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Single-cell RNA sequencing (scRNA-seq) is able to give an insight into the gene–gene associations or transcriptional networks among cell populations based on the sequencing of a large number of cells. However, traditional network methods are limited to the grouped cells instead of each single cell, and thus the heterogeneity of single cells will be erased. We present a new method to construct a cell-specific network (CSN) for each single cell from scRNA-seq data (i.e. one network for one cell), which transforms the data from ‘unstable’ gene expression form to ‘stable’ gene association form on a single-cell basis. In particular, it is for the first time that we can identify the gene associations/network at a single-cell resolution level. By CSN method, scRNA-seq data can be analyzed for clustering and pseudo-trajectory from network perspective by any existing method, which opens a new way to scRNA-seq data analyses. In addition, CSN is able to find differential gene associations for each single cell, and even ‘dark’ genes that play important roles at the network level but are generally ignored by traditional differential gene expression analyses. In addition, CSN can be applied to construct individual network of each sample bulk RNA-seq data. Experiments on various scRNA-seq datasets validated the effectiveness of CSN in terms of accuracy and robustness.

Type 1 diabetes (T1D) is a highly heritable disease with much lower incidence but more adult-onset cases in the Chinese population. Although genome-wide association studies (GWAS) have identified >60 T1D loci in Caucasians, less is known in Asians. RESEARCH DESIGN AND METHODS We performed the first two-stage GWAS of T1D using 2,596 autoantibody-positive T1D case subjects and 5,082 control subjects in a Chinese Han population and evaluated the associations between the identified T1D risk loci and age and fasting C-peptide levels at T1D diagnosis. RESULTS We observed a high genetic correlation between children/adolescents and adult T1D case subjects (r g = 0.87), as well as subgroups of autoantibody status (r g ‡ 0.90). We identified four T1D risk loci reaching genome-wide significance in the Chinese Han population, including two novel loci, rs4320356 near BTN3A1 (odds ratio [OR] 1.26,