an-adapted-optical-density-based-microplate-assay-for-characterizing

An Adapted Optical Density-Based Microplate Assay for Characterizing Actinobacteriophage Infection

Bacteriophages are a key part of natural environments, and they have a powerful ability to shape bacterial populations. To understand how individual ...

University of Arkansas

Gaussian Bayesian networks have become a widely used framework to estimate directed associations between joint Gaussian variables, where the network structure encodes the decomposition of multivariate normal density into local terms. However, the resulting estimates can be inaccurate when the normality assumption is moderately or severely violated, making it unsuitable for dealing with recent genomic data such as the Cancer Genome Atlas data. In the present paper, we propose a mixture copula Bayesian network model which provides great flexibility in modeling non-Gaussian and multimodal data for causal inference. The parameters in mixture copula functions can be efficiently estimated by a routine expectation-maximization algorithm. A heuristic search algorithm based on Bayesian information criterion is developed to estimate the network structure, and prediction can be further improved by the best-scoring network out of multiple predictions from random initial values. Our method outperforms Gaussian Bayesian networks and regular copula Bayesian networks in terms of modeling flexibility and prediction accuracy, as demonstrated using a cell signaling data set. We apply the proposed methods to the Cancer Genome Atlas data to study the genetic and epigenetic pathways that underlie serous ovarian cancer.

A mixture copula Bayesian network model for multimodal genomic data.

Differential co-expression analysis, as a complement of differential expression analysis, offers significant insights into the changes in molecular mechanism of different phenotypes. A prevailing approach to detecting differentially co-expressed genes is to compare Pearson's correlation coefficients in two phenotypes. However, due to the limitations of Pearson's correlation measure, this approach lacks the power to detect nonlinear changes in gene co-expression which is common in gene regulatory networks.

A powerful nonparametric method for detecting differentially co-expressed genes: distance correlation screening and edge-count test.

Feature screening has become a real prerequisite for the analysis of high-dimensional genomic data, as it is effective in reducing dimensionality and removing redundant features. However, existing methods for feature screening have been mostly relying on the assumptions of linear effects and independence (or weak dependence) between features, which might be inappropriate in real practice. In this paper, we consider the problem of selecting continuous features for a categorical outcome from high-dimensional data. We propose a powerful statistical procedure that consists of two steps, a nonparametric significance test based on edge count and a multiple testing procedure with dependence adjustment for false discovery rate control. The new method presents two novelties. First, the edge-count test directly targets distributional difference between groups, therefore it is sensitive to nonlinear effects. Second, we relax the independence assumption and adapt Efron's procedure to adjust for the dependence between features. The performance of the proposed procedure, in terms of statistical power and false discovery rate, is illustrated by simulated data. We apply the new method to three genomic datasets to identify genes associated with colon, cervical and prostate cancers.

Model-free feature screening for categorical outcomes: Nonlinear effect detection and false discovery rate control.

The nonparanormal graphical model has emerged as an important tool for modeling dependency structure between variables because it is flexible to non-Gaussian data while maintaining the good interpretability and computational convenience of Gaussian graphical models. In this paper, we consider the problem of detecting differential substructure between two nonparanormal graphical models with false discovery rate control. We construct a new statistic based on a truncated estimator of the unknown transformation functions, together with a bias-corrected sample covariance. Furthermore, we show that the new test statistic converges to the same distribution as its oracle counterpart does. Both synthetic data and real cancer genomic data are used to illustrate the promise of the new method. Our proposed testing framework is simple and scalable, facilitating its applications to large-scale data. The computational pipeline has been implemented in the R package , which is freely available through the Comprehensive R Archive Network.

Testing Differential Gene Networks under Nonparanormal Graphical Models with False Discovery Rate Control.

Cancer is in general not a result of an abnormality of a single gene but a consequence of changes in many genes, it is therefore of great importance to understand the roles of different oncogenic and tumor suppressor pathways in tumorigenesis. In recent years, there have been many computational models developed to study the genetic alterations of different pathways in the evolutionary process of cancer. However, most of the methods are knowledge-based enrichment analyses and inflexible to analyze user-defined pathways or gene sets. In this paper, we develop a nonparametric and data-driven approach to testing for the dynamic changes of pathways over the cancer progression. Our method is based on an expansion and refinement of the pathway being studied, followed by a graph-based multivariate test, which is very easy to implement in practice. The new test is applied to the rich Cancer Genome Atlas data to study the (epi)genetic alterations of 186 KEGG pathways in the development of serous ovarian cancer. To make use of the comprehensive data, we incorporate three data types in the analysis representing gene expression level, copy number and DNA methylation level. Our analysis suggests a list of nine pathways that are closely associated with serous ovarian cancer progression, including cell cycle, ERBB, JAK-STAT signaling and p53 signaling pathways. By pairwise tests, we found that most of the identified pathways contribute only to a particular transition step. For instance, the cell cycle and ERBB pathways play key roles in the early-stage transition, while the ECM receptor and apoptosis pathways contribute to the progression from stage III to stage IV. The proposed computational pipeline is powerful in detecting important pathways and gene sets that drive cancers at certain stage(s). It offers new insights into the understanding of molecular mechanism of cancer initiation and progression.

A graph-based multi-sample test for identifying pathways associated with cancer progression.

Compositional data refer to the data that lie on a simplex, which are common in many scientific domains such as genomics, geology and economics. As the components in a composition must sum to one, traditional tests based on unconstrained data become inappropriate, and new statistical methods are needed to analyze this special type of data.

A distance based multisample test for high-dimensional compositional data with applications to the human microbiome.

Bacterial infection and inflammation of the airways are the leading causes of morbidity and mortality in persons with cystic fibrosis (CF). The ecology of the bacterial communities inhabiting CF airways is poorly understood, especially with respect to how community structure, dynamics, and microbial metabolic activity relate to clinical outcomes. In this study, the bacterial communities in 818 sputum samples from 109 persons with CF were analyzed by sequencing bacterial 16S rRNA gene amplicons. We identified eight alternative community types (pulmotypes) by using a Dirichlet multinomial mixture model and studied their temporal dynamics in the cohort. Across patients, the pulmotypes displayed chronological patterns in the transition among each other. Furthermore, significant correlations between pulmotypes and patient clinical status were detected by using multinomial mixed effects models, principal components regression, and statistical testing. Constructing pulmotype-specific metabolic activity profiles, we found that pulmotype microbiota drive distinct community functions including mucus degradation or increased acid production. These results indicate that pulmotypes are the result of ordered, underlying drivers such as predominant metabolism, ecological competition, and niche construction and can form the basis for quantitative, predictive models supporting clinical treatment decisions.

Article	Year
A mixture copula Bayesian network model for multimodal genomic data. Cancer informatics\| PubMed ID: 28469391	2017
A powerful nonparametric method for detecting differentially co-expressed genes: distance correlation screening and edge-count test. BMC systems biology\| PubMed ID: 29769129	2018
Model-free feature screening for categorical outcomes: Nonlinear effect detection and false discovery rate control. PloS one\| PubMed ID: 31150453	2019
Testing Differential Gene Networks under Nonparanormal Graphical Models with False Discovery Rate Control. Genes\| PubMed ID: 32033447	2020
A graph-based multi-sample test for identifying pathways associated with cancer progression. Computational biology and chemistry\| PubMed ID: 32521496	2020
A distance based multisample test for high-dimensional compositional data with applications to the human microbiome. BMC bioinformatics\| PubMed ID: 33272203	2020
Association of bacterial community types, functional microbial processes and lung disease in cystic fibrosis airways. The ISME journal\| PubMed ID: 34689185	2022

Qingyang Zhang

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