Literature DB >> 30886411

Scalable analysis of cell-type composition from single-cell transcriptomics using deep recurrent learning.

Yue Deng1, Feng Bao2, Qionghai Dai2, Lani F Wu3, Steven J Altschuler4.   

Abstract

Recent advances in large-scale single-cell RNA-seq enable fine-grained characterization of phenotypically distinct cellular states in heterogeneous tissues. We present scScope, a scalable deep-learning-based approach that can accurately and rapidly identify cell-type composition from millions of noisy single-cell gene-expression profiles.

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Year:  2019        PMID： 30886411      PMCID： PMC6774994          DOI： 10.1038/s41592-019-0353-7

Source DB:  PubMed          Journal:  Nat Methods        ISSN： 1548-7091            Impact factor:   28.547


  24 in total

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Authors:  Charles Gawad; Winston Koh; Stephen R Quake
Journal:  Nat Rev Genet       Date:  2016-01-25       Impact factor: 53.242

2.  The major cell populations of the mouse retina.

Authors:  C J Jeon; E Strettoi; R H Masland
Journal:  J Neurosci       Date:  1998-11-01       Impact factor: 6.167

3.  Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning.

Authors:  Bo Wang; Junjie Zhu; Emma Pierson; Daniele Ramazzotti; Serafim Batzoglou
Journal:  Nat Methods       Date:  2017-03-06       Impact factor: 28.547

4.  Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes.

Authors:  Lude Franke; Harm van Bakel; Like Fokkens; Edwin D de Jong; Michael Egmont-Petersen; Cisca Wijmenga
Journal:  Am J Hum Genet       Date:  2006-04-25       Impact factor: 11.025

5.  Integrating single-cell transcriptomic data across different conditions, technologies, and species.

Authors:  Andrew Butler; Paul Hoffman; Peter Smibert; Efthymia Papalexi; Rahul Satija
Journal:  Nat Biotechnol       Date:  2018-04-02       Impact factor: 54.908

6.  Mapping the Mouse Cell Atlas by Microwell-Seq.

Authors:  Xiaoping Han; Renying Wang; Yincong Zhou; Lijiang Fei; Huiyu Sun; Shujing Lai; Assieh Saadatpour; Ziming Zhou; Haide Chen; Fang Ye; Daosheng Huang; Yang Xu; Wentao Huang; Mengmeng Jiang; Xinyi Jiang; Jie Mao; Yao Chen; Chenyu Lu; Jin Xie; Qun Fang; Yibin Wang; Rui Yue; Tiefeng Li; He Huang; Stuart H Orkin; Guo-Cheng Yuan; Ming Chen; Guoji Guo
Journal:  Cell       Date:  2018-02-22       Impact factor: 41.582

7.  Simultaneous epitope and transcriptome measurement in single cells.

Authors:  Marlon Stoeckius; Christoph Hafemeister; William Stephenson; Brian Houck-Loomis; Pratip K Chattopadhyay; Harold Swerdlow; Rahul Satija; Peter Smibert
Journal:  Nat Methods       Date:  2017-07-31       Impact factor: 28.547

8.  A single-cell survey of the small intestinal epithelium.

Authors:  Adam L Haber; Moshe Biton; Noga Rogel; Rebecca H Herbst; Karthik Shekhar; Christopher Smillie; Grace Burgin; Toni M Delorey; Michael R Howitt; Yarden Katz; Itay Tirosh; Semir Beyaz; Danielle Dionne; Mei Zhang; Raktima Raychowdhury; Wendy S Garrett; Orit Rozenblatt-Rosen; Hai Ning Shi; Omer Yilmaz; Ramnik J Xavier; Aviv Regev
Journal:  Nature       Date:  2017-11-08       Impact factor: 49.962

9.  Deep generative modeling for single-cell transcriptomics.

Authors:  Romain Lopez; Jeffrey Regier; Michael B Cole; Michael I Jordan; Nir Yosef
Journal:  Nat Methods       Date:  2018-11-30       Impact factor: 28.547

10.  Splatter: simulation of single-cell RNA sequencing data.

Authors:  Luke Zappia; Belinda Phipson; Alicia Oshlack
Journal:  Genome Biol       Date:  2017-09-12       Impact factor: 13.583
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  33 in total

1.  SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references.

Authors:  Meichen Dong; Aatish Thennavan; Eugene Urrutia; Yun Li; Charles M Perou; Fei Zou; Yuchao Jiang
Journal:  Brief Bioinform       Date:  2021-01-18       Impact factor: 11.622

Review 2.  Tools for the analysis of high-dimensional single-cell RNA sequencing data.

Authors:  Yan Wu; Kun Zhang
Journal:  Nat Rev Nephrol       Date:  2020-03-27       Impact factor: 28.314

3.  Context-Specific Transcription Factor Functions Regulate Epigenomic and Transcriptional Dynamics during Cardiac Reprogramming.

Authors:  Nicole R Stone; Casey A Gifford; Reuben Thomas; Karishma J B Pratt; Kaitlen Samse-Knapp; Tamer M A Mohamed; Ethan M Radzinsky; Amelia Schricker; Lin Ye; Pengzhi Yu; Joke G van Bemmel; Kathryn N Ivey; Katherine S Pollard; Deepak Srivastava
Journal:  Cell Stem Cell       Date:  2019-07-03       Impact factor: 24.633

4.  Integrative spatial analysis of cell morphologies and transcriptional states with MUSE.

Authors:  Feng Bao; Yue Deng; Sen Wan; Susan Q Shen; Bo Wang; Qionghai Dai; Steven J Altschuler; Lani F Wu
Journal:  Nat Biotechnol       Date:  2022-03-28       Impact factor: 68.164

5.  A novel method for single-cell data imputation using subspace regression.

Authors:  Duc Tran; Bang Tran; Hung Nguyen; Tin Nguyen
Journal:  Sci Rep       Date:  2022-02-17       Impact factor: 4.996

6.  Deep learning tackles single-cell analysis-a survey of deep learning for scRNA-seq analysis.

Authors:  Mario Flores; Zhentao Liu; Tinghe Zhang; Md Musaddaqui Hasib; Yu-Chiao Chiu; Zhenqing Ye; Karla Paniagua; Sumin Jo; Jianqiu Zhang; Shou-Jiang Gao; Yu-Fang Jin; Yidong Chen; Yufei Huang
Journal:  Brief Bioinform       Date:  2022-01-17       Impact factor: 13.994

7.  Using Cell Type-Specific Genes to Identify Cell-Type Transitions Between Different in vitro Culture Conditions.

Authors:  Xuelin He; Li Liu; Baode Chen; Chao Wu
Journal:  Front Cell Dev Biol       Date:  2021-06-25

8.  A Bayesian framework for inter-cellular information sharing improves dscRNA-seq quantification.

Authors:  Avi Srivastava; Laraib Malik; Hirak Sarkar; Rob Patro
Journal:  Bioinformatics       Date:  2020-07-01       Impact factor: 6.937

9.  Searching large-scale scRNA-seq databases via unbiased cell embedding with Cell BLAST.

Authors:  Zhi-Jie Cao; Lin Wei; Shen Lu; De-Chang Yang; Ge Gao
Journal:  Nat Commun       Date:  2020-07-10       Impact factor: 14.919

10.  Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data.

Authors:  Nikolaus Fortelny; Christoph Bock
Journal:  Genome Biol       Date:  2020-08-03       Impact factor: 13.583
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