Categorical Data Analysis for High-Dimensional Sparse Gene Expression Data.

Categorical data analysis becomes challenging when high-dimensional sparse covariates are involved, which is often the case for omics data. We introduce a statistical procedure based on multinomial logistic regression analysis for such scenarios, including variable screening, model selection, order selection for response categories, and variable selection. We perform our procedure on high-dimensional gene expression data with 801 patients, 2426 genes, and five types of cancerous tumors. As a result, we recommend three finalized models: one with 74 genes achieves extremely low cross-entropy loss and zero predictive error rate based on a five-fold cross-validation; and two other models with 31 and 4 genes, respectively, are recommended for prognostic multi-gene signatures.

Variable Selection for Sparse Data with Applications to Vaginal Microbiome and Gene Expression Data.

Sparse data with a high portion of zeros arise in various disciplines. Modeling sparse high-dimensional data is a challenging and growing research area. In this paper, we provide statistical methods and tools for analyzing sparse data in a fairly general and complex context. We utilize two real scientific applications as illustrations, including a longitudinal vaginal microbiome data and a high dimensional gene expression data. We recommend zero-inflated model selections and significance tests to identify the time intervals when the pregnant and non-pregnant groups of women are significantly different in terms of Lactobacillus species. We apply the same techniques to select the best 50 genes out of 2426 sparse gene expression data. The classification based on our selected genes achieves 100% prediction accuracy. Furthermore, the first four principal components based on the selected genes can explain as high as 83% of the model variability.

Arsenic Exposure Decreases Adiposity During High-Fat Feeding.

OBJECTIVE: Arsenic is an endocrine-disrupting chemical associated with diabetes risk. Increased adiposity is a significant risk factor for diabetes and its comorbidities. Here, the impact of chronic arsenic exposure on adiposity and metabolic health was assessed in mice. METHODS: Male C57BL/6J mice were provided ad libitum access to a normal or high-fat diet and water +/- 50 mg/L of sodium arsenite. Changes in body weight, body composition, insulin sensitivity, energy expenditure, and locomotor activity were measured. Measures of adiposity were compared with accumulated arsenic in the liver. RESULTS: Despite uniform arsenic exposure, internal arsenic levels varied significantly among arsenic-exposed mice. Hepatic arsenic levels in exposed mice negatively correlated with overall weight gain, individual adipose depot masses, and hepatic triglyceride accumulation. No effects were observed in mice on a normal diet. For mice on a high-fat diet, arsenic exposure reduced fasting insulin levels, homeostatic model assessment of insulin resistance and ß-cell function, and systemic insulin resistance. Arsenic exposure did not alter energy expenditure or activity. CONCLUSIONS: Collectively, these data indicate that arsenic is antiobesogenic and that concentration at the source poorly predicts arsenic accumulation and phenotypic outcomes. In future studies, investigators should consider internal accumulation of arsenic rather than source concentration when assessing the outcomes of arsenic exposure.

Developmental exposure to the endocrine disruptor tolylfluanid induces sex-specific later-life metabolic dysfunction.

Endocrine-disrupting chemicals (EDCs) are implicated in the developmental mis-programming of energy metabolism. This study examined the impact of combined gestational and lactational exposure to the fungicide tolylfluanid (TF) on metabolic physiology in adult offspring. C57BL/6 J dams received standard rodent chow or the same diet containing 67 mg/kg TF. Offspring growth and metabolism were assessed up to 22 weeks of age. TF-exposed offspring exhibited reduced weaning weight. Body weight among female offspring remained low throughout the study, while male offspring matched controls by 17 weeks of age. Female offspring exhibited reduced glucose tolerance, markedly enhanced systemic insulin sensitivity, reduced adiposity, and normal gluconeogenic capacity during adulthood. In contrast, male offspring exhibited impaired glucose tolerance with unchanged insulin sensitivity, no differences in adiposity, and increased gluconeogenic capacity. These data indicate that developmental exposure to TF induces sex-specific metabolic disruptions that recapitulate key aspects of other in utero growth restriction models.