Single-cell RNA-seq methods to interrogate virus-host interactions
The twenty-first century has seen the emergence of many epidemic and pandemic viruses,
with the most recent being the SARS-CoV-2-driven COVID-19 pandemic. As obligate …
with the most recent being the SARS-CoV-2-driven COVID-19 pandemic. As obligate …
Single cell RNA‐sequencing: A powerful yet still challenging technology to study cellular heterogeneity
Almost all biomedical research to date has relied upon mean measurements from cell
populations, however it is well established that what it is observed at this macroscopic level …
populations, however it is well established that what it is observed at this macroscopic level …
scAAGA: Single cell data analysis framework using asymmetric autoencoder with gene attention
R Meng, S Yin, J Sun, H Hu, Q Zhao - Computers in biology and medicine, 2023 - Elsevier
In recent years, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful
technique for investigating cellular heterogeneity and structure. However, analyzing scRNA …
technique for investigating cellular heterogeneity and structure. However, analyzing scRNA …
A review of single-cell RNA-Seq annotation, integration, and cell–cell communication
Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating
cellular biology at an unprecedented resolution, enabling the characterization of cellular …
cellular biology at an unprecedented resolution, enabling the characterization of cellular …
Integrated analysis of multimodal single-cell data with structural similarity
Multimodal single-cell sequencing technologies provide unprecedented information on
cellular heterogeneity from multiple layers of genomic readouts. However, joint analysis of …
cellular heterogeneity from multiple layers of genomic readouts. However, joint analysis of …
Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapy
ZHE Zhang, X Wei - Seminars in Cancer Biology, 2023 - Elsevier
The rapid development of artificial intelligence (AI) technologies in the context of the vast
amount of collectable data obtained from high-throughput sequencing has led to an …
amount of collectable data obtained from high-throughput sequencing has led to an …
Clustering ensemble in scRNA-seq data analysis: Methods, applications and challenges
With the rapid development of single-cell RNA-sequencing techniques, various
computational methods and tools were proposed to analyze these high-throughput data …
computational methods and tools were proposed to analyze these high-throughput data …
An introduction to representation learning for single-cell data analysis
Single-cell-resolved systems biology methods, including omics-and imaging-based
measurement modalities, generate a wealth of high-dimensional data characterizing the …
measurement modalities, generate a wealth of high-dimensional data characterizing the …
Prediction of tumor-reactive T cell receptors from scRNA-seq data for personalized T cell therapy
The identification of patient-derived, tumor-reactive T cell receptors (TCRs) as a basis for
personalized transgenic T cell therapies remains a time-and cost-intensive endeavor …
personalized transgenic T cell therapies remains a time-and cost-intensive endeavor …
Self-supervised deep clustering of single-cell RNA-seq data to hierarchically detect rare cell populations
Single-cell RNA sequencing (scRNA-seq) is a widely used technique for characterizing
individual cells and studying gene expression at the single-cell level. Clustering plays a vital …
individual cells and studying gene expression at the single-cell level. Clustering plays a vital …