A5085824436- Single-cell and spatial transcriptomics
- Gene expression and cancer classification
- Genomics and Chromatin Dynamics
- Advanced oxidation water treatment
- Environmental remediation with nanomaterials
- Epigenetics and DNA Methylation
- Bioinformatics and Genomic Networks
- Cell Image Analysis Techniques
- Advanced Proteomics Techniques and Applications
- Microbial bioremediation and biosurfactants
- Advanced Photocatalysis Techniques
- Metabolomics and Mass Spectrometry Studies
- Immune cells in cancer
- Neuroinflammation and Neurodegeneration Mechanisms
- RNA Research and Splicing
- Microplastics and Plastic Pollution
- Cancer-related molecular mechanisms research
- Marine Ecology and Invasive Species
- Adsorption and biosorption for pollutant removal
- Blockchain Technology Applications and Security
- Microbial Fuel Cells and Bioremediation
- Nanomaterials for catalytic reactions
- Hydrocarbon exploration and reservoir analysis
- Planarian Biology and Electrostimulation
- Spectroscopy Techniques in Biomedical and Chemical Research
Renmin University of China
2024-2025
Academy of Mathematics and Systems Science
2019-2024
University of Chinese Academy of Sciences
2019-2024
National Center for Mathematics and Interdisciplinary Sciences
2019-2024
China University of Petroleum, East China
2022-2024
Chinese Academy of Sciences
2019-2024
Recent advances in spatially resolved transcriptomics have enabled comprehensive measurements of gene expression patterns while retaining the spatial context tissue microenvironment. Deciphering spots a needs to use their information carefully. To this end, we develop graph attention auto-encoder framework STAGATE accurately identify domains by learning low-dimensional latent embeddings via integrating and profiles. better characterize similarity at boundary domains, adopts an mechanism...
Abstract Spatial transcriptomics characterizes gene expression profiles while retaining the information of spatial context, providing an unprecedented opportunity to understand cellular systems. One essential tasks in such data analysis is determine spatially variable genes (SVGs), which demonstrate patterns. Existing methods only consider individually and fail model inter-dependence genes. To this end, we present analytic tool STAMarker for robustly determining domain-specific SVGs with...
Abstract Whole-body regeneration of planarians is a natural wonder but how it occurs remains elusive. It requires coordinated responses from each cell in the remaining tissue with spatial awareness to regenerate new cells and missing body parts. While previous studies identified genes essential regeneration, more efficient screening approach that can identify regeneration-associated context needed. Here, we present comprehensive three-dimensional spatiotemporal transcriptomic landscape...
Abstract Spatial transcriptome technologies have enabled the measurement of gene expression while maintaining spatial location information for deciphering heterogeneity biological tissues. However, they were heavily limited by sparse resolution and low data quality. To this end, we develop a location-supervised auto-encoder generator STAGE generating high-density transcriptomics (ST). takes advantage customized supervised to learn continuous patterns in space generate high-resolution...
Abstract Recent advances in single-cell spatial transcriptomics (scST) have enabled the analysis of gene transcription levels individual cells while preserving their positions. Cell-type mapping and annotation are crucial understanding complex interactions between microenvironments within a context. To this end, we develop heterogeneous graph neural network, STAMapper, to transfer cell-type labels from RNA-seq data scST data. STAMapper captures both expression similarity among relationships...
ABSTRACT Recent advances in spatially resolved transcriptomics have enabled comprehensive measurements of gene expression patterns while retaining the spatial context tissue microenvironment. Deciphering spots a needs to use their information carefully. To this end, we developed graph attention auto-encoder framework STAGATE accurately identify domains by learning low-dimensional latent embeddings via integrating and profiles. better characterize similarity at boundary domains, adopts an...
Abstract With the rapid generation of spatial transcriptomics (ST) data, integrative analysis multiple ST datasets from different conditions, technologies, and developmental stages is becoming increasingly important. However, identifying shared specific domains across slices remains challenging. To this end, we develop a graph attention neural network STAligner for integrating aligning datasets, enabling spatially-aware data integration, simultaneous domain identification, downstream...
Single-cell RNA sequencing (scRNA-seq) provides a powerful tool to analyze the expression level of tissues at cellular resolution. However, it could not capture spatial organization cells in tissue. The spatially resolved transcriptomics technologies (ST) have been developed address this issue. emerging STs are still inefficient single-cell resolution and/or fail sufficient reads. To end, we adopted partial least squares-based method (spatial modular patterns [SpaMOD]) simultaneously...
The rapid accumulation of single-cell chromatin accessibility data offers a unique opportunity to investigate common and specific regulatory mechanisms across different cell types. However, existing methods for cis-regulatory network reconstruction using were only designed cells belonging one type, resulting networks may be incomparable directly due diverse numbers Here, we adopt computational method jointly reconstruct interaction maps (JRIM) multiple populations based on patterns...
Abstract Spatial transcriptomics characterizes gene expression profiles while retaining the information of spatial context, providing an unprecedented opportunity to understand cellular systems. One essential tasks in such data analysis is determine spatially variable genes (SVGs), which demonstrate patterns. Existing methods only consider individually and fail model inter-dependence genes. To this end, we present analytic tool STAMarker for robustly determining domain-specific SVGs with...
Abstract Imaging mass spectrometry (IMS) is one of the powerful tools in spatial metabolomics for obtaining metabolite data and probing internal microenvironment organisms. It has dramatically advanced understanding structure biological tissues drug treatment diseases. However, complexity IMS hinders further acquisition biomarkers study certain specific activities To this end, we introduce an artificial intelligence tool, SmartGate, to enable automatic peak selection identification iterative...
N and O dual-doped porous carbon (ONPC) is obtained using a simple one-step pyrolysis method for TC degradation. Graphitic N, pyridinic CO can synergistically catalyze PMS the generation of 2 ˙ − 1 .
Abstract Topologically associating domains (TADs) emerge as indispensable units in three-dimensional (3D) genome organization, playing a critical role gene regulation. However, accurately identifying TADs from sparse chromatin contact maps and exploring the structural functional elements within remain challenging. To this end, we develop graph attention auto-encoder, TADGATE, to identify even ultra-sparse generate imputed while preserving or enhancing underlying topological structures....
To enhance crude oil recovery under complex reservoir conditions and reduce the environmental impact of displacement system, a biosurfactant lipid peptide (TH) was combined with four chemical surfactants. From these combinations, those capable achieving an ultralow interfacial tension region (<10