A Dirichlet-Multinomial Bayes Classifier for Disease Diagnosis with Microbial Compositions

Naught all zeros in sequence count data are the same

Genomic studies feature multivariate count data from high-throughput DNA sequencing experiments, which often contain many zero values. These zeros can cause artifacts for statistical analyses and multiple modeling approaches have been developed in response. Here, we apply different zero-handling models to gene-expression and microbiome datasets and show models can disagree substantially in terms of identifying the most differentially expressed sequences. Next, to rationally examine how different zero handling models behave, we developed a conceptual framework outlining four types of processes that may give rise to zero values in sequence count data. Last, we performed simulations to test how zero handling models behave in the presence of these different zero generating processes. Our simulations showed that simple count models are sufficient across multiple processes, even when the true underlying process is unknown. On the other hand, a common zero handling technique known as "zero-inflation" was only suitable under a zero generating process associated with an unlikely set of biological and experimental conditions. In concert, our work here suggests several specific guidelines for developing and choosing state-of-the-art models for analyzing sparse sequence count data.

A Bayesian classification model for discriminating common infectious diseases in Zhejiang province, China

To develop a classification model for accurately discriminating common infectious diseases in Zhejiang province, China.Symptoms and signs, abnormal lab test results, epidemiological features, as well as the incidence rates were treated as predictors, and were collected from the published literature and a national surveillance system of infectious disease. A classification model was established using naïve Bayesian classifier. Dataset from historical outbreaks was applied for model validation, while sensitivity, specificity, accuracy, area under the receiver operating characteristic curve (AUC) and M-index were presented.A total of 146 predictors were included in the classification model, for discriminating 25 common infectious diseases. The sensitivity ranged from 44.44% for hepatitis E to 96.67% for measles. The specificity varied from 96.36% for dengue fever to 100% for 5 diseases. The median of total accuracy was 97.41% (range: 93.85%-99.04%). The AUCs exceeded 0.98 in 11 of 12 diseases, except in dengue fever (0.613). The M-index was 0.960 (95%CI 0.941-0.978).A novel classification model was constructed based on Bayesian approach to discriminate common infectious diseases in Zhejiang province, China. After entering symptoms and signs, abnormal lab test results, epidemiological features and city of disease origin, an output list of possible diseases ranked according to the calculated probabilities can be provided. The discrimination performance was reasonably good, making it useful in epidemiological applications.

Leveraging Human Microbiome Features to Diagnose and Stratify Children with Irritable Bowel Syndrome

Accurate diagnosis and stratification of children with irritable bowel syndrome (IBS) remain challenging. Given the central role of recurrent abdominal pain in IBS, we evaluated the relationships of pediatric IBS and abdominal pain with intestinal microbes and fecal metabolites using a comprehensive clinical characterization and multiomics strategy. Using rigorous clinical phenotyping, we identified preadolescent children (aged 7 to 12 years) with Rome III IBS (n = 23) and healthy controls (n = 22) and characterized their fecal microbial communities using whole-genome shotgun metagenomics and global unbiased fecal metabolomic profiling. Correlation-based approaches and machine learning algorithms identified associations between microbes, metabolites, and abdominal pain. IBS cases differed from controls with respect to key bacterial taxa (eg, Flavonifractor plautii and Lachnospiraceae bacterium 7_1_58FAA), metagenomic functions (eg, carbohydrate metabolism and amino acid metabolism), and higher-order metabolites (eg, secondary bile acids, sterols, and steroid-like compounds). Significant associations between abdominal pain frequency and severity and intestinal microbial features were identified. A random forest classifier built on metagenomic and metabolic markers successfully distinguished IBS cases from controls (area under the curve, 0.93). Leveraging multiple lines of evidence, intestinal microbes, genes/pathways, and metabolites were associated with IBS, and these features were capable of distinguishing children with IBS from healthy children. These multi-omics features, and their links to childhood IBS coupled with nutritional interventions, may lead to new microbiome-guided diagnostic and therapeutic strategies.