AIMC Topic: Single-Cell Analysis

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DeepCCI: a deep learning framework for identifying cell-cell interactions from single-cell RNA sequencing data.

Bioinformatics (Oxford, England) Oct 3, 2023
MOTIVATION: Cell-cell interactions (CCIs) play critical roles in many biological processes such as cellular differentiation, tissue homeostasis, and immune response. With the rapid development of high throughput single-cell RNA sequencing (scRNA-seq)...
Software Single-Cell Analysis Sequence Analysis, RNA Cluster Analysis Gene Expression Profiling Deep Learning
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The Deep Generative Decoder: MAP estimation of representations improves modelling of single-cell RNA data.

Bioinformatics (Oxford, England) Sep 2, 2023
MOTIVATION: Learning low-dimensional representations of single-cell transcriptomics has become instrumental to its downstream analysis. The state of the art is currently represented by neural network models, such as variational autoencoders, which us...
Normal Distribution RNA Probability Gene Expression Profiling Single-Cell Analysis Neural Networks, Computer
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BEENE: deep learning-based nonlinear embedding improves batch effect estimation.

Bioinformatics (Oxford, England) Aug 1, 2023
MOTIVATION: Analyzing large-scale single-cell transcriptomic datasets generated using different technologies is challenging due to the presence of batch-specific systematic variations known as batch effects. Since biological and technological differe...
Animals Leukocytes, Mononuclear Gene Expression Profiling Sequence Analysis, RNA RNA-Seq Mice Deep Learning Single-Cell Analysis
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Ensemble deep learning of embeddings for clustering multimodal single-cell omics data.

Bioinformatics (Oxford, England) Jun 1, 2023
MOTIVATION: Recent advances in multimodal single-cell omics technologies enable multiple modalities of molecular attributes, such as gene expression, chromatin accessibility, and protein abundance, to be profiled simultaneously at a global level in i...
Deep Learning Single-Cell Analysis Algorithms Cluster Analysis Chromatin
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A scalable unsupervised learning of scRNAseq data detects rare cells through integration of structure-preserving embedding, clustering and outlier detection.

Briefings in bioinformatics May 19, 2023
Single-cell RNA-seq analysis has become a powerful tool to analyse the transcriptomes of individual cells. In turn, it has fostered the possibility of screening thousands of single cells in parallel. Thus, contrary to the traditional bulk measurement...
Unsupervised Machine Learning Single-Cell Analysis Single-Cell Gene Expression Analysis Sequence Analysis, RNA Cluster Analysis Algorithms Gene Expression Profiling
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Adversarial dense graph convolutional networks for single-cell classification.

Bioinformatics (Oxford, England) Feb 3, 2023
MOTIVATION: In single-cell transcriptomics applications, effective identification of cell types in multicellular organisms and in-depth study of the relationships between genes has become one of the main goals of bioinformatics research. However, dat...
Software Single-Cell Analysis Neural Networks, Computer
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AgeAnno: a knowledgebase of single-cell annotation of aging in human.

Nucleic acids research Jan 6, 2023
Aging is a complex process that accompanied by molecular and cellular alterations. The identification of tissue-/cell type-specific biomarkers of aging and elucidation of the detailed biological mechanisms of aging-related genes at the single-cell le...
Aged, 80 and over Child Young Adult Adolescent Single-Cell Analysis Infant, Newborn Chromatin Adult Middle Aged Child, Preschool Humans Aged Aging Knowledge Bases Infant
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dynDeepDRIM: a dynamic deep learning model to infer direct regulatory interactions using time-course single-cell gene expression data.

Briefings in bioinformatics Nov 19, 2022
Time-course single-cell RNA sequencing (scRNA-seq) data have been widely used to explore dynamic changes in gene expression of transcription factors (TFs) and their target genes. This information is useful to reconstruct cell-type-specific gene regul...
Gene Expression Single-Cell Analysis Sequence Analysis, RNA Gene Expression Profiling Deep Learning
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CellDART: cell type inference by domain adaptation of single-cell and spatial transcriptomic data.

Nucleic acids research Jun 10, 2022
Deciphering the cellular composition in genome-wide spatially resolved transcriptomic data is a critical task to clarify the spatial context of cells in a tissue. In this study, we developed a method, CellDART, which estimates the spatial distributio...
Single-Cell Analysis Mice Lung Transcriptome Humans Neural Networks, Computer Brain Animals
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Deep learning shapes single-cell data analysis.

Nature reviews. Molecular cell biology May 1, 2022
Deep learning has tremendous potential in single-cell data analyses, but numerous challenges and possible new developments remain to be explored. In this commentary, we consider the progress, limitations, best practices and outlook of adapting deep l...
Deep Learning Data Analysis Single-Cell Analysis Algorithms
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