Spatial-MGCN: a novel multi-view graph convolutional network for identifying spatial domains with attention mechanism.

Motivation: Recent advances in spatial transcriptomics technologies have enabled gene expression profiles while preserving spatial context. Accurately identifying spatial domains is crucial for downstream analysis and it requires the effective integration of gene expression profiles and spatial information. While increasingly computational methods have been developed for spatial domain detection, most of them cannot adaptively learn the complex relationship between gene expression and spatial information, leading to sub-optimal performance.

scGCL: an imputation method for scRNA-seq data based on graph contrastive learning.

Motivation: Single-cell RNA-sequencing (scRNA-seq) is widely used to reveal cellular heterogeneity, complex disease mechanisms and cell differentiation processes. Due to high sparsity and complex gene expression patterns, scRNA-seq data present a large number of dropout events, affecting downstream tasks such as cell clustering and pseudo-time analysis. Restoring the expression levels of genes is essential for reducing technical noise and facilitating downstream analysis.

scSemiGAN: a single-cell semi-supervised annotation and dimensionality reduction framework based on generative adversarial network.

Motivation: Cell-type annotation plays a crucial role in single-cell RNA-seq (scRNA-seq) data analysis. As more and more well-annotated scRNA-seq reference data are publicly available, automatical label transference algorithms are gaining popularity over manual marker gene-based annotation methods. However, most existing methods fail to unify cell-type annotation with dimensionality reduction and are unable to generate deep latent representation from the perspective of data generation.

TMT-based comparative proteomics reveals the role of acyl-CoA oxidase 4 in enhancing the drought stress tolerance in common buckwheat (Fagopyrum esculentum).

Drought stress has been the main abiotic factor affecting the growth, development and production of common buckwheat (Fagopyrum esculentum). To explore the response mechanisms of regulating buckwheat drought stress on the post-transcriptional and translational levels, a comparative proteomic analysis was applied to monitor the short-term proteomic variations under the drought stress in the seedling stage. From which 593 differentially abundant proteins (DAPs) were identified using the TMT-based proteomics analysis.