Identification of Type 2 Diabetes Biomarkers From Mixed Single-Cell Sequencing Data With Feature Selection Methods - Li, Zhandong; Pan, Xiaoyong; Cai, Yu-Dong (Frontiers in Bioengineering and Biotechnology, 2022)
Diabetes is a most common disease and a major threaten to human health. Type 2 diabetes (T2D) make up about 90% of all cases. With the development of high-throughput sequencing technologies, more and more fundamental pathogenesis of T2D at genetic and transcriptomic levels have been revealed. The recent single cell sequencing can further reveal the cellular heterogenicity of complex diseases in an unprecedented way. With expectation on the molecular essence of T2D across multiple cell types, we investigated the expression profiling of more than 1600 single cells (949 cells from T2D patients and 651 cells from normal controls) and identified the differential expression profiling and characteristics at transcriptomics level that can distinguish such two groups of cells at single cell level. The expression profile was analyzed by several machine learning algorithms, including Monte Carlo feature selection, support vector machine and RIPPER. On one hand, some T2D associated genes (MTND4P24, MTND2P28, LOC100128906, etc.) were discovered. On the other hand, we revealed novel potential pathogenic mechanisms in a rule manner. They are induced by newly recognized genes and neglected by traditional bulk sequencing techniques. Particularly, the newly identified T2D genes were shown to follow specific quantitative rules with diabetes prediction potentials, and such rules further indicated several potential functional crosstalk involved in T2D.