AIMC Topic: Single-Cell Analysis

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CNRein: an evolution-aware deep reinforcement learning algorithm for single-cell DNA copy number calling.

Genome biology
Low-pass single-cell DNA sequencing technologies and algorithmic advancements have enabled haplotype-specific copy number calling on thousands of cells within tumors. However, measurement uncertainty may result in spurious CNAs inconsistent with real...

scAMZI: attention-based deep autoencoder with zero-inflated layer for clustering scRNA-seq data.

BMC genomics
BACKGROUND: Clustering scRNA-seq data plays a vital role in scRNA-seq data analysis and downstream analyses. Many computational methods have been proposed and achieved remarkable results. However, there are several limitations of these methods. First...

Large-scale bulk and single-cell RNA sequencing combined with machine learning reveals glioblastoma-associated neutrophil heterogeneity and establishes a VEGFA neutrophil prognostic model.

Biology direct
BACKGROUND: Neutrophils play a key role in the tumor microenvironment (TME); however, their functions in glioblastoma (GBM) are overlooked and insufficiently studied. A detailed analysis of GBM-associated neutrophil (GBMAN) subpopulations may offer n...

101 Machine Learning Algorithms for Mining Esophageal Squamous Cell Carcinoma Neoantigen Prognostic Models in Single-Cell Data.

International journal of molecular sciences
Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive malignant tumors in the digestive tract, characterized by a high recurrence rate and inadequate immunotherapy options. We analyzed mutation data of ESCC from public databases and...

MIST: An interpretable and flexible deep learning framework for single-T cell transcriptome and receptor analysis.

Science advances
Joint analysis of transcriptomic and T cell receptor (TCR) features at single-cell resolution provides a powerful approach for in-depth T cell immune function research. Here, we introduce a deep learning framework for single-T cell transcriptome and ...

Self-Supervised Graph Representation Learning for Single-Cell Classification.

Interdisciplinary sciences, computational life sciences
Accurately identifying cell types in single-cell RNA sequencing data is critical for understanding cellular differentiation and pathological mechanisms in downstream analysis. As traditional biological approaches are laborious and time-intensive, it ...

Mitigating ambient RNA and doublets effects on single cell transcriptomics analysis in cancer research.

Cancer letters
In cancer biology, where understanding the tumor microenvironment at high resolution is vital, ambient RNA contamination becomes a considerable problem. This hinders accurate delineation of intratumoral heterogeneity, complicates the identification o...

Predicting cell cycle stage from 3D single-cell nuclear-stained images.

Life science alliance
The cell cycle governs the proliferation of all eukaryotic cells. Profiling cell cycle dynamics is therefore central to basic and biomedical research. However, current approaches to cell cycle profiling involve complex interventions that may confound...

Integrating single-cell RNA sequencing, WGCNA, and machine learning to identify key biomarkers in hepatocellular carcinoma.

Scientific reports
The microarray and single-cell RNA-sequencing (scRNA-seq) datasets of hepatocellular carcinoma (HCC) were downloaded from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis (W...