Results 21 to 30 of about 86,859 (350)

scRNA-seq: oh, the joys [PDF]

Nature Methods
Vivien Marx
openalex   +3 more sources

Meta-analysis of honey bee neurogenomic response links deformed wing virus type A to precocious behavioral maturation [PDF]

, 2020
Crop pollination by the western honey bee Apis mellifera is vital to agriculture but threatened by alarmingly high levels of colony mortality, especially in Europe and North America.
Ahmed, Amy Cash, Bukhari, Syed Abbas, Hamilton, Adam R., Kevill, Jessica, Naeger, Nicholas L., Robinson, Gene E., Schroeder, Declan C., Traniello, Ian M.   +7 more
core   +1 more source

Single-Cell RNA-Seq Technologies and Related Computational Data Analysis

Frontiers in Genetics, 2019
Single-cell RNA sequencing (scRNA-seq) technologies allow the dissection of gene expression at single-cell resolution, which greatly revolutionizes transcriptomic studies.
Geng Chen, Baitang Ning, Tieliu Shi
doaj   +1 more source

Global Gene Expression Analysis Identifies Age-Related Differences in Knee Joint Transcriptome during the Development of Post-Traumatic Osteoarthritis in Mice. [PDF]

, 2020
Aging and injury are two major risk factors for osteoarthritis (OA). Yet, very little is known about how aging and injury interact and contribute to OA pathogenesis.
Christiansen, Blaine A, Hum, Nicholas R, Loots, Gabriela G, McCool, Jillian L, Mendez, Melanie E, Murugesh, Deepa K, Rios-Arce, Naiomy D, Sebastian, Aimy   +7 more
core   +2 more sources

Applications of single-cell RNA sequencing in atopic dermatitis and psoriasis

Frontiers in Immunology, 2022
Single-cell RNA sequencing (scRNA-seq) is a novel technology that characterizes molecular heterogeneity at the single-cell level. With the development of more automated, sensitive, and cost-effective single-cell isolation methods, the sensitivity and ...
Dengmei Xia, Dengmei Xia, Dengmei Xia, Yiyi Wang, Yiyi Wang, Yue Xiao, Yue Xiao, Wei Li, Wei Li   +8 more
doaj   +1 more source

Contrastive self-supervised clustering of scRNA-seq data [PDF]

BMC Bioinformatics, 2021
Abstract Background Single-cell RNA sequencing (scRNA-seq) has emerged has a main strategy to study transcriptional activity at the cellular level. Clustering analysis is routinely performed on scRNA-seq data to explore, recognize or discover underlying cell identities.
Ciortan, Madalina, Defrance, Matthieu
openaire   +4 more sources

Probing infectious disease by single-cell RNA sequencing: Progresses and perspectives

Computational and Structural Biotechnology Journal, 2020
The increasing application of single-cell RNA sequencing (scRNA-seq) technology in life science and biomedical research has significantly increased our understanding of the cellular heterogeneities in immunology, oncology and developmental biology.
Geyang Luo, Qian Gao, Shuye Zhang, Bo Yan   +3 more
doaj   +1 more source

Missing Value Imputation With Low-Rank Matrix Completion in Single-Cell RNA-Seq Data by Considering Cell Heterogeneity

Frontiers in Genetics, 2022
Single-cell RNA-sequencing (scRNA-seq) technologies enable the measurements of gene expressions in individual cells, which is helpful for exploring cancer heterogeneity and precision medicine.
Meng Huang, Xiucai Ye, Xiucai Ye, Hongmin Li, Tetsuya Sakurai, Tetsuya Sakurai   +5 more
doaj   +1 more source

Pulmonary alveolar type I cell population consists of two distinct subtypes that differ in cell fate. [PDF]

, 2018
Pulmonary alveolar type I (AT1) cells cover more than 95% of alveolar surface and are essential for the air-blood barrier function of lungs. AT1 cells have been shown to retain developmental plasticity during alveolar regeneration.
Cai, Tao, Dai, Huaping, Huang, Huanwei, Li, Jiao, Li, Juan, Tang, Nan, Tang, Zan, Wang, Fengchao, Wang, Yanjie, Wang, Zheng, Yu, Yuanyuan, Zhang, Chengwei   +11 more
core   +1 more source

scNPF: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data

BMC Genomics, 2019
Background Single-cell RNA-sequencing (scRNA-seq) is fast becoming a powerful tool for profiling genome-scale transcriptomes of individual cells and capturing transcriptome-wide cell-to-cell variability.
Wenbin Ye, Guoli Ji, Pengchao Ye, Yuqi Long, Xuesong Xiao, Shuchao Li, Yaru Su, Xiaohui Wu   +7 more
doaj   +1 more source