lifecourse Mendelian randomization (MR). Protective effects of taller childhood height on risk of later life coronary artery disease (OR = 0.78 per change in height category, 95% CI = 0.70 to 0.86, P = 4 × 10- 10) and stroke (OR = 0.93, 95% CI = 0.86 to 1.00, P = 0.03) using data from large-scale consortia were found using a univariable model, although evidence of these effects attenuated in a multivariable setting upon accounting for adulthood height. In contrast, direct effects of taller childhood height on increased risk of later life atrial fibrillation (OR = 1.61, 95% CI = 1.42 to 1.79, P = 5 × 10- 7) and thoracic aortic aneurysm (OR = 1.55, 95% CI = 1.16 to 1.95, P = 0.03) were found even after accounting for adulthood height. Evidence for both of these direct effects was replicated in the Million Veterans Program. The protective effect of childhood height on risk of coronary artery disease and stroke can be largely explained by taller children typically becoming taller individuals in later life. Conversely, the independent effect of childhood height on increased risk of atrial fibrillation and thoracic aortic aneurysm may point towards developmental mechanisms in early life which confer a lifelong risk on these disease outcomes.

Blood group O attributes to prolonged PFS, OS and five-year survival in IDH-wildtype glioblastoma patients with MGMT promoter methylation

Abstract Background Glioblastoma (GBM) remains incurable despite multimodal therapeutic approaches. Here, we assessed the relevance of ABO blood groups for progression-free (PFS), overall survival (OS), and long-term survival in a large cohort of IDH-wildtype (wt) GBM patients. Methods Consecutive GBM patients (2009-2020) at a large tertiary brain tumour centre were included, and clinical data were retrospectively abstracted. We dichotomised patients into those with blood group O and those with a Non-O blood group. Patient, tumour, and treatment characteristics were compared between these groups. Association with outcomes was assessed in univariable and multivariable settings via log-rank tests and Cox proportional hazards regressions, respectively. Results 554 GBM IDH-wt had available ABO information. There were no substantial differences in patient, tumour, or treatment characteristics between group O and group Non-O. In contrast, blood group O patients showed increased PFS, OS, and five-year survivals in both univariable and multivariable analyses. Differences were strongly pronounced in patients with MGMT promoter methylated tumours receiving standardised radiochemotherapy (OS blood group O 24.6 months (95%CI 17.8–31.4) versus Non-O 17.7 months (14.1–21.3), p=0.015 log-rank analysis, hazard ratio 0.70 (95%CI 0.53-0.94)), but not apparent in MGMT promoter unmethylated tumours and in patients without (standardised) adjuvant therapy. Conclusion Blood group O status in conjunction with MGMT promoter methylation (including weak methylation) is an independent favourable prognostic marker in GBM IDH-wt patients receiving standardised radiochemotherapy. This finding is unprecedented, suggesting a linkage between the downregulation of a DNA repair protein and the absence of a functional blood cell surface glycosyltransferase.

Increase in the percentage of attendance at outpatient consultations in the Health Area of Santiago de Compostela and Barbanza, after modifying the appointment management system

Increase in the percentage of attendance at outpatient consultations in the Health Area of Santiago de Compostela and Barbanza, after modifying the appointment management system

Prediction and decoding of metaverse coin dynamics: a granular quest using MODWT-Facebook’s prophet-TBATS and XAI methodology

The growing media buzz and industry focus on the emergence and rapid development of Metaverse technology have paved the way for the escalation of multifaceted research. Specific Metaverse coins have come into existence, but they have barely seen any traction among practitioners despite their tremendous potential. The current work endeavors to deeply analyze the temporal characteristics of 6 Metaverse coins through the lens of predictive analytics and explain the forecasting process. The dearth of research imposes serious challenges in building the forecasting model. We resort to a granular prediction setup incorporating the Maximal Overlap Discrete Wavelet Transformation (MODWT) technique to disentangle the original series into subseries. Facebook's Prophet and TBATS algorithms are utilized to individually draw predictions on granular components. Aggregating components-wise forecasted figures achieve the final forecast. Facebook's Prophet is deployed in a multivariate setting, applying a set of explanatory features covering macroeconomic, technical, and social media indicators. Rigorous performance checks justify the efficiency of the integrated forecasting framework. Additionally, to interpret the black box typed prediction framework, two explainable artificial intelligence (XAI) frameworks, SHAP and LIME, are used to gauge the nature of the influence of the predictor variables, which serve several practical insights.

Reading Between the Lines: Complete Blood Count-Derived Parameters as Prognostic Factors in Patients With Newly Diagnosed Acute Myeloid Leukemia.

Research proved the prognostic significance of the neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), and red blood cell distribution width (RDW) in some hematological malignancies. This study aimed to analyze the role of pretreatment NLR, LMR, PLR, RDW coefficient of variation (RDW-CV), and RDW standard deviation (RDW-SD) as prognostic markers for acute myeloid leukemia (AML). This retrospective cohort study included 204 patients newly diagnosed with AML in the Department of Hematooncology and Bone Marrow Transplantation of the Medical University of Lublin. In the univariate models, higher RDW-CV and lower LMR predicted a poorer response to induction chemotherapy (odds ratio [OR] = 1.21, 95% confidence interval [CI95] = [1.09-2.36], P < .001; OR = 0.95, CI95 = [0.89-0.99], P = .045, respectively). In the multivariate model, age of diagnosis, ECOG (Performance Status Assessment by Eastern Cooperative Oncology Group) score, cytogenetic risk, NLR, and RDW-CV affected the odds of no response to chemotherapy significantly. The risk of progression was influenced by NLR and RDW-CV in the univariate analysis (hazard ratio [HR] = 1.20, CI95 = [1.09-1.33], P < .001; HR = 1.10, CI95 = [1.04-1.17], P = .002, respectively). In the multivariate settings, cytogenetic risk, leukocyte count, and RDW-CV affected progression free survival (PFS) significantly. Based on univariate models, the risk of death, when overall survival (OS) was taken into account, was influenced by NLR, LMR, and RDW-CV (HR = 1.05, CI95 = [1.00-1.09], P = .034; HR = 0.94, CI95 = [0.89-0.98], P = .010; HR = 1.06, CI95 = [1.01-1.10], P = .014, respectively). Higher NLR, higher RDW, lower LMR, and possibly lower PLR are poor prognostic factors that may help in the risk stratification of patients with AML.

Root‐Zone Water‐Storage Capacity and Uncertainty: An Intrinsic Factor Affecting Agroecosystem Resilience to Drought

AbstractMapping ecosystem function indicators helps identify areas susceptible to drought, heat stress, and reduced agricultural production. This information can be used to prioritize areas for targeted interventions to tackle adverse climatic conditions and changes in land use. Root‐zone water‐storage capacity (SR) is a commonly used variable of agroecosystem functioning, representing the maximum value of water stored within the root zone and accessible to vegetation for its productive growth. Mapping SR over large spatial scales is only feasible through an oversimplification of real‐world conditions. Under such circumstances, we propose to resort to soil‐hydraulic‐energy indices, namely the integral mean water capacity (IMWC) and the integral energy (IE) and an effective root‐zone depth (zR). Accordingly, a more efficient and environmentally sensitive, albeit still simplistic, determination of the root‐zone water‐storage capacity is computed as SR,IMWC = zR × IMWC, and validated against soil moisture measurements carried out along a transect. Subsequently, the SR,IMWC indicator was mapped in Campania, a 13,700 km2 region in southern Italy. This study also addressed the issue of the propagation of epistemic uncertainty in input soil hydraulic parameters to the output response variable IMWC. This was accomplished using a Monte Carlo simulation technique that generated several equiprobable stochastic realizations from the multivariate set of data inputs. Finally, we assessed the potential utility of the integral capacity energy (ICE) composite indicator, computed as the ratio IMWC/IE in %, as a scoring parameter to identify Priority Intervention Areas (PIAs) where resilience to environmental challenges, including water scarcity, drought events, and post‐fire conditions, could be enhanced.

Spherical Heinz transform of operator tuples

In this paper, we introduce the concept of the spherical Heinz transform for operator tuples, thereby extending the notion of the generalized mean transform in one-dimensional case to a multivariable operator setting. We study some of its properties and prove several inequalities involving the joint numerical radius and the joint operator norm of the spherical Aluthge and Heinz transforms. Moreover, several generalizations and refinements of some fundamental inequalities are also obtained.

Comparing Cochlear Implant Outcomes in 100 Patients With Sporadic Vestibular Schwannoma Managed With Observation, Radiosurgery, or Microsurgery: A Multi-Institutional Review.

To compare cochlear implant (CI) speech perception outcomes in patients with sporadic vestibular schwannoma (VS) managed with observation, radiosurgery, or microsurgery. Retrospective review. Eleven tertiary academic medical centers. One hundred patients with sporadic VS who received an ipsilateral CI. Ipsilateral cochlear implantation. Pure-tone thresholds, monosyllabic speech perception testing scores, and rates of open-set speech acquisition. Of the 100 patients studied, 54 underwent microsurgery, 26 underwent radiosurgery, 19 continued observation, and 1 underwent multimodal therapy. Among all patients, the median post-implantation pure-tone average was 31 dB (interquartile range [IQR] 25-39 dB) and the median monosyllabic speech perception score was 30% (IQR 0-60%) at a median of 12 months (IQR 5-25 months) post-implantation. Patients who were managed with microsurgery (median speech perception score 11%, IQR 0-52%) exhibited poorer implant outcomes overall compared with those managed with observation (median speech perception score 52%, IQR 40-72%) or radiosurgery (median speech perception score 30%, IQR 16-60%). Open-set speech perception was achieved in 61% of patients managed with microsurgery, 100% with observation, and 80% with radiosurgery. In a multivariable setting, those managed with observation (p = 0.02) or who underwent radiosurgery (p = 0.04) were significantly more likely to achieve open-set speech perception compared with patients who underwent microsurgery. Cochlear implants offer benefit in selected patients with sporadic VS. Although achieved in over half of people after microsurgery, open-set speech perception is more reliably attained in patients who are treated with observation or radiosurgery compared with microsurgical resection. These data may inform patient counseling and VS tumor management in people who may benefit from implantation.

Nexus among Economic Uncertainty, Geopolitical Risk, Oil Price Volatility and Economic Complexity in Saudi Arabia: Fresh Insights from Wavelet Local Multiple Correlations

Enhancing future economic growth through higher economic complexity is vital for sustainable growth strategies. Even though most studies are concerned with the key factors governing the economic complexity process, there needs to be more studies about the impact of risk factors on economic sophistication evolvement, particularly for oil-rentier economies. The present paper intends to delve into the dynamic connectedness between some risk factors, such as economic, political, and geopolitical risks and oil price volatility and the progress of economic complexity. We utilized quarterly data (1995Q1-2021Q4) for Saudi Arabia as a heavy oil-rich economy. As the first study to delve into the short-long term connections at various time scales and frequencies within a multivariable setting, we contribute to the literature by offering a spotless picture of economic complexity risk factors. In doing so, we resort to a novel wavelet local multiple correlation method, which can explore the varying patterns of time periods in the interconnections between the variables. Our findings disclose that oil volatility, geopolitical risk, and global uncertainties are positively connected to economic complexity over the long run. In contrast, their short-term effect is weak and mostly insignificant. These results indicate the resiliency of the Saudi economy to external to oil volatility shocks and intensification of global and local uncertainties. These outcomes offer policymakers new insights and prominent policy recommendations when designing economic strategies to achieve higher economic sophistication and sustainable growth.

Optimization-aided construction of multivariate Chebyshev polynomials

Optimization-aided construction of multivariate Chebyshev polynomials

The Impact of Import, Savings, Consumtion and Inflation Rate on the Economic Growth of Denmark

This paper investigates the dynamic relationships between imports of goods and services (annual % growth), adjusted savings: consumption of fixed capital (current US$), final consumption expenditure (annual % growth), inflation GDP deflator (annual %) and economic growth in Denmark, using multivariate setting to examine time- series data. The secondary records from 1973 to 2022 that were collected from the World Bank comprise the data used in this analysis. The results of the study show that annual percentage increases in final consumption expenditure and imports of goods and services both considerably boost economic growth. However, the annual percentage of inflation as indicated by the GDP deflator and adjusted savings, which is expressed as the consumption of fixed capital in current US dollars, have a negative impact on economic growth.

Feature Bagging with Nested Rotations (FBNR) for anomaly detection in multivariate time series

Feature Bagging with Nested Rotations (FBNR) for anomaly detection in multivariate time series

A wee exploration of techniques for risk assessments of extreme events

Assessing the behaviour of extreme events in univariate and multivariate settings entails many challenges, from the need to capture different sources of non-stationarity to adequately extrapolate into the tail of the distribution and compute probabilities of extreme events associated with high-dimensional vectors. Motivated by these common issues, we use a combination of methods from extreme-value theory, dimensionality reduction, non-parametric statistics, copula theory, and bootstrap model averaging to provide estimates of risk measures associated with environmental extremes. The work is tailored to the four data challenges presented in the EVA (2023) Conference Data Challenge competition, and the methods introduced here represent the approach taken by the Wee Extremes group.

Improving data interpretability with new differential sample variance gene set tests.

Gene set analysis methods have played a major role in generating biological interpretations from omics data such as gene expression datasets. However, most methods focus on detecting homogenous pattern changes in mean expression and methods detecting pattern changes in variance remain poorly explored. While a few studies attempted to use gene-level variance analysis, such approach remains under-utilized. When comparing two phenotypes, gene sets with distinct changes in subgroups under one phenotype are overlooked by available methods although they reflect meaningful biological differences between two phenotypes. Multivariate sample-level variance analysis methods are needed to detect such pattern changes. We use ranking schemes based on minimum spanning tree to generalize the Cramer-Von Mises and Anderson-Darling univariate statistics into multivariate gene set analysis methods to detect differential sample variance or mean. We characterize these methods in addition to two methods developed earlier using simulation results with different parameters. We apply the developed methods to microarray gene expression dataset of prednisolone-resistant and prednisolone-sensitive children diagnosed with B-lineage acute lymphoblastic leukemia and bulk RNA-sequencing gene expression dataset of benign hyperplastic polyps and potentially malignant sessile serrated adenoma/polyps. One or both of the two compared phenotypes in each of these datasets have distinct molecular subtypes that contribute to heterogeneous differences. Our results show that methods designed to detect differential sample variance are able to detect specific hallmark signaling pathways associated with the two compared phenotypes as documented in available literature. The results in this study demonstrate the usefulness of methods designed to detect differential sample variance in providing biological interpretations when biologically relevant but heterogeneous changes between two phenotypes are prevalent in specific signaling pathways. Software implementation of the developed methods is available with detailed documentation from Bioconductor package GSAR. The available methods are applicable to gene expression datasets in a normalized matrix form and could be used with other omics datasets in a normalized matrix form with available collection of feature sets.

Modern extreme value statistics for Utopian extremes. EVA (2023) Conference Data Challenge: Team Yalla

Capturing the extremal behaviour of data often requires bespoke marginal and dependence models which are grounded in rigorous asymptotic theory, and hence provide reliable extrapolation into the upper tails of the data-generating distribution. We present a modern toolbox of four methodological frameworks, motivated by classical extreme value theory, that can be used to accurately estimate extreme exceedance probabilities or the corresponding level in either a univariate or multivariate setting. Our frameworks were used to facilitate the winning contribution of Team Yalla to the EVA (2023) Conference Data Challenge, which was organised for the 13th International Conference on Extreme Value Analysis. This competition comprised seven teams competing across four separate sub-challenges, with each requiring the modelling of data simulated from known, yet highly complex, statistical distributions, and extrapolation far beyond the range of the available samples in order to predict probabilities of extreme events. Data were constructed to be representative of real environmental data, sampled from the fantasy country of “Utopia”.

Clinical impact of sex differences and procedural setting in transcatheter aortic valve implantation

Clinical impact of sex differences and procedural setting in transcatheter aortic valve implantation

A dynamic duration approach to venture capital exit

A dynamic duration approach to venture capital exit