.png){kind=icon}
返回
Computational methods for single-cell omics across modalities
.png){kind=icon}
PRE
AI
DOI：10.1038/s41592-019-0692-4.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Single-cell omics approaches provide high-resolution data on cellular phenotypes, developmental dynamics and communication networks in diverse tissues and conditions. Emerging technologies now measure different modalities of individual cells, such as genomes, epigenomes, transcriptomes and proteomes, in addition to spatial profiling. Combined with analytical approaches, these data open new avenues for accurate reconstruction of gene-regulatory and signaling networks driving cellular identity and function. Here we summarize computational methods for analysis and integration of single-cell omics data across different modalities and discuss their applications, challenges and future directions.
Keyword:
SPATIAL RECONSTRUCTION
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
32.1
论文数:
7.2K
被引数:
12.7W
机构
引用论文
MATCHER: manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics.png)
GENOME BIOLOGY
OAAI
GENOME BIOLOGYIF9.4
OAAI分享收藏High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin单细胞rna-seq数据到原始组织的高通量空间映射NATURE BIOTECHNOLOGYOAAI
NATURE BIOTECHNOLOGYIF41.7
OAAI分享收藏PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models
OAAI
OAAI分享收藏
查看更多内容