ABSTRACT
Although hypoxia is a critical factor that can drive the progression of various diseases, the mechanism underlying hypoxia itself remains unclear. Recently, m6A has been proposed as an important factor driving hypoxia. Despite successful analyses, potential genes were not selected with statistical significance but were selected based solely on fold changes. Because the number of genes is large while the number of samples is small, it was impossible to select genes using conventional feature selection methods with statistical significance. In this study, we applied the recently proposed principal component analysis (PCA), tensor decomposition (TD), and kernel tensor decomposition (KTD)-based unsupervised feature extraction (FE) to a hypoxia data set. We found that PCA, TD, and KTD-based unsupervised FE could successfully identify a limited number of genes associated with altered gene expression and m6A profiles, as well as the enrichment of hypoxia-related biological terms, with improved statistical significance.
Subject(s)
Algorithms , Databases, Nucleic Acid , Gene Expression Profiling , Gene Expression Regulation , Hypoxia , Models, Biological , Humans , Hypoxia/genetics , Hypoxia/metabolismABSTRACT
A hypothetical evolutionary relationship was generated between the nuclear reprogramming factors for induced pluripotent stem (iPS) cells generation. Utilizing bioinformatics techniques, sequence analyses and phylogenetic tree algorithms, a comparative study has been performed to understand the evolutionary relationship of human nuclear reprogramming factors of induced pluripotent stem cells (iPSCs) generation. Among the total six nuclear reprogramming factors, the four reprogramming factors (SOX2, C-MYC, KLF4, and LIN28) have significant evolutionary origin. Our study shows SOX2 and C-MYC have evolutionary relationship and common point of origin. Likewise, KLF4 and LIN28 are having evolutionary relationship and have common point of origin. Based on these evidences, we propose that our study may be a great help to the future researchers to understand the mechanism(s) as well as pathway of nuclear reprogramming process.