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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,

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Motivation Joint profiling of single-cell transcriptomics and epigenomics data enables us to characterize cell states and transcriptomics regulatory programs related to cellular heterogeneity. However, the highly different features on sparsity, heterogeneity, and dimensionality between multi-omics data have severely hindered its integrative analysis. Results We proposed deep cross-omics cycle attention (DCCA) model, a computational tool for joint analysis of single-cell multi-omics data, by combining variational autoencoders (VAEs) and attention-transfer. Specifically, we show that DCCA can leverage one omics data to fine-tune the network trained for another omics data, given a dataset of parallel multi-omics data within the same cell. Studies on both simulated and real datasets from various platforms, DCCA demonstrates its superior capability: (i) dissecting cellular heterogeneity; (ii) denoising and aggregating data; and (iii) constructing the link between multi-omics data, which is used to infer new transcriptional regulatory relations. In our applications, DCCA was demonstrated to have a superior power to generate missing stages or omics in a biologically meaningful manner, which provides a new way to analyze and also understand complicated biological processes. Availability and implementation DCCA source code is available at https://github.com/cmzuo11/DCCA, and has been deposited in archived format at https://doi.org/10.5281/zenodo.4762065. Supplementary information Supplementary data are available at Bioinformatics online.

Identifying potential protein-ligand interactions is central to the field of drug discovery as it facilitates the identification of potential novel drug leads, contributes to advancement from hits to leads, predicts potential off-target explanations for side effects of approved drugs or candidates, as well as de-orphans phenotypic hits. For the rapid identification of protein-ligand interactions, we here present a novel chemogenomics algorithm for the prediction of protein-ligand interactions using a new machine learning approach and novel class of descriptor. The algorithm applies Bayesian Additive Regression Trees (BART) on a newly proposed proteochemical space, termed the bow-pharmacological space. The space spans three distinctive sub-spaces that cover the protein space, the ligand space, and the interaction space. Thereby, the model extends the scope of classical target prediction or chemogenomic modelling that relies on one or two of these subspaces. Our model demonstrated excellent prediction power, reaching accuracies of up to 94.5–98.4% when evaluated on four human target datasets constituting enzymes, nuclear receptors, ion channels, and G-protein-coupled receptors . BART provided a reliable probabilistic description of the likelihood of interaction between proteins and ligands, which can be used in the prioritization of assays to be performed in both discovery and vigilance phases of small molecule development.

The initiation and development of storage roots (SRs) are intricately regulated by a transcriptional regulatory network. One key challenge is to accurately pinpoint the tipping point during the transition from pre-swelling to SRs and to identify the core regulators governing such a critical transition. To solve this problem, we performed a dynamic network biomarker (DNB) analysis of transcriptomic dynamics during root development in Ipomoea batatas (sweet potato). First, our analysis identified stage-specific expression patterns for a significant proportion (>9%) of the sweet potato genes and unraveled the chronology of events that happen at the early and later stages of root development. Then, the results showed that different root developmental stages can be depicted by co-expressed modules of sweet potato genes. Moreover, we identified the key components and transcriptional regulatory network that determine root development. Furthermore, through DNB analysis an early stage, with a root diameter of 3.5 mm, was identified as the critical period of SR swelling initiation, which is consistent with morphological and metabolic changes. In particular, we identified a NAM/ATAF/CUC (NAC) domain transcription factor, IbNAC083, as a core regulator of this initiation in the DNB-associated network. Further analyses and experiments showed that IbNAC083, along with its associated differentially expressed genes, induced dysfunction of metabolism processes, including the biosynthesis of lignin, flavonol and starch, thus leading to the transition to swelling roots.

To investigate whether CTLA-4 +49 G/A (rs231775), a tagSNP in Asian, is a functional T1D SNP, we genotyped this SNP with 1035 T1D patients and 2575 controls in Chinese Han population. And 1280 controls measured insulin release and sensitivity based on an oral glucose tolerance test; 283 newly diagnosed T1D patients assayed C-peptide level based on a mixed-meal tolerance test. 31 controls were analyzed for different T cell subsets by multi-color flow cytometry. Under additive model, we found that CTLA-4 +49 G/A was significantly associated with T1D (P = 2.82E-04, OR = 1.25, 95% CI: 1.12–1.41), which was further confirmed by meta-analysis (P = 1.19E-08, OR = 1.65, 95% CI: 1.38–1.96) in Chinese Han population. Although we did not find any association between this SNP and beta-cell function in either healthy individuals or newly diagnosed T1D patients, healthy individuals carrying GG/GA genotypes had lower CTLA-4 expression in naïve or activated CD4 Treg subsets (P = 0.0046 and 0.0317 respectively). A higher positive rate of IA-2A was observed among T1D patients with GG genotype compared with AA (OR = 0.51, 95% CI: 0.30–0.84, p = 0.008). Collectively, CTLA-4 +49 G/A reached a GWAS significant association with T1D risk in Chinese Han population, affects CTLA-4 expression in Treg subsets and subsequently humoral immunity in T1D patients.