Study on fatty liver based on Pseudotime analysis

Abstract

In recent decades, the unhealthy diet and sedentary lifestyles of people are taking their toll on growing cases of metabolic diseases worldwide, one of them being Nonalcoholic Fatty Liver Disease (NAFLD). This disease has become one of the most sophisticated medical and physiological puzzles because of its convoluted mechanisms of progression.Existing gene expression analysis methods like microarray or RNA-sequencing are unable to resolve the complex mechanisms of progression of non-alcoholic fatty liver disease (NAFLD) due to insufficient accuracy and lack of phenotypic data. Particularly, incomplete phenotypic data in public liver gene expression cohorts have cumbered many studies on the progression of NAFLD. To address this issue, the cutting-edge pseudotime analysis is adopted to estimate liver health status in human liver gene expression data. The identified DE genes separate the NAFLD patients and the healthy controls in hierarchical clustering, and their related biological pathways are highly relevant to liver signaling and injury, implying the close relationship between the DE gene expressions and NAFLD. What's more, the pseudotime analysis we conducted simulates the deterioration of NAFLD by using liver fat percent to represent NAFLD severity and aligning the candidate samples on the estimated trajectory according to their respective gene expression and covariates; we verified the pseudotime model using another microarray cohort.