Data integration and inference of gene regulation using single-cell temporal multimodal data with scTIE.

Single-cell technologies offer unprecedented opportunities to dissect gene regulatory mechanisms in context-specific ways. Although there are computational methods for extracting gene regulatory relationships from scRNA-seq and scATAC-seq data, the data integration problem, essential for accurate cell type identification, has been mostly treated as a standalone challenge. Here we present scTIE, a unified method that integrates temporal multimodal data and infers regulatory relationships predictive of cellular state changes.

Heritability enrichment in context-specific regulatory networks improves phenotype-relevant tissue identification.

Systems genetics holds the promise to decipher complex traits by interpreting their associated SNPs through gene regulatory networks derived from comprehensive multi-omics data of cell types, tissues, and organs. Here, we propose SpecVar to integrate paired chromatin accessibility and gene expression data into context-specific regulatory network atlas and regulatory categories, conduct heritability enrichment analysis with genome-wide association studies (GWAS) summary statistics, identify relevant tissues, and estimate relevance correlation to depict common genetic factors acting in the shared regulatory networks between traits. Our method improves power upon existing approaches by associating SNPs with context-specific regulatory elements to assess heritability enrichments and by explicitly prioritizing gene regulations underlying relevant tissues.

Regulatory analysis of single cell multiome gene expression and chromatin accessibility data with scREG.

Technological development has enabled the profiling of gene expression and chromatin accessibility from the same cell. We develop scREG, a dimension reduction methodology, based on the concept of cis-regulatory potential, for single cell multiome data. This concept is further used for the construction of subpopulation-specific cis-regulatory networks.

Sc-compReg enables the comparison of gene regulatory networks between conditions using single-cell data.

The comparison of gene regulatory networks between diseased versus healthy individuals or between two different treatments is an important scientific problem. Here, we propose sc-compReg as a method for the comparative analysis of gene expression regulatory networks between two conditions using single cell gene expression (scRNA-seq) and single cell chromatin accessibility data (scATAC-seq). Our software, sc-compReg, can be used as a stand-alone package that provides joint clustering and embedding of the cells from both scRNA-seq and scATAC-seq, and the construction of differential regulatory networks across two conditions.

Chromatin accessibility landscape and regulatory network of high-altitude hypoxia adaptation.

High-altitude adaptation of Tibetans represents a remarkable case of natural selection during recent human evolution. Previous genome-wide scans found many non-coding variants under selection, suggesting a pressing need to understand the functional role of non-coding regulatory elements (REs). Here, we generate time courses of paired ATAC-seq and RNA-seq data on cultured HUVECs under hypoxic and normoxic conditions.

Time course regulatory analysis based on paired expression and chromatin accessibility data.

A time course experiment is a widely used design in the study of cellular processes such as differentiation or response to stimuli. In this paper, we propose course ulatory analysis (TimeReg) as a method for the analysis of gene regulatory networks based on paired gene expression and chromatin accessibility data from a time course. TimeReg can be used to prioritize regulatory elements, to extract core regulatory modules at each time point, to identify key regulators driving changes of the cellular state, and to causally connect the modules across different time points.

TFAP2C- and p63-Dependent Networks Sequentially Rearrange Chromatin Landscapes to Drive Human Epidermal Lineage Commitment.

Tissue development results from lineage-specific transcription factors (TFs) programming a dynamic chromatin landscape through progressive cell fate transitions. Here, we define epigenomic landscape during epidermal differentiation of human pluripotent stem cells (PSCs) and create inference networks that integrate gene expression, chromatin accessibility, and TF binding to define regulatory mechanisms during keratinocyte specification. We found two critical chromatin networks during surface ectoderm initiation and keratinocyte maturation, which are driven by TFAP2C and p63, respectively.

Lipid-gene regulatory network reveals coregulations of triacylglycerol with phosphatidylinositol/lysophosphatidylinositol and with hexosyl-ceramide.

Lipid homeostasis is important for executing normal cellular functions and maintaining physiological conditions. The biophysical properties and intricate metabolic network of lipids underlie the coordinated regulation of different lipid species in lipid homeostasis. To reveal the homeostatic response among different lipids, we systematically knocked down 40 lipid metabolism genes in Drosophila S2 cells by RNAi and profiled the lipidomic changes.

A Semisupervised Classification Approach for Multidomain Networks With Domain Selection.

Multidomain network classification has attracted significant attention in data integration and machine learning, which can enhance network classification or prediction performance by integrating information from different sources. Despite the previous success, existing multidomain network learning methods usually assume that different views are available for the same set of instances, and thus, they seek a consistent classification result for all domains. However, in many real-world problems, each domain has its specific instance set, and one instance in one domain may correspond to multiple instances in another domain.

Drosophila TRF2 and TAF9 regulate lipid droplet size and phospholipid fatty acid composition.

The general transcription factor TBP (TATA-box binding protein) and its associated factors (TAFs) together form the TFIID complex, which directs transcription initiation. Through RNAi and mutant analysis, we identified a specific TBP family protein, TRF2, and a set of TAFs that regulate lipid droplet (LD) size in the Drosophila larval fat body. Among the three Drosophila TBP genes, trf2, tbp and trf1, only loss of function of trf2 results in increased LD size.

Identifying network biomarkers based on protein-protein interactions and expression data.

Identifying effective biomarkers to battle complex diseases is an important but challenging task in biomedical research today. Molecular data of complex diseases is increasingly abundant due to the rapid advance of high throughput technologies. However, a great gap remains in identifying the massive molecular data to phenotypic changes, in particular, at a network level, i.