Advanced machine learning techniques for microarray spot quality classification

Comparative Evaluation of MS-based Metabolomics Software and Its Application to Preclinical Alzheimer's Disease

Mass spectrometry-based metabolomics has undergone significant progresses in the past decade, with a variety of software packages being developed for data analysis. However, systematic comparison of different metabolomics software tools has rarely been conducted. In this study, several representative software packages were comparatively evaluated throughout the entire pipeline of metabolomics data analysis, including data processing, statistical analysis, feature selection, metabolite identification, pathway analysis, and classification model construction. LC-MS-based metabolomics was applied to preclinical Alzheimer's disease (AD) using a small cohort of human cerebrospinal fluid (CSF) samples (N = 30). All three software packages, XCMS Online, SIEVE, and Compound Discoverer, provided consistent and reproducible data processing results. A hybrid method combining statistical test and support vector machine feature selection was employed to screen key metabolites, achieving a complementary selection of candidate biomarkers from three software packages. Machine learning classification using candidate biomarkers generated highly accurate and predictive models to classify patients into preclinical AD or control category. Overall, our study demonstrated a systematic evaluation of different MS-based metabolomics software packages for the entire data analysis pipeline which was applied to the candidate biomarker discovery of preclinical AD.

In-Depth Characterization and Validation of Human Urine Metabolomes Reveal Novel Metabolic Signatures of Lower Urinary Tract Symptoms

Lower urinary tract symptoms (LUTS) are a range of irritative or obstructive symptoms that commonly afflict aging population. The diagnosis is mostly based on patient-reported symptoms, and current medication often fails to completely eliminate these symptoms. There is a pressing need for objective non-invasive approaches to measure symptoms and understand disease mechanisms. We developed an in-depth workflow combining urine metabolomics analysis and machine learning bioinformatics to characterize metabolic alterations and support objective diagnosis of LUTS. Machine learning feature selection and statistical tests were combined to identify candidate biomarkers, which were statistically validated with leave-one-patient-out cross-validation and absolutely quantified by selected reaction monitoring assay. Receiver operating characteristic analysis showed highly-accurate prediction power of candidate biomarkers to stratify patients into disease or non-diseased categories. The key metabolites and pathways may be possibly correlated with smooth muscle tone changes, increased collagen content, and inflammation, which have been identified as potential contributors to urinary dysfunction in humans and rodents. Periurethral tissue staining revealed a significant increase in collagen content and tissue stiffness in men with LUTS. Together, our study provides the first characterization and validation of LUTS urinary metabolites and pathways to support the future development of a urine-based diagnostic test for LUTS.