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Single-Cell Analysis

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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...

Application of Deep Learning on Single-cell RNA Sequencing Data Analysis: A Review.

Genomics, proteomics & bioinformatics
Single-cell RNA sequencing (scRNA-seq) has become a routinely used technique to quantify the gene expression profile of thousands of single cells simultaneously. Analysis of scRNA-seq data plays an important role in the study of cell states and pheno...

Clustering of single-cell multi-omics data with a multimodal deep learning method.

Nature communications
Single-cell multimodal sequencing technologies are developed to simultaneously profile different modalities of data in the same cell. It provides a unique opportunity to jointly analyze multimodal data at the single-cell level for the identification ...

A model-based constrained deep learning clustering approach for spatially resolved single-cell data.

Genome research
Spatially resolved scRNA-seq (sp-scRNA-seq) technologies provide the potential to comprehensively profile gene expression patterns in tissue context. However, the development of computational methods lags behind the advances in these technologies, wh...

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...

Panoramic Manifold Projection (Panoramap) for Single-Cell Data Dimensionality Reduction and Visualization.

International journal of molecular sciences
Nonlinear dimensionality reduction (NLDR) methods such as t-Distributed Stochastic Neighbour Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) have been widely used for biological data exploration, especially in single-cell a...

scDLC: a deep learning framework to classify large sample single-cell RNA-seq data.

BMC genomics
BACKGROUND: Using single-cell RNA sequencing (scRNA-seq) data to diagnose disease is an effective technique in medical research. Several statistical methods have been developed for the classification of RNA sequencing (RNA-seq) data, including, for e...

Chord: an ensemble machine learning algorithm to identify doublets in single-cell RNA sequencing data.

Communications biology
High-throughput single-cell RNA sequencing (scRNA-seq) is a popular method, but it is accompanied by doublet rate problems that disturb the downstream analysis. Several computational approaches have been developed to detect doublets. However, most of...

Cell cycle gene regulation dynamics revealed by RNA velocity and deep-learning.

Nature communications
Despite the fact that the cell cycle is a fundamental process of life, a detailed quantitative understanding of gene regulation dynamics throughout the cell cycle is far from complete. Single-cell RNA-sequencing (scRNA-seq) technology gives access to...

Cross-tissue immune cell analysis reveals tissue-specific features in humans.

Science (New York, N.Y.)
Despite their crucial role in health and disease, our knowledge of immune cells within human tissues remains limited. We surveyed the immune compartment of 16 tissues from 12 adult donors by single-cell RNA sequencing and VDJ sequencing generating a ...