标题
Automated methods for cell type annotation on scRNA-seq data
作者
关键词
scRNA-seq, Cell type, Cell state, Automatic annotation
出版物
Computational and Structural Biotechnology Journal
Volume -, Issue -, Pages -
出版商
Elsevier BV
发表日期
2021-01-19
DOI
10.1016/j.csbj.2021.01.015
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- CIPR: a web-based R/shiny app and R package to annotate cell clusters in single cell RNA sequencing experiments
- (2020) H. Atakan Ekiz et al. BMC BIOINFORMATICS
- Identifying cell types to interpret scRNA-seq data: how, why and more possibilities
- (2020) Ziwei Wang et al. Briefings in Functional Genomics
- Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis
- (2020) Chiaowen Joyce Hsiao et al. GENOME RESEARCH
- Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
- (2020) Elisabetta Mereu et al. NATURE BIOTECHNOLOGY
- SCSA: A Cell Type Annotation Tool for Single-Cell RNA-seq Data
- (2020) Yinghao Cao et al. Frontiers in Genetics
- Comparative transcriptomics of primary cells in vertebrates
- (2020) Tanvir Alam et al. GENOME RESEARCH
- scClassify: sample size estimation and multiscale classification of cells using single and multiple reference
- (2020) Yingxin Lin et al. Molecular Systems Biology
- Tissue Stem Cells: Architects of Their Niches
- (2020) Elaine Fuchs et al. Cell Stem Cell
- Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage
- (2019) Dvir Aran et al. NATURE IMMUNOLOGY
- Challenges in unsupervised clustering of single-cell RNA-seq data
- (2019) Vladimir Yu Kiselev et al. NATURE REVIEWS GENETICS
- SuperCT: a supervised-learning framework for enhanced characterization of single-cell transcriptomic profiles
- (2019) Peng Xie et al. NUCLEIC ACIDS RESEARCH
- Integrative single-cell analysis
- (2019) Tim Stuart et al. NATURE REVIEWS GENETICS
- LAmbDA: Label Ambiguous Domain Adaptation Dataset Integration Reduces Batch Effects and Improves Subtype Detection
- (2019) Travis S Johnson et al. BIOINFORMATICS
- scMatch: a single-cell gene expression profile annotation tool using reference datasets
- (2019) Rui Hou et al. BIOINFORMATICS
- Deep Learning in the Biomedical Applications: Recent and Future Status
- (2019) Ryad Zemouri et al. Applied Sciences-Basel
- Current best practices in single‐cell RNA‐seq analysis: a tutorial
- (2019) Malte D Luecken et al. Molecular Systems Biology
- Conserved cell types with divergent features in human versus mouse cortex
- (2019) Rebecca D. Hodge et al. NATURE
- Supervised classification enables rapid annotation of cell atlases
- (2019) Hannah A. Pliner et al. NATURE METHODS
- Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling
- (2019) Allen W. Zhang et al. NATURE METHODS
- Evaluation of single-cell classifiers for single-cell RNA sequencing data sets
- (2019) Xinlei Zhao et al. BRIEFINGS IN BIOINFORMATICS
- High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell
- (2019) Song Chen et al. NATURE BIOTECHNOLOGY
- An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome
- (2019) Chenxu Zhu et al. NATURE STRUCTURAL & MOLECULAR BIOLOGY
- Random forest based similarity learning for single cell RNA sequencing data
- (2018) Maziyar Baran Pouyan et al. BIOINFORMATICS
- Integrating single-cell transcriptomic data across different conditions, technologies, and species
- (2018) Andrew Butler et al. NATURE BIOTECHNOLOGY
- scmap: projection of single-cell RNA-seq data across data sets
- (2018) Vladimir Yu Kiselev et al. NATURE METHODS
- scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells
- (2018) Stephen J. Clark et al. Nature Communications
- Deep learning in biomedicine
- (2018) Michael Wainberg et al. NATURE BIOTECHNOLOGY
- OUP accepted manuscript
- (2018) NUCLEIC ACIDS RESEARCH
- M3Drop: dropout-based feature selection for scRNASeq
- (2018) Tallulah S Andrews et al. BIOINFORMATICS
- Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris
- (2018) et al. NATURE
- Single-cell mapping of lineage and identity in direct reprogramming
- (2018) Brent A. Biddy et al. NATURE
- Dimensionality reduction for visualizing single-cell data using UMAP
- (2018) Etienne Becht et al. NATURE BIOTECHNOLOGY
- CancerSEA: a cancer single-cell state atlas
- (2018) Huating Yuan et al. NUCLEIC ACIDS RESEARCH
- Correction: CaSTLe - Classification of single cells by transfer learning: Harnessing the power of publicly available single cell RNA sequencing experiments to annotate new experiments
- (2018) Yuval Lieberman et al. PLoS One
- Multiplexed quantification of proteins and transcripts in single cells
- (2017) Vanessa M Peterson et al. NATURE BIOTECHNOLOGY
- Simultaneous epitope and transcriptome measurement in single cells
- (2017) Marlon Stoeckius et al. NATURE METHODS
- The International Human Epigenome Consortium: A Blueprint for Scientific Collaboration and Discovery
- (2016) Hendrik G. Stunnenberg et al. CELL
- Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq
- (2016) Barbara Treutlein et al. NATURE
- Update of the FANTOM web resource: high resolution transcriptome of diverse cell types in mammals
- (2016) Marina Lizio et al. NUCLEIC ACIDS RESEARCH
- The Technology and Biology of Single-Cell RNA Sequencing
- (2015) Aleksandra A. Kolodziejczyk et al. MOLECULAR CELL
- G&T-seq: parallel sequencing of single-cell genomes and transcriptomes
- (2015) Iain C Macaulay et al. NATURE METHODS
- CellNet: Network Biology Applied to Stem Cell Engineering
- (2014) Patrick Cahan et al. CELL
- An expression atlas of human primary cells: inference of gene function from coexpression networks
- (2013) Neil A Mabbott et al. BMC GENOMICS
- The promise of single-cell sequencing
- (2013) James Eberwine et al. NATURE METHODS
- BioNumbers—the database of key numbers in molecular and cell biology
- (2009) Ron Milo et al. NUCLEIC ACIDS RESEARCH
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