ABSTRACT
BACKGROUND: Asthma is an inflammatory lung disease that constitutes the most common noncommunicable chronic disease in childhood. Childhood asthma shows large heterogeneity regarding onset of disease, symptoms, severity, prognosis, and response to therapy. MAIN BODY: Evidence suggests that this variability is due to distinct pathophysiological mechanisms, which has led to an exhaustive research effort to understand and characterize these distinct entities currently designated as "endotypes." Initially, studies focused on identifying specific groups using clinical variables yielding different "clinical phenotypes." In addition, the identification of specific patterns based on inflammatory cell counts and cytokine data has resulted in "inflammatory endotypes." More recently, an increasing number of molecular data from high-throughput technology ("omics" data) have allowed to investigate more complex "molecular endotypes." CONCLUSION: A better definition and comprehension of childhood asthma heterogeneity is key for improving diagnosis and treatment. This review aims at summarizing the current knowledge on this topic and discusses some limitations in their application as well as recommendations for future studies.
ABSTRACT
Large metabolomics datasets inevitably contain unwanted technical variations which can obscure meaningful biological signals and affect how this information is applied to personalized healthcare. Many methods have been developed to handle unwanted variations. However, the underlying assumptions of many existing methods only hold for a few specific scenarios. Some tools remove technical variations with models trained on quality control (QC) samples which may not generalize well on subject samples. Additionally, almost none of the existing methods supports datasets with multiple types of QC samples, which greatly limits their performance and flexibility. To address these issues, a non-parametric method TIGER (Technical variation elImination with ensemble learninG architEctuRe) is developed in this study and released as an R package (https://CRAN.R-project.org/package=TIGERr). TIGER integrates the random forest algorithm into an adaptable ensemble learning architecture. Evaluation results show that TIGER outperforms four popular methods with respect to robustness and reliability on three human cohort datasets constructed with targeted or untargeted metabolomics data. Additionally, a case study aiming to identify age-associated metabolites is performed to illustrate how TIGER can be used for cross-kit adjustment in a longitudinal analysis with experimental data of three time-points generated by different analytical kits. A dynamic website is developed to help evaluate the performance of TIGER and examine the patterns revealed in our longitudinal analysis (https://han-siyu.github.io/TIGER_web/). Overall, TIGER is expected to be a powerful tool for metabolomics data analysis.
