AIMC Topic: Single-Cell Gene Expression Analysis

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On the use of QDE-SVM for gene feature selection and cell type classification from scRNA-seq data.

PloS one
Cell type identification is one of the fundamental tasks in single-cell RNA sequencing (scRNA-seq) studies. It is a key step to facilitate downstream interpretations such as differential expression, trajectory inference, etc. scRNA-seq data contains ...

Methods for cell-type annotation on scRNA-seq data: A recent overview.

Journal of bioinformatics and computational biology
The evolution of single-cell technology is ongoing, continually generating massive amounts of data that reveal many mysteries surrounding intricate diseases. However, their drawbacks continue to constrain us. Among these, annotating cell types in sin...

scTIGER: A Deep-Learning Method for Inferring Gene Regulatory Networks from Case versus Control scRNA-seq Datasets.

International journal of molecular sciences
Inferring gene regulatory networks (GRNs) from single-cell RNA-seq (scRNA-seq) data is an important computational question to find regulatory mechanisms involved in fundamental cellular processes. Although many computational methods have been designe...

Transfer learning enables predictions in network biology.

Nature
Mapping gene networks requires large amounts of transcriptomic data to learn the connections between genes, which impedes discoveries in settings with limited data, including rare diseases and diseases affecting clinically inaccessible tissues. Recen...

Single-cell RNA-seq data analysis based on directed graph neural network.

Methods (San Diego, Calif.)
Single-cell RNA sequencing (scRNA-seq) data scale surges with high-throughput sequencing technology development. However, although single-cell data analysis is a powerful tool, various issues have been reported, such as sequencing sparsity and comple...

Cross-species cell-type assignment from single-cell RNA-seq data by a heterogeneous graph neural network.

Genome research
Cross-species comparative analyses of single-cell RNA sequencing (scRNA-seq) data allow us to explore, at single-cell resolution, the origins of the cellular diversity and evolutionary mechanisms that shape cellular form and function. Cell-type assig...

Protocol for fast scRNA-seq raw data processing using scKB and non-arbitrary quality control with COPILOT.

STAR protocols
We describe a protocol to perform fast and non-arbitrary quality control of single-cell RNA sequencing (scRNA-seq) raw data using scKB and COPILOT. scKB is a wrapper script of kallisto and bustools for accelerated alignment and transcript count matri...

Multi-omics identification of circulating protein biomarkers for intervertebral disc degeneration using Mendelian randomization and scRNA-seq.

Clinical rheumatology
BACKGROUND: Intervertebral disc degeneration (IVDD) is a primary cause of chronic low back pain, significantly impacting quality of life and healthcare systems globally. Despite its prevalence, the molecular mechanisms underlying IVDD remain unclear,...

Differentiable graph clustering with structural grouping for single-cell RNA-seq data.

Bioinformatics (Oxford, England)
MOTIVATION: Clustering cells into subpopulations is one of the most crucial tasks in single-cell RNA sequencing (scRNA-seq) data analysis, which provides support for biological research at cellular level. With the development of graph neural networks...

Identification of potential biomarkers in cardiovascular calcification based on bioinformatics combined with single-cell RNA-seq and multiple machine learning analysis.

Cellular signalling
BACKGROUND: The molecular and genetic mechanisms underlying vascular calcification remain unclear. This study aimed to determine the differences in calcification marker-related gene expression in macrophages.