A5006051114- Single-cell and spatial transcriptomics
- Immune cells in cancer
- Cancer Genomics and Diagnostics
- Cancer-related molecular mechanisms research
- Ocular Oncology and Treatments
- Gene expression and cancer classification
- Vascular Tumors and Angiosarcomas
- MicroRNA in disease regulation
- Ear and Head Tumors
- AI in cancer detection
- Cytomegalovirus and herpesvirus research
- T-cell and B-cell Immunology
- Retinal Development and Disorders
- Cell Image Analysis Techniques
- Advanced Optical Sensing Technologies
Tsinghua University
2022-2025
Gansu Provincial Hospital
2025
Gansu University of Traditional Chinese Medicine
2025
Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) techniques hold great value in evaluating the heterogeneity characteristics of hematopoietic cells within tissues. These two are highly complementary, with scRNA-seq offering single-cell resolution ST retaining information. However, there is an urgent demand for well-organized user-friendly toolkits capable handling Here, we present HemaScope, a specialized bioinformatics toolkit featuring modular designs to analyze data...
Abstract Sequencing-based spatial transcriptomics (ST) is an emerging technology to study in situ gene expression patterns at the whole-genome scale. Currently, ST data analysis still complicated by high technical noises and low resolution. In addition transcriptomic data, matched histopathological images are usually generated for same tissue sample along experiment. The high-resolution provide complementary cellular phenotypical information, providing opportunity mitigate data. We present a...
Abstract Summary Single-cell RNA-seq (scRNA-seq) is a powerful technique for decoding the complex cellular compositions in tumor microenvironment (TME). As previous studies have defined many meaningful cell subtypes several types, there great need to computationally transfer these labels new datasets. Also, different used approaches or criteria define same major lineages. The relationships between should be carefully evaluated. In this updated package scCancer2, designed integrative...
Large-scale transcriptomic data are crucial for understanding the molecular features of hepatocellular carcinoma (HCC). Integrated 15 datasets HCC clinical samples, first version database (HCCDB v1.0) was released in 2018. Through meta-analysis differentially expressed genes and prognosis-related across multiple datasets, it provides a systematic view altered biological processes inter-patient heterogeneities with high reproducibility robustness. With four years having passed, now needs...
Abstract The liver performs several vital functions such as metabolism, toxin removal and glucose storage through the coordination of various cell types. type compositions cellular states undergo significant changes in abnormal conditions fatty liver, cirrhosis cancer. As recent breakthrough single-cell/single-nucleus RNA-seq (sc/snRNA-seq) techniques, there is a great opportunity to establish reference map at single resolution with transcriptome-wise features. In this study, we build...
Abstract Large-scale transcriptomic data are crucial for understanding the molecular features of hepatocellular carcinoma (HCC). By integrating 15 datasets HCC clinical samples, first version HCCDB was released in 2018. The meta-analysis differentially expressed genes and prognosis-related across multiple provides a systematic view altered biological processes inter-patient heterogeneities with high reproducibility robustness. After four years, database needs to integrate recently published...
Abstract Single-cell RNA-seq (scRNA-seq) is a powerful technique for decoding the complex cellular compositions in tumor microenvironment (TME). As previous studies have defined many meaningful cell subtypes several types, there great need to computationally transfer these labels new datasets. Also, different used approaches or criteria define same major lineages. The relationships between should be carefully evaluated. In this updated package scCancer2, designed integrative scRNA-seq data...
Abstract Sequencing-based spatial transcriptomics (ST) is an emerging technique to study in situ gene expression patterns at the whole-genome scale. In addition transcriptomic data, usually generates matched histopathological images for same tissue sample. ST data analysis complicated by severe technical noise; with high continuity and resolution introduce complementary cellular phenotypical information provide a chance mitigate noise data. Hence, we propose novel method called transcriptome...