A5069224841- Single-cell and spatial transcriptomics
- Molecular Biology Techniques and Applications
- Primate Behavior and Ecology
- Cancer Genomics and Diagnostics
- Cancer-related molecular mechanisms research
- MicroRNA in disease regulation
- Gut microbiota and health
- Agriculture Sustainability and Environmental Impact
- Gene expression and cancer classification
Sun Yat-sen University
2021-2024
Biocon (Switzerland)
2024
University of Bath
2024
South China Agricultural University
2021
With the tremendous increase of publicly available single-cell RNA-sequencing (scRNA-seq) datasets, bioinformatics methods based on gene co-expression network are becoming efficient tools for analyzing scRNA-seq data, improving cell type prediction accuracy and in turn facilitating biological discovery. However, current mainly overall correlation overlook that exists only a subset cells, thus fail to discover certain rare types sensitive batch effect. Here, we developed independent component...
Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for uncovering cellular heterogeneity. However, the high costs associated with this technique have rendered it impractical studying large patient cohorts. We introduce ENIGMA (Deconvolution based on Regularized Matrix Completion), method that addresses limitation through accurately deconvoluting bulk tissue RNA-seq data into readout cell-type resolution by leveraging information from scRNA-seq data. By employing matrix...
Gut microbiotas have important impacts on host health, reproductive success, and survival. While extensive research in mammals has identified the exogenous (e.g. environment) endogenous phylogeny, sex, age) factors that shape gut microbiota composition functionality, yet avian systems remain comparatively less understood. Shorebirds, characterized by a well-resolved phylogeny diverse life-history traits, present an ideal model for dissecting modulating dynamics. Here, we provide insight into...
Abstract Single cell RNA-seq (scRNA-seq) has been widely used to uncover cellular heterogeneity, however, the constraints of cost make it impractical as a routine on large patient cohorts. Here we present ENIGMA, method that accurately deconvolute bulk tissue into single cell-type resolution given knowledge gained from scRNA-seq. ENIGMA applies matrix completion strategy minimize distance between mixture transcriptome and weighted combination type-specific expression, allowing quantification...