Single-cell gene expression analysis reveals β-cell dysfunction and deficit mechanisms in type 2 diabetes.

ABSTRACT:

Background

Type 2 diabetes (T2D) is one of the most common chronic diseases. Studies on T2D are mainly built upon bulk-cell data analysis, which measures the average gene expression levels for a population of cells and cannot capture the inter-cell heterogeneity. The single-cell RNA-sequencing technology can provide additional information about the molecular mechanisms of T2D at single-cell level.

Results

In this work, we analyze three datasets of single-cell transcriptomes to reveal β-cell dysfunction and deficit mechanisms in T2D. Focused on the expression levels of key genes, we conduct discrimination of healthy and T2D β-cells using five machine learning classifiers, and extracted major influential factors by calculating correlation coefficients and mutual information. O

SUBMITTER: Ma L 

PROVIDER: S-EPMC6311914 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature