Results 1 to 10 of about 1,198,587 (324)

Single-cell RNA sequencing technologies and bioinformatics pipelines

Experimental and Molecular Medicine, 2018
Genetic data: Zooming in on single cells Showing which genes are expressed, or switched on, in individual cells may help to reveal the first signs of disease.
Byungjin Hwang, Ji Hyun Lee, Duhee Bang
doaj   +4 more sources

Splatter: simulation of single-cell RNA sequencing data

Genome Biology, 2017
As single-cell RNA sequencing (scRNA-seq) technologies have rapidly developed, so have analysis methods. Many methods have been tested, developed, and validated using simulated datasets.
Luke Zappia, Belinda Phipson, Alicia Oshlack   +2 more
doaj   +5 more sources

Single‐cell RNA sequencing in cancer research [PDF]

Journal of Experimental & Clinical Cancer Research, 2021
Single-cell RNA sequencing (scRNA-seq), a technology that analyzes transcriptomes of complex tissues at single-cell levels, can identify differential gene expression and epigenetic factors caused by mutations in unicellular genomes, as well as new cell ...
Yijie Zhang, Dan Wang, Miao Peng, Le Tang, Jiawei Ouyang, Fang Xiong, Can Guo, Yanyan Tang, Yujuan Zhou, Qianjin Liao, Xu Wu, Hui Wang, Jianjun Yu, Yong Li, Xiaoling Li, Guiyuan Li, Zhaoyang Zeng, Yixin Tan, Wei Xiong   +18 more
doaj   +3 more sources

Simulating multiple faceted variability in single cell RNA sequencing [PDF]

Nature Communications, 2019
Simulated single cell RNA sequencing data is useful for method development and comparison. Here, the authors developed SymSim, a simulator that explicitly models the main factors of variation in single cell data.
Xiuwei Zhang, Chenling Xu, Nir Yosef
doaj   +2 more sources

Advances in application of single-cell RNA sequencing in cardiovascular research

Frontiers in Cardiovascular Medicine, 2022
Single-cell RNA sequencing (scRNA-seq) provides high-resolution information on transcriptomic changes at the single-cell level, which is of great significance for distinguishing cell subtypes, identifying stem cell differentiation processes, and ...
Yue Hu, Ying Zhang, Yutong Liu, Yan Gao, Tiantian San, Xiaoying Li, Xiaoying Li, Sensen Song, Binglong Yan, Zhuo Zhao, Zhuo Zhao   +10 more
doaj   +1 more source

Recent advances in deciphering hippocampus complexity using single-cell transcriptomics

Neurobiology of Disease, 2023
Single-cell and single-nucleus RNA sequencing (scRNA-seq and snRNA-seq) technologies have emerged as revolutionary and powerful tools, which have helped in achieving significant progress in biomedical research over the last decade.
Chenxu Chang, Hongyan Zuo, Yang Li
doaj   +1 more source

Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology

Nature Cancer, 2022
Inferring single-cell compositions and their contributions to global gene expression changes from bulk RNA sequencing (RNA-seq) datasets is a major challenge in oncology.
Tinyi Chu, Zhong Wang, D. Pe’er, C. Danko   +3 more
semanticscholar   +1 more source

Application of single-cell RNA sequencing technologies in aortic dilatation diseases [PDF]

Jichu yixue yu linchuang, 2022
Aortic dilation disease, represented by aortic dissection and aortic aneurysm, is the most dangerous disease in vascular surgery. The pathogenesis of this disease is not very clear.
ZHENG Kai, REN Hua-liang, ZHANG Wang-de, LI Chun-min
doaj   +1 more source

Overview of single‐cell RNA sequencing analysis and its application to spermatogenesis research

Reproductive Medicine and Biology, 2023
Background Single‐cell transcriptomics allows parallel analysis of multiple cell types in tissues. Because testes comprise somatic cells and germ cells at various stages of spermatogenesis, single‐cell RNA sequencing is a powerful tool for investigating ...
Takahiro Suzuki
doaj   +1 more source

Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets

Genome Biology, 2023
Deconvolution of cell mixtures in “bulk” transcriptomic samples from homogenate human tissue is important for understanding disease pathologies. However, several experimental and computational challenges impede transcriptomics-based deconvolution ...
Sean K. Maden, Sang Ho Kwon, Louise A. Huuki-Myers, Leonardo Collado-Torres, Stephanie C. Hicks, Kristen R. Maynard   +5 more
doaj   +1 more source