Systematic mapping of organism-scale gene-regulatory networks in aging using population asynchrony.

In aging, physiologic networks decline in function at rates that differ between individuals, producing a wide distribution of lifespan. Though 70% of human lifespan variance remains unexplained by heritable factors, little is known about the intrinsic sources of physiologic heterogeneity in aging. To understand how complex physiologic networks generate lifespan variation, new methods are needed. Here, we present Asynch-seq, an approach that uses gene-expression heterogeneity within isogenic populations to study the processes generating lifespan variation. By collecting thousands of single-individual transcriptomes, we capture the Caenorhabditis elegans "pan-transcriptome"-a highly resolved atlas of non-genetic variation. We use our atlas to guide a large-scale perturbation screen that identifies the decoupling of total mRNA content between germline and soma as the largest source of physiologic heterogeneity in aging, driven by pleiotropic genes whose knockdown dramatically reduces lifespan variance. Our work demonstrates how systematic mapping of physiologic heterogeneity can be applied to reduce inter-individual disparities in aging.

Time-resolved systems immunology reveals a late juncture linked to fatal COVID-19.

COVID-19 exhibits extensive patient-to-patient heterogeneity. To link immune response variation to disease severity and outcome over time, we longitudinally assessed circulating proteins as well as 188 surface protein markers, transcriptome, and T cell receptor sequence simultaneously in single peripheral immune cells from COVID-19 patients. Conditional-independence network analysis revealed primary correlates of disease severity, including gene expression signatures of apoptosis in plasmacytoid dendritic cells and attenuated inflammation but increased fatty acid metabolism in CD56dimCD16hi NK cells linked positively to circulating interleukin (IL)-15. CD8+ T cell activation was apparent without signs of exhaustion. Although cellular inflammation was depressed in severe patients early after hospitalization, it became elevated by days 17-23 post symptom onset, suggestive of a late wave of inflammatory responses. Furthermore, circulating protein trajectories at this time were divergent between and predictive of recovery versus fatal outcomes. Our findings stress the importance of timing in the analysis, clinical monitoring, and therapeutic intervention of COVID-19.

Reassessing pharmacogenomic cell sensitivity with multilevel statistical models.

Pharmacogenomic experiments allow for the systematic testing of drugs, at varying dosage concentrations, to study how genomic markers correlate with cell sensitivity to treatment. The first step in the analysis is to quantify the response of cell lines to variable dosage concentrations of the drugs being tested. The signal to noise in these measurements can be low due to biological and experimental variability. However, the increasing availability of pharmacogenomic studies provides replicated data sets that can be leveraged to gain power. To do this, we formulate a hierarchical mixture model to estimate the drug-specific mixture distributions for estimating cell sensitivity and for assessing drug effect type as either broad or targeted effect. We use this formulation to propose a unified approach that can yield posterior probability of a cell being susceptible to a drug conditional on being a targeted effect or relative effect sizes conditioned on the cell being broad. We demonstrate the usefulness of our approach via case studies. First, we assess pairwise agreements for cell lines/drugs within the intersection of two data sets and confirm the moderate pairwise agreement between many publicly available pharmacogenomic data sets. We then present an analysis that identifies sensitivity to the drug crizotinib for cells harboring EML4-ALK or NPM1-ALK gene fusions, as well as significantly down-regulated cell-matrix pathways associated with crizotinib sensitivity.

Predicting humoral responses to primary and booster SARS-CoV-2 mRNA vaccination in people living with HIV: a machine learning approach.

BACKGROUND: SARS-CoV-2 mRNA vaccines are highly immunogenic in people living with HIV (PLWH) on effective antiretroviral therapy (ART). However, whether viro-immunologic parameters or other factors affect immune responses to vaccination is debated. This study aimed to develop a machine learning-based model able to predict the humoral response to mRNA vaccines in PLWH and to assess the impact of demographic and clinical variables on antibody production over time. METHODS: Different machine learning algorithms have been compared in the setting of a longitudinal observational study involving 497 PLWH, after primary and booster SARS-CoV-2 mRNA vaccination. Both Generalized Linear Models and non-linear Models (Tree Regression and Random Forest) were trained and tested. RESULTS: Non-linear algorithms showed better ability to predict vaccine-elicited humoral responses. The best-performing Random Forest model identified a few variables as more influential, within 39 clinical, demographic, and immunological factors. In particular, previous SARS-CoV-2 infection, BMI, CD4 T-cell count and CD4/CD8 ratio were positively associated with the primary cycle immunogenicity, yet their predictive value diminished with the administration of booster doses. CONCLUSIONS: In the present work we have built a non-linear Random Forest model capable of accurately predicting humoral responses to SARS-CoV-2 mRNA vaccination, and identifying relevant factors that influence the vaccine response in PLWH. In clinical contexts, the application of this model provides promising opportunities for predicting individual vaccine responses, thus facilitating the development of vaccination strategies tailored for PLWH.

Estimating the Effect of Radical Prostatectomy: Combining Data From the SPCG4 and PIVOT Randomized Trials With Contemporary Cohorts.

PURPOSE: Two randomized trials (SPCG4 and PIVOT) have compared surgery to conservative management for localized prostate cancer. The applicability of these trials to contemporary practice remains uncertain. We aimed to develop an individualized prediction model for prostate cancer mortality comparing immediate surgery at a high-volume center to active surveillance. MATERIALS AND METHODS: We determined whether the relative risk of prostate cancer mortality with surgery vs observation varied by baseline risk. We then used various estimates of relative risk to estimate 15-year mortality with and without surgery using, as a predictor, risk of biochemical recurrence calculated from a model. RESULTS: We saw no evidence that relative risk varied by baseline risk, supporting the use of a constant relative risk. Compared with observation, surgery was associated with negligible benefit for patients with Grade Group (GG) 1 disease (0.2% mortality reduction at 15 years) and small benefit for patients with GG2 with lower PSA and stage (≤5% mortality reduction). Benefit was greater (6%-9%) for patients with GG3 or GG4 though still modest, but effect estimates varied widely depending on choice of hazard ratio for surgery (6%-36% absolute risk reduction). CONCLUSIONS: Surgery should be avoided for men with low-risk (GG1) prostate cancer and for many men with GG2 disease. Surgical benefits are greater in men with higher-risk disease. Integration of findings with a life expectancy model will allow patients to make informed treatment decisions given their oncologic risk, risk of death from other causes, and estimated effects of surgery.

Genome-wide quantification of copy-number aberration impact on gene expression in ovarian high-grade serous carcinoma.

Copy-number alterations (CNAs) are a hallmark of cancer and can regulate cancer cell states via altered gene expression values. Herein, we have developed a copy-number impact (CNI) analysis method that quantifies the degree to which a gene expression value is impacted by CNAs and leveraged this analysis at the pathway level. Our results show that a high CNA is not necessarily reflected at the gene expression level, and our method is capable of detecting genes and pathways whose activity is strongly influenced by CNAs. Furthermore, the CNI analysis enables unbiased categorization of CNA categories, such as deletions and amplifications. We identified six CNI-driven pathways associated with poor treatment response in ovarian high-grade serous carcinoma (HGSC), which we found to be the most CNA-driven cancer across 14 cancer types. The key driver in most of these pathways was amplified wild-type KRAS, which we validated functionally using CRISPR modulation. Our results suggest that wild-type KRAS amplification is a driver of chemotherapy resistance in HGSC and may serve as a potential treatment target.

Sex diversity in the 21st century: Concepts, frameworks, and approaches for the future of neuroendocrinology.

Sex is ubiquitous and variable throughout the animal kingdom. Historically, scientists have used reductionist methodologies that rely on a priori sex categorizations, in which two discrete sexes are inextricably linked with gamete type. However, this binarized operationalization does not adequately reflect the diversity of sex observed in nature. This is due, in part, to the fact that sex exists across many levels of biological analysis, including genetic, molecular, cellular, morphological, behavioral, and population levels. Furthermore, the biological mechanisms governing sex are embedded in complex networks that dynamically interact with other systems. To produce the most accurate and scientifically rigorous work examining sex in neuroendocrinology and to capture the full range of sex variability and diversity present in animal systems, we must critically assess the frameworks, experimental designs, and analytical methods used in our research. In this perspective piece, we first propose a new conceptual framework to guide the integrative study of sex. Then, we provide practical guidance on research approaches for studying sex-associated variables, including factors to consider in study design, selection of model organisms, experimental methodologies, and statistical analyses. We invite fellow scientists to conscientiously apply these modernized approaches to advance our biological understanding of sex and to encourage academically and socially responsible outcomes of our work. By expanding our conceptual frameworks and methodological approaches to the study of sex, we will gain insight into the unique ways that sex exists across levels of biological organization to produce the vast array of variability and diversity observed in nature.

Decomposing mechanisms of COVID-19 mortality in empirical datasets: A modeling study.

Our objective was to decompose mortality mechanisms during the coronavirus disease 2019 (COVID-19) pandemic to estimate direct, indirect, and associated deaths from COVID-19. Given the confirmatory diagnosis of COVID-19, a death event that was not necessarily caused by respiratory complications but stemmed from other complications was categorized as an indirect death from COVID-19. Associated deaths occurred in patients who did not have COVID-19 but died during the surge in COVID-19 cases when overwhelming pressure was exerted on the healthcare system. Analyzing the sixth wave (i.e., the first epidemic wave of the Omicron B.1.1.529 variant from January to May 2022), decomposition was achieved using the binomial and Poisson sampling process models fitted to two pieces of data (i.e., COVID-19 death certificate and excess data by major cause of death). The total numbers of direct, indirect, and associated deaths during the sixth wave in Osaka were estimated at 1,071; 948; and 2,157; respectively. The number of associated deaths was greater than the sum of direct and indirect deaths. We further observed that the composition of indirect and associated deaths differed by major cause of death, and deaths caused by circulatory disease included a greater proportion of indirect deaths compared with deaths by other causes. The goals of healthcare services for endemic COVID-19 include the sustainable provision of services to avoid preventable deaths. Therefore, gaining an in-depth understanding of mechanisms that lead to excess death is vital for improving future pandemic response efforts.

Is the time right for a new initiative in mathematical modeling of the lower urinary tract? ICI-RS 2023.

INTRODUCTION: A session at the 2023 International Consultation on Incontinence - Research Society (ICI-RS) held in Bristol, UK, focused on the question: Is the time right for a new initiative in mathematical modeling of the lower urinary tract (LUT)? The LUT is a complex system, comprising various synergetic components (i.e., bladder, urethra, neural control), each with its own dynamic functioning and high interindividual variability. This has led to a variety of different types of models for different purposes, each with advantages and disadvantages. METHODS: When addressing the LUT, the modeling approach should be selected and sized according to the specific purpose, the targeted level of detail, and the available computational resources. Four areas were selected as examples to discuss: utility of nomograms in clinical use, value of fluid mechanical modeling, applications of models to simplify urodynamics, and utility of statistical models. RESULTS: A brief literature review is provided along with discussion of the merits of different types of models for different applications. Remaining research questions are provided. CONCLUSIONS: Inadequacies in current (outdated) models of the LUT as well as recent advances in computing power (e.g., quantum computing) and methods (e.g., artificial intelligence/machine learning), would dictate that the answer is an emphatic "Yes, the time is right for a new initiative in mathematical modeling of the LUT."

Expectation violations produce error signals in mouse V1.

Repeated exposure to visual sequences changes the form of evoked activity in the primary visual cortex (V1). Predictive coding theory provides a potential explanation for this, namely that plasticity shapes cortical circuits to encode spatiotemporal predictions and that subsequent responses are modulated by the degree to which actual inputs match these expectations. Here we use a recently developed statistical modeling technique called Model-Based Targeted Dimensionality Reduction (MbTDR) to study visually evoked dynamics in mouse V1 in the context of an experimental paradigm called "sequence learning." We report that evoked spiking activity changed significantly with training, in a manner generally consistent with the predictive coding framework. Neural responses to expected stimuli were suppressed in a late window (100-150 ms) after stimulus onset following training, whereas responses to novel stimuli were not. Substituting a novel stimulus for a familiar one led to increases in firing that persisted for at least 300 ms. Omitting predictable stimuli in trained animals also led to increased firing at the expected time of stimulus onset. Finally, we show that spiking data can be used to accurately decode time within the sequence. Our findings are consistent with the idea that plasticity in early visual circuits is involved in coding spatiotemporal information.

Replacing concurrent controls with virtual control groups in rat toxicity studies.

Virtual control groups (VCGs) in nonclinical toxicity represent the concept of using appropriate historical control data for replacing concurrent control group animals. Historical control data collected from standardized studies can serve as base for constructing VCGs and legacy study reports can be used as a benchmark to evaluate the VCG performance. Replacing concurrent controls of legacy studies with VCGs should ideally reproduce the results of these studies. Based on three four-week rat oral toxicity legacy studies with varying degrees of toxicity findings we developed a concept to evaluate VCG performance on different levels: the ability of VCGs to (i) reproduce statistically significant deviations from the concurrent control, (ii) reproduce test substance-related effects, and (iii) reproduce the conclusion of the toxicity study in terms of threshold dose, target organs, toxicological biomarkers (clinical pathology) and reversibility. Although VCGs have shown a low to moderate ability to reproduce statistical results, the general study conclusions remained unchanged. Our results provide a first indication that carefully selected historical control data can be used to replace concurrent control without impairing the general study conclusion. Additionally, the developed procedures and workflows lay the foundation for the future validation of virtual controls for a use in regulatory toxicology.

Accounting for spatial sampling patterns in Bayesian phylogeography.

Statistical phylogeography provides useful tools to characterize and quantify the spread of organisms during the course of evolution. Analyzing georeferenced genetic data often relies on the assumption that samples are preferentially collected in densely populated areas of the habitat. Deviation from this assumption negatively impacts the inference of the spatial and demographic dynamics. This issue is pervasive in phylogeography. It affects analyses that approximate the habitat as a set of discrete demes as well as those that treat it as a continuum. The present study introduces a Bayesian modeling approach that explicitly accommodates for spatial sampling strategies. An original inference technique, based on recent advances in statistical computing, is then described that is most suited to modeling data where sequences are preferentially collected at certain locations, independently of the outcome of the evolutionary process. The analysis of georeferenced genetic sequences from the West Nile virus in North America along with simulated data shows how assumptions about spatial sampling may impact our understanding of the forces shaping biodiversity across time and space.

Assessing Cognitive Workload in Motor Decision-Making through Functional Connectivity Analysis: Towards Early Detection and Monitoring of Neurodegenerative Diseases.

Neurodegenerative diseases (NDs), such as Alzheimer's, Parkinson's, amyotrophic lateral sclerosis, and frontotemporal dementia, among others, are increasingly prevalent in the global population. The clinical diagnosis of these NDs is based on the detection and characterization of motor and non-motor symptoms. However, when these diagnoses are made, the subjects are often in advanced stages where neuromuscular alterations are frequently irreversible. In this context, we propose a methodology to evaluate the cognitive workload (CWL) of motor tasks involving decision-making processes. CWL is a concept widely used to address the balance between task demand and the subject's available resources to complete that task. In this study, multiple models for motor planning during a motor decision-making task were developed by recording EEG and EMG signals in n=17 healthy volunteers (9 males, 8 females, age 28.66±8.8 years). In the proposed test, volunteers have to make decisions about which hand should be moved based on the onset of a visual stimulus. We computed functional connectivity between the cortex and muscles, as well as among muscles using both corticomuscular and intermuscular coherence. Despite three models being generated, just one of them had strong performance. The results showed two types of motor decision-making processes depending on the hand to move. Moreover, the central processing of decision-making for the left hand movement can be accurately estimated using behavioral measures such as planning time combined with peripheral recordings like EMG signals. The models provided in this study could be considered as a methodological foundation to detect neuromuscular alterations in asymptomatic patients, as well as to monitor the process of a degenerative disease.

Association Between the United States Department of Health and Human Services' COVID-19 Public Education Campaign and Initial Adult COVID-19 Vaccination Uptake by Race and Ethnicity in the United States, 2020-2022.

Non-Hispanic Black (Black) and Hispanic/Latino (Latino) populations face an increased risk of COVID-19 infection, hospitalization, and death from COVID-19 relative to non-Hispanic White (White) populations. When COVID-19 vaccines became available in December 2020, Black and Latino adults were less likely than White adults to get vaccinated due to factors such as racial discrimination and structural barriers to uptake. In April 2021, the U.S. HHS COVID-19 public education campaign (the Campaign) was launched to promote vaccination through general and audience-tailored messaging. As of March 2022, Black and Latino adults had reached parity with White adults in COVID-19 vaccine uptake. This study evaluated the relationship between Campaign exposure and subsequent vaccine uptake among Black, Latino, and White adults in the United States and assessed whether participant race/ethnicity moderated the relationship between Campaign exposure and vaccine uptake. Campaign media delivery data was merged with survey data collected from a sample of U.S. adults (n = 2,923) over four waves from January 2021 to March 2022. Logistic regression analysis showed that cumulative Campaign digital impressions had a positive, statistically significant association with COVID-19 vaccine uptake, and that participant race/ethnicity moderated this association. Compared with White adults, the magnitude of the relationship between cumulative impressions and vaccination was greater among Black and Latino adults. Results from a simulation model suggested that the Campaign may have been responsible for closing 5.0% of the gap in COVID-19 vaccination by race/ethnicity from April to mid-September 2021. We discuss implications for future public education campaigns that aim to reduce health disparities.

Development of rapid prediction model for silicosis risk among stone carvers in Nakhon Ratchasima, Thailand.

Exposure to silica dust presents a risk for the development of lung disease for stone carvers in Nakhon Ratchasima province, Thailand. This study aimed to develop a rapid prediction model for the assessment of silicosis risk among 243 stone carvers who were exposed to silica at work from August and October 2023 in Nakhon Ratchasima, Thailand. Demographic characteristics collected in questionnaires were work information; basic health information; health behavior data, including prevention and control of silicosis; knowledge; attitude; and practices for surveillance, prevention, and control of silicosis. Respirable crystalline silica (RCS) exposures were measured by conducting personal air sampling. Risk scores of silicosis were calculated and a rapid prediction model for assessment of silicosis risk among stone carvers was determined. It was found that 11 variables were significantly associated with silicosis risk scores (p < 0.05). However, it was demonstrated that only four significant influential variables, including the concentration of silica dust exposure (mg/m3), working hours per day, underlying diseases, and separation of residence from a workplace were acceptable for conducting a silicosis risk assessment using multiple regression analysis (R2 = 0.675). This study indicated that a prediction model can be used for the assessment of silicosis risk among stone carvers. Therefore, the use of this prediction model is recommended to evaluate the risk associated with exposure to RCS of stone carvers in Nakhon Ratchasima province, Thailand due to its simplicity, accuracy, and time-saving attributes.

Assessing prevalence, factors and health consequences and academic performance of undergraduate students with breakfast skipping during COVID-19 using statistical modeling: a cross-sectional study.

The study was conducted in order to study breakfast skipping (BKS) frequency, factors associated with, health consequence and undergraduate students academic performance during Covid-19 pandemic as earliest studies focusing on this area. A cross-sectional study was carried out among 2225 of undergraduate students. The study was carried between the period of 15/1/2020 to 3/4/2020 using an online self-report Breakfast Eating Habit Survey (BEHS). The BEHS survey was divided into two sections. The first sections included sociodemographic information (gender, BMI, age, smoking, residency, parental education, family income, studying system and stage (public or private), and studying institution (university or institute) academic performance. The second part included questions regarding breakfast eating habits including frequency of skipping meals, factors related to BKS health consequences and types of snacks. Logistic regression is a common technique used for modeling outcomes that fall into the range of 1 and 0. For this purpose, a logistic regression was performed to find adjusted odds ratio and crude odds ratio. The results showed that the majority of participants were female (1238, 55.7%). Out of 2,224 students, 2059 are aged between 18 to 24 years. Most of the participants were from first level (26.5%), second level (32.8%), third level (17.6%) or the fourth level (21.3%). Over 92% of participants were single and about 68% came from families of medium income families. The statistical analysis showed that the odds of BKS is reduced among students who live in accommodation by 54% (odds ratio = 54%, CI (41-71%), p value = 0.000). It seems that students with low income and normal or higher BMI are more likely to skip breakfast more regularly. The odds of skipping breakfast among students with BMI of 18-24.9 is reduced by 41% (odds ratio = 59%, CI (27%-93%), p value = 0.027) and the odds of BKS is reduced among students with BMI of 25-29.9 by 45% (odds ratio = 55%, CI (31-95%). Additionally, students with medium or high incomes are more likely to skip breakfast as much as twofold in comparison with students with low income (medium income (odds ratio = 1.85, CI (1.08-3.17), p-value = 0.024), high income (odds ratio = 1.98, CI (1.12-3.51), p-value = 0.019). The most common reasons for skipping breakfast included include time constraint, not hungry, breakfast is not ready, afraid to be overweight and lack of appetite. The consequences of skipping breakfast were feeling hungry throughout the day, feeling tired, and not paying attention in class and low academic performance. To concluded, BKS during Covid-19 is more common among students with higher BMI, higher income and living in accommodation. The main reason is time constraint and the most common health problems are being tired and luck of attention.

A Bayesian Approach for Modeling and Forecasting Solar Photovoltaic Power Generation.

In this paper, we propose a Bayesian approach to estimate the curve of a function f(·) that models the solar power generated at k moments per day for n days and to forecast the curve for the (n+1)th day by using the history of recorded values. We assume that f(·) is an unknown function and adopt a Bayesian model with a Gaussian-process prior on the vector of values f(t)=f(1),…, f(k). An advantage of this approach is that we may estimate the curves of f(·) and fn+1(·) as "smooth functions" obtained by interpolating between the points generated from a k-variate normal distribution with appropriate mean vector and covariance matrix. Since the joint posterior distribution for the parameters of interest does not have a known mathematical form, we describe how to implement a Gibbs sampling algorithm to obtain estimates for the parameters. The good performance of the proposed approach is illustrated using two simulation studies and an application to a real dataset. As performance measures, we calculate the absolute percentage error, the mean absolute percentage error (MAPE), and the root-mean-square error (RMSE). In all simulated cases and in the application to real-world data, the MAPE and RMSE values were all near 0, indicating the very good performance of the proposed approach.

MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale.

The MSstats R-Bioconductor family of packages is widely used for statistical analyses of quantitative bottom-up mass spectrometry-based proteomic experiments to detect differentially abundant proteins. It is applicable to a variety of experimental designs and data acquisition strategies and is compatible with many data processing tools used to identify and quantify spectral features. In the face of ever-increasing complexities of experiments and data processing strategies, the core package of the family, with the same name MSstats, has undergone a series of substantial updates. Its new version MSstats v4.0 improves the usability, versatility, and accuracy of statistical methodology, and the usage of computational resources. New converters integrate the output of upstream processing tools directly with MSstats, requiring less manual work by the user. The package's statistical models have been updated to a more robust workflow. Finally, MSstats' code has been substantially refactored to improve memory use and computation speed. Here we detail these updates, highlighting methodological differences between the new and old versions. An empirical comparison of MSstats v4.0 to its previous implementations, as well as to the packages MSqRob and DEqMS, on controlled mixtures and biological experiments demonstrated a stronger performance and better usability of MSstats v4.0 as compared to existing methods.

Statistical models of complex brain networks: a maximum entropy approach.

The brain is a highly complex system. Most of such complexity stems from the intermingled connections between its parts, which give rise to rich dynamics and to the emergence of high-level cognitive functions. Disentangling the underlying network structure is crucial to understand the brain functioning under both healthy and pathological conditions. Yet, analyzing brain networks is challenging, in part because their structure represents only one possible realization of a generative stochastic process which is in general unknown. Having a formal way to cope with such intrinsic variability is therefore central for the characterization of brain network properties. Addressing this issue entails the development of appropriate tools mostly adapted from network science and statistics. Here, we focus on a particular class of maximum entropy models for networks, i.e. exponential random graph models, as a parsimonious approach to identify the local connection mechanisms behind observed global network structure. Efforts are reviewed on the quest for basic organizational properties of human brain networks, as well as on the identification of predictive biomarkers of neurological diseases such as stroke. We conclude with a discussion on how emerging results and tools from statistical graph modeling, associated with forthcoming improvements in experimental data acquisition, could lead to a finer probabilistic description of complex systems in network neuroscience.

Accurate Identification of Subclones in Tumor Genomes.

Understanding intratumor heterogeneity is critical for studying tumorigenesis and designing personalized treatments. To decompose the mixed cell population in a tumor, subclones are inferred computationally based on variant allele frequency (VAF) from bulk sequencing data. In this study, we showed that sequencing depth, mean VAF, and variance of VAF of a subclone are confounded. Without considering this effect, current methods require deep-sequencing data (>300× depth) to reliably infer subclones. Here, we present a novel algorithm that incorporates depth-variance and mean-variance dependencies in a clustering error model and successfully identifies subclones in tumors sequenced at depths of as low as 30×. We implemented the algorithm as a model-based adaptive grouping of subclones (MAGOS) method. Analyses of computer simulated data and empirical sequencing data showed that MAGOS outperformed existing methods on minimum sequencing depth, decomposition accuracy, and computation efficiency. The most prominent improvements were observed in analyzing tumors sequenced at depths between 30× and 200×, whereas the performance was comparable between MAGOS and existing methods on deeply sequenced tumors. MAGOS supports analysis of single-nucleotide variants and copy number variants from a single sample or multiple samples of a tumor. We applied MAGOS to whole-exome data of late-stage liver cancers and discovered that high subclone count in a tumor was a significant risk factor of poor prognosis. Lastly, our analysis suggested that sequencing multiple samples of the same tumor at standard depth is more cost-effective and robust for subclone characterization than deep sequencing a single sample. MAGOS is available at github (https://github.com/liliulab/magos).