scIMC: a platform for benchmarking comparison and visualization analysis of scRNA-seq data imputation methods
出版年份 2022 全文链接
标题
scIMC: a platform for benchmarking comparison and visualization analysis of scRNA-seq data imputation methods
作者
关键词
-
出版物
NUCLEIC ACIDS RESEARCH
Volume 50, Issue 9, Pages 4877-4899
出版商
Oxford University Press (OUP)
发表日期
2022-04-21
DOI
10.1093/nar/gkac317
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Deep learning shapes single-cell data analysis
- (2022) Qin Ma et al. NATURE REVIEWS MOLECULAR CELL BIOLOGY
- scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses
- (2021) Juexin Wang et al. Nature Communications
- Imputing single-cell RNA-seq data by combining graph convolution and autoencoder neural networks
- (2021) Jiahua Rao et al. iScience
- SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data
- (2021) Jing Qi et al. PLoS Computational Biology
- scTSSR: gene expression recovery for single-cell RNA sequencing using two-side sparse self-representation
- (2020) Ke Jin et al. BIOINFORMATICS
- netNMF-sc: leveraging gene–gene interactions for imputation and dimensionality reduction in single-cell expression analysis
- (2020) Rebecca Elyanow et al. GENOME RESEARCH
- scIGANs: single-cell RNA-seq imputation using generative adversarial networks
- (2020) Yungang Xu et al. NUCLEIC ACIDS RESEARCH
- Imputing missing RNA-sequencing data from DNA methylation by using a transfer learning–based neural network
- (2020) Xiang Zhou et al. GigaScience
- Impact of data preprocessing on cell-type clustering based on single-cell RNA-seq data
- (2020) Chunxiang Wang et al. BMC BIOINFORMATICS
- Single-cell RNA-seq denoising using a deep count autoencoder
- (2019) Gökcen Eraslan et al. Nature Communications
- scNPF: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data
- (2019) Wenbin Ye et al. BMC GENOMICS
- Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM
- (2019) Huidong Chen et al. Nature Communications
- Single-cell RNA-Seq: a next generation sequencing tool for a high-resolution view of the individual cell
- (2019) Kevin Stevenson et al. JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS
- DrImpute: imputing dropout events in single cell RNA sequencing data
- (2018) Wuming Gong et al. BMC BIOINFORMATICS
- Recovering Gene Interactions from Single-Cell Data Using Data Diffusion
- (2018) David van Dijk et al. CELL
- Opportunities and obstacles for deep learning in biology and medicine
- (2018) Travers Ching et al. Journal of the Royal Society Interface
- Identifying cell populations with scRNASeq
- (2018) Tallulah S. Andrews et al. MOLECULAR ASPECTS OF MEDICINE
- SAVER: gene expression recovery for single-cell RNA sequencing
- (2018) Mo Huang et al. NATURE METHODS
- An accurate and robust imputation method scImpute for single-cell RNA-seq data
- (2018) Wei Vivian Li et al. Nature Communications
- Uncovering pseudotemporal trajectories with covariates from single cell and bulk expression data
- (2018) Kieran R Campbell et al. Nature Communications
- Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data
- (2018) Travers Ching et al. PLoS Computational Biology
- Dimensionality reduction for visualizing single-cell data using UMAP
- (2018) Etienne Becht et al. NATURE BIOTECHNOLOGY
- Deep generative modeling for single-cell transcriptomics
- (2018) Romain Lopez et al. NATURE METHODS
- AutoImpute: Autoencoder based imputation of single-cell RNA-seq data
- (2018) Divyanshu Talwar et al. Scientific Reports
- Reversed graph embedding resolves complex single-cell trajectories
- (2017) Xiaojie Qiu et al. NATURE METHODS
- Accounting for technical noise in differential expression analysis of single-cell RNA sequencing data
- (2017) Cheng Jia et al. NUCLEIC ACIDS RESEARCH
- Advances in single-cell RNA sequencing and its applications in cancer research
- (2017) Sibo Zhu et al. Oncotarget
- Revealing the vectors of cellular identity with single-cell genomics
- (2016) Allon Wagner et al. NATURE BIOTECHNOLOGY
- Wishbone identifies bifurcating developmental trajectories from single-cell data
- (2016) Manu Setty et al. NATURE BIOTECHNOLOGY
- Diffusion pseudotime robustly reconstructs lineage branching
- (2016) Laleh Haghverdi et al. NATURE METHODS
- TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis
- (2016) Zhicheng Ji et al. NUCLEIC ACIDS RESEARCH
- Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons
- (2016) Naomi Habib et al. SCIENCE
- Condensing Raman spectrum for single-cell phenotype analysis
- (2015) Shiwei Sun et al. BMC BIOINFORMATICS
- Single-Cell RNA-Seq with Waterfall Reveals Molecular Cascades underlying Adult Neurogenesis
- (2015) Jaehoon Shin et al. Cell Stem Cell
- Computational and analytical challenges in single-cell transcriptomics
- (2015) Oliver Stegle et al. NATURE REVIEWS GENETICS
- Single-Cell Trajectory Detection Uncovers Progression and Regulatory Coordination in Human B Cell Development
- (2014) Sean C. Bendall et al. CELL
- Bayesian approach to single-cell differential expression analysis
- (2014) Peter V Kharchenko et al. NATURE METHODS
- Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape
- (2014) Eugenio Marco et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- An estimation of the number of cells in the human body
- (2013) Eva Bianconi et al. ANNALS OF HUMAN BIOLOGY
- From single-cell to cell-pool transcriptomes: Stochasticity in gene expression and RNA splicing
- (2013) G. K. Marinov et al. GENOME RESEARCH
- Quantitative single-cell RNA-seq with unique molecular identifiers
- (2013) Saiful Islam et al. NATURE METHODS
- edgeR: a Bioconductor package for differential expression analysis of digital gene expression data
- (2009) M. D. Robinson et al. BIOINFORMATICS
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