Collection
Single-cell technologies
Recent years have seen rapid progress in the development of single-cell sequencing technologies. They can provide valuable insights into complex biological systems as well as allow researchers to make new discoveries. These powerful methods are used, for example, to generate high resolution transcriptomes, identify complex and rare cell populations, discover regulatory relationships between genes and assess cellular heterogeneity.
Editors
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Baohua Wang
Dr Baohua Wang is a professor in the School of Life Sciences at Nantong University. He got his Ph.D. from Nanjing Agricultural University, and worked as a postdoctoral research fellow in the University of Georgia for three years. Currently, he serves as Editorial Board Member for two journals, namely BMC Genomics & Frontiers in Plant Science. As the project leader, he undertook more than 10 scientific research projects including a National Key R & D Project, and published more than 60 relevant academic papers. His research interests focus on molecular genetics, epigenetics, quantitative genetics especially in germplasm enhancement.
Articles (10 in this collection)
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Detection of continuous hierarchical heterogeneity by single-cell surface antigen analysis in the prognosis evaluation of acute myeloid leukaemia
Authors (first, second and last of 9)
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A cell abundance analysis based on efficient PAM clustering for a better understanding of the dynamics of endometrial remodelling
Authors (first, second and last of 6)
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ScLSTM: single-cell type detection by siamese recurrent network and hierarchical clustering
Authors (first, second and last of 4)
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deltaXpress (ΔXpress): a tool for mapping differentially correlated genes using single-cell qPCR data
Authors (first, second and last of 5)
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scSNPdemux: a sensitive demultiplexing pipeline using single nucleotide polymorphisms for improved pooled single-cell RNA sequencing analysis
Authors (first, second and last of 7)
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The Poisson distribution model fits UMI-based single-cell RNA-sequencing data
Authors (first, second and last of 6)
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A mixed-effects stochastic model reveals clonal dominance in gene therapy safety studies
Authors (first, second and last of 4)
