AIMC Topic: Single-Cell Analysis

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COME: contrastive mapping learning for spatial reconstruction of single-cell RNA sequencing data.

Bioinformatics (Oxford, England)
MOTIVATION: Single-cell RNA sequencing (scRNA-seq) enables high-throughput transcriptomic profiling at single-cell resolution. The inherent spatial location is crucial for understanding how single cells orchestrate multicellular functions and drive d...

Enhancing single-cell classification accuracy using image conversion and deep learning.

Yi chuan = Hereditas
Single-cell transcriptome sequencing (scRNA-seq) is widely used in the fields of animal and plant developmental biology and important trait analysis by obtaining single-cell transcript abundance data in high throughput, which can deeply reveal cell t...

An Attention-Based Deep Neural Network Model to Detect Cis-Regulatory Elements at the Single-Cell Level From Multi-Omics Data.

Genes to cells : devoted to molecular & cellular mechanisms
Cis-regulatory elements (cREs) play a crucial role in regulating gene expression and determining cell differentiation and state transitions. To capture the heterogeneous transitions of cell states associated with these processes, detecting cRE activi...

stAI: a deep learning-based model for missing gene imputation and cell-type annotation of spatial transcriptomics.

Nucleic acids research
Spatial transcriptomics technology has revolutionized our understanding of cellular systems by capturing RNA transcript levels in their original spatial context. Single-cell spatial transcriptomics (scST) offers single-cell resolution expression leve...

Deep learning-based cell-specific gene regulatory networks inferred from single-cell multiome data.

Nucleic acids research
Gene regulatory networks (GRNs) provide a global representation of how genetic/genomic information is transferred in living systems and are a key component in understanding genome regulation. Single-cell multiome data provide unprecedented opportunit...

Towards the Next Generation of Data-Driven Therapeutics Using Spatially Resolved Single-Cell Technologies and Generative AI.

European journal of immunology
Recent advances in multi-omics and spatially resolved single-cell technologies have revolutionised our ability to profile millions of cellular states, offering unprecedented opportunities to understand the complex molecular landscapes of human tissue...

CSI-GEP: A GPU-based unsupervised machine learning approach for recovering gene expression programs in atlas-scale single-cell RNA-seq data.

Cell genomics
Exploratory analysis of single-cell RNA sequencing (scRNA-seq) typically relies on hard clustering over two-dimensional projections like uniform manifold approximation and projection (UMAP). However, such methods can severely distort the data and hav...

scTWAS Atlas: an integrative knowledgebase of single-cell transcriptome-wide association studies.

Nucleic acids research
Single-cell transcriptome-wide association studies (scTWAS) is a new method for conducting TWAS analysis at the cellular level to identify gene-trait associations with higher precision. This approach helps overcome the challenge of interpreting cell-...

Investigating the molecular mechanisms associated with ulcerative colitis through the application of single-cell combined spatial transcriptome sequencing.

Frontiers in immunology
BACKGROUND: Ulcerative colitis (UC) is a chronic inflammatory bowel disease marked by dysregulated immune responses, resulting in sustained inflammation and ulceration of the colonic and rectal mucosa. To elucidate the cellular subtypes and gene expr...