Focused analysis of RNFL decay in glaucomatous eyes using circular statistics on high-resolution OCT data.

We generated Optical Coherence Tomography (OCT) data of much higher resolution than usual on retinal nerve fiber layer (RNFL) thickness of a given eye. These consist of measurements made at hundreds of angular-points defined on a circular coordinate system. Traditional analysis of OCT RNFL data does not utilize insightful characteristics such as its circularity and granularity for common downstream applications. To address this, we present a new circular statistical framework that defines an Angular Decay function and thereby provides a directionally precise representation of an eye with attention to patterns of focused RNFL loss. By applying to a clinical cohort of Asian Indian eyes, the generated circular data were modeled with a finite mixture of von Mises distributions, which led to an unsupervised identification in different age-groups of recurrent clusters of glaucomatous eyes with distinct directional signatures of RNFL decay. New indices of global and local RNFL loss were computed for comparing the structural differences between these glaucoma clusters across the age-groups and improving classification. Further, we built a catalog of directionally precise statistical distributions of RNFL thickness for the said population of normal eyes as stratified by their age and optic disc size.

Geographically weighted linear combination test for gene-set analysis of a continuous spatial phenotype as applied to intratumor heterogeneity.

Background: The impact of gene-sets on a spatial phenotype is not necessarily uniform across different locations of cancer tissue. This study introduces a computational platform, GWLCT, for combining gene set analysis with spatial data modeling to provide a new statistical test for location-specific association of phenotypes and molecular pathways in spatial single-cell RNA-seq data collected from an input tumor sample. Methods: The main advantage of GWLCT consists of an analysis beyond global significance, allowing the association between the gene-set and the phenotype to vary across the tumor space. At each location, the most significant linear combination is found using a geographically weighted shrunken covariance matrix and kernel function. Whether a fixed or adaptive bandwidth is determined based on a cross-validation cross procedure. Our proposed method is compared to the global version of linear combination test (LCT), bulk and random-forest based gene-set enrichment analyses using data created by the Visium Spatial Gene Expression technique on an invasive breast cancer tissue sample, as well as 144 different simulation scenarios. Results: In an illustrative example, the new geographically weighted linear combination test, GWLCT, identifies the cancer hallmark gene-sets that are significantly associated at each location with the five spatially continuous phenotypic contexts in the tumors defined by different well-known markers of cancer-associated fibroblasts. Scan statistics revealed clustering in the number of significant gene-sets. A spatial heatmap of combined significance over all selected gene-sets is also produced. Extensive simulation studies demonstrate that our proposed approach outperforms other methods in the considered scenarios, especially when the spatial association increases. Conclusion: Our proposed approach considers the spatial covariance of gene expression to detect the most significant gene-sets affecting a continuous phenotype. It reveals spatially detailed information in tissue space and can thus play a key role in understanding the contextual heterogeneity of cancer cells.

Food insecurity in the Eastern Indo-Gangetic plain: Taking a closer look.

OBJECTIVE: Food security is an important policy issue in India. As India recently ranked 107th out of 121 countries in the 2022 Global Hunger Index, there is an urgent need to dissect, and gain insights into, such a major decline at the national level. However, the existing surveys, due to small sample sizes, cannot be used directly to produce reliable estimates at local administrative levels such as districts. DESIGN: The latest round of available data from the Household Consumer Expenditure Survey (HCES 2011-12) done by the National Sample Survey Office of India used stratified multi-stage random sampling with districts as strata, villages as first stage and households as second stage units. SETTING: Our Small Area Estimation approach estimated food insecurity prevalence, gap, and severity of each rural district of the Eastern Indo-Gangetic Plain (EIGP) region by modeling the HCES data, guided by local covariates from the 2011 Indian Population Census. PARTICIPANTS: In HCES, 5915 (34429), 3310 (17534) and 3566 (15223) households (persons) were surveyed from the 71, 38 and 18 districts of the EIGP states of Uttar Pradesh, Bihar and West Bengal respectively. RESULTS: We estimated the district-specific food insecurity indicators, and mapped their local disparities over the EIGP region. By comparing food insecurity with indicators of climate vulnerability, poverty and crop diversity, we shortlisted the vulnerable districts in EIGP. CONCLUSIONS: Our district-level estimates and maps can be effective for informed policy-making to build local resiliency and address systemic vulnerabilities where they matter most in the post-pandemic era. ADVANCES: Our study computed, for the Indian states in the EIGP region, the first area-level small area estimates of food insecurity as well as poverty over the past decade, and generated a ranked list of districts upon combining these data with measures of crop diversity and climatic vulnerability.

Re-scaling and small area estimation of behavioral risk survey guided by social vulnerability data.

BACKGROUND: Local governments and other public health entities often need population health measures at the county or subcounty level for activities such as resource allocation and targeting public health interventions, among others. Information collected via national surveys alone cannot fill these needs. We propose a novel, two-step method for rescaling health survey data and creating small area estimates (SAEs) of smoking rates using a Behavioral Risk Factor Surveillance System survey administered in 2015 to participants living in Allegheny County, Pennsylvania, USA. METHODS: The first step consisted of a spatial microsimulation to rescale location of survey respondents from zip codes to tracts based on census population distributions by age, sex, race, and education. The rescaling allowed us, in the second step, to utilize available census tract-specific ancillary data on social vulnerability for small area estimation of local health risk using an area-level version of a logistic linear mixed model. To demonstrate this new two-step algorithm, we estimated the ever-smoking rate for the census tracts of Allegheny County. RESULTS: The ever-smoking rate was above 70% for two census tracts to the southeast of the city of Pittsburgh. Several tracts in the southern and eastern sections of Pittsburgh also had relatively high (> 65%) ever-smoking rates. CONCLUSIONS: These SAEs may be used in local public health efforts to target interventions and educational resources aimed at reducing cigarette smoking. Further, our new two-step methodology may be extended to small area estimation for other locations and health outcomes.

Geostatistical Modeling and Heterogeneity Analysis of Tumor Molecular Landscape.

Intratumor heterogeneity (ITH) is associated with therapeutic resistance and poor prognosis in cancer patients, and attributed to genetic, epigenetic, and microenvironmental factors. We developed a new computational platform, GATHER, for geostatistical modeling of single cell RNA-seq data to synthesize high-resolution and continuous gene expression landscapes of a given tumor sample. Such landscapes allow GATHER to map the enriched regions of pathways of interest in the tumor space and identify genes that have spatial differential expressions at locations representing specific phenotypic contexts using measures based on optimal transport. GATHER provides new applications of spatial entropy measures for quantification and objective characterization of ITH. It includes new tools for insightful visualization of spatial transcriptomic phenomena. We illustrate the capabilities of GATHER using real data from breast cancer tumor to study hallmarks of cancer in the phenotypic contexts defined by cancer associated fibroblasts.

Systematic mining of patterns of polysubstance use in a nationwide population survey.

OBJECTIVES: To identify patterns of association and transition in polysubstance use based on National Survey of Drug Use and Health (NSDUH) in the United States. METHODS: We developed a new computational platform for PolySubstance Use data Mining for Associations and Transitions (PSUMAnT). It is based on the computation of weighted support, a measure of popularity, for the use of every combination of one or more substances, termed as a drugset, over a period of 5 decades (1965-2014) based on NSDUH data. It uses an efficient bitstring representation with exact and approximate string matching capabilities to search for patterns of association between drugsets and demographics of user groups at different time-intervals. Moreover, it introduces a quantitative definition of a rule of transition between pairs of substances used within a given time-interval, and provides a function for mining them. RESULTS: We identified the frequent drugsets from individual substance use database, and determined their representation among different demographic groups at different intervals. An interesting pattern of use of pain relievers and tranquilizers was detected for the age-group of 26-34 years. In addition, transition rules for heroin use in the last decade (2004-2015) of the given data were mined. CONCLUSIONS: Computation of weighted supports over time for every possible combination of substances in the survey, and their association with specific user groups, allows PSUMAnT to generate and test novel, interesting hypotheses in polysubstance use. PSUMAnT can be used for mining combinations of substances used among diverse demographic groups including those that have received less attention in this problem.

Circular functional analysis of OCT data for precise identification of structural phenotypes in the eye.

Progressive optic neuropathies such as glaucoma are major causes of blindness globally. Multiple sources of subjectivity and analytical challenges are often encountered by clinicians in the process of early diagnosis and clinical management of these diseases. In glaucoma, the structural damage is often characterized by neuroretinal rim (NRR) thinning of the optic nerve head, and other clinical parameters. Baseline structural heterogeneity in the eyes can play a key role in the progression of optic neuropathies, and present challenges to clinical decision-making. We generated a dataset of Optical Coherence Tomography (OCT) based high-resolution circular measurements on NRR phenotypes, along with other clinical covariates, of 3973 healthy eyes as part of an established clinical cohort of Asian Indian participants. We introduced CIFU, a new computational pipeline for CIrcular FUnctional data modeling and analysis. We demonstrated CIFU by unsupervised circular functional clustering of the OCT NRR data, followed by meta-clustering to characterize the clusters using clinical covariates, and presented a circular visualization of the results. Upon stratification by age, we identified a healthy NRR phenotype cluster in the age group 40-49 years with predictive potential for glaucoma. Our dataset also addresses the disparity of representation of this particular population in normative OCT databases.

Multivariate Tail Probabilities: Predicting Regional Pertussis Cases in Washington State.

In disease modeling, a key statistical problem is the estimation of lower and upper tail probabilities of health events from given data sets of small size and limited range. Assuming such constraints, we describe a computational framework for the systematic fusion of observations from multiple sources to compute tail probabilities that could not be obtained otherwise due to a lack of lower or upper tail data. The estimation of multivariate lower and upper tail probabilities from a given small reference data set that lacks complete information about such tail data is addressed in terms of pertussis case count data. Fusion of data from multiple sources in conjunction with the density ratio model is used to give probability estimates that are non-obtainable from the empirical distribution. Based on a density ratio model with variable tilts, we first present a univariate fit and, subsequently, improve it with a multivariate extension. In the multivariate analysis, we selected the best model in terms of the Akaike Information Criterion (AIC). Regional prediction, in Washington state, of the number of pertussis cases is approached by providing joint probabilities using fused data from several relatively small samples following the selected density ratio model. The model is validated by a graphical goodness-of-fit plot comparing the estimated reference distribution obtained from the fused data with that of the empirical distribution obtained from the reference sample only.

Differential Patterns of Social Media Use Associated with Loneliness and Health Outcomes in Selected Socioeconomic Groups.

Loneliness has emerged as a chronic and persistent problem for a considerable fraction of the general population in the developed world. Concurrently, use of online social media by the same societies has steadily increased over the past two decades. The present study analyzed a recent large country-wide loneliness survey of 20,096 adults in the US using an unsupervised approach for systematic identification of clusters of respondents in terms of their social media use and representation among different socioeconomic subgroups. We studied the underlying population heterogeneity with a computational pipeline that was developed to gain insights into cluster- or group-specific patterns of loneliness. In particular, distributions of high loneliness were observed in groups of female users of Facebook and YouTube of certain age, race, marital, and socioeconomic status. For instance, among the group of predominantly YouTube users, we noted that non-Hispanic white female respondents of age 25-44 years who have high school or less education level and are single or never married have more significant high loneliness distribution. In fact, their high loneliness scores also seem to be associated with self-reported poorer physical and mental health outcomes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s41347-021-00208-4.

Clustering Patterns Connecting COVID-19 Dynamics and Human Mobility Using Optimal Transport.

Social distancing and stay-at-home are among the few measures that are known to be effective in checking the spread of a pandemic such as COVID-19 in a given population. The patterns of dependency between such measures and their effects on disease incidence may vary dynamically and across different populations. We described a new computational framework to measure and compare the temporal relationships between human mobility and new cases of COVID-19 across more than 150 cities of the United States with relatively high incidence of the disease. We used a novel application of Optimal Transport for computing the distance between the normalized patterns induced by bivariate time series for each pair of cities. Thus, we identified 10 clusters of cities with similar temporal dependencies, and computed the Wasserstein barycenter to describe the overall dynamic pattern for each cluster. Finally, we used city-specific socioeconomic covariates to analyze the composition of each cluster.

Protocol for a cluster randomised trial evaluating a multifaceted intervention starting preconceptionally-Early Interventions to Support Trajectories for Healthy Life in India (EINSTEIN): a Healthy Life Trajectories Initiative (HeLTI) Study.

INTRODUCTION: The Healthy Life Trajectories Initiative is an international consortium comprising four harmonised but independently powered trials to evaluate whether an integrated intervention starting preconceptionally will reduce non-communicable disease risk in their children. This paper describes the protocol of the India study. METHODS AND ANALYSIS: The study set in rural Mysore will recruit ~6000 married women over the age of 18 years. The village-based cluster randomised design has three arms (preconception, pregnancy and control; 35 villages per arm). The longitudinal multifaceted intervention package will be delivered by community health workers and comprise: (1) measures to optimise nutrition; (2) a group parenting programme integrated with cognitive-behavioral therapy; (3) a lifestyle behaviour change intervention to support women to achieve a diverse diet, exclusive breast feeding for the first 6 months, timely introduction of diverse and nutritious infant weaning foods, and adopt appropriate hygiene measures; and (4) the reduction of environmental pollution focusing on indoor air pollution and toxin avoidance.The primary outcome is adiposity in children at age 5 years, measured by fat mass index. We will report on a host of intermediate and process outcomes. We will collect a range of biospecimens including blood, urine, stool and saliva from the mothers, as well as umbilical cord blood, placenta and specimens from the offspring.An intention-to-treat analysis will be adopted to assess the effect of interventions on outcomes. We will also undertake process and economic evaluations to determine scalability and public health translation. ETHICS AND DISSEMINATION: The study has been approved by the institutional ethics committee of the lead institute. Findings will be published in peer-reviewed journals. We will interact with policy makers at local, national and international agencies to enable translation. We will also share the findings with the participants and local community through community meetings, newsletters and local radio. TRIAL REGISTRATION NUMBER: ISRCTN20161479, CTRI/2020/12/030134; Pre-results.

Determination of critical community size from an HIV/AIDS model.

After an epidemic outbreak, the infection persists in a community long enough to engulf the entire susceptible population. Local extinction of the disease could be possible if the susceptible population gets depleted. In large communities, the tendency of eventual damp down of recurrent epidemics is balanced by random variability. But, in small communities, the infection would die out when the number of susceptible falls below a certain threshold. Critical community size (CCS) is considered to be the mentioned threshold, at which the infection is as likely as not to die out after a major epidemic for small communities unless reintroduced from outside. The determination of CCS could aid in devising systematic control strategies to eradicate the infectious disease from small communities. In this article, we have come up with a simplified computation based approach to deduce the CCS of HIV disease dynamics. We consider a deterministic HIV model proposed by Silva and Torres, and following Nåsell, introduce stochasticity in the model through time-varying population sizes of different compartments. Besides, Metcalf's group observed that the relative risk of extinction of some infections on islands is almost double that in the mainlands i.e. infections cease to exist at a significantly higher rate in islands compared to the mainlands. They attributed this phenomenon to the greater recolonization in the mainlands. Interestingly, the application of our method on demographic facts and figures of countries in the AIDS belt of Africa led us to expect that existing control measures and isolated locations would assist in temporary eradication of HIV infection much faster. For example, our method suggests that through systematic control strategies, after 7.36 years HIV epidemics will temporarily be eradicated from different communes of island nation Madagascar, where the population size falls below its CCS value, unless the disease is reintroduced from outside.

Estimation of Tail Probabilities by Repeated Augmented Reality.

Synthetic data, when properly used, can enhance patterns in real data and thus provide insights into different problems. Here, the estimation of tail probabilities of rare events from a moderately large number of observations is considered. The problem is approached by a large number of augmentations or fusions of the real data with computer-generated synthetic samples. The tail probability of interest is approximated by subsequences created by a novel iterative process. The estimates are found to be quite precise.

Single-Cell Transcriptional Profiling of Cells Derived From Regenerating Alveolar Ducts.

Lung regeneration occurs in a variety of adult mammals after surgical removal of one lung (pneumonectomy). Previous studies of murine post-pneumonectomy lung growth have identified regenerative "hotspots" in subpleural alveolar ducts; however, the cell-types participating in this process remain unclear. To identify the single cells participating in post-pneumonectomy lung growth, we used laser microdissection, enzymatic digestion and microfluidic isolation. Single-cell transcriptional analysis of the murine alveolar duct cells was performed using the C1 integrated fluidic circuit (Fluidigm) and a custom PCR panel designed for lung growth and repair genes. The multi-dimensional data set was analyzed using visualization software based on the tSNE algorithm. The analysis identified 6 cell clusters; 1 cell cluster was present only after pneumonectomy. This post-pneumonectomy cluster was significantly less transcriptionally active than 3 other clusters and may represent a transitional cell population. A provisional cluster identity for 4 of the 6 cell clusters was obtained by embedding bulk transcriptional data into the tSNE analysis. The transcriptional pattern of the 6 clusters was further analyzed for genes associated with lung repair, matrix production, and angiogenesis. The data demonstrated that multiple cell-types (clusters) transcribed genes linked to these basic functions. We conclude that the coordinated gene expression across multiple cell clusters is likely a response to a shared regenerative microenvironment within the subpleural alveolar ducts.

High-speed automatic characterization of rare events in flow cytometric data.

A new computational framework for FLow cytometric Analysis of Rare Events (FLARE) has been developed specifically for fast and automatic identification of rare cell populations in very large samples generated by platforms like multi-parametric flow cytometry. Using a hierarchical Bayesian model and information-sharing via parallel computation, FLARE rapidly explores the high-dimensional marker-space to detect highly rare populations that are consistent across multiple samples. Further it can focus within specified regions of interest in marker-space to detect subpopulations with desired precision.

Gene set analysis and reduction for a continuous phenotype: Identifying markers of birth weight variation based on embryonic stem cells and immunologic signatures.

BACKGROUND: Gene set analysis is a popular approach to examine the association between a predefined gene set and a phenotype. Few methods have been developed for a continuous phenotype. However, often not all the genes within a significant gene set contribute to its significance. There is no gene set reduction method developed for continuous phenotype. We developed a computationally efficient analytical tool, called linear combination test for gene set reduction (LCT-GSR) to identify core subsets of gene sets associated with a continuous phenotype. Identifying the core subset enhances our understanding of the biological mechanism and reduces costs of disease risk assessment, diagnosis and treatment. RESULTS: We evaluated the performance of our analytical tool by applying it to two real microarray studies. In the first application, we analyzed pathway expression measurements in newborns' blood to discover core genes contributing to the variation in birth weight. On average, we were able to reduce the number of genes in the 33 significant gene sets of embryonic stem cell signatures by 84.3% resulting in 229 unique genes. Using immunologic signatures, on average we reduced the number of genes in the 210 significant gene sets by 89% leading to 1603 unique genes. There were 180 unique core genes overlapping across the two databases. In the second application, we analyzed pathway expression measurements in a cohort of lethal prostate cancer patients from Swedish Watchful Waiting cohort to identify main genes associated with tumor volume. On average, we were able to reduce the number of genes in the 17 gene sets by 90% resulting in 47 unique genes. CONCLUSIONS: We conclude that LCT-GSR is a statistically sound analytical tool that can be used to extract core genes associated with a continuous phenotype. It can be applied to a wide range of studies in which dichotomizing the continuous phenotype is neither easy nor meaningful. Reduction to the most predictive genes is crucial in advancing our understanding of issues such as disease prevention, faster and more efficient diagnosis, intervention strategies and personalized medicine.

Association between bivariate expression of key oncogenes and metabolic phenotypes of patients with prostate cancer.

BACKGROUND: AKT and MYC are two of the most prevalent oncogenes associated with prostate cancer. The precise effects of overexpression of these two key oncogenes on the regulation of metabolic pathways in prostate cancer are under active investigation; however, few studies have investigated their bivariate oncogene-pair expressions in metabolic prostate cancer phenotypes. This is primarily due to the lack of a suitable statistical method to analyze the data in the presence of oncogene interactions and within-metabolite-set correlations. METHODS: We analyzed data on the expressions of phosphorylated AKT1 and MYC and the concentrations of 228 metabolites from 60 human prostate tumor samples and 16 normal tissue samples. The metabolomic data allowed us to study not only the measurement of individual metabolites, which can exhibit a dynamic range, but the enriched phenotypes in terms of "metabolite sets" that come from known metabolic pathways. We studied 71 metabolite sets defined by KEGG annotation. We used a modification of linear combination test (LCT) for multiple continuous outcomes to find associations between metabolite sets and oncogenic expressions, after accounting for the correlation between AKT1 and MYC expressions and the correlation between metabolites in a metabolite set. The LCT performance was evaluated using a simulation study. RESULTS: Through a comprehensive analytical method, our study linked oncogenomics and metabolomics data from patients to improve our understanding of the interconnected mechanisms underlying prostate cancer. This study showed that dysregulations of AKT1 and MYC significantly alter the metabolic pathways activated by nonglucose nutrient sources and their downstream targets. Our findings highlighted the role of MYC as the leading, but not the only, oncogene in prostate oncogenesis. In our simulation study, the LCT performed better than the known alternative method, gene-set enrichment analysis (GSEA). CONCLUSIONS: Our study offers a solution for linking genomics and metabolomics, working directly with multiple continuous and correlated measurements.

A brief review of single-cell transcriptomic technologies.

In recent years, there has been an effort to develop new technologies for measuring gene expression and sequence information from thousands of individual cells. Large data sets that were obtained using these 'single cell' technologies have allowed scientists to address fundamental questions in biomedicine ranging from stems cells and development to cancer and immunology. Here, we provide a brief review of recent developments in single-cell technology. Our intention is to provide a quick background for newcomers to the field as well as a deeper description of some of the leading technologies to date.

Ca2+/nuclear factor of activated T cells signaling is enriched in early-onset rectal tumors devoid of canonical Wnt activation.

Our previous extensive analysis revealed a significant proportion of early-onset colorectal tumors from India to be localized to the rectum in younger individuals and devoid of deregulated Wnt/ß-catenin signaling. In the current study, we performed a comprehensive genome-wide analysis of clinically well-annotated microsatellite stable early-onset sporadic rectal cancer (EOSRC) samples. Results revealed extensive DNA copy number alterations in rectal tumors in the absence of deregulated Wnt/ß-catenin signaling. More importantly, transcriptome profiling revealed a (non-Wnt/ß-catenin, non-MSI) genetic signature that could efficiently and specifically identify Wnt- rectal cancer. The genetic signature included a significant representation of genes belonging to Ca2+/NFAT signaling pathways that were validated in additional samples. The validated NFAT target genes exhibited significantly higher expression levels than canonical Wnt/ß-catenin targets in Wnt- samples, an observation confirmed in other CRC expression data sets as well. We confirmed the validated genes to be transcriptionally regulated by NFATc1 by (a) evaluating their respective transcript levels and (b) performing promoter-luciferase and chromatin immunoprecipitation assays following ectopic expression as well as knockdown of NFATc1 in CRC cells. NFATc1 and its targets RUNX2 and GSN could drive increased migration in CRC cells. Finally, the validated genes were associated with poor survival in the cancer genome atlas CRC expression data set. This study is the first comprehensive molecular characterization of EOSRC that appears to be driven by noncanonical tumorigenesis pathways. KEY MESSAGES: Early-onset sporadic rectal cancer exhibits DNA gain and loss without Wnt activation. Ca2+/NFAT signaling appears to be activated in the absence of Wnt activation. An eight-gene genetic signature distinguishes Wnt+ and Wnt- rectal tumors. NFAT and its target genes regulate tumorigenic properties in CRC cells.