Multigroup analysis of compositions of microbiomes with covariate adjustments and repeated measures.

Microbiome differential abundance analysis methods for two groups are well-established in the literature. However, many microbiome studies involve more than two groups, sometimes even ordered groups such as stages of a disease, and require different types of comparison. Standard pairwise comparisons are inefficient in terms of power and false discovery rates. In this Article, we propose a general framework, ANCOM-BC2, for performing a wide range of multigroup analyses with covariate adjustments and repeated measures. We illustrate our methodology through two real datasets. The first example explores the effects of aridity on the soil microbiome, and the second example investigates the effects of surgical interventions on the microbiome of patients with inflammatory bowel disease.

isocir: An R Package for Constrained Inference using Isotonic Regression for Circular Data, with an Application to Cell Biology.

In many applications one may be interested in drawing inferences regarding the order of a collection of points on a unit circle. Due to the underlying geometry of the circle standard constrained inference procedures developed for Euclidean space data are not applicable. Recently, statistical inference for parameters under such order constraints on a unit circle was discussed in Rueda et al. (2009); Fernández et al. (2012). In this paper we introduce an R package called isocir which provides a set of functions that can be used for analyzing angular data subject to order constraints on a unit circle. Since this work is motivated by applications in cell biology, we illustrate the proposed package using a relevant cell cycle data.

Multi-group Analysis of Compositions of Microbiomes with Covariate Adjustments and Repeated Measures.

Microbiome differential abundance analysis methods for a pair of groups are well established in the literature. However, many microbiome studies involve multiple groups, sometimes even ordered groups, such as stages of a disease, and require different types of comparisons. Standard pairwise comparisons are not only inefficient in terms of power and false discovery rates, but they may not address the scientific question of interest. In this paper, we propose a general framework for performing a wide range of multi-group analyses with covariate adjustments and repeated measures. We demonstrate the effectiveness of our methodology through two real data sets. The first example explores the effects of aridity on the soil microbiome, and the second example investigates the effects of surgical interventions on the microbiome of IBD patients.

Linear and nonlinear correlation estimators unveil undescribed taxa interactions in microbiome data.

It is well-known that human gut microbiota form an ecosystem where microbes interact with each other. Due to complex underlying interactions, some microbes may correlate nonlinearly. There are no measures in the microbiome literature we know of that quantify these nonlinear relationships. Here, we develop a methodology called Sparse Estimation of Correlations among Microbiomes (SECOM) for estimating linear and nonlinear relationships among microbes while maintaining the sparsity. SECOM accounts for both sample and taxon-specific biases in its model. Its statistical properties are evaluated analytically and by comprehensive simulation studies. We test SECOM in two real data sets, namely, forehead and palm microbiome data from college-age adults, and Norwegian infant gut microbiome data. Given that forehead and palm are related to skin, as desired, SECOM discovers each genus to be highly correlated between the two sites, but that is not the case with any of the competing methods. It is well-known that infant gut evolves as the child grows. Using SECOM, for the first time in the literature, we characterize temporal changes in correlations among bacterial families during a baby's first year after birth.

Analysis of compositions of microbiomes with bias correction.

Differential abundance (DA) analysis of microbiome data continues to be a challenging problem due to the complexity of the data. In this article we define the notion of "sampling fraction" and demonstrate a major hurdle in performing DA analysis of microbiome data is the bias introduced by differences in the sampling fractions across samples. We introduce a methodology called Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC), which estimates the unknown sampling fractions and corrects the bias induced by their differences among samples. The absolute abundance data are modeled using a linear regression framework. This formulation makes a fundamental advancement in the field because, unlike the existing methods, it (a) provides statistically valid test with appropriate p-values, (b) provides confidence intervals for differential abundance of each taxon, (c) controls the False Discovery Rate (FDR), (d) maintains adequate power, and (e) is computationally simple to implement.

Analysis of microbial compositions: a review of normalization and differential abundance analysis.

Increasingly, researchers are discovering associations between microbiome and a wide range of human diseases such as obesity, inflammatory bowel diseases, HIV, and so on. The first step towards microbiome wide association studies is the characterization of the composition of human microbiome under different conditions. Determination of differentially abundant microbes between two or more environments, known as differential abundance (DA) analysis, is a challenging and an important problem that has received considerable interest during the past decade. It is well documented in the literature that the observed microbiome data (OTU/SV table) are relative abundances with an excess of zeros. Since relative abundances sum to a constant, these data are necessarily compositional. In this article we review some recent methods for DA analysis and describe their strengths and weaknesses.

Health literacy: Impact on quality of life in head and neck cancer survivors.

OBJECTIVES: Although inadequate health literacy has been shown to impact health outcomes in other cancers, little is known about its impact in head and neck cancer (HNC). This study aimed to determine the prevalence and predictors of inadequate health literacy and evaluate the association between health literacy and quality of life (QOL) in HNC survivors. METHODS: We conducted a retrospective analysis of HNC survivors evaluated in a multidisciplinary HNC survivorship clinic. Survivors had to be ≥1-year postcompletion of treatment to be included in the analysis. Health literacy was assessed via self-report with the Brief Health Literacy Screen (score <10 indicating inadequate health literacy), and QOL was measured using the University of Washington QOL questionnaire. Linear regression with robust standard errors was utilized to evaluate the association between health literacy and QOL. RESULTS: Of the 218 survivors evaluated, 13.8% (n = 30) demonstrated inadequate health literacy. After adjusting for age, marital status, site, stage, treatment modality, and years since treatment completion, social-emotional QOL scores for survivors with adequate health literacy were estimated to be 10.67 points higher than those with inadequate health literacy (P = .013). Health literacy was not significantly associated with physical QOL after adjusting for covariates (P = .130). CONCLUSION: Inadequate health literacy is associated with a lower social QOL in HNC survivors, and among those with inadequate health literacy, interventions to ameliorate the impact on QOL are needed. LEVEL OF EVIDENCE: 4 Laryngoscope, 130:2354-2359, 2020.

Impact of Neck Disability on Health-Related Quality of Life among Head and Neck Cancer Survivors.

OBJECTIVE: Although neck impairment has been described following surgical resection, limited studies have investigated its prevalence in nonsurgical treatment. The purpose of this study is to determine the prevalence and predictors of neck disability following head and neck cancer (HNC) treatment and to explore its association with quality of life (QOL). STUDY DESIGN: Cross-sectional study. SETTING: HNC survivorship clinic. SUBJECTS AND METHODS: We identified 214 survivors who completed treatment ≥1 year prior to evaluation in the clinic. Self-reported neck impairment was measured using the Neck Disability Index. QOL was measured using the University of Washington QOL Questionnaire, with physical and social subscale scores calculated. Regression analysis and trend tests were employed to explore associations. RESULTS: Over half of survivors (54.2%) reported neck disability. The odds of neck disability in survivors who received nonsurgical treatment and those who received surgery plus adjuvant treatment were 3.46 and 4.98 times higher compared to surgery alone (P = .008, P = .004). Survivors who underwent surgery only had higher physical and social QOL than those who received nonsurgical treatment (physical QOL: P < .001, social QOL: P = .023) and those who received surgery plus adjuvant treatment (physical QOL: P < .001, social QOL: P = .039). CONCLUSION: This study revealed a high prevalence of neck disability following nonsurgical treatment. While neck disability is an established sequela of surgical resection, the impact of nonsurgical treatment has gone unrecognized. Early identification and intervention to prevent progression of neck disability are crucial to optimize QOL.

Prevalence of major depressive episodes in rural women using primary care.

Women carry a disproportionate burden of depression in part because situational and other factors enhance their risk. Rural women may be at particular risk because of poverty and lack of treatment opportunities. For this study we investigated the rate of current major depressive episodes (MDE) in impoverished rural women seeking care in a community health center (CHC) in the rural South. We screened 982 women for MDE during a routine primary care visit: about half were positive for depressive symptoms. Of women positive at screening, 194 were then assessed for psychiatric disorder. A current MDE was observed in 14.3% of women screened for depression and 72.2% of women assessed for psychiatric disorder. Recognizing that neither of these percentages reflects the likely rate of MDE among the larger population of rural impoverished women, we used probability theory and binary logistic regression to estimate a depression rate that could be applied as one factor associated with unmet need in this population of women. We estimate that 44.3% of the population of women using the CHC had MDE. These findings underscore the need for mental health services in rural primary care, especially in facilities serving impoverished women.

Estimation of Parameters Subject to Order Restrictions on a Circle With Application to Estimation of Phase Angles of Cell Cycle Genes.

Motivated by a problem encountered in the analysis of cell cycle gene expression data, this article deals with the estimation of parameters subject to order restrictions on a unit circle. A normal eukaryotic cell cycle has four major phases during cell division, and a cell cycle gene has its peak expression (phase angle) during the phase that may correspond to its biological function. Because the phases are ordered along a circle, the phase angles of cell cycle genes are ordered unknown parameters on a unit circle. The problem of interest is to estimate the phase angles using the information regarding the order among them. We address this problem by developing a circular version of the well-known isotonic regression for Euclidean data. Because of the underlying geometry, the standard pool adjacent violator algorithm (PAVA) cannot be used for deriving the circular isotonic regression estimator (CIRE). However, PAVA can be modified to obtain a computationally efficient algorithm for deriving the CIRE. We illustrate the CIRE by estimating the phase angles of some of well-known cell cycle genes using the unrestricted estimators obtained in the literature.