True Proportions Research Articles

Validation of Environmental DNA for Estimating Proportional and Absolute Biomass

ABSTRACTEnvironmental DNA (eDNA) has significant potential to improve the efficiency of biological sampling and detect species that pose challenges for traditional sampling methods. However, a key obstacle in utilizing eDNA data for ecosystem management is uncertainty surrounding the ability to estimate abundance or biomass of multiple species simultaneously. In this study, we use experimental trials with known biomasses of multiple species to explore the feasibility of (1) estimating species proportions from eDNA metabarcoding data and (2) estimating absolute eDNA concentrations of multiple species by scaling metabarcoding proportions with eDNA concentrations of a single species obtained from qPCR. The focal species for this study were three gadid fishes that are key components of marine ecosystems in Alaska and vary in their distribution and habitat use: Walleye pollock (Gadus chalcogrammus), Pacific cod (Gadus macrocephalus), and Arctic cod (Boreogadus saida). After designing gadid‐specific metabarcoding primers and accounting for PCR biases in the metabarcoding data, we found corrected read proportions closely approximated the true biomass proportions of species. Furthermore, we found strong positive relationships between absolute eDNA concentration and absolute biomass for Arctic cod and Pacific cod using quantitative metabarcoding data combined with estimates of Walleye pollock eDNA concentration derived from qPCR. These findings suggest that it is possible to accurately quantify species compositions and estimate metrics of biomass for gadids in real‐world scenarios. Furthermore, this work provides a framework for developing primers and analytical approaches that can be applied to other species to improve the quantitative utility of eDNA.

A New Criterion for Determining a Cutoff Value Based on the Biases of Incidence Proportions in the Presence of Non-differential Outcome Misclassifications.

When conducting database studies, researchers sometimes use an algorithm known as "case definition," "outcome definition," or "computable phenotype" to identify the outcome of interest. Generally, algorithms are created by combining multiple variables and codes, and we need to select the most appropriate one to apply to the database study. Validation studies compare algorithms with the gold standard and calculate indicators such as sensitivity and specificity to assess their validities. As the indicators are calculated for each algorithm, selecting an algorithm is equivalent to choosing a pair of sensitivity and specificity. Therefore, receiver operating characteristic curves can be utilized, and two intuitive criteria are commonly used. However, neither was conceived to reduce the biases of effect measures (e.g., risk difference and risk ratio), which are important in database studies. In this study, we evaluated two existing criteria from perspectives of the biases and found that one of them, called the Youden index always minimizes the bias of the risk difference regardless of the true incidence proportions under nondifferential outcome misclassifications. However, both criteria may lead to inaccurate estimates of absolute risks, and such property is undesirable in decision-making. Therefore, we propose a new criterion based on minimizing the sum of the squared biases of absolute risks to estimate them more accurately. Subsequently, we apply all criteria to the data from the actual validation study on postsurgical infections and present the results of a sensitivity analysis to examine the robustness of the assumption our proposed criterion requires.

Machine learning assisted microfluidics dual fluorescence flow cytometry for detecting bladder tumor cells based on morphological characteristic parameters

BackgroundBladder cancer (BC) is the most common malignant tumor and has become a major public health problem, leading the causes of death worldwide. The detection of BC cells is of great significance for clinical diagnosis and disease treatment. Urinary cytology based liquid biopsy remains high specificity for early diagnosis of BC, however, it still requires microscopy examination which heavily relies on manual operations. It is imperative to investigate the potential of automated and indiscriminate cell differentiation technology to enhance the sensitivity and efficiency of urine cytology. ResultsHere, we developed a machine learning algorithm empowered dual-fluorescence flow cytometry platform (μ-FCM) for urinary cytology analysis. A phenotype characteristic parameter (CP) which correlated with the size of the cell and nucleus was defined to achieve the differentiation of the BC cells and uroepithelial cells with high throughput and high accuracy. Based on CP analysis, SV-HUC-1 cells were almost differentiated from EJ cells and effectively reduced the overlap with 5637 cells. To further differentiate SV-HUC-1 cells and 5637 cells, support vector machine (SVM) machine learning algorithm was optimized to assist data analysis with the highest accuracies of 84.7 % for cell differentiation including the specificity of 91.0 % and the sensitivity of 75.0 %. Furthermore, the false positive rate (FPR) compensation enabled the detection rates of rare BC cells predicted by the well-trained SVM model were close to the true proportions with the recognition error in 0.4 % for the tumor cells. SignificanceAs a proof of concept, the developed μ-FCM system successfully demonstrates the capacity to identify the distribution of exfoliated cells in real urine samples. This system underscores the significance of integrating AI with microfluidics to perform high-throughput phenotyping of exfoliated cells, offering a pathway toward scalable, efficient, and automatic microfluidic systems in the fields of both biosensing and in vitro diagnosis of BC.

An evaluation of confidence intervals for a cumulative proportion to enable decisions at interim reviews of single-arm trials

BackgroundClinical trials often include interim analyses of the proportion of participants experiencing an event by a fixed time-point. A pre-specified proportion excluded from a corresponding confidence interval (CI) may lead an independent monitoring committee to recommend stopping the trial. Frequently this cumulative proportion is estimated by the Kaplan-Meier estimator with a Wald approximate CI, which may have coverage issues with small samples. MethodsWe reviewed four alternative CI methods for cumulative proportions (Beta Product Confidence Procedure (BPCP), BPCP Mid P, Rothman-Wilson, Thomas-Grunkemeier) and two CI methods for simple proportions (Clopper-Pearson, Wilson). We conducted a simulation study comparing CI methods across true event proportions for 12 scenarios differentiated by sample sizes and censoring patterns. We re-analyzed interim data from A5340, a HIV cure trial considering the proportion of participants experiencing virologic failure. ResultsOur simulation study highlights the lower and upper tail error probabilities for each CI method. Across scenarios, we found differences in the performance of lower versus upper bounds. No single method is always preferred. The upper bound of a Wald approximate CI performed reasonably with some error inflation, whereas the lower bound of the BPCP Mid P method performed well. For a trial design similar to A5340, we recommend BPCP Mid P. ConclusionsThe design of future single-arm interim analyses of event proportions should consider the most appropriate CI method based on the relevant bound, anticipated sample size and event proportion. Our paper summarizes available methods, demonstrates performance in a simulation study, and includes code for implementation.

A tradeoff between false discovery and true positive proportions for sparse high-dimensional logistic regression

A tradeoff between false discovery and true positive proportions for sparse high-dimensional logistic regression

Deep learning-based automatic image classification of oral cancer cells acquiring chemoresistance in vitro.

Cell shape reflects the spatial configuration resulting from the equilibrium of cellular and environmental signals and is considered a highly relevant indicator of its function and biological properties. For cancer cells, various physiological and environmental challenges, including chemotherapy, cause a cell state transition, which is accompanied by a continuous morphological alteration that is often extremely difficult to recognize even by direct microscopic inspection. To determine whether deep learning-based image analysis enables the detection of cell shape reflecting a crucial cell state alteration, we used the oral cancer cell line resistant to chemotherapy but having cell morphology nearly indiscernible from its non-resistant parental cells. We then implemented the automatic approach via deep learning methods based on EfficienNet-B3 models, along with over- and down-sampling techniques to determine whether image analysis of the Convolutional Neural Network (CNN) can accomplish three-class classification of non-cancer cells vs. cancer cells with and without chemoresistance. We also examine the capability of CNN-based image analysis to approximate the composition of chemoresistant cancer cells within a population. We show that the classification model achieves at least 98.33% accuracy by the CNN model trained with over- and down-sampling techniques. For heterogeneous populations, the best model can approximate the true proportions of non-chemoresistant and chemoresistant cancer cells with Root Mean Square Error (RMSE) reduced to 0.16 by Ensemble Learning (EL). In conclusion, our study demonstrates the potential of CNN models to identify altered cell shapes that are visually challenging to recognize, thus supporting future applications with this automatic approach to image analysis.

Analysis of two binomial proportions in noninferiority confirmatory trials.

In this article, we propose considering an approximate exact score (AES) test for noninferiority comparisons and we derive its test-based confidence interval for the difference between two independent binomial proportions. This test was published in the literature, but not its associated confidence interval. The p-value for this test is obtained by using exact binomial probabilities with the nuisance parameter being replaced by its restricted maximum likelihood estimate. Calculated type I errors revealed that the AES method has important advantages for noninferiority comparisons over popular asymptotic methods for adequately powered confirmatory clinical trials, at 80% or 90% statistical power. For unbalanced sample sizes of the compared groups, type I errors for the asymptotic score method were shown to be higher than the nominal level in a systematic pattern over a range of true proportions, but the AES method did not suffer from such a problem. On average, the true type I error of the AES method was closer to the nominal level than all considered methods in the empirical comparisons. In rare cases, type I errors of the AES test exceeded the nominal level, but only by a small amount. Presented examples showed that the AES method can be more attractive in practice than practical exact methods. In addition, p-value and confidence interval of the AES method can be obtained in <30 s of computer time for most confirmatory trials. Theoretical arguments, combined with empirical evidence and fast computation time should make the AES method attractive in statistical practice.

Males die young, which may cause an Allee effect during a population collapse of the Vendace Coregonus albula

AbstractObjectiveWe estimated the difference in mortality between mature male and female Vendace Coregonus albula based on a large data set of catch samples from 25 locations in Finland. We then used this estimate and age distribution data from Lake Etelä‐Konnevesi to illustrate how the sex ratio (females per one male) might decrease as the average age of spawners increases during a several‐year‐long period of recruitment failure.MethodsWe estimated mortalities first from sex‐specific age–ln(catch) curves and second from the average age‐specific proportions of different sexes.ResultThe estimate of the additional mortality of males was around 0.2–0.4, depending on the method of estimation, and assumed true proportions in the population at age 1 (two growing seasons), when Vendace reaches sexual maturity. When using the additional mortality estimate and age distribution data from Lake Etelä‐Konnevesi, the hypothetical sex ratio in the most extreme year was even as skewed as four females per one male.ConclusionIf the lack of males per female spawner during a population decline reduces the per capita recruitment success of females, this is a depensatory density‐dependent effect, the Allee effect. This phenomenon may partly explain the rapid population collapses and contribute to 2‐year cyclicity typical of the dynamics of Vendace populations.

Design-based composite estimation of small proportions in small domains

Traditional direct estimation methods are inefficient for domains of a survey population with small sample sizes. To estimate the domain proportions, we combine the direct estimators and the regression-synthetic estimators based on domain-level auxiliary information. For the case of small true proportions, we propose the design-based linear combination that is a robust alternative to the empirical best linear unbiased predictor (EBLUP) based on the Fay–Herriot model.&#x0D; We imitate the Lithuanian Labor Force Survey, where we estimate the proportions of the unemployed and employed in municipalities. We show where the proposed design-based composition and estimator of its mean square error are competitive for EBLUP and its accuracy estimation.

Permutation-based true discovery proportions for functional magnetic resonance imaging cluster analysis.

We propose a permutation-based method for testing a large collection of hypotheses simultaneously. Our method provides lower bounds for the number of true discoveries in any selected subset of hypotheses. These bounds are simultaneously valid with high confidence. The methodology is particularly useful in functional Magnetic Resonance Imaging cluster analysis, where it provides a confidence statement on the percentage of truly activated voxels within clusters of voxels, avoiding the well-known spatial specificity paradox. We offer a user-friendly tool to estimate the percentage of true discoveries for each cluster while controlling the family-wise error rate for multiple testing and taking into account that the cluster was chosen in a data-driven way. The method adapts to the spatial correlation structure that characterizes functional Magnetic Resonance Imaging data, gaining power over parametric approaches.

Application of Biostatistical Models in Public Health under Mpox Virus

Since January 1, 2022 last year, many countries outside Africa have begun to have outbreaks of Monkeypox (Mpox) virus. The first case of Mpox transfusion was confirmed on 16 September 2022 Chinese mainland. In order to prevent major public health events, the aim of this study is to predict which populations in China are more susceptible to Mpox virus infection. There are two main research methods used in this paper: literature research method and statistical analysis method. The proportion of male cases in different groups was mainly analyzed separately, and the proportion of the data was determined by a 95% confidence interval. Second, analyze the true proportions of these factors through demographic information. Semen samples and rectal swabs of the type from the confirmed case are then transmitted to determine how Mpox virus is transmitted. Finally, sex-age (proportion hospitalized) was counted. The results of the experiment are: the proportion of male cases is the highest, and the true proportion of male sex and sexual transmission ratio of the influencing factors is the highest. The main types of transmission are: sexual contact, other, and between people. Men in the 18-49 age group and women in the 18-59 age range had the greatest hospitalization rates, respectively. The conclusion is that men who have sex with men, sex actors, HIV-positive patients, women aged 18-59 and men aged 18-49 are the main target groups in China and are susceptible to Mpox virus.

Genetic Validation of Psoriasis Phenotyping in UK Biobank Supports the Utility of Self-Reported Data and Composite Definitions for Large Genetic and Epidemiological Studies

Genetic Validation of Psoriasis Phenotyping in UK Biobank Supports the Utility of Self-Reported Data and Composite Definitions for Large Genetic and Epidemiological Studies

Uncertainty quantification of reference-based cellular deconvolution algorithms

ABSTRACT The majority of epigenetic epidemiology studies to date have generated genome-wide profiles from bulk tissues (e.g., whole blood) however these are vulnerable to confounding from variation in cellular composition. Proxies for cellular composition can be mathematically derived from the bulk tissue profiles using a deconvolution algorithm; however, there is no method to assess the validity of these estimates for a dataset where the true cellular proportions are unknown. In this study, we describe, validate and characterize a sample level accuracy metric for derived cellular heterogeneity variables. The CETYGO score captures the deviation between a sample’s DNA methylation profile and its expected profile given the estimated cellular proportions and cell type reference profiles. We demonstrate that the CETYGO score consistently distinguishes inaccurate and incomplete deconvolutions when applied to reconstructed whole blood profiles. By applying our novel metric to >6,300 empirical whole blood profiles, we find that estimating accurate cellular composition is influenced by both technical and biological variation. In particular, we show that when using a common reference panel for whole blood, less accurate estimates are generated for females, neonates, older individuals and smokers. Our results highlight the utility of a metric to assess the accuracy of cellular deconvolution, and describe how it can enhance studies of DNA methylation that are reliant on statistical proxies for cellular heterogeneity. To facilitate incorporating our methodology into existing pipelines, we have made it freely available as an R package (https://github.com/ds420/CETYGO).

NetTDP: permutation-based true discovery proportions for differential co-expression network analysis.

Existing methods for differential network analysis could only infer whether two networks of interest have differences between two groups of samples, but could not quantify and localize network differences. In this work, a novel method, permutation-based Network True Discovery Proportions (NetTDP), is proposed to quantify the number of edges (correlations) or nodes (genes) for which the co-expression networks are different. In the NetTDP method, we propose an edge-level statistic and a node-level statistic, and detect true discoveries of edges and nodes in the sense of differential co-expression network, respectively, by the permutation-based sumSome method. Furthermore, the NetTDP method could further localize the differences by inferring the TDPs for edge or gene subsets of interest, which can be selected post hoc. Our NetTDP method allows inference on data-driven modules or biology-driven gene sets, and remains valid even when these sub-networks are optimized using the same data. Experimental results on both simulation data sets and five real data sets show the effectiveness of the proposed method in inferring the quantification and localization of differential co-expression networks. The R code is available at https://github.com/LiminLi-xjtu/NetTDP.

Estimating the relative proportions of SARS-CoV-2 haplotypes from wastewater samples

Wastewater surveillance has become essential for monitoring the spread of SARS-CoV-2. The quantification of SARS-CoV-2 RNA in wastewater correlates with the Covid-19 caseload in a community. However, estimating the proportions of different SARS-CoV-2 haplotypes has remained technically difficult. We present a phylogenetic imputation method for improving the SARS-CoV-2 reference database and a method for estimating the relative proportions of SARS-CoV-2 haplotypes from wastewater samples. The phylogenetic imputation method uses the global SARS-CoV-2 phylogeny and imputes based on the maximum of the posterior probability of each nucleotide. We show that the imputation method has error rates comparable to, or lower than, typical sequencing error rates which substantially improves the reference database and allows for accurate inferences of haplotype composition. Our method for estimating relative proportions of haplotypes uses an initial step to remove unlikely haplotypes and an Expectation-Maximization (EM) algorithm for obtaining maximum likelihood estimates of the proportions of different haplotypes in a sample. Using simulations with a reference database of >3 million SARS-CoV-2 genomes, we show that the estimated proportions reflect the true proportions given sufficiently high sequencing depth.

Classifying etiology of infantile spasms syndrome in resource-limited settings: A study from the South Asian region.

ObjectiveEtiological classification of infantile spasms syndrome (ISS) is important, considering the influence on prognosis based on the presence or absence of a known etiology. This study was performed to describe the limitations and difficulties experienced within the South Asian region when classifying the etiology of ISS according to the current recommendation.MethodData on healthcare indices and facilities related to management of ISS for the nine countries in the South Asian region were gathered by the South Asian West Syndrome Research Group. A Google survey was performed among three hundred and thirty pediatric neurologists in the region. The capacity within each country for investigating etiology of ISS according to current described benchmarks was evaluated. The difficulties experienced in this regard and the potential solutions were investigated.ResultsOne hundred and sixty pediatric neurologists (response rate 48%) from Bangladesh (19/25), India (94/255), Myanmar (11/11), Nepal (6/8), Pakistan (19/25), and from Sri Lanka (7/8) responded. Three countries had no pediatric neurology services. Fifty‐six percent attempted to classify ISS etiology according to classification outlined by International League Against Epilepsy in 2017. The facilities to perform metabolic, genetic, and immunological investigations were very limited. Lack of funding for investigations and poor laboratory support were the two most frequent barriers encountered. Sixty percent indicated that a separate classification is suitable for low‐income setting; 78% suggested inclusion of separate category as “incompletely investigated” as an alternative solution to mitigate the barrier of achieving a better understanding of the etiological subtypes seen more frequently in this region.SignificanceThe resources in South Asian region are limited to meet the recommendations for investigating etiology of ISS. Including the etiological subcategory “incompletely investigated” is proposed as an alternative to understand the true proportions of children in this region, with a definite known etiology and those with an unknown etiology.

A targeted solution for estimating the cell-type composition of bulk samples

BackgroundTo avoid false-positive findings and detect cell-type specific associations in methylation and transcription investigations with bulk samples, it is critical to know the proportions of the major cell-types.ResultsWe present a novel approach that allows for precise estimation of cell-type proportions using only a few highly informative methylation markers. The most reliable estimates were obtained with 17 amplicons (34 CpGs) using the MuSiC estimator, for which the average correlations between the estimated and the true cell-type proportions were 0.889. Furthermore, the estimates were not significantly different from the true values (P = 0.95) indicating that the estimator is unbiased and the standard deviation of the estimates further indicate high precision. Moreover, the overall variability of the estimates as measured by the Root Mean Squared Error (RMSE), which is a function of both bias and precision, was low (mean RMSE = 0.038). Taken together, these results indicate that the approach produced reliable estimates that are both unbiased and highly precise.ConclusionThis cost-effective approach for estimating cell-type proportions in bulk samples allows for enhanced targeted analysis, which in turn will minimize the risk of reporting false-positive findings and allowing for detection of cell-type specific associations. The approach is applicable across platforms and can be extended to assess cell-type proportions for various tissues.

Likelihood-based deconvolution of bulk gene expression data using single-cell references.

Direct comparison of bulk gene expression profiles is complicated by distinct cell type mixtures in each sample that obscure whether observed differences are actually caused by changes in the expression levels themselves or are simply a result of differing cell type compositions. Single-cell technology has made it possible to measure gene expression in individual cells, achieving higher resolution at the expense of increased noise. If carefully incorporated, such single-cell data can be used to deconvolve bulk samples to yield accurate estimates of the true cell type proportions, thus enabling one to disentangle the effects of differential expression and cell type mixtures. Here, we propose a generative model and a likelihood-based inference method that uses asymptotic statistical theory and a novel optimization procedure to perform deconvolution of bulk RNA-seq data to produce accurate cell type proportion estimates. We show the effectiveness of our method, called RNA-Sieve, across a diverse array of scenarios involving real data and discuss extensions made uniquely possible by our probabilistic framework, including a demonstration of well-calibrated confidence intervals.

Precision and accuracy of modal analysis methods for clastic deposits and rocks: A statistical and numerical modeling approach

Quantifying the proportions of certain components in rocks and deposits (modal analysis or componentry) is important in earth sciences. Relevant methods for cross-sections (two- dimensional exposures) of clastic rocks include point counts or line counts. The accuracy of these methods has been supposed to be good in the literature but not necessarily verified empirically. Natural materials are inappropriate for assessing accuracy because the true proportions of each component are unknown. The precision of modal analysis methods has traditionally been evaluated from statistical models (primarily the normal approximation to the binomial distribution) but again rarely verified in practice because it is also extremely difficult to obtain different slices through the same material at outcrop scale. Here we create a set of numerical models of red and blue spheres with different proportions and sizes and cut 60 slices through the models, on which we perform point counts and line counts. We show that both of these methods are indeed able to retrieve the correct volumetric proportions of components, on average, when enough fragments are counted or intersected. As already known, precision is controlled by component abundance and the number of points counted or clasts intersected. However, we show that other important factors include differences between slices, which are relevant for our unequal-size models, and the proportion of voids, matrix, and/or cement in the rock. We present empirical precision charts for clast counts and line counts based on our models and make recommendations for future field studies.

HPV Vaccine Recommendations and Parental Intent

* Abbreviations: CI — : confidence interval dPP — : difference in predicted probability HPV — : human papillomavirus NIS — : National Immunization Survey Adolescents’ receipt of the human papillomavirus (HPV) vaccine is primarily a decision of their parents. Health care providers’ recommendation increases parents’ likelihood of initiating the HPV vaccine for their child.1 Rates of HPV vaccine recommendations and the proportion of up-to-date adolescents have improved over time2; however, in 2017–2018, 7.3 million vaccine-eligible US adolescents were unvaccinated.1 Notably, despite a provider recommendation, parents of 60.6% of unvaccinated adolescents had no intention to initiate the HPV vaccine series.1 It is unclear whether the sentiment of HPV vaccine hesitancy is growing (because of the rise of the antivaccination movement), has dissipated (with a growing body of evidence pertaining to postlicensure efficacy and the safety of the HPV vaccine), or has remained unchanged over time. To understand the trajectory of acute HPV vaccine hesitancy, we evaluated trends in the proportion of parents lacking the intent to initiate HPV vaccination for their unvaccinated adolescent despite having received a provider recommendation. We analyzed the 2012–2018 National Immunization Survey (NIS)–Teen, a national … Address correspondence to Kalyani Sonawane, PhD, Center for Healthcare Data, Department of Management, Policy and Community Health, School of Public Health, The University of Texas Health Science Center at Houston, 1200 Pressler St, RAS-E 1013, Houston, TX 77478. E-mail: kalyani.b.sonawane{at}uth.tmc.edu